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@ -1,32 +1,3 @@
|
|||
BasedOnStyle: LLVM
|
||||
|
||||
Language: Cpp
|
||||
Standard: Cpp11
|
||||
|
||||
AccessModifierOffset: -1
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||||
AlignEscapedNewlines: Left
|
||||
AlwaysBreakAfterReturnType: None
|
||||
AlwaysBreakTemplateDeclarations: Yes
|
||||
AllowAllParametersOfDeclarationOnNextLine: false
|
||||
AllowShortFunctionsOnASingleLine: Inline
|
||||
BinPackParameters: false
|
||||
BreakBeforeBinaryOperators: NonAssignment
|
||||
BreakBeforeTernaryOperators: true
|
||||
BreakConstructorInitializers: AfterColon
|
||||
BreakInheritanceList: AfterColon
|
||||
ColumnLimit: 120
|
||||
ConstructorInitializerAllOnOneLineOrOnePerLine: true
|
||||
ContinuationIndentWidth: 4
|
||||
DerivePointerAlignment: false
|
||||
IndentCaseLabels: true
|
||||
IndentPPDirectives: None
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||||
BasedOnStyle: Microsoft
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||||
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MaxEmptyLinesToKeep: 1
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||||
PointerAlignment: Left
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||||
ReflowComments: true
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||||
SortIncludes: false
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SortUsingDeclarations: true
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||||
SpaceAfterTemplateKeyword: false
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||||
SpacesBeforeTrailingComments: 2
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||||
TabWidth: 4
|
||||
UseTab: Never
|
||||
ReflowComments: false
|
||||
|
|
@ -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
|
||||
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]
|
||||
|
|
|
|||
|
|
@ -75,33 +75,33 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "ascii-canvas"
|
||||
version = "4.0.0"
|
||||
version = "3.0.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "ef1e3e699d84ab1b0911a1010c5c106aa34ae89aeac103be5ce0c3859db1e891"
|
||||
checksum = "8824ecca2e851cec16968d54a01dd372ef8f95b244fb84b84e70128be347c3c6"
|
||||
dependencies = [
|
||||
"term",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "autocfg"
|
||||
version = "1.4.0"
|
||||
version = "1.3.0"
|
||||
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||||
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||||
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||||
|
||||
[[package]]
|
||||
name = "bit-set"
|
||||
version = "0.8.0"
|
||||
version = "0.5.3"
|
||||
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||||
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|
||||
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|
||||
dependencies = [
|
||||
"bit-vec",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "bit-vec"
|
||||
version = "0.8.0"
|
||||
version = "0.6.3"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "bitflags"
|
||||
|
|
@ -109,15 +109,6 @@ version = "2.6.0"
|
|||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "b048fb63fd8b5923fc5aa7b340d8e156aec7ec02f0c78fa8a6ddc2613f6f71de"
|
||||
|
||||
[[package]]
|
||||
name = "block-buffer"
|
||||
version = "0.10.4"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "3078c7629b62d3f0439517fa394996acacc5cbc91c5a20d8c658e77abd503a71"
|
||||
dependencies = [
|
||||
"generic-array",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "byteorder"
|
||||
version = "1.5.0"
|
||||
|
|
@ -126,9 +117,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
|
|||
|
||||
[[package]]
|
||||
name = "cc"
|
||||
version = "1.1.24"
|
||||
version = "1.1.13"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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|
||||
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|
||||
dependencies = [
|
||||
"shlex",
|
||||
]
|
||||
|
|
@ -141,9 +132,9 @@ checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
|
|||
|
||||
[[package]]
|
||||
name = "clap"
|
||||
version = "4.5.19"
|
||||
version = "4.5.16"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "7be5744db7978a28d9df86a214130d106a89ce49644cbc4e3f0c22c3fba30615"
|
||||
checksum = "ed6719fffa43d0d87e5fd8caeab59be1554fb028cd30edc88fc4369b17971019"
|
||||
dependencies = [
|
||||
"clap_builder",
|
||||
"clap_derive",
|
||||
|
|
@ -151,9 +142,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "clap_builder"
|
||||
version = "4.5.19"
|
||||
version = "4.5.15"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "a5fbc17d3ef8278f55b282b2a2e75ae6f6c7d4bb70ed3d0382375104bfafdb4b"
|
||||
checksum = "216aec2b177652e3846684cbfe25c9964d18ec45234f0f5da5157b207ed1aab6"
|
||||
dependencies = [
|
||||
"anstream",
|
||||
"anstyle",
|
||||
|
|
@ -163,14 +154,14 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "clap_derive"
|
||||
version = "4.5.18"
|
||||
version = "4.5.13"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "4ac6a0c7b1a9e9a5186361f67dfa1b88213572f427fb9ab038efb2bd8c582dab"
|
||||
checksum = "501d359d5f3dcaf6ecdeee48833ae73ec6e42723a1e52419c79abf9507eec0a0"
|
||||
dependencies = [
|
||||
"heck 0.5.0",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.79",
|
||||
"syn 2.0.75",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -197,15 +188,6 @@ dependencies = [
|
|||
"windows-sys 0.52.0",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cpufeatures"
|
||||
version = "0.2.14"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "608697df725056feaccfa42cffdaeeec3fccc4ffc38358ecd19b243e716a78e0"
|
||||
dependencies = [
|
||||
"libc",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "crossbeam"
|
||||
version = "0.8.4"
|
||||
|
|
@ -263,23 +245,30 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
|
|||
checksum = "22ec99545bb0ed0ea7bb9b8e1e9122ea386ff8a48c0922e43f36d45ab09e0e80"
|
||||
|
||||
[[package]]
|
||||
name = "crypto-common"
|
||||
version = "0.1.6"
|
||||
name = "crunchy"
|
||||
version = "0.2.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1bfb12502f3fc46cca1bb51ac28df9d618d813cdc3d2f25b9fe775a34af26bb3"
|
||||
checksum = "7a81dae078cea95a014a339291cec439d2f232ebe854a9d672b796c6afafa9b7"
|
||||
|
||||
[[package]]
|
||||
name = "dirs-next"
|
||||
version = "2.0.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "b98cf8ebf19c3d1b223e151f99a4f9f0690dca41414773390fc824184ac833e1"
|
||||
dependencies = [
|
||||
"generic-array",
|
||||
"typenum",
|
||||
"cfg-if",
|
||||
"dirs-sys-next",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "digest"
|
||||
version = "0.10.7"
|
||||
name = "dirs-sys-next"
|
||||
version = "0.1.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "9ed9a281f7bc9b7576e61468ba615a66a5c8cfdff42420a70aa82701a3b1e292"
|
||||
checksum = "4ebda144c4fe02d1f7ea1a7d9641b6fc6b580adcfa024ae48797ecdeb6825b4d"
|
||||
dependencies = [
|
||||
"block-buffer",
|
||||
"crypto-common",
|
||||
"libc",
|
||||
"redox_users",
|
||||
"winapi",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -321,9 +310,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "fastrand"
|
||||
version = "2.1.1"
|
||||
version = "2.1.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "e8c02a5121d4ea3eb16a80748c74f5549a5665e4c21333c6098f283870fbdea6"
|
||||
checksum = "9fc0510504f03c51ada170672ac806f1f105a88aa97a5281117e1ddc3368e51a"
|
||||
|
||||
[[package]]
|
||||
name = "fixedbitset"
|
||||
|
|
@ -340,16 +329,6 @@ dependencies = [
|
|||
"byteorder",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "generic-array"
|
||||
version = "0.14.7"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "85649ca51fd72272d7821adaf274ad91c288277713d9c18820d8499a7ff69e9a"
|
||||
dependencies = [
|
||||
"typenum",
|
||||
"version_check",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "getopts"
|
||||
version = "0.2.21"
|
||||
|
|
@ -385,12 +364,6 @@ dependencies = [
|
|||
"ahash",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "hashbrown"
|
||||
version = "0.15.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "1e087f84d4f86bf4b218b927129862374b72199ae7d8657835f1e89000eea4fb"
|
||||
|
||||
[[package]]
|
||||
name = "heck"
|
||||
version = "0.4.1"
|
||||
|
|
@ -403,15 +376,6 @@ version = "0.5.0"
|
|||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2304e00983f87ffb38b55b444b5e3b60a884b5d30c0fca7d82fe33449bbe55ea"
|
||||
|
||||
[[package]]
|
||||
name = "home"
|
||||
version = "0.5.9"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "e3d1354bf6b7235cb4a0576c2619fd4ed18183f689b12b006a0ee7329eeff9a5"
|
||||
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|
||||
"windows-sys 0.52.0",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "indexmap"
|
||||
version = "1.9.3"
|
||||
|
|
@ -424,12 +388,12 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "indexmap"
|
||||
version = "2.6.0"
|
||||
version = "2.4.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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|
||||
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|
||||
dependencies = [
|
||||
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|
||||
"hashbrown 0.15.0",
|
||||
"hashbrown 0.14.5",
|
||||
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|
||||
|
||||
[[package]]
|
||||
|
|
@ -440,9 +404,9 @@ checksum = "b248f5224d1d606005e02c97f5aa4e88eeb230488bcc03bc9ca4d7991399f2b5"
|
|||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dependencies = [
|
||||
"either",
|
||||
"inkwell_internals",
|
||||
|
|
@ -454,13 +418,13 @@ 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 2.0.79",
|
||||
"syn 2.0.75",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -483,6 +447,15 @@ 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 +471,35 @@ version = "1.0.11"
|
|||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b"
|
||||
|
||||
[[package]]
|
||||
name = "keccak"
|
||||
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|
||||
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 +510,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
|
|||
|
||||
[[package]]
|
||||
name = "libc"
|
||||
version = "0.2.159"
|
||||
version = "0.2.157"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "561d97a539a36e26a9a5fad1ea11a3039a67714694aaa379433e580854bc3dc5"
|
||||
checksum = "374af5f94e54fa97cf75e945cce8a6b201e88a1a07e688b47dfd2a59c66dbd86"
|
||||
|
||||
[[package]]
|
||||
name = "libloading"
|
||||
|
|
@ -561,6 +524,16 @@ 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 +594,11 @@ dependencies = [
|
|||
name = "nac3artiq"
|
||||
version = "0.1.0"
|
||||
dependencies = [
|
||||
"itertools",
|
||||
"inkwell",
|
||||
"itertools 0.13.0",
|
||||
"nac3core",
|
||||
"nac3ld",
|
||||
"nac3parser",
|
||||
"parking_lot",
|
||||
"pyo3",
|
||||
"tempfile",
|
||||
|
|
@ -644,11 +619,11 @@ name = "nac3core"
|
|||
version = "0.1.0"
|
||||
dependencies = [
|
||||
"crossbeam",
|
||||
"indexmap 2.6.0",
|
||||
"indexmap 2.4.0",
|
||||
"indoc",
|
||||
"inkwell",
|
||||
"insta",
|
||||
"itertools",
|
||||
"itertools 0.13.0",
|
||||
"nac3parser",
|
||||
"parking_lot",
|
||||
"rayon",
|
||||
|
|
@ -686,7 +661,9 @@ name = "nac3standalone"
|
|||
version = "0.1.0"
|
||||
dependencies = [
|
||||
"clap",
|
||||
"inkwell",
|
||||
"nac3core",
|
||||
"nac3parser",
|
||||
"parking_lot",
|
||||
]
|
||||
|
||||
|
|
@ -698,12 +675,9 @@ checksum = "650eef8c711430f1a879fdd01d4745a7deea475becfb90269c06775983bbf086"
|
|||
|
||||
[[package]]
|
||||
name = "once_cell"
|
||||
version = "1.20.1"
|
||||
version = "1.19.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "82881c4be219ab5faaf2ad5e5e5ecdff8c66bd7402ca3160975c93b24961afd1"
|
||||
dependencies = [
|
||||
"portable-atomic",
|
||||
]
|
||||
checksum = "3fdb12b2476b595f9358c5161aa467c2438859caa136dec86c26fdd2efe17b92"
|
||||
|
||||
[[package]]
|
||||
name = "parking_lot"
|
||||
|
|
@ -735,7 +709,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
|
|||
checksum = "b4c5cc86750666a3ed20bdaf5ca2a0344f9c67674cae0515bec2da16fbaa47db"
|
||||
dependencies = [
|
||||
"fixedbitset",
|
||||
"indexmap 2.6.0",
|
||||
"indexmap 2.4.0",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -778,7 +752,7 @@ dependencies = [
|
|||
"phf_shared 0.11.2",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.79",
|
||||
"syn 2.0.75",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -807,9 +781,9 @@ 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"
|
||||
|
|
@ -882,7 +856,7 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"pyo3-macros-backend",
|
||||
"quote",
|
||||
"syn 2.0.79",
|
||||
"syn 2.0.75",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -895,14 +869,14 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"pyo3-build-config",
|
||||
"quote",
|
||||
"syn 2.0.79",
|
||||
"syn 2.0.75",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "quote"
|
||||
version = "1.0.37"
|
||||
version = "1.0.36"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "b5b9d34b8991d19d98081b46eacdd8eb58c6f2b201139f7c5f643cc155a633af"
|
||||
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|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
]
|
||||
|
|
@ -959,18 +933,29 @@ 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"
|
||||
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|
||||
dependencies = [
|
||||
"bitflags",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "regex"
|
||||
version = "1.11.0"
|
||||
name = "redox_users"
|
||||
version = "0.4.5"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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|
||||
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|
||||
dependencies = [
|
||||
"getrandom",
|
||||
"libredox",
|
||||
"thiserror",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "regex"
|
||||
version = "1.10.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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|
||||
dependencies = [
|
||||
"aho-corasick",
|
||||
"memchr",
|
||||
|
|
@ -980,9 +965,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "regex-automata"
|
||||
version = "0.4.8"
|
||||
version = "0.4.7"
|
||||
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|
||||
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||||
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|
||||
dependencies = [
|
||||
"aho-corasick",
|
||||
"memchr",
|
||||
|
|
@ -991,9 +976,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "regex-syntax"
|
||||
version = "0.8.5"
|
||||
version = "0.8.4"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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|
||||
checksum = "7a66a03ae7c801facd77a29370b4faec201768915ac14a721ba36f20bc9c209b"
|
||||
|
||||
[[package]]
|
||||
name = "runkernel"
|
||||
|
|
@ -1004,9 +989,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "rustix"
|
||||
version = "0.38.37"
|
||||
version = "0.38.34"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "8acb788b847c24f28525660c4d7758620a7210875711f79e7f663cc152726811"
|
||||
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|
||||
dependencies = [
|
||||
"bitflags",
|
||||
"errno",
|
||||
|
|
@ -1050,29 +1035,29 @@ checksum = "61697e0a1c7e512e84a621326239844a24d8207b4669b41bc18b32ea5cbf988b"
|
|||
|
||||
[[package]]
|
||||
name = "serde"
|
||||
version = "1.0.210"
|
||||
version = "1.0.208"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "c8e3592472072e6e22e0a54d5904d9febf8508f65fb8552499a1abc7d1078c3a"
|
||||
checksum = "cff085d2cb684faa248efb494c39b68e522822ac0de72ccf08109abde717cfb2"
|
||||
dependencies = [
|
||||
"serde_derive",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "serde_derive"
|
||||
version = "1.0.210"
|
||||
version = "1.0.208"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "243902eda00fad750862fc144cea25caca5e20d615af0a81bee94ca738f1df1f"
|
||||
checksum = "24008e81ff7613ed8e5ba0cfaf24e2c2f1e5b8a0495711e44fcd4882fca62bcf"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.79",
|
||||
"syn 2.0.75",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "serde_json"
|
||||
version = "1.0.128"
|
||||
version = "1.0.125"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "6ff5456707a1de34e7e37f2a6fd3d3f808c318259cbd01ab6377795054b483d8"
|
||||
checksum = "83c8e735a073ccf5be70aa8066aa984eaf2fa000db6c8d0100ae605b366d31ed"
|
||||
dependencies = [
|
||||
"itoa",
|
||||
"memchr",
|
||||
|
|
@ -1092,16 +1077,6 @@ 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"
|
||||
|
|
@ -1172,7 +1147,7 @@ dependencies = [
|
|||
"proc-macro2",
|
||||
"quote",
|
||||
"rustversion",
|
||||
"syn 2.0.79",
|
||||
"syn 2.0.75",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1188,9 +1163,9 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "syn"
|
||||
version = "2.0.79"
|
||||
version = "2.0.75"
|
||||
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|
||||
checksum = "89132cd0bf050864e1d38dc3bbc07a0eb8e7530af26344d3d2bbbef83499f590"
|
||||
checksum = "f6af063034fc1935ede7be0122941bafa9bacb949334d090b77ca98b5817c7d9"
|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -1205,9 +1180,9 @@ checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1"
|
|||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dependencies = [
|
||||
"cfg-if",
|
||||
"fastrand",
|
||||
|
|
@ -1218,12 +1193,13 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
name = "term"
|
||||
version = "1.0.0"
|
||||
version = "0.7.0"
|
||||
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|
||||
checksum = "4df4175de05129f31b80458c6df371a15e7fc3fd367272e6bf938e5c351c7ea0"
|
||||
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|
||||
dependencies = [
|
||||
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|
||||
"windows-sys 0.52.0",
|
||||
"dirs-next",
|
||||
"rustversion",
|
||||
"winapi",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1241,29 +1217,32 @@ dependencies = [
|
|||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
dependencies = [
|
||||
"thiserror-impl",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "thiserror-impl"
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"syn 2.0.75",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typenum"
|
||||
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|
||||
name = "tiny-keccak"
|
||||
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|
||||
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|
||||
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|
||||
checksum = "2c9d3793400a45f954c52e73d068316d76b6f4e36977e3fcebb13a2721e80237"
|
||||
dependencies = [
|
||||
"crunchy",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "unic-char-property"
|
||||
|
|
@ -1319,27 +1298,27 @@ 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",
|
||||
|
|
@ -1347,9 +1326,9 @@ 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",
|
||||
|
|
@ -1391,6 +1370,22 @@ 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"
|
||||
|
|
@ -1400,6 +1395,12 @@ 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"
|
||||
|
|
@ -1509,5 +1510,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
|
|||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.79",
|
||||
"syn 2.0.75",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -2,11 +2,11 @@
|
|||
"nodes": {
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1727348695,
|
||||
"narHash": "sha256-J+PeFKSDV+pHL7ukkfpVzCOO7mBSrrpJ3svwBFABbhI=",
|
||||
"lastModified": 1723637854,
|
||||
"narHash": "sha256-med8+5DSWa2UnOqtdICndjDAEjxr5D7zaIiK4pn0Q7c=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "1925c603f17fc89f4c8f6bf6f631a802ad85d784",
|
||||
"rev": "c3aa7b8938b17aebd2deecf7be0636000d62a2b9",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
|
|
|
|||
|
|
@ -12,10 +12,16 @@ 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."""
|
||||
|
|
|
|||
|
|
@ -1,3 +1,36 @@
|
|||
use nac3core::{
|
||||
codegen::{
|
||||
classes::{ListValue, RangeValue, UntypedArrayLikeAccessor},
|
||||
expr::{destructure_range, gen_call},
|
||||
llvm_intrinsics::{call_int_smax, call_stackrestore, call_stacksave},
|
||||
model::*,
|
||||
object::{any::AnyObject, ndarray::NDArrayObject},
|
||||
stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{helper::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, PointerValue, 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 +39,6 @@ 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 crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
|
||||
|
||||
/// The parallelism mode within a block.
|
||||
#[derive(Copy, Clone, Eq, PartialEq)]
|
||||
enum ParallelMode {
|
||||
|
|
@ -454,55 +454,41 @@ fn format_rpc_arg<'ctx>(
|
|||
// 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 ndarray = AnyObject { ty: arg_ty, value: arg };
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
|
||||
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 dtype = ctx.get_llvm_type(generator, ndarray.dtype);
|
||||
let ndims = ndarray.ndims_llvm(generator, ctx.ctx);
|
||||
|
||||
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();
|
||||
// `ndarray.data` is possibly not contiguous, and we need it to be contiguous for
|
||||
// the reader.
|
||||
let carray = ndarray.make_contiguous_ndarray(generator, ctx, Any(dtype));
|
||||
|
||||
let dims_buf_sz =
|
||||
ctx.builder.build_int_mul(llvm_arg.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
|
||||
let sizeof_sizet = Int(SizeT).sizeof(generator, ctx.ctx);
|
||||
let sizeof_sizet = Int(SizeT).truncate_or_bit_cast(generator, ctx, sizeof_sizet);
|
||||
|
||||
let buffer_size =
|
||||
ctx.builder.build_int_add(dims_buf_sz, llvm_pdata_sizeof, "").unwrap();
|
||||
let sizeof_pdata = Ptr(Any(dtype)).sizeof(generator, ctx.ctx);
|
||||
let sizeof_pdata = Int(SizeT).truncate_or_bit_cast(generator, ctx, sizeof_pdata);
|
||||
|
||||
let buffer = ctx.builder.build_array_alloca(llvm_i8, buffer_size, "rpc.arg").unwrap();
|
||||
let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, Some("rpc.arg"));
|
||||
let sizeof_buf_shape = sizeof_sizet.mul(ctx, ndims);
|
||||
let sizeof_buf = sizeof_buf_shape.add(ctx, sizeof_pdata);
|
||||
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
buffer.base_ptr(ctx, generator),
|
||||
llvm_arg.ptr_to_data(ctx),
|
||||
llvm_pdata_sizeof,
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
// buf = { data: void*, shape: [size_t; ndims]; }
|
||||
let buf = Int(Byte).array_alloca(generator, ctx, sizeof_buf.value);
|
||||
let buf_data = buf;
|
||||
let buf_shape = buf_data.offset(ctx, sizeof_pdata.value);
|
||||
|
||||
let pbuffer_dims_begin =
|
||||
unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
pbuffer_dims_begin,
|
||||
llvm_arg.dim_sizes().base_ptr(ctx, generator),
|
||||
dims_buf_sz,
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
// Write to `buf->data`
|
||||
let carray_data = carray.get(generator, ctx, |f| f.data); // has type Ptr<Any>
|
||||
let carray_data = carray_data.pointer_cast(generator, ctx, Int(Byte));
|
||||
buf_data.copy_from(generator, ctx, carray_data, sizeof_pdata.value);
|
||||
|
||||
buffer.base_ptr(ctx, generator)
|
||||
// Write to `buf->shape`
|
||||
let carray_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
|
||||
let carray_shape_i8 = carray_shape.pointer_cast(generator, ctx, Int(Byte));
|
||||
buf_shape.copy_from(generator, ctx, carray_shape_i8, sizeof_buf_shape.value);
|
||||
|
||||
buf.value
|
||||
}
|
||||
|
||||
_ => {
|
||||
|
|
@ -512,7 +498,7 @@ fn format_rpc_arg<'ctx>(
|
|||
ctx.builder.build_store(arg_slot, arg).unwrap();
|
||||
|
||||
ctx.builder
|
||||
.build_bit_cast(arg_slot, llvm_pi8, "rpc.arg")
|
||||
.build_bitcast(arg_slot, llvm_pi8, "rpc.arg")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
|
@ -523,305 +509,12 @@ fn format_rpc_arg<'ctx>(
|
|||
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 int8 = ctx.ctx.i8_type();
|
||||
let int32 = ctx.ctx.i32_type();
|
||||
|
|
@ -930,64 +623,84 @@ 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 +795,7 @@ 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 +805,9 @@ 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)
|
||||
})))
|
||||
}
|
||||
|
||||
|
|
@ -1359,56 +1072,46 @@ fn polymorphic_print<'ctx>(
|
|||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
|
||||
|
||||
fmt.push_str("array([");
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
|
||||
let val = NDArrayValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
|
||||
let len = call_ndarray_calc_size(generator, ctx, &val.dim_sizes(), (None, None));
|
||||
let last =
|
||||
ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
|
||||
let ndarray = AnyObject { ty, value };
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(len, false),
|
||||
|generator, ctx, _, i| {
|
||||
let elem = unsafe { val.data().get_unchecked(ctx, generator, &i, None) };
|
||||
let num_0 = Int(SizeT).const_0(generator, ctx.ctx);
|
||||
|
||||
polymorphic_print(
|
||||
ctx,
|
||||
generator,
|
||||
&[(elem_ty, elem.into())],
|
||||
"",
|
||||
None,
|
||||
true,
|
||||
as_rtio,
|
||||
)?;
|
||||
// Print `ndarray` as a flat list delimited by interspersed with ", \0"
|
||||
ndarray.foreach(generator, ctx, |generator, ctx, _, hdl| {
|
||||
let i = hdl.get_index(generator, ctx);
|
||||
let scalar = hdl.get_scalar(generator, ctx);
|
||||
|
||||
gen_if_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::ULT, i, last, "")
|
||||
.unwrap())
|
||||
},
|
||||
|generator, ctx| {
|
||||
printf(ctx, generator, ", \0".into(), Vec::default());
|
||||
// if (i != 0) { puts(", "); }
|
||||
gen_if_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
let not_first = i.compare(ctx, IntPredicate::NE, num_0);
|
||||
Ok(not_first.value)
|
||||
},
|
||||
|generator, ctx| {
|
||||
printf(ctx, generator, ", \0".into(), Vec::default());
|
||||
Ok(())
|
||||
},
|
||||
|_, _| Ok(()),
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|_, _| Ok(()),
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
// Print element
|
||||
polymorphic_print(
|
||||
ctx,
|
||||
generator,
|
||||
&[(scalar.ty, scalar.value.into())],
|
||||
"",
|
||||
None,
|
||||
true,
|
||||
as_rtio,
|
||||
)?;
|
||||
Ok(())
|
||||
})?;
|
||||
|
||||
fmt.push_str(")]");
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
|
|
|
|||
|
|
@ -16,53 +16,49 @@
|
|||
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 crate::{
|
||||
|
|
@ -70,13 +66,15 @@ use crate::{
|
|||
attributes_writeback, gen_core_log, gen_rtio_log, rpc_codegen_callback, ArtiqCodeGenerator,
|
||||
},
|
||||
symbol_resolver::{DeferredEvaluationStore, InnerResolver, PythonHelper, Resolver},
|
||||
timeline::TimeFns,
|
||||
};
|
||||
use tempfile::{self, TempDir};
|
||||
|
||||
mod codegen;
|
||||
mod symbol_resolver;
|
||||
mod timeline;
|
||||
|
||||
use timeline::TimeFns;
|
||||
|
||||
#[derive(PartialEq, Clone, Copy)]
|
||||
enum Isa {
|
||||
Host,
|
||||
|
|
@ -197,8 +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
|
||||
}
|
||||
|
|
@ -211,8 +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
|
||||
}
|
||||
|
|
@ -448,6 +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(),
|
||||
|
|
@ -478,25 +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, .. } => {
|
||||
|
|
@ -504,26 +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();
|
||||
|
|
@ -562,6 +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(),
|
||||
|
|
@ -579,8 +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() };
|
||||
|
|
@ -619,12 +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();
|
||||
|
|
@ -635,7 +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,
|
||||
|
|
@ -650,11 +632,6 @@ impl Nac3 {
|
|||
}
|
||||
}
|
||||
}
|
||||
TopLevelDef::Variable { .. } => {
|
||||
return Err(CompileError::new_err(String::from(
|
||||
"Unsupported @rpc annotation on global variable",
|
||||
)))
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -714,7 +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 };
|
||||
|
|
@ -733,7 +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());
|
||||
|
|
@ -871,41 +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 +1,17 @@
|
|||
use std::{
|
||||
collections::{HashMap, HashSet},
|
||||
sync::{
|
||||
atomic::{AtomicBool, Ordering::Relaxed},
|
||||
Arc,
|
||||
},
|
||||
use crate::PrimitivePythonId;
|
||||
use inkwell::{
|
||||
module::Linkage,
|
||||
types::BasicType,
|
||||
values::{BasicValue, BasicValueEnum},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use itertools::Itertools;
|
||||
use parking_lot::RwLock;
|
||||
use pyo3::{
|
||||
types::{PyDict, PyTuple},
|
||||
PyAny, PyObject, PyResult, Python,
|
||||
};
|
||||
|
||||
use nac3core::{
|
||||
codegen::{
|
||||
classes::{NDArrayType, ProxyType},
|
||||
model::*,
|
||||
object::ndarray::{make_contiguous_strides, NDArray},
|
||||
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 +23,19 @@ use nac3core::{
|
|||
typedef::{into_var_map, iter_type_vars, Type, TypeEnum, TypeVar, Unifier, VarMap},
|
||||
},
|
||||
};
|
||||
|
||||
use crate::PrimitivePythonId;
|
||||
use nac3parser::ast::{self, StrRef};
|
||||
use parking_lot::{Mutex, RwLock};
|
||||
use pyo3::{
|
||||
types::{PyDict, PyTuple},
|
||||
PyAny, PyErr, PyObject, PyResult, Python,
|
||||
};
|
||||
use std::{
|
||||
collections::{HashMap, HashSet},
|
||||
sync::{
|
||||
atomic::{AtomicBool, Ordering::Relaxed},
|
||||
Arc,
|
||||
},
|
||||
};
|
||||
|
||||
pub enum PrimitiveValue {
|
||||
I32(i32),
|
||||
|
|
@ -82,6 +80,7 @@ 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,
|
||||
|
|
@ -1088,15 +1087,12 @@ impl InnerResolver {
|
|||
let (ndarray_dtype, ndarray_ndims) =
|
||||
unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let ndarray_dtype_llvm_ty = ctx.get_llvm_type(generator, ndarray_dtype);
|
||||
let ndarray_llvm_ty = NDArrayType::new(generator, ctx.ctx, ndarray_dtype_llvm_ty);
|
||||
|
||||
let dtype = Any(ctx.get_llvm_type(generator, ndarray_dtype));
|
||||
{
|
||||
if self.global_value_ids.read().contains_key(&id) {
|
||||
let global = ctx.module.get_global(&id_str).unwrap_or_else(|| {
|
||||
ctx.module.add_global(
|
||||
ndarray_llvm_ty.as_underlying_type(),
|
||||
Struct(NDArray).get_type(generator, ctx.ctx),
|
||||
Some(AddressSpace::default()),
|
||||
&id_str,
|
||||
)
|
||||
|
|
@ -1116,100 +1112,138 @@ impl InnerResolver {
|
|||
} else {
|
||||
todo!("Unpacking literal of more than one element unimplemented")
|
||||
};
|
||||
let Ok(ndarray_ndims) = u64::try_from(ndarray_ndims) else {
|
||||
let Ok(ndims) = u64::try_from(ndarray_ndims) else {
|
||||
unreachable!("Expected u64 value for ndarray_ndims")
|
||||
};
|
||||
|
||||
// Obtain the shape of the ndarray
|
||||
let shape_tuple: &PyTuple = obj.getattr("shape")?.downcast()?;
|
||||
assert_eq!(shape_tuple.len(), ndarray_ndims as usize);
|
||||
let shape_values: Result<Option<Vec<_>>, _> = shape_tuple
|
||||
assert_eq!(shape_tuple.len(), ndims as usize);
|
||||
|
||||
// The Rust type inferencer cannot figure this out
|
||||
let shape_values: Result<Vec<Instance<'ctx, Int<SizeT>>>, PyErr> = shape_tuple
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, elem)| {
|
||||
self.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize()).map_err(
|
||||
|e| super::CompileError::new_err(format!("Error getting element {i}: {e}")),
|
||||
)
|
||||
let value = self
|
||||
.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize())
|
||||
.map_err(|e| {
|
||||
super::CompileError::new_err(format!("Error getting element {i}: {e}"))
|
||||
})?
|
||||
.unwrap();
|
||||
let value = Int(SizeT).check_value(generator, ctx.ctx, value).unwrap();
|
||||
Ok(value)
|
||||
})
|
||||
.collect();
|
||||
let shape_values = shape_values?.unwrap();
|
||||
let shape_values = llvm_usize.const_array(
|
||||
&shape_values.into_iter().map(BasicValueEnum::into_int_value).collect_vec(),
|
||||
);
|
||||
let shape_values = shape_values?;
|
||||
|
||||
// Also use this opportunity to get the constant values of `shape_values` for calculating strides.
|
||||
let shape_u64s = shape_values
|
||||
.iter()
|
||||
.map(|dim| {
|
||||
assert!(dim.value.is_const());
|
||||
dim.value.get_zero_extended_constant().unwrap()
|
||||
})
|
||||
.collect_vec();
|
||||
let shape_values = Int(SizeT).const_array(generator, ctx.ctx, &shape_values);
|
||||
|
||||
// create a global for ndarray.shape and initialize it using the shape
|
||||
let shape_global = ctx.module.add_global(
|
||||
llvm_usize.array_type(ndarray_ndims as u32),
|
||||
Array { len: AnyLen(ndims as u32), item: Int(SizeT) }.get_type(generator, ctx.ctx),
|
||||
Some(AddressSpace::default()),
|
||||
&(id_str.clone() + ".shape"),
|
||||
);
|
||||
shape_global.set_initializer(&shape_values);
|
||||
shape_global.set_initializer(&shape_values.value);
|
||||
|
||||
// Obtain the (flattened) elements of the ndarray
|
||||
let sz: usize = obj.getattr("size")?.extract()?;
|
||||
let data: Result<Option<Vec<_>>, _> = (0..sz)
|
||||
let data_values: Vec<Instance<'ctx, Any>> = (0..sz)
|
||||
.map(|i| {
|
||||
obj.getattr("flat")?.get_item(i).and_then(|elem| {
|
||||
self.get_obj_value(py, elem, ctx, generator, ndarray_dtype).map_err(|e| {
|
||||
super::CompileError::new_err(format!("Error getting element {i}: {e}"))
|
||||
})
|
||||
let value = self
|
||||
.get_obj_value(py, elem, ctx, generator, ndarray_dtype)
|
||||
.map_err(|e| {
|
||||
super::CompileError::new_err(format!(
|
||||
"Error getting element {i}: {e}"
|
||||
))
|
||||
})?
|
||||
.unwrap();
|
||||
|
||||
let value = dtype.check_value(generator, ctx.ctx, value).unwrap();
|
||||
Ok(value)
|
||||
})
|
||||
})
|
||||
.collect();
|
||||
let data = data?.unwrap().into_iter();
|
||||
let data = match ndarray_dtype_llvm_ty {
|
||||
BasicTypeEnum::ArrayType(ty) => {
|
||||
ty.const_array(&data.map(BasicValueEnum::into_array_value).collect_vec())
|
||||
}
|
||||
|
||||
BasicTypeEnum::FloatType(ty) => {
|
||||
ty.const_array(&data.map(BasicValueEnum::into_float_value).collect_vec())
|
||||
}
|
||||
|
||||
BasicTypeEnum::IntType(ty) => {
|
||||
ty.const_array(&data.map(BasicValueEnum::into_int_value).collect_vec())
|
||||
}
|
||||
|
||||
BasicTypeEnum::PointerType(ty) => {
|
||||
ty.const_array(&data.map(BasicValueEnum::into_pointer_value).collect_vec())
|
||||
}
|
||||
|
||||
BasicTypeEnum::StructType(ty) => {
|
||||
ty.const_array(&data.map(BasicValueEnum::into_struct_value).collect_vec())
|
||||
}
|
||||
|
||||
BasicTypeEnum::VectorType(_) => unreachable!(),
|
||||
};
|
||||
.try_collect()?;
|
||||
let data = dtype.const_array(generator, ctx.ctx, &data_values);
|
||||
|
||||
// create a global for ndarray.data and initialize it using the elements
|
||||
//
|
||||
// NOTE: NDArray's `data` is `u8*`. Here, `data_global` is an array of `dtype`.
|
||||
// We will have to cast it to an `u8*` later.
|
||||
let data_global = ctx.module.add_global(
|
||||
ndarray_dtype_llvm_ty.array_type(sz as u32),
|
||||
Array { len: AnyLen(sz as u32), item: dtype }.get_type(generator, ctx.ctx),
|
||||
Some(AddressSpace::default()),
|
||||
&(id_str.clone() + ".data"),
|
||||
);
|
||||
data_global.set_initializer(&data);
|
||||
data_global.set_initializer(&data.value);
|
||||
|
||||
// Get the constant itemsize.
|
||||
let itemsize = dtype.get_type(generator, ctx.ctx).size_of().unwrap();
|
||||
let itemsize = itemsize.get_zero_extended_constant().unwrap();
|
||||
|
||||
// Create the strides needed for ndarray.strides
|
||||
let strides = make_contiguous_strides(itemsize, ndims, &shape_u64s);
|
||||
let strides = strides
|
||||
.into_iter()
|
||||
.map(|stride| Int(SizeT).const_int(generator, ctx.ctx, stride))
|
||||
.collect_vec();
|
||||
let strides = Int(SizeT).const_array(generator, ctx.ctx, &strides);
|
||||
|
||||
// create a global for ndarray.strides and initialize it
|
||||
let strides_global = ctx.module.add_global(
|
||||
Array { len: AnyLen(ndims as u32), item: Int(Byte) }.get_type(generator, ctx.ctx),
|
||||
Some(AddressSpace::default()),
|
||||
&(id_str.clone() + ".strides"),
|
||||
);
|
||||
strides_global.set_initializer(&strides.value);
|
||||
|
||||
// create a global for the ndarray object and initialize it
|
||||
let value = ndarray_llvm_ty.as_underlying_type().const_named_struct(&[
|
||||
llvm_usize.const_int(ndarray_ndims, false).into(),
|
||||
shape_global
|
||||
.as_pointer_value()
|
||||
.const_cast(llvm_usize.ptr_type(AddressSpace::default()))
|
||||
.into(),
|
||||
data_global
|
||||
.as_pointer_value()
|
||||
.const_cast(ndarray_dtype_llvm_ty.ptr_type(AddressSpace::default()))
|
||||
.into(),
|
||||
]);
|
||||
// We are also doing [`Model::check_value`] instead of [`Model::believe_value`] to catch bugs.
|
||||
|
||||
let ndarray = ctx.module.add_global(
|
||||
ndarray_llvm_ty.as_underlying_type(),
|
||||
// NOTE: data_global is an array of dtype, we want a `u8*`.
|
||||
let ndarray_data = Ptr(dtype).check_value(generator, ctx.ctx, data_global).unwrap();
|
||||
let ndarray_data = Ptr(Int(Byte)).pointer_cast(generator, ctx, ndarray_data.value);
|
||||
|
||||
let ndarray_itemsize = Int(SizeT).const_int(generator, ctx.ctx, itemsize);
|
||||
|
||||
let ndarray_ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims);
|
||||
|
||||
let ndarray_shape =
|
||||
Ptr(Int(SizeT)).check_value(generator, ctx.ctx, shape_global).unwrap();
|
||||
|
||||
let ndarray_strides =
|
||||
Ptr(Int(SizeT)).check_value(generator, ctx.ctx, strides_global).unwrap();
|
||||
|
||||
let ndarray = Struct(NDArray).const_struct(
|
||||
generator,
|
||||
ctx.ctx,
|
||||
&[
|
||||
ndarray_data.value.as_basic_value_enum(),
|
||||
ndarray_itemsize.value.as_basic_value_enum(),
|
||||
ndarray_ndims.value.as_basic_value_enum(),
|
||||
ndarray_shape.value.as_basic_value_enum(),
|
||||
ndarray_strides.value.as_basic_value_enum(),
|
||||
],
|
||||
);
|
||||
|
||||
let ndarray_global = ctx.module.add_global(
|
||||
Struct(NDArray).get_type(generator, ctx.ctx),
|
||||
Some(AddressSpace::default()),
|
||||
&id_str,
|
||||
);
|
||||
ndarray.set_initializer(&value);
|
||||
ndarray_global.set_initializer(&ndarray.value);
|
||||
|
||||
Ok(Some(ndarray.as_pointer_value().into()))
|
||||
Ok(Some(ndarray_global.as_pointer_value().into()))
|
||||
} else if ty_id == self.primitive_ids.tuple {
|
||||
let expected_ty_enum = ctx.unifier.get_ty_immutable(expected_ty);
|
||||
let TypeEnum::TTuple { ty, is_vararg_ctx: false } = expected_ty_enum.as_ref() else {
|
||||
|
|
@ -1470,7 +1504,6 @@ 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();
|
||||
|
||||
|
|
|
|||
|
|
@ -10,17 +10,17 @@ 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"
|
||||
|
||||
[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"
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
use regex::Regex;
|
||||
use std::{
|
||||
env,
|
||||
fs::File,
|
||||
|
|
@ -6,8 +7,6 @@ use std::{
|
|||
process::{Command, Stdio},
|
||||
};
|
||||
|
||||
use regex::Regex;
|
||||
|
||||
fn main() {
|
||||
let out_dir = env::var("OUT_DIR").unwrap();
|
||||
let out_dir = Path::new(&out_dir);
|
||||
|
|
@ -23,7 +22,6 @@ fn main() {
|
|||
"--target=wasm32",
|
||||
"-x",
|
||||
"c++",
|
||||
"-std=c++20",
|
||||
"-fno-discard-value-names",
|
||||
"-fno-exceptions",
|
||||
"-fno-rtti",
|
||||
|
|
|
|||
|
|
@ -1,6 +1,16 @@
|
|||
#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"
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/list.hpp>
|
||||
#include <irrt/math_util.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/matmul.hpp>
|
||||
#include <irrt/ndarray/reshape.hpp>
|
||||
#include <irrt/ndarray/transpose.hpp>
|
||||
#include <irrt/original.hpp>
|
||||
#include <irrt/range.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
|
@ -1,9 +1,9 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt/int_types.hpp"
|
||||
#include <irrt/int_types.hpp>
|
||||
|
||||
template<typename SizeT>
|
||||
struct CSlice {
|
||||
uint8_t* base;
|
||||
template <typename SizeT> struct CSlice
|
||||
{
|
||||
uint8_t *base;
|
||||
SizeT len;
|
||||
};
|
||||
|
|
@ -0,0 +1,20 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_types.hpp>
|
||||
|
||||
namespace cstr
|
||||
{
|
||||
/**
|
||||
* @brief Implementation of `strlen()`.
|
||||
*/
|
||||
uint32_t length(const char *str)
|
||||
{
|
||||
uint32_t length = 0;
|
||||
while (*str != '\0')
|
||||
{
|
||||
length++;
|
||||
str++;
|
||||
}
|
||||
return length;
|
||||
}
|
||||
} // namespace cstr
|
||||
|
|
@ -7,19 +7,17 @@
|
|||
#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)
|
||||
#define raise_debug_assert(SizeT, msg, param1, param2, param3) \
|
||||
raise_exception(SizeT, EXN_ASSERTION_ERROR, "IRRT debug assert failed: " msg, param1, param2, param3);
|
||||
|
||||
#define debug_assert_eq(SizeT, lhs, rhs) \
|
||||
if 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); \
|
||||
}
|
||||
|
|
@ -1,7 +1,8 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt/cslice.hpp"
|
||||
#include "irrt/int_types.hpp"
|
||||
#include <irrt/cslice.hpp>
|
||||
#include <irrt/cstr_util.hpp>
|
||||
#include <irrt/int_types.hpp>
|
||||
|
||||
/**
|
||||
* @brief The int type of ARTIQ exception IDs.
|
||||
|
|
@ -12,11 +13,12 @@ typedef int32_t ExceptionId;
|
|||
* Set of exceptions C++ IRRT can use.
|
||||
* Must be synchronized with `setup_irrt_exceptions` in `nac3core/src/codegen/irrt/mod.rs`.
|
||||
*/
|
||||
extern "C" {
|
||||
ExceptionId EXN_INDEX_ERROR;
|
||||
ExceptionId EXN_VALUE_ERROR;
|
||||
ExceptionId EXN_ASSERTION_ERROR;
|
||||
ExceptionId EXN_TYPE_ERROR;
|
||||
extern "C"
|
||||
{
|
||||
ExceptionId EXN_INDEX_ERROR;
|
||||
ExceptionId EXN_VALUE_ERROR;
|
||||
ExceptionId EXN_ASSERTION_ERROR;
|
||||
ExceptionId EXN_TYPE_ERROR;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -25,14 +27,15 @@ ExceptionId EXN_TYPE_ERROR;
|
|||
* 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);
|
||||
extern "C" void __nac3_raise(void *err);
|
||||
|
||||
namespace {
|
||||
namespace
|
||||
{
|
||||
/**
|
||||
* @brief NAC3's Exception struct
|
||||
*/
|
||||
template<typename SizeT>
|
||||
struct Exception {
|
||||
template <typename SizeT> struct Exception
|
||||
{
|
||||
ExceptionId id;
|
||||
CSlice<SizeT> filename;
|
||||
int32_t line;
|
||||
|
|
@ -42,29 +45,24 @@ struct Exception {
|
|||
int64_t params[3];
|
||||
};
|
||||
|
||||
constexpr int64_t NO_PARAM = 0;
|
||||
const int64_t NO_PARAM = 0;
|
||||
|
||||
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) {
|
||||
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::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::length(function)},
|
||||
.msg = {.base = (uint8_t *)msg, .len = (int32_t)cstr::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();
|
||||
}
|
||||
|
||||
|
|
@ -77,6 +75,6 @@ void _raise_exception_helper(ExceptionId id,
|
|||
* `param0` to `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) \
|
||||
#define raise_exception(SizeT, id, msg, param0, param1, param2) \
|
||||
_raise_exception_helper<SizeT>(id, __FILE__, __LINE__, __FUNCTION__, msg, param0, param1, param2)
|
||||
} // namespace
|
||||
} // namespace
|
||||
|
|
@ -1,22 +1,8 @@
|
|||
#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 +1,19 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt/int_types.hpp"
|
||||
#include "irrt/math_util.hpp"
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
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;
|
||||
}
|
||||
} // extern "C"
|
||||
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
|
||||
|
|
@ -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 +1,14 @@
|
|||
#pragma once
|
||||
|
||||
namespace {
|
||||
template<typename T>
|
||||
const T& max(const T& a, const T& b) {
|
||||
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) {
|
||||
template <typename T> const T &min(const T &a, const T &b)
|
||||
{
|
||||
return a > b ? b : a;
|
||||
}
|
||||
} // namespace
|
||||
} // 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 +1,157 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/debug.hpp>
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/list.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace
|
||||
{
|
||||
namespace ndarray
|
||||
{
|
||||
namespace array
|
||||
{
|
||||
/**
|
||||
* @brief In the context of `np.array(<list>)`, deduce the ndarray's shape produced by `<list>` and raise
|
||||
* an exception if there is anything wrong with `<shape>` (e.g., inconsistent dimensions `np.array([[1.0, 2.0], [3.0]])`)
|
||||
*
|
||||
* If this function finds no issues with `<list>`, the deduced shape is written to `shape`. The caller has the responsibility to
|
||||
* allocate `[SizeT; ndims]` for `shape`. The caller must also initialize `shape` with `-1`s because of implementation details.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void set_and_validate_list_shape_helper(SizeT axis, List<SizeT> *list, SizeT ndims, SizeT *shape)
|
||||
{
|
||||
if (shape[axis] == -1)
|
||||
{
|
||||
// Dimension is unspecified. Set it.
|
||||
shape[axis] = list->len;
|
||||
}
|
||||
else
|
||||
{
|
||||
// Dimension is specified. Check.
|
||||
if (shape[axis] != list->len)
|
||||
{
|
||||
// Mismatch, throw an error.
|
||||
// NOTE: NumPy's error message is more complex and needs more PARAMS to display.
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"The requested array has an inhomogenous shape "
|
||||
"after {0} dimension(s).",
|
||||
axis, shape[axis], list->len);
|
||||
}
|
||||
}
|
||||
|
||||
if (axis + 1 == ndims)
|
||||
{
|
||||
// `list` has type `list[ItemType]`
|
||||
// Do nothing
|
||||
}
|
||||
else
|
||||
{
|
||||
// `list` has type `list[list[...]]`
|
||||
List<SizeT> **lists = (List<SizeT> **)(list->items);
|
||||
for (SizeT i = 0; i < list->len; i++)
|
||||
{
|
||||
set_and_validate_list_shape_helper<SizeT>(axis + 1, lists[i], ndims, shape);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief See `set_and_validate_list_shape_helper`.
|
||||
*/
|
||||
template <typename SizeT> void set_and_validate_list_shape(List<SizeT> *list, SizeT ndims, SizeT *shape)
|
||||
{
|
||||
for (SizeT axis = 0; axis < ndims; axis++)
|
||||
{
|
||||
shape[axis] = -1; // Sentinel to say this dimension is unspecified.
|
||||
}
|
||||
set_and_validate_list_shape_helper<SizeT>(0, list, ndims, shape);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief In the context of `np.array(<list>)`, copied the contents stored in `list` to `ndarray`.
|
||||
*
|
||||
* `list` is assumed to be "legal". (i.e., no inconsistent dimensions)
|
||||
*
|
||||
* # Notes on `ndarray`
|
||||
* The caller is responsible for allocating space for `ndarray`.
|
||||
* Here is what this function expects from `ndarray` when called:
|
||||
* - `ndarray->data` has to be allocated, contiguous, and may contain uninitialized values.
|
||||
* - `ndarray->itemsize` has to be initialized.
|
||||
* - `ndarray->ndims` has to be initialized.
|
||||
* - `ndarray->shape` has to be initialized.
|
||||
* - `ndarray->strides` is ignored, but note that `ndarray->data` is contiguous.
|
||||
* When this function call ends:
|
||||
* - `ndarray->data` is written with contents from `<list>`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void write_list_to_array_helper(SizeT axis, SizeT *index, List<SizeT> *list, NDArray<SizeT> *ndarray)
|
||||
{
|
||||
debug_assert_eq(SizeT, list->len, ndarray->shape[axis]);
|
||||
if (IRRT_DEBUG_ASSERT_BOOL)
|
||||
{
|
||||
if (!ndarray::basic::is_c_contiguous(ndarray))
|
||||
{
|
||||
raise_debug_assert(SizeT, "ndarray is not C-contiguous", ndarray->strides[0], ndarray->strides[1],
|
||||
NO_PARAM);
|
||||
}
|
||||
}
|
||||
|
||||
if (axis + 1 == ndarray->ndims)
|
||||
{
|
||||
// `list` has type `list[scalar]`
|
||||
// `ndarray` is contiguous, so we can do this, and this is fast.
|
||||
uint8_t *dst = ndarray->data + (ndarray->itemsize * (*index));
|
||||
__builtin_memcpy(dst, list->items, ndarray->itemsize * list->len);
|
||||
*index += list->len;
|
||||
}
|
||||
else
|
||||
{
|
||||
// `list` has type `list[list[...]]`
|
||||
List<SizeT> **lists = (List<SizeT> **)(list->items);
|
||||
|
||||
for (SizeT i = 0; i < list->len; i++)
|
||||
{
|
||||
write_list_to_array_helper<SizeT>(axis + 1, index, lists[i], ndarray);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief See `write_list_to_array_helper`.
|
||||
*/
|
||||
template <typename SizeT> void write_list_to_array(List<SizeT> *list, NDArray<SizeT> *ndarray)
|
||||
{
|
||||
SizeT index = 0;
|
||||
write_list_to_array_helper<SizeT>((SizeT)0, &index, list, ndarray);
|
||||
}
|
||||
} // namespace array
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C"
|
||||
{
|
||||
using namespace ndarray::array;
|
||||
|
||||
void __nac3_ndarray_array_set_and_validate_list_shape(List<int32_t> *list, int32_t ndims, int32_t *shape)
|
||||
{
|
||||
set_and_validate_list_shape(list, ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_array_set_and_validate_list_shape64(List<int64_t> *list, int64_t ndims, int64_t *shape)
|
||||
{
|
||||
set_and_validate_list_shape(list, ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_array_write_list_to_array(List<int32_t> *list, NDArray<int32_t> *ndarray)
|
||||
{
|
||||
write_list_to_array(list, ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_array_write_list_to_array64(List<int64_t> *list, NDArray<int64_t> *ndarray)
|
||||
{
|
||||
write_list_to_array(list, ndarray);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,371 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/debug.hpp>
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace
|
||||
{
|
||||
namespace ndarray
|
||||
{
|
||||
namespace basic
|
||||
{
|
||||
/**
|
||||
* @brief Assert that `shape` does not contain negative dimensions.
|
||||
*
|
||||
* @param ndims Number of dimensions in `shape`
|
||||
* @param shape The shape to check on
|
||||
*/
|
||||
template <typename SizeT> void assert_shape_no_negative(SizeT ndims, const SizeT *shape)
|
||||
{
|
||||
for (SizeT axis = 0; axis < ndims; axis++)
|
||||
{
|
||||
if (shape[axis] < 0)
|
||||
{
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"negative dimensions are not allowed; axis {0} "
|
||||
"has dimension {1}",
|
||||
axis, shape[axis], NO_PARAM);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Assert that two shapes are the same in the context of writing output to an ndarray.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void assert_output_shape_same(SizeT ndarray_ndims, const SizeT *ndarray_shape, SizeT output_ndims,
|
||||
const SizeT *output_shape)
|
||||
{
|
||||
if (ndarray_ndims != output_ndims)
|
||||
{
|
||||
// There is no corresponding NumPy error message like this.
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot write output of ndims {0} to an ndarray with ndims {1}",
|
||||
output_ndims, ndarray_ndims, NO_PARAM);
|
||||
}
|
||||
|
||||
for (SizeT axis = 0; axis < ndarray_ndims; axis++)
|
||||
{
|
||||
if (ndarray_shape[axis] != output_shape[axis])
|
||||
{
|
||||
// There is no corresponding NumPy error message like this.
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"Mismatched dimensions on axis {0}, output has "
|
||||
"dimension {1}, but destination ndarray has dimension {2}.",
|
||||
axis, output_shape[axis], ndarray_shape[axis]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return the number of elements of an ndarray given its shape.
|
||||
*
|
||||
* @param ndims Number of dimensions in `shape`
|
||||
* @param shape The shape of the ndarray
|
||||
*/
|
||||
template <typename SizeT> SizeT calc_size_from_shape(SizeT ndims, const SizeT *shape)
|
||||
{
|
||||
SizeT size = 1;
|
||||
for (SizeT axis = 0; axis < ndims; axis++)
|
||||
size *= shape[axis];
|
||||
return size;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Compute the array indices of the `nth` (0-based) element of an ndarray given only its shape.
|
||||
*
|
||||
* @param ndims Number of elements in `shape` and `indices`
|
||||
* @param shape The shape of the ndarray
|
||||
* @param indices The returned indices indexing the ndarray with shape `shape`.
|
||||
* @param nth The index of the element of interest.
|
||||
*/
|
||||
template <typename SizeT> void set_indices_by_nth(SizeT ndims, const SizeT *shape, SizeT *indices, SizeT nth)
|
||||
{
|
||||
for (SizeT i = 0; i < ndims; i++)
|
||||
{
|
||||
SizeT axis = ndims - i - 1;
|
||||
SizeT dim = shape[axis];
|
||||
|
||||
indices[axis] = nth % dim;
|
||||
nth /= dim;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return the number of elements of an `ndarray`
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.size`
|
||||
*/
|
||||
template <typename SizeT> SizeT size(const NDArray<SizeT> *ndarray)
|
||||
{
|
||||
return calc_size_from_shape(ndarray->ndims, ndarray->shape);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return of the number of its content of an `ndarray`.
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.nbytes`.
|
||||
*/
|
||||
template <typename SizeT> SizeT nbytes(const NDArray<SizeT> *ndarray)
|
||||
{
|
||||
return size(ndarray) * ndarray->itemsize;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
|
||||
*
|
||||
* This function corresponds to `<an_ndarray>.__len__`.
|
||||
*
|
||||
* @param dst_length The length.
|
||||
*/
|
||||
template <typename SizeT> SizeT len(const NDArray<SizeT> *ndarray)
|
||||
{
|
||||
// numpy prohibits `__len__` on unsized objects
|
||||
if (ndarray->ndims == 0)
|
||||
{
|
||||
raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object", NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
else
|
||||
{
|
||||
return ndarray->shape[0];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return a boolean indicating if `ndarray` is (C-)contiguous.
|
||||
*
|
||||
* You may want to see ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
|
||||
*/
|
||||
template <typename SizeT> bool is_c_contiguous(const NDArray<SizeT> *ndarray)
|
||||
{
|
||||
// References:
|
||||
// - tinynumpy's implementation: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L102
|
||||
// - ndarray's flags["C_CONTIGUOUS"]: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags
|
||||
// - ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
|
||||
|
||||
// From https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45:
|
||||
//
|
||||
// The traditional rule is that for an array to be flagged as C contiguous,
|
||||
// the following must hold:
|
||||
//
|
||||
// strides[-1] == itemsize
|
||||
// strides[i] == shape[i+1] * strides[i + 1]
|
||||
// [...]
|
||||
// According to these rules, a 0- or 1-dimensional array is either both
|
||||
// C- and F-contiguous, or neither; and an array with 2+ dimensions
|
||||
// can be C- or F- contiguous, or neither, but not both. Though there
|
||||
// there are exceptions for arrays with zero or one item, in the first
|
||||
// case the check is relaxed up to and including the first dimension
|
||||
// with shape[i] == 0. In the second case `strides == itemsize` will
|
||||
// can be true for all dimensions and both flags are set.
|
||||
|
||||
if (ndarray->ndims == 0)
|
||||
{
|
||||
return true;
|
||||
}
|
||||
|
||||
if (ndarray->strides[ndarray->ndims - 1] != ndarray->itemsize)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
for (SizeT i = 1; i < ndarray->ndims; i++)
|
||||
{
|
||||
SizeT axis_i = ndarray->ndims - i - 1;
|
||||
if (ndarray->strides[axis_i] != ndarray->shape[axis_i + 1] * ndarray->strides[axis_i + 1])
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return the pointer to the element indexed by `indices` along the ndarray's axes.
|
||||
*
|
||||
* This function does no bound check.
|
||||
*/
|
||||
template <typename SizeT> uint8_t *get_pelement_by_indices(const NDArray<SizeT> *ndarray, const SizeT *indices)
|
||||
{
|
||||
uint8_t *element = ndarray->data;
|
||||
for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
|
||||
element += indices[dim_i] * ndarray->strides[dim_i];
|
||||
return element;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Return the pointer to the nth (0-based) element of `ndarray` in flattened view.
|
||||
*
|
||||
* This function does no bound check.
|
||||
*/
|
||||
template <typename SizeT> uint8_t *get_nth_pelement(const NDArray<SizeT> *ndarray, SizeT nth)
|
||||
{
|
||||
uint8_t *element = ndarray->data;
|
||||
for (SizeT i = 0; i < ndarray->ndims; i++)
|
||||
{
|
||||
SizeT axis = ndarray->ndims - i - 1;
|
||||
SizeT dim = ndarray->shape[axis];
|
||||
element += ndarray->strides[axis] * (nth % dim);
|
||||
nth /= dim;
|
||||
}
|
||||
return element;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Update the strides of an ndarray given an ndarray `shape` to be contiguous.
|
||||
*
|
||||
* You might want to read https://ajcr.net/stride-guide-part-1/.
|
||||
*/
|
||||
template <typename SizeT> void set_strides_by_shape(NDArray<SizeT> *ndarray)
|
||||
{
|
||||
SizeT stride_product = 1;
|
||||
for (SizeT i = 0; i < ndarray->ndims; i++)
|
||||
{
|
||||
SizeT axis = ndarray->ndims - i - 1;
|
||||
ndarray->strides[axis] = stride_product * ndarray->itemsize;
|
||||
stride_product *= ndarray->shape[axis];
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Set an element in `ndarray`.
|
||||
*
|
||||
* @param pelement Pointer to the element in `ndarray` to be set.
|
||||
* @param pvalue Pointer to the value `pelement` will be set to.
|
||||
*/
|
||||
template <typename SizeT> void set_pelement_value(NDArray<SizeT> *ndarray, uint8_t *pelement, const uint8_t *pvalue)
|
||||
{
|
||||
__builtin_memcpy(pelement, pvalue, ndarray->itemsize);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Copy data from one ndarray to another of the exact same size and itemsize.
|
||||
*
|
||||
* Both ndarrays will be viewed in their flatten views when copying the elements.
|
||||
*/
|
||||
template <typename SizeT> void copy_data(const NDArray<SizeT> *src_ndarray, NDArray<SizeT> *dst_ndarray)
|
||||
{
|
||||
// TODO: Make this faster with memcpy when we see a contiguous segment.
|
||||
// TODO: Handle overlapping.
|
||||
|
||||
debug_assert_eq(SizeT, src_ndarray->itemsize, dst_ndarray->itemsize);
|
||||
|
||||
for (SizeT i = 0; i < size(src_ndarray); i++)
|
||||
{
|
||||
auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
|
||||
auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
|
||||
ndarray::basic::set_pelement_value(dst_ndarray, dst_element, src_element);
|
||||
}
|
||||
}
|
||||
} // namespace basic
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C"
|
||||
{
|
||||
using namespace ndarray::basic;
|
||||
|
||||
void __nac3_ndarray_util_assert_shape_no_negative(int32_t ndims, int32_t *shape)
|
||||
{
|
||||
assert_shape_no_negative(ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims, int64_t *shape)
|
||||
{
|
||||
assert_shape_no_negative(ndims, shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_output_shape_same(int32_t ndarray_ndims, const int32_t *ndarray_shape,
|
||||
int32_t output_ndims, const int32_t *output_shape)
|
||||
{
|
||||
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims, output_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_util_assert_output_shape_same64(int64_t ndarray_ndims, const int64_t *ndarray_shape,
|
||||
int64_t output_ndims, const int64_t *output_shape)
|
||||
{
|
||||
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims, output_shape);
|
||||
}
|
||||
|
||||
uint32_t __nac3_ndarray_size(NDArray<int32_t> *ndarray)
|
||||
{
|
||||
return size(ndarray);
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_size64(NDArray<int64_t> *ndarray)
|
||||
{
|
||||
return size(ndarray);
|
||||
}
|
||||
|
||||
uint32_t __nac3_ndarray_nbytes(NDArray<int32_t> *ndarray)
|
||||
{
|
||||
return nbytes(ndarray);
|
||||
}
|
||||
|
||||
uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t> *ndarray)
|
||||
{
|
||||
return nbytes(ndarray);
|
||||
}
|
||||
|
||||
int32_t __nac3_ndarray_len(NDArray<int32_t> *ndarray)
|
||||
{
|
||||
return len(ndarray);
|
||||
}
|
||||
|
||||
int64_t __nac3_ndarray_len64(NDArray<int64_t> *ndarray)
|
||||
{
|
||||
return len(ndarray);
|
||||
}
|
||||
|
||||
bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t> *ndarray)
|
||||
{
|
||||
return is_c_contiguous(ndarray);
|
||||
}
|
||||
|
||||
bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t> *ndarray)
|
||||
{
|
||||
return is_c_contiguous(ndarray);
|
||||
}
|
||||
|
||||
uint8_t *__nac3_ndarray_get_nth_pelement(const NDArray<int32_t> *ndarray, int32_t nth)
|
||||
{
|
||||
return get_nth_pelement(ndarray, nth);
|
||||
}
|
||||
|
||||
uint8_t *__nac3_ndarray_get_nth_pelement64(const NDArray<int64_t> *ndarray, int64_t nth)
|
||||
{
|
||||
return get_nth_pelement(ndarray, nth);
|
||||
}
|
||||
|
||||
uint8_t *__nac3_ndarray_get_pelement_by_indices(const NDArray<int32_t> *ndarray, int32_t *indices)
|
||||
{
|
||||
return get_pelement_by_indices(ndarray, indices);
|
||||
}
|
||||
|
||||
uint8_t *__nac3_ndarray_get_pelement_by_indices64(const NDArray<int64_t> *ndarray, int64_t *indices)
|
||||
{
|
||||
return get_pelement_by_indices(ndarray, indices);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t> *ndarray)
|
||||
{
|
||||
set_strides_by_shape(ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t> *ndarray)
|
||||
{
|
||||
set_strides_by_shape(ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_copy_data(NDArray<int32_t> *src_ndarray, NDArray<int32_t> *dst_ndarray)
|
||||
{
|
||||
copy_data(src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_copy_data64(NDArray<int64_t> *src_ndarray, NDArray<int64_t> *dst_ndarray)
|
||||
{
|
||||
copy_data(src_ndarray, dst_ndarray);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,188 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
namespace
|
||||
{
|
||||
template <typename SizeT> struct ShapeEntry
|
||||
{
|
||||
SizeT ndims;
|
||||
SizeT *shape;
|
||||
};
|
||||
} // namespace
|
||||
|
||||
namespace
|
||||
{
|
||||
namespace ndarray
|
||||
{
|
||||
namespace broadcast
|
||||
{
|
||||
/**
|
||||
* @brief Return true if `src_shape` can broadcast to `dst_shape`.
|
||||
*
|
||||
* See https://numpy.org/doc/stable/user/basics.broadcasting.html
|
||||
*/
|
||||
template <typename SizeT>
|
||||
bool can_broadcast_shape_to(SizeT target_ndims, const SizeT *target_shape, SizeT src_ndims, const SizeT *src_shape)
|
||||
{
|
||||
if (src_ndims > target_ndims)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
for (SizeT i = 0; i < src_ndims; i++)
|
||||
{
|
||||
SizeT target_dim = target_shape[target_ndims - i - 1];
|
||||
SizeT src_dim = src_shape[src_ndims - i - 1];
|
||||
if (!(src_dim == 1 || target_dim == src_dim))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Performs `np.broadcast_shapes(<shapes>)`
|
||||
*
|
||||
* @param num_shapes Number of entries in `shapes`
|
||||
* @param shapes The list of shape to do `np.broadcast_shapes` on.
|
||||
* @param dst_ndims The length of `dst_shape`.
|
||||
* `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it.
|
||||
* for this function since they should already know in order to allocate `dst_shape` in the first place.
|
||||
* @param dst_shape The resulting shape. Must be pre-allocated by the caller. This function calculate the result
|
||||
* of `np.broadcast_shapes` and write it here.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT> *shapes, SizeT dst_ndims, SizeT *dst_shape)
|
||||
{
|
||||
for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++)
|
||||
{
|
||||
dst_shape[dst_axis] = 1;
|
||||
}
|
||||
|
||||
#ifdef IRRT_DEBUG_ASSERT
|
||||
SizeT max_ndims_found = 0;
|
||||
#endif
|
||||
|
||||
for (SizeT i = 0; i < num_shapes; i++)
|
||||
{
|
||||
ShapeEntry<SizeT> entry = shapes[i];
|
||||
|
||||
// Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
|
||||
debug_assert(SizeT, entry.ndims <= dst_ndims);
|
||||
|
||||
#ifdef IRRT_DEBUG_ASSERT
|
||||
max_ndims_found = max(max_ndims_found, entry.ndims);
|
||||
#endif
|
||||
|
||||
for (SizeT j = 0; j < entry.ndims; j++)
|
||||
{
|
||||
SizeT entry_axis = entry.ndims - j - 1;
|
||||
SizeT dst_axis = dst_ndims - j - 1;
|
||||
|
||||
SizeT entry_dim = entry.shape[entry_axis];
|
||||
SizeT dst_dim = dst_shape[dst_axis];
|
||||
|
||||
if (dst_dim == 1)
|
||||
{
|
||||
dst_shape[dst_axis] = entry_dim;
|
||||
}
|
||||
else if (entry_dim == 1 || entry_dim == dst_dim)
|
||||
{
|
||||
// Do nothing
|
||||
}
|
||||
else
|
||||
{
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR,
|
||||
"shape mismatch: objects cannot be broadcast "
|
||||
"to a single shape.",
|
||||
NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
|
||||
debug_assert_eq(SizeT, max_ndims_found, dst_ndims);
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Perform `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
|
||||
*
|
||||
* This function attempts to broadcast `src_ndarray` to a new shape defined by `dst_ndarray.shape`,
|
||||
* and return the result by modifying `dst_ndarray`.
|
||||
*
|
||||
* # Notes on `dst_ndarray`
|
||||
* The caller is responsible for allocating space for the resulting ndarray.
|
||||
* Here is what this function expects from `dst_ndarray` when called:
|
||||
* - `dst_ndarray->data` does not have to be initialized.
|
||||
* - `dst_ndarray->itemsize` does not have to be initialized.
|
||||
* - `dst_ndarray->ndims` must be initialized, determining the length of `dst_ndarray->shape`
|
||||
* - `dst_ndarray->shape` must be allocated, and must contain the desired target broadcast shape.
|
||||
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
|
||||
* When this function call ends:
|
||||
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
|
||||
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
|
||||
* - `dst_ndarray->ndims` is unchanged.
|
||||
* - `dst_ndarray->shape` is unchanged.
|
||||
* - `dst_ndarray->strides` is updated accordingly by how ndarray broadcast_to works.
|
||||
*/
|
||||
template <typename SizeT> void broadcast_to(const NDArray<SizeT> *src_ndarray, NDArray<SizeT> *dst_ndarray)
|
||||
{
|
||||
if (!ndarray::broadcast::can_broadcast_shape_to(dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
|
||||
src_ndarray->shape))
|
||||
{
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "operands could not be broadcast together", NO_PARAM, NO_PARAM,
|
||||
NO_PARAM);
|
||||
}
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
for (SizeT i = 0; i < dst_ndarray->ndims; i++)
|
||||
{
|
||||
SizeT src_axis = src_ndarray->ndims - i - 1;
|
||||
SizeT dst_axis = dst_ndarray->ndims - i - 1;
|
||||
if (src_axis < 0 || (src_ndarray->shape[src_axis] == 1 && dst_ndarray->shape[dst_axis] != 1))
|
||||
{
|
||||
// Freeze the steps in-place
|
||||
dst_ndarray->strides[dst_axis] = 0;
|
||||
}
|
||||
else
|
||||
{
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace broadcast
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C"
|
||||
{
|
||||
using namespace ndarray::broadcast;
|
||||
|
||||
void __nac3_ndarray_broadcast_to(NDArray<int32_t> *src_ndarray, NDArray<int32_t> *dst_ndarray)
|
||||
{
|
||||
broadcast_to(src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_to64(NDArray<int64_t> *src_ndarray, NDArray<int64_t> *dst_ndarray)
|
||||
{
|
||||
broadcast_to(src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_shapes(int32_t num_shapes, const ShapeEntry<int32_t> *shapes, int32_t dst_ndims,
|
||||
int32_t *dst_shape)
|
||||
{
|
||||
broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_broadcast_shapes64(int64_t num_shapes, const ShapeEntry<int64_t> *shapes, int64_t dst_ndims,
|
||||
int64_t *dst_shape)
|
||||
{
|
||||
broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_types.hpp>
|
||||
|
||||
namespace
|
||||
{
|
||||
/**
|
||||
* @brief The NDArray object
|
||||
*
|
||||
* Official numpy implementation: https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst
|
||||
*/
|
||||
template <typename SizeT> struct NDArray
|
||||
{
|
||||
/**
|
||||
* @brief The underlying data this `ndarray` is pointing to.
|
||||
*/
|
||||
uint8_t *data;
|
||||
|
||||
/**
|
||||
* @brief The number of bytes of a single element in `data`.
|
||||
*/
|
||||
SizeT itemsize;
|
||||
|
||||
/**
|
||||
* @brief The number of dimensions of this shape.
|
||||
*/
|
||||
SizeT ndims;
|
||||
|
||||
/**
|
||||
* @brief The NDArray shape, with length equal to `ndims`.
|
||||
*
|
||||
* Note that it may contain 0.
|
||||
*/
|
||||
SizeT *shape;
|
||||
|
||||
/**
|
||||
* @brief Array strides, with length equal to `ndims`
|
||||
*
|
||||
* The stride values are in units of bytes, not number of elements.
|
||||
*
|
||||
* Note that `strides` can have negative values or contain 0.
|
||||
*/
|
||||
SizeT *strides;
|
||||
};
|
||||
} // namespace
|
||||
|
|
@ -0,0 +1,249 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/range.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
namespace
|
||||
{
|
||||
typedef uint8_t NDIndexType;
|
||||
|
||||
/**
|
||||
* @brief A single element index
|
||||
*
|
||||
* `data` points to a `int32_t`.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
|
||||
|
||||
/**
|
||||
* @brief A slice index
|
||||
*
|
||||
* `data` points to a `Slice<int32_t>`.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_SLICE = 1;
|
||||
|
||||
/**
|
||||
* @brief `np.newaxis` / `None`
|
||||
*
|
||||
* `data` is unused.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_NEWAXIS = 2;
|
||||
|
||||
/**
|
||||
* @brief `Ellipsis` / `...`
|
||||
*
|
||||
* `data` is unused.
|
||||
*/
|
||||
const NDIndexType ND_INDEX_TYPE_ELLIPSIS = 3;
|
||||
|
||||
/**
|
||||
* @brief An index used in ndarray indexing
|
||||
*
|
||||
* That is:
|
||||
* ```
|
||||
* my_ndarray[::-1, 3, ..., np.newaxis]
|
||||
* ^^^^ ^ ^^^ ^^^^^^^^^^ each of these is represented by an NDIndex.
|
||||
* ```
|
||||
*/
|
||||
struct NDIndex
|
||||
{
|
||||
/**
|
||||
* @brief Enum tag to specify the type of index.
|
||||
*
|
||||
* Please see the comment of each enum constant.
|
||||
*/
|
||||
NDIndexType type;
|
||||
|
||||
/**
|
||||
* @brief The accompanying data associated with `type`.
|
||||
*
|
||||
* Please see the comment of each enum constant.
|
||||
*/
|
||||
uint8_t *data;
|
||||
};
|
||||
} // namespace
|
||||
|
||||
namespace
|
||||
{
|
||||
namespace ndarray
|
||||
{
|
||||
namespace indexing
|
||||
{
|
||||
/**
|
||||
* @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
|
||||
*
|
||||
* This function is very similar to performing `dst_ndarray = src_ndarray[indices]` in Python.
|
||||
*
|
||||
* This function also does proper assertions on `indices` to check for out of bounds access and more.
|
||||
*
|
||||
* # Notes on `dst_ndarray`
|
||||
* The caller is responsible for allocating space for the resulting ndarray.
|
||||
* Here is what this function expects from `dst_ndarray` when called:
|
||||
* - `dst_ndarray->data` does not have to be initialized.
|
||||
* - `dst_ndarray->itemsize` does not have to be initialized.
|
||||
* - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
|
||||
* indexing `src_ndarray` with `indices`.
|
||||
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
|
||||
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
|
||||
* When this function call ends:
|
||||
* - `dst_ndarray->data` is set to `src_ndarray->data`.
|
||||
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`.
|
||||
* - `dst_ndarray->ndims` is unchanged.
|
||||
* - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
|
||||
* - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
|
||||
*
|
||||
* @param indices indices to index `src_ndarray`, ordered in the same way you would write them in Python.
|
||||
* @param src_ndarray The NDArray to be indexed.
|
||||
* @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void index(SizeT num_indices, const NDIndex *indices, const NDArray<SizeT> *src_ndarray, NDArray<SizeT> *dst_ndarray)
|
||||
{
|
||||
// Validate `indices`.
|
||||
|
||||
// Expected value of `dst_ndarray->ndims`.
|
||||
SizeT expected_dst_ndims = src_ndarray->ndims;
|
||||
// To check for "too many indices for array: array is ?-dimensional, but ? were indexed"
|
||||
SizeT num_indexed = 0;
|
||||
// There may be ellipsis `...` in `indices`. There can only be 0 or 1 ellipsis.
|
||||
SizeT num_ellipsis = 0;
|
||||
|
||||
for (SizeT i = 0; i < num_indices; i++)
|
||||
{
|
||||
if (indices[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT)
|
||||
{
|
||||
expected_dst_ndims--;
|
||||
num_indexed++;
|
||||
}
|
||||
else if (indices[i].type == ND_INDEX_TYPE_SLICE)
|
||||
{
|
||||
num_indexed++;
|
||||
}
|
||||
else if (indices[i].type == ND_INDEX_TYPE_NEWAXIS)
|
||||
{
|
||||
expected_dst_ndims++;
|
||||
}
|
||||
else if (indices[i].type == ND_INDEX_TYPE_ELLIPSIS)
|
||||
{
|
||||
num_ellipsis++;
|
||||
if (num_ellipsis > 1)
|
||||
{
|
||||
raise_exception(SizeT, EXN_INDEX_ERROR, "an index can only have a single ellipsis ('...')", NO_PARAM,
|
||||
NO_PARAM, NO_PARAM);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
|
||||
debug_assert_eq(SizeT, expected_dst_ndims, dst_ndarray->ndims);
|
||||
|
||||
if (src_ndarray->ndims - num_indexed < 0)
|
||||
{
|
||||
raise_exception(SizeT, EXN_INDEX_ERROR,
|
||||
"too many indices for array: array is {0}-dimensional, "
|
||||
"but {1} were indexed",
|
||||
src_ndarray->ndims, num_indices, NO_PARAM);
|
||||
}
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
// Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
|
||||
SizeT src_axis = 0;
|
||||
SizeT dst_axis = 0;
|
||||
|
||||
for (int32_t i = 0; i < num_indices; i++)
|
||||
{
|
||||
const NDIndex *index = &indices[i];
|
||||
if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT)
|
||||
{
|
||||
SizeT input = (SizeT) * ((int32_t *)index->data);
|
||||
|
||||
SizeT k = slice::resolve_index_in_length(src_ndarray->shape[src_axis], input);
|
||||
if (k == -1)
|
||||
{
|
||||
raise_exception(SizeT, EXN_INDEX_ERROR,
|
||||
"index {0} is out of bounds for axis {1} "
|
||||
"with size {2}",
|
||||
input, src_axis, src_ndarray->shape[src_axis]);
|
||||
}
|
||||
|
||||
dst_ndarray->data += k * src_ndarray->strides[src_axis];
|
||||
|
||||
src_axis++;
|
||||
}
|
||||
else if (index->type == ND_INDEX_TYPE_SLICE)
|
||||
{
|
||||
Slice<int32_t> *slice = (Slice<int32_t> *)index->data;
|
||||
|
||||
Range<int32_t> range = slice->indices_checked<SizeT>(src_ndarray->shape[src_axis]);
|
||||
|
||||
dst_ndarray->data += (SizeT)range.start * src_ndarray->strides[src_axis];
|
||||
dst_ndarray->strides[dst_axis] = ((SizeT)range.step) * src_ndarray->strides[src_axis];
|
||||
dst_ndarray->shape[dst_axis] = (SizeT)range.len<SizeT>();
|
||||
|
||||
dst_axis++;
|
||||
src_axis++;
|
||||
}
|
||||
else if (index->type == ND_INDEX_TYPE_NEWAXIS)
|
||||
{
|
||||
dst_ndarray->strides[dst_axis] = 0;
|
||||
dst_ndarray->shape[dst_axis] = 1;
|
||||
|
||||
dst_axis++;
|
||||
}
|
||||
else if (index->type == ND_INDEX_TYPE_ELLIPSIS)
|
||||
{
|
||||
// The number of ':' entries this '...' implies.
|
||||
SizeT ellipsis_size = src_ndarray->ndims - num_indexed;
|
||||
|
||||
for (SizeT j = 0; j < ellipsis_size; j++)
|
||||
{
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
|
||||
|
||||
dst_axis++;
|
||||
src_axis++;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
|
||||
for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++)
|
||||
{
|
||||
dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
|
||||
dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
|
||||
}
|
||||
|
||||
debug_assert_eq(SizeT, src_ndarray->ndims, src_axis);
|
||||
debug_assert_eq(SizeT, dst_ndarray->ndims, dst_axis);
|
||||
}
|
||||
} // namespace indexing
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C"
|
||||
{
|
||||
using namespace ndarray::indexing;
|
||||
|
||||
void __nac3_ndarray_index(int32_t num_indices, NDIndex *indices, NDArray<int32_t> *src_ndarray,
|
||||
NDArray<int32_t> *dst_ndarray)
|
||||
{
|
||||
index(num_indices, indices, src_ndarray, dst_ndarray);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_index64(int64_t num_indices, NDIndex *indices, NDArray<int64_t> *src_ndarray,
|
||||
NDArray<int64_t> *dst_ndarray)
|
||||
{
|
||||
index(num_indices, indices, src_ndarray, dst_ndarray);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,139 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace
|
||||
{
|
||||
/**
|
||||
* @brief Helper struct to enumerate through an ndarray *efficiently*.
|
||||
*
|
||||
* Interesting cases:
|
||||
* - If ndims == 0, there is one iteration.
|
||||
* - If shape contains zeroes, there are no iterations.
|
||||
*/
|
||||
template <typename SizeT> struct NDIter
|
||||
{
|
||||
// Information about the ndarray being iterated over.
|
||||
SizeT ndims;
|
||||
SizeT *shape;
|
||||
SizeT *strides;
|
||||
|
||||
/**
|
||||
* @brief The current indices.
|
||||
*
|
||||
* Must be allocated by the caller.
|
||||
*/
|
||||
SizeT *indices;
|
||||
|
||||
/**
|
||||
* @brief The nth (0-based) index of the current indices.
|
||||
*
|
||||
* Initially this is all 0s.
|
||||
*/
|
||||
SizeT nth;
|
||||
|
||||
/**
|
||||
* @brief Pointer to the current element.
|
||||
*
|
||||
* Initially this points to first element of the ndarray.
|
||||
*/
|
||||
uint8_t *element;
|
||||
|
||||
/**
|
||||
* @brief Cache for the product of shape.
|
||||
*
|
||||
* Could be 0 if `shape` has 0s in it.
|
||||
*/
|
||||
SizeT size;
|
||||
|
||||
// TODO:: Not implemented: 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.
|
||||
|
||||
void initialize(SizeT ndims, SizeT *shape, SizeT *strides, uint8_t *element, SizeT *indices)
|
||||
{
|
||||
this->ndims = ndims;
|
||||
this->shape = shape;
|
||||
this->strides = strides;
|
||||
|
||||
this->indices = indices;
|
||||
this->element = element;
|
||||
|
||||
// Compute size
|
||||
this->size = 1;
|
||||
for (SizeT i = 0; i < ndims; i++)
|
||||
{
|
||||
this->size *= shape[i];
|
||||
}
|
||||
|
||||
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 +1,92 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/debug.hpp>
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/ndarray/basic.hpp>
|
||||
#include <irrt/ndarray/broadcast.hpp>
|
||||
#include <irrt/ndarray/iter.hpp>
|
||||
|
||||
// NOTE: Everything would be much easier and elegant if einsum is implemented.
|
||||
|
||||
namespace
|
||||
{
|
||||
namespace ndarray
|
||||
{
|
||||
namespace matmul
|
||||
{
|
||||
|
||||
/**
|
||||
* @brief Perform the broadcast in `np.einsum("...ij,...jk->...ik", a, b)`.
|
||||
*
|
||||
* Example:
|
||||
* Suppose `a_shape == [1, 97, 4, 2]`
|
||||
* and `b_shape == [99, 98, 1, 2, 5]`,
|
||||
*
|
||||
* ...then `new_a_shape == [99, 98, 97, 4, 2]`,
|
||||
* `new_b_shape == [99, 98, 97, 2, 5]`,
|
||||
* and `dst_shape == [99, 98, 97, 4, 5]`.
|
||||
* ^^^^^^^^^^ ^^^^
|
||||
* (broadcasted) (4x2 @ 2x5 => 4x5)
|
||||
*
|
||||
* @param a_ndims Length of `a_shape`.
|
||||
* @param a_shape Shape of `a`.
|
||||
* @param b_ndims Length of `b_shape`.
|
||||
* @param b_shape Shape of `b`.
|
||||
* @param final_ndims Should be equal to `max(a_ndims, b_ndims)`. This is the length of `new_a_shape`,
|
||||
* `new_b_shape`, and `dst_shape` - the number of dimensions after broadcasting.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void calculate_shapes(SizeT a_ndims, SizeT *a_shape, SizeT b_ndims, SizeT *b_shape, SizeT final_ndims,
|
||||
SizeT *new_a_shape, SizeT *new_b_shape, SizeT *dst_shape)
|
||||
{
|
||||
debug_assert(SizeT, a_ndims >= 2);
|
||||
debug_assert(SizeT, b_ndims >= 2);
|
||||
debug_assert_eq(SizeT, max(a_ndims, b_ndims), final_ndims);
|
||||
|
||||
// Check that a and b are compatible for matmul
|
||||
if (a_shape[a_ndims - 1] != b_shape[b_ndims - 2])
|
||||
{
|
||||
// This is a custom error message. Different from NumPy.
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot multiply LHS (shape ?x{0}) with RHS (shape {1}x?})",
|
||||
a_shape[a_ndims - 1], b_shape[b_ndims - 2], NO_PARAM);
|
||||
}
|
||||
|
||||
const SizeT num_entries = 2;
|
||||
ShapeEntry<SizeT> entries[num_entries] = {{.ndims = a_ndims - 2, .shape = a_shape},
|
||||
{.ndims = b_ndims - 2, .shape = b_shape}};
|
||||
|
||||
// TODO: Optimize this
|
||||
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_a_shape);
|
||||
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_b_shape);
|
||||
ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, dst_shape);
|
||||
|
||||
new_a_shape[final_ndims - 2] = a_shape[a_ndims - 2];
|
||||
new_a_shape[final_ndims - 1] = a_shape[a_ndims - 1];
|
||||
new_b_shape[final_ndims - 2] = b_shape[b_ndims - 2];
|
||||
new_b_shape[final_ndims - 1] = b_shape[b_ndims - 1];
|
||||
dst_shape[final_ndims - 2] = a_shape[a_ndims - 2];
|
||||
dst_shape[final_ndims - 1] = b_shape[b_ndims - 1];
|
||||
}
|
||||
} // namespace matmul
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C"
|
||||
{
|
||||
using namespace ndarray::matmul;
|
||||
|
||||
void __nac3_ndarray_matmul_calculate_shapes(int32_t a_ndims, int32_t *a_shape, int32_t b_ndims, int32_t *b_shape,
|
||||
int32_t final_ndims, int32_t *new_a_shape, int32_t *new_b_shape,
|
||||
int32_t *dst_shape)
|
||||
{
|
||||
calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_matmul_calculate_shapes64(int64_t a_ndims, int64_t *a_shape, int64_t b_ndims, int64_t *b_shape,
|
||||
int64_t final_ndims, int64_t *new_a_shape, int64_t *new_b_shape,
|
||||
int64_t *dst_shape)
|
||||
{
|
||||
calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,125 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
|
||||
namespace
|
||||
{
|
||||
namespace ndarray
|
||||
{
|
||||
namespace reshape
|
||||
{
|
||||
/**
|
||||
* @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
|
||||
*
|
||||
* If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
|
||||
* modified to contain the resolved dimension.
|
||||
*
|
||||
* To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
|
||||
* `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
|
||||
*
|
||||
* @param size The `.size` of `<ndarray>`
|
||||
* @param new_ndims Number of elements in `new_shape`
|
||||
* @param new_shape Target shape to reshape to
|
||||
*/
|
||||
template <typename SizeT> void resolve_and_check_new_shape(SizeT size, SizeT new_ndims, SizeT *new_shape)
|
||||
{
|
||||
// Is there a -1 in `new_shape`?
|
||||
bool neg1_exists = false;
|
||||
// Location of -1, only initialized if `neg1_exists` is true
|
||||
SizeT neg1_axis_i;
|
||||
// The computed ndarray size of `new_shape`
|
||||
SizeT new_size = 1;
|
||||
|
||||
for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++)
|
||||
{
|
||||
SizeT dim = new_shape[axis_i];
|
||||
if (dim < 0)
|
||||
{
|
||||
if (dim == -1)
|
||||
{
|
||||
if (neg1_exists)
|
||||
{
|
||||
// Multiple `-1` found. Throw an error.
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "can only specify one unknown dimension", NO_PARAM,
|
||||
NO_PARAM, NO_PARAM);
|
||||
}
|
||||
else
|
||||
{
|
||||
neg1_exists = true;
|
||||
neg1_axis_i = axis_i;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// TODO: What? In `np.reshape` any negative dimensions is
|
||||
// treated like its `-1`.
|
||||
//
|
||||
// Try running `np.zeros((3, 4)).reshape((-999, 2))`
|
||||
//
|
||||
// It is not documented by numpy.
|
||||
// Throw an error for now...
|
||||
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "Found non -1 negative dimension {0} on axis {1}", dim, axis_i,
|
||||
NO_PARAM);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
new_size *= dim;
|
||||
}
|
||||
}
|
||||
|
||||
bool can_reshape;
|
||||
if (neg1_exists)
|
||||
{
|
||||
// Let `x` be the unknown dimension
|
||||
// Solve `x * <new_size> = <size>`
|
||||
if (new_size == 0 && size == 0)
|
||||
{
|
||||
// `x` has infinitely many solutions
|
||||
can_reshape = false;
|
||||
}
|
||||
else if (new_size == 0 && size != 0)
|
||||
{
|
||||
// `x` has no solutions
|
||||
can_reshape = false;
|
||||
}
|
||||
else if (size % new_size != 0)
|
||||
{
|
||||
// `x` has no integer solutions
|
||||
can_reshape = false;
|
||||
}
|
||||
else
|
||||
{
|
||||
can_reshape = true;
|
||||
new_shape[neg1_axis_i] = size / new_size; // Resolve dimension
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
can_reshape = (new_size == size);
|
||||
}
|
||||
|
||||
if (!can_reshape)
|
||||
{
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "cannot reshape array of size {0} into given shape", size, NO_PARAM,
|
||||
NO_PARAM);
|
||||
}
|
||||
}
|
||||
} // namespace reshape
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C"
|
||||
{
|
||||
void __nac3_ndarray_reshape_resolve_and_check_new_shape(int32_t size, int32_t new_ndims, int32_t *new_shape)
|
||||
{
|
||||
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_reshape_resolve_and_check_new_shape64(int64_t size, int64_t new_ndims, int64_t *new_shape)
|
||||
{
|
||||
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,155 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/ndarray/def.hpp>
|
||||
#include <irrt/slice.hpp>
|
||||
|
||||
/*
|
||||
* Notes on `np.transpose(<array>, <axes>)`
|
||||
*
|
||||
* TODO: `axes`, if specified, can actually contain negative indices,
|
||||
* but it is not documented in numpy.
|
||||
*
|
||||
* Supporting it for now.
|
||||
*/
|
||||
|
||||
namespace
|
||||
{
|
||||
namespace ndarray
|
||||
{
|
||||
namespace transpose
|
||||
{
|
||||
/**
|
||||
* @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
|
||||
*
|
||||
* Note that `np.transpose`'s `<axe>` argument is optional. If the argument
|
||||
* is specified but the user, use this function to do assertions on it.
|
||||
*
|
||||
* @param ndims The number of dimensions of `<array>`
|
||||
* @param num_axes Number of elements in `<axes>` as specified by the user.
|
||||
* This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
|
||||
* @param axes The user specified `<axes>`.
|
||||
*/
|
||||
template <typename SizeT> void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT *axes)
|
||||
{
|
||||
if (ndims != num_axes)
|
||||
{
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array", NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
// TODO: Optimize this
|
||||
bool *axe_specified = (bool *)__builtin_alloca(sizeof(bool) * ndims);
|
||||
for (SizeT i = 0; i < ndims; i++)
|
||||
axe_specified[i] = false;
|
||||
|
||||
for (SizeT i = 0; i < ndims; i++)
|
||||
{
|
||||
SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
|
||||
if (axis == -1)
|
||||
{
|
||||
// TODO: numpy actually throws a `numpy.exceptions.AxisError`
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "axis {0} is out of bounds for array of dimension {1}", axis, ndims,
|
||||
NO_PARAM);
|
||||
}
|
||||
|
||||
if (axe_specified[axis])
|
||||
{
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "repeated axis in transpose", NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
axe_specified[axis] = true;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Create a transpose view of `src_ndarray` and perform proper assertions.
|
||||
*
|
||||
* This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
|
||||
* If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
|
||||
*
|
||||
* The transpose view created is returned by modifying `dst_ndarray`.
|
||||
*
|
||||
* The caller is responsible for setting up `dst_ndarray` before calling this function.
|
||||
* Here is what this function expects from `dst_ndarray` when called:
|
||||
* - `dst_ndarray->data` does not have to be initialized.
|
||||
* - `dst_ndarray->itemsize` does not have to be initialized.
|
||||
* - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
|
||||
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
|
||||
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
|
||||
* When this function call ends:
|
||||
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
|
||||
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
|
||||
* - `dst_ndarray->ndims` is unchanged
|
||||
* - `dst_ndarray->shape` is updated according to how `np.transpose` works
|
||||
* - `dst_ndarray->strides` is updated according to how `np.transpose` works
|
||||
*
|
||||
* @param src_ndarray The NDArray to build a transpose view on
|
||||
* @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
|
||||
* @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
|
||||
* @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
|
||||
*/
|
||||
template <typename SizeT>
|
||||
void transpose(const NDArray<SizeT> *src_ndarray, NDArray<SizeT> *dst_ndarray, SizeT num_axes, const SizeT *axes)
|
||||
{
|
||||
debug_assert_eq(SizeT, src_ndarray->ndims, dst_ndarray->ndims);
|
||||
const auto ndims = src_ndarray->ndims;
|
||||
|
||||
if (axes != nullptr)
|
||||
assert_transpose_axes(ndims, num_axes, axes);
|
||||
|
||||
dst_ndarray->data = src_ndarray->data;
|
||||
dst_ndarray->itemsize = src_ndarray->itemsize;
|
||||
|
||||
// Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
|
||||
if (axes == nullptr)
|
||||
{
|
||||
// `np.transpose(<array>, axes=None)`
|
||||
|
||||
/*
|
||||
* Minor note: `np.transpose(<array>, axes=None)` is equivalent to
|
||||
* `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
|
||||
* is reversing the order of strides and shape.
|
||||
*
|
||||
* This is a fast implementation to handle this special (but very common) case.
|
||||
*/
|
||||
|
||||
for (SizeT axis = 0; axis < ndims; axis++)
|
||||
{
|
||||
dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
|
||||
dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// `np.transpose(<array>, <axes>)`
|
||||
|
||||
// Permute strides and shape according to `axes`, while resolving negative indices in `axes`
|
||||
for (SizeT axis = 0; axis < ndims; axis++)
|
||||
{
|
||||
// `i` cannot be OUT_OF_BOUNDS because of assertions
|
||||
SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
|
||||
|
||||
dst_ndarray->shape[axis] = src_ndarray->shape[i];
|
||||
dst_ndarray->strides[axis] = src_ndarray->strides[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace transpose
|
||||
} // namespace ndarray
|
||||
} // namespace
|
||||
|
||||
extern "C"
|
||||
{
|
||||
using namespace ndarray::transpose;
|
||||
void __nac3_ndarray_transpose(const NDArray<int32_t> *src_ndarray, NDArray<int32_t> *dst_ndarray, int32_t num_axes,
|
||||
const int32_t *axes)
|
||||
{
|
||||
transpose(src_ndarray, dst_ndarray, num_axes, axes);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_transpose64(const NDArray<int64_t> *src_ndarray, NDArray<int64_t> *dst_ndarray,
|
||||
int64_t num_axes, const int64_t *axes)
|
||||
{
|
||||
transpose(src_ndarray, dst_ndarray, num_axes, axes);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,215 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/math_util.hpp>
|
||||
|
||||
// The type of an index or a value describing the length of a range/slice is always `int32_t`.
|
||||
using SliceIndex = int32_t;
|
||||
|
||||
namespace
|
||||
{
|
||||
// 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
|
||||
|
||||
extern "C"
|
||||
{
|
||||
#define DEF_nac3_int_exp_(T) \
|
||||
T __nac3_int_exp_##T(T base, T exp) \
|
||||
{ \
|
||||
return __nac3_int_exp_impl(base, exp); \
|
||||
}
|
||||
|
||||
DEF_nac3_int_exp_(int32_t) DEF_nac3_int_exp_(int64_t) DEF_nac3_int_exp_(uint32_t) DEF_nac3_int_exp_(uint64_t)
|
||||
|
||||
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len)
|
||||
{
|
||||
if (i < 0)
|
||||
{
|
||||
i = len + i;
|
||||
}
|
||||
if (i < 0)
|
||||
{
|
||||
return 0;
|
||||
}
|
||||
else if (i > len)
|
||||
{
|
||||
return len;
|
||||
}
|
||||
return i;
|
||||
}
|
||||
|
||||
SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step)
|
||||
{
|
||||
SliceIndex diff = end - start;
|
||||
if (diff > 0 && step > 0)
|
||||
{
|
||||
return ((diff - 1) / step) + 1;
|
||||
}
|
||||
else if (diff < 0 && step < 0)
|
||||
{
|
||||
return ((diff + 1) / step) + 1;
|
||||
}
|
||||
else
|
||||
{
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
// Handle list assignment and dropping part of the list when
|
||||
// both dest_step and src_step are +1.
|
||||
// - All the index must *not* be out-of-bound or negative,
|
||||
// - The end index is *inclusive*,
|
||||
// - The length of src and dest slice size should already
|
||||
// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
|
||||
SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start, SliceIndex dest_end, SliceIndex dest_step,
|
||||
uint8_t *dest_arr, SliceIndex dest_arr_len, SliceIndex src_start,
|
||||
SliceIndex src_end, SliceIndex src_step, uint8_t *src_arr,
|
||||
SliceIndex src_arr_len, const SliceIndex size)
|
||||
{
|
||||
/* if dest_arr_len == 0, do nothing since we do not support extending list */
|
||||
if (dest_arr_len == 0)
|
||||
return dest_arr_len;
|
||||
/* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
|
||||
if (src_step == dest_step && dest_step == 1)
|
||||
{
|
||||
const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
|
||||
const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
|
||||
if (src_len > 0)
|
||||
{
|
||||
__builtin_memmove(dest_arr + dest_start * size, src_arr + src_start * size, src_len * size);
|
||||
}
|
||||
if (dest_len > 0)
|
||||
{
|
||||
/* dropping */
|
||||
__builtin_memmove(dest_arr + (dest_start + src_len) * size, dest_arr + (dest_end + 1) * size,
|
||||
(dest_arr_len - dest_end - 1) * size);
|
||||
}
|
||||
/* shrink size */
|
||||
return dest_arr_len - (dest_len - src_len);
|
||||
}
|
||||
/* if two range overlaps, need alloca */
|
||||
uint8_t need_alloca = (dest_arr == src_arr) && !(max(dest_start, dest_end) < min(src_start, src_end) ||
|
||||
max(src_start, src_end) < min(dest_start, dest_end));
|
||||
if (need_alloca)
|
||||
{
|
||||
uint8_t *tmp = reinterpret_cast<uint8_t *>(__builtin_alloca(src_arr_len * size));
|
||||
__builtin_memcpy(tmp, src_arr, src_arr_len * size);
|
||||
src_arr = tmp;
|
||||
}
|
||||
SliceIndex src_ind = src_start;
|
||||
SliceIndex dest_ind = dest_start;
|
||||
for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end); src_ind += src_step, dest_ind += dest_step)
|
||||
{
|
||||
/* for constant optimization */
|
||||
if (size == 1)
|
||||
{
|
||||
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
|
||||
}
|
||||
else if (size == 4)
|
||||
{
|
||||
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
|
||||
}
|
||||
else if (size == 8)
|
||||
{
|
||||
__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
|
||||
}
|
||||
else
|
||||
{
|
||||
/* memcpy for var size, cannot overlap after previous alloca */
|
||||
__builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
|
||||
}
|
||||
}
|
||||
/* only dest_step == 1 can we shrink the dest list. */
|
||||
/* size should be ensured prior to calling this function */
|
||||
if (dest_step == 1 && dest_end >= dest_start)
|
||||
{
|
||||
__builtin_memmove(dest_arr + dest_ind * size, dest_arr + (dest_end + 1) * size,
|
||||
(dest_arr_len - dest_end - 1) * size);
|
||||
return dest_arr_len - (dest_end - dest_ind) - 1;
|
||||
}
|
||||
return dest_arr_len;
|
||||
}
|
||||
|
||||
int32_t __nac3_isinf(double x)
|
||||
{
|
||||
return __builtin_isinf(x);
|
||||
}
|
||||
|
||||
int32_t __nac3_isnan(double x)
|
||||
{
|
||||
return __builtin_isnan(x);
|
||||
}
|
||||
|
||||
double tgamma(double arg);
|
||||
|
||||
double __nac3_gamma(double z)
|
||||
{
|
||||
// Handling for denormals
|
||||
// | x | Python gamma(x) | C tgamma(x) |
|
||||
// --- | ----------------- | --------------- | ----------- |
|
||||
// (1) | nan | nan | nan |
|
||||
// (2) | -inf | -inf | inf |
|
||||
// (3) | inf | inf | inf |
|
||||
// (4) | 0.0 | inf | inf |
|
||||
// (5) | {-1.0, -2.0, ...} | inf | nan |
|
||||
|
||||
// (1)-(3)
|
||||
if (__builtin_isinf(z) || __builtin_isnan(z))
|
||||
{
|
||||
return z;
|
||||
}
|
||||
|
||||
double v = tgamma(z);
|
||||
|
||||
// (4)-(5)
|
||||
return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
|
||||
}
|
||||
|
||||
double lgamma(double arg);
|
||||
|
||||
double __nac3_gammaln(double x)
|
||||
{
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: gammaln(-inf) -> -inf
|
||||
// - libm : lgamma(-inf) -> inf
|
||||
|
||||
if (__builtin_isinf(x))
|
||||
{
|
||||
return x;
|
||||
}
|
||||
|
||||
return lgamma(x);
|
||||
}
|
||||
|
||||
double j0(double x);
|
||||
|
||||
double __nac3_j0(double x)
|
||||
{
|
||||
// libm's handling of value overflows differs from scipy:
|
||||
// - scipy: j0(inf) -> nan
|
||||
// - libm : j0(inf) -> 0.0
|
||||
|
||||
if (__builtin_isinf(x))
|
||||
{
|
||||
return __builtin_nan("");
|
||||
}
|
||||
|
||||
return j0(x);
|
||||
}
|
||||
} // extern "C"
|
||||
|
|
@ -0,0 +1,41 @@
|
|||
#pragma once
|
||||
|
||||
#include <irrt/debug.hpp>
|
||||
#include <irrt/int_types.hpp>
|
||||
|
||||
namespace
|
||||
{
|
||||
namespace range
|
||||
{
|
||||
template <typename T> T len(T start, T stop, T step)
|
||||
{
|
||||
// Reference:
|
||||
// https://github.com/python/cpython/blob/9dbd12375561a393eaec4b21ee4ac568a407cdb0/Objects/rangeobject.c#L933
|
||||
if (step > 0 && start < stop)
|
||||
return 1 + (stop - 1 - start) / step;
|
||||
else if (step < 0 && start > stop)
|
||||
return 1 + (start - 1 - stop) / (-step);
|
||||
else
|
||||
return 0;
|
||||
}
|
||||
} // namespace range
|
||||
|
||||
/**
|
||||
* @brief A Python range.
|
||||
*/
|
||||
template <typename T> struct Range
|
||||
{
|
||||
T start;
|
||||
T stop;
|
||||
T step;
|
||||
|
||||
/**
|
||||
* @brief Calculate the `len()` of this range.
|
||||
*/
|
||||
template <typename SizeT> T len()
|
||||
{
|
||||
debug_assert(SizeT, step != 0);
|
||||
return range::len(start, stop, step);
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
|
@ -1,28 +1,158 @@
|
|||
#pragma once
|
||||
|
||||
#include "irrt/int_types.hpp"
|
||||
#include <irrt/debug.hpp>
|
||||
#include <irrt/exception.hpp>
|
||||
#include <irrt/int_types.hpp>
|
||||
#include <irrt/math_util.hpp>
|
||||
#include <irrt/range.hpp>
|
||||
|
||||
extern "C" {
|
||||
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
|
||||
if (i < 0) {
|
||||
i = len + i;
|
||||
namespace
|
||||
{
|
||||
namespace slice
|
||||
{
|
||||
/**
|
||||
* @brief Resolve a possibly negative index in a list of a known length.
|
||||
*
|
||||
* Returns -1 if the resolved index is out of the list's bounds.
|
||||
*/
|
||||
template <typename T> T resolve_index_in_length(T length, T index)
|
||||
{
|
||||
T resolved = index < 0 ? length + index : index;
|
||||
if (0 <= resolved && resolved < length)
|
||||
{
|
||||
return resolved;
|
||||
}
|
||||
if (i < 0) {
|
||||
return 0;
|
||||
} else if (i > len) {
|
||||
return len;
|
||||
else
|
||||
{
|
||||
return -1;
|
||||
}
|
||||
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;
|
||||
/**
|
||||
* @brief Resolve a slice as a range.
|
||||
*
|
||||
* This is equivalent to `range(*slice(start, stop, step).indices(length))` in Python.
|
||||
*/
|
||||
template <typename T>
|
||||
void indices(bool start_defined, T start, bool stop_defined, T stop, bool step_defined, T step, T length,
|
||||
T *range_start, T *range_stop, T *range_step)
|
||||
{
|
||||
// Reference: https://github.com/python/cpython/blob/main/Objects/sliceobject.c#L388
|
||||
*range_step = step_defined ? step : 1;
|
||||
bool step_is_negative = *range_step < 0;
|
||||
|
||||
T lower, upper;
|
||||
if (step_is_negative)
|
||||
{
|
||||
lower = -1;
|
||||
upper = length - 1;
|
||||
}
|
||||
else
|
||||
{
|
||||
lower = 0;
|
||||
upper = length;
|
||||
}
|
||||
|
||||
if (start_defined)
|
||||
{
|
||||
*range_start = start < 0 ? max(lower, start + length) : min(upper, start);
|
||||
}
|
||||
else
|
||||
{
|
||||
*range_start = step_is_negative ? upper : lower;
|
||||
}
|
||||
|
||||
if (stop_defined)
|
||||
{
|
||||
*range_stop = stop < 0 ? max(lower, stop + length) : min(upper, stop);
|
||||
}
|
||||
else
|
||||
{
|
||||
*range_stop = step_is_negative ? lower : upper;
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace slice
|
||||
|
||||
/**
|
||||
* @brief A Python-like slice with **unresolved** indices.
|
||||
*/
|
||||
template <typename T> struct Slice
|
||||
{
|
||||
bool start_defined;
|
||||
T start;
|
||||
|
||||
bool stop_defined;
|
||||
T stop;
|
||||
|
||||
bool step_defined;
|
||||
T step;
|
||||
|
||||
Slice()
|
||||
{
|
||||
this->reset();
|
||||
}
|
||||
|
||||
void reset()
|
||||
{
|
||||
this->start_defined = false;
|
||||
this->stop_defined = false;
|
||||
this->step_defined = false;
|
||||
}
|
||||
|
||||
void set_start(T start)
|
||||
{
|
||||
this->start_defined = true;
|
||||
this->start = start;
|
||||
}
|
||||
|
||||
void set_stop(T stop)
|
||||
{
|
||||
this->stop_defined = true;
|
||||
this->stop = stop;
|
||||
}
|
||||
|
||||
void set_step(T step)
|
||||
{
|
||||
this->step_defined = true;
|
||||
this->step = step;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Resolve this slice as a range.
|
||||
*
|
||||
* In Python, this would be `range(*slice(start, stop, step).indices(length))`.
|
||||
*/
|
||||
template <typename SizeT> Range<T> indices(T length)
|
||||
{
|
||||
// Reference:
|
||||
// https://github.com/python/cpython/blob/main/Objects/sliceobject.c#L388
|
||||
debug_assert(SizeT, length >= 0);
|
||||
|
||||
Range<T> result;
|
||||
slice::indices(start_defined, start, stop_defined, stop, step_defined, step, length, &result.start,
|
||||
&result.stop, &result.step);
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Like `.indices()` but with assertions.
|
||||
*/
|
||||
template <typename SizeT> Range<T> indices_checked(T length)
|
||||
{
|
||||
// TODO: Switch to `SizeT length`
|
||||
|
||||
if (length < 0)
|
||||
{
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "length should not be negative, got {0}", length, NO_PARAM,
|
||||
NO_PARAM);
|
||||
}
|
||||
|
||||
if (this->step_defined && this->step == 0)
|
||||
{
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero", NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
return this->indices<SizeT>(length);
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,17 +1,13 @@
|
|||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
use inkwell::context::Context;
|
||||
use inkwell::types::{ArrayType, BasicType, StructType};
|
||||
use inkwell::values::{ArrayValue, BasicValue, StructValue};
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{AnyTypeEnum, ArrayType, BasicType, BasicTypeEnum, IntType, PointerType, StructType},
|
||||
values::{ArrayValue, BasicValue, BasicValueEnum, IntValue, PointerValue, StructValue},
|
||||
types::{AnyTypeEnum, BasicTypeEnum, IntType, PointerType},
|
||||
values::{BasicValueEnum, IntValue, PointerValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
|
||||
use crate::codegen::{
|
||||
irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
|
||||
llvm_intrinsics::call_int_umin,
|
||||
stmt::gen_for_callback_incrementing,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// A LLVM type that is used to represent a non-primitive type in NAC3.
|
||||
pub trait ProxyType<'ctx>: Into<Self::Base> {
|
||||
/// The LLVM type of which values of this type possess. This is usually a
|
||||
|
|
@ -1140,626 +1136,3 @@ impl<'ctx> From<RangeValue<'ctx>> for PointerValue<'ctx> {
|
|||
value.as_base_value()
|
||||
}
|
||||
}
|
||||
|
||||
/// Proxy type for a `ndarray` type in LLVM.
|
||||
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
|
||||
pub struct NDArrayType<'ctx> {
|
||||
ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayType<'ctx> {
|
||||
/// Checks whether `llvm_ty` represents a `ndarray` type, returning [Err] if it does not.
|
||||
pub fn is_type(llvm_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Result<(), String> {
|
||||
let llvm_ndarray_ty = llvm_ty.get_element_type();
|
||||
let AnyTypeEnum::StructType(llvm_ndarray_ty) = llvm_ndarray_ty else {
|
||||
return Err(format!("Expected struct type for `NDArray` type, got {llvm_ndarray_ty}"));
|
||||
};
|
||||
if llvm_ndarray_ty.count_fields() != 3 {
|
||||
return Err(format!(
|
||||
"Expected 3 fields in `NDArray`, got {}",
|
||||
llvm_ndarray_ty.count_fields()
|
||||
));
|
||||
}
|
||||
|
||||
let ndarray_ndims_ty = llvm_ndarray_ty.get_field_type_at_index(0).unwrap();
|
||||
let Ok(ndarray_ndims_ty) = IntType::try_from(ndarray_ndims_ty) else {
|
||||
return Err(format!("Expected int type for `ndarray.0`, got {ndarray_ndims_ty}"));
|
||||
};
|
||||
if ndarray_ndims_ty.get_bit_width() != llvm_usize.get_bit_width() {
|
||||
return Err(format!(
|
||||
"Expected {}-bit int type for `ndarray.0`, got {}-bit int",
|
||||
llvm_usize.get_bit_width(),
|
||||
ndarray_ndims_ty.get_bit_width()
|
||||
));
|
||||
}
|
||||
|
||||
let ndarray_dims_ty = llvm_ndarray_ty.get_field_type_at_index(1).unwrap();
|
||||
let Ok(ndarray_pdims) = PointerType::try_from(ndarray_dims_ty) else {
|
||||
return Err(format!("Expected pointer type for `ndarray.1`, got {ndarray_dims_ty}"));
|
||||
};
|
||||
let ndarray_dims = ndarray_pdims.get_element_type();
|
||||
let Ok(ndarray_dims) = IntType::try_from(ndarray_dims) else {
|
||||
return Err(format!(
|
||||
"Expected pointer-to-int type for `ndarray.1`, got pointer-to-{ndarray_dims}"
|
||||
));
|
||||
};
|
||||
if ndarray_dims.get_bit_width() != llvm_usize.get_bit_width() {
|
||||
return Err(format!(
|
||||
"Expected pointer-to-{}-bit int type for `ndarray.1`, got pointer-to-{}-bit int",
|
||||
llvm_usize.get_bit_width(),
|
||||
ndarray_dims.get_bit_width()
|
||||
));
|
||||
}
|
||||
|
||||
let ndarray_data_ty = llvm_ndarray_ty.get_field_type_at_index(2).unwrap();
|
||||
let Ok(_) = PointerType::try_from(ndarray_data_ty) else {
|
||||
return Err(format!("Expected pointer type for `ndarray.2`, got {ndarray_data_ty}"));
|
||||
};
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Creates an instance of [`ListType`].
|
||||
#[must_use]
|
||||
pub fn new<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
dtype: BasicTypeEnum<'ctx>,
|
||||
) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
|
||||
// struct NDArray { num_dims: size_t, dims: size_t*, data: T* }
|
||||
//
|
||||
// * num_dims: Number of dimensions in the array
|
||||
// * dims: Pointer to an array containing the size of each dimension
|
||||
// * data: Pointer to an array containing the array data
|
||||
let llvm_ndarray = ctx
|
||||
.struct_type(
|
||||
&[
|
||||
llvm_usize.into(),
|
||||
llvm_usize.ptr_type(AddressSpace::default()).into(),
|
||||
dtype.ptr_type(AddressSpace::default()).into(),
|
||||
],
|
||||
false,
|
||||
)
|
||||
.ptr_type(AddressSpace::default());
|
||||
|
||||
NDArrayType::from_type(llvm_ndarray, llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an [`NDArrayType`] from a [`PointerType`].
|
||||
#[must_use]
|
||||
pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
|
||||
debug_assert!(Self::is_type(ptr_ty, llvm_usize).is_ok());
|
||||
|
||||
NDArrayType { ty: ptr_ty, llvm_usize }
|
||||
}
|
||||
|
||||
/// Returns the type of the `size` field of this `ndarray` type.
|
||||
#[must_use]
|
||||
pub fn size_type(&self) -> IntType<'ctx> {
|
||||
self.as_base_type()
|
||||
.get_element_type()
|
||||
.into_struct_type()
|
||||
.get_field_type_at_index(0)
|
||||
.map(BasicTypeEnum::into_int_type)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Returns the element type of this `ndarray` type.
|
||||
#[must_use]
|
||||
pub fn element_type(&self) -> AnyTypeEnum<'ctx> {
|
||||
self.as_base_type()
|
||||
.get_element_type()
|
||||
.into_struct_type()
|
||||
.get_field_type_at_index(2)
|
||||
.map(BasicTypeEnum::into_pointer_type)
|
||||
.map(PointerType::get_element_type)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyType<'ctx> for NDArrayType<'ctx> {
|
||||
type Base = PointerType<'ctx>;
|
||||
type Underlying = StructType<'ctx>;
|
||||
type Value = NDArrayValue<'ctx>;
|
||||
|
||||
fn new_value<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self::Value {
|
||||
self.create_value(
|
||||
generator.gen_var_alloc(ctx, self.as_underlying_type().into(), name).unwrap(),
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
fn create_value(
|
||||
&self,
|
||||
value: <Self::Value as ProxyValue<'ctx>>::Base,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self::Value {
|
||||
debug_assert_eq!(value.get_type(), self.as_base_type());
|
||||
|
||||
NDArrayValue { value, llvm_usize: self.llvm_usize, name }
|
||||
}
|
||||
|
||||
fn as_base_type(&self) -> Self::Base {
|
||||
self.ty
|
||||
}
|
||||
|
||||
fn as_underlying_type(&self) -> Self::Underlying {
|
||||
self.as_base_type().get_element_type().into_struct_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<NDArrayType<'ctx>> for PointerType<'ctx> {
|
||||
fn from(value: NDArrayType<'ctx>) -> Self {
|
||||
value.as_base_type()
|
||||
}
|
||||
}
|
||||
|
||||
/// Proxy type for accessing an `NDArray` value in LLVM.
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct NDArrayValue<'ctx> {
|
||||
value: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayValue<'ctx> {
|
||||
/// Checks whether `value` is an instance of `NDArray`, returning [Err] if `value` is not an
|
||||
/// instance.
|
||||
pub fn is_instance(value: PointerValue<'ctx>, llvm_usize: IntType<'ctx>) -> Result<(), String> {
|
||||
NDArrayType::is_type(value.get_type(), llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an [`NDArrayValue`] from a [`PointerValue`].
|
||||
#[must_use]
|
||||
pub fn from_ptr_val(
|
||||
ptr: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_instance(ptr, llvm_usize).is_ok());
|
||||
|
||||
<Self as ProxyValue<'ctx>>::Type::from_type(ptr.get_type(), llvm_usize)
|
||||
.create_value(ptr, name)
|
||||
}
|
||||
|
||||
/// Returns the pointer to the field storing the number of dimensions of this `NDArray`.
|
||||
fn ptr_to_ndims(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let var_name = self.name.map(|v| format!("{v}.ndims.addr")).unwrap_or_default();
|
||||
|
||||
unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.as_base_value(),
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_zero()],
|
||||
var_name.as_str(),
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
/// Stores the number of dimensions `ndims` into this instance.
|
||||
pub fn store_ndims<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ndims: IntValue<'ctx>,
|
||||
) {
|
||||
debug_assert_eq!(ndims.get_type(), generator.get_size_type(ctx.ctx));
|
||||
|
||||
let pndims = self.ptr_to_ndims(ctx);
|
||||
ctx.builder.build_store(pndims, ndims).unwrap();
|
||||
}
|
||||
|
||||
/// Returns the number of dimensions of this `NDArray` as a value.
|
||||
pub fn load_ndims(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
let pndims = self.ptr_to_ndims(ctx);
|
||||
ctx.builder.build_load(pndims, "").map(BasicValueEnum::into_int_value).unwrap()
|
||||
}
|
||||
|
||||
/// Returns the double-indirection pointer to the `dims` array, as if by calling `getelementptr`
|
||||
/// on the field.
|
||||
fn ptr_to_dims(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let var_name = self.name.map(|v| format!("{v}.dims.addr")).unwrap_or_default();
|
||||
|
||||
unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.as_base_value(),
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_int(1, true)],
|
||||
var_name.as_str(),
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
/// Stores the array of dimension sizes `dims` into this instance.
|
||||
fn store_dim_sizes(&self, ctx: &CodeGenContext<'ctx, '_>, dims: PointerValue<'ctx>) {
|
||||
ctx.builder.build_store(self.ptr_to_dims(ctx), dims).unwrap();
|
||||
}
|
||||
|
||||
/// Convenience method for creating a new array storing dimension sizes with the given `size`.
|
||||
pub fn create_dim_sizes(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
size: IntValue<'ctx>,
|
||||
) {
|
||||
self.store_dim_sizes(ctx, ctx.builder.build_array_alloca(llvm_usize, size, "").unwrap());
|
||||
}
|
||||
|
||||
/// Returns a proxy object to the field storing the size of each dimension of this `NDArray`.
|
||||
#[must_use]
|
||||
pub fn dim_sizes(&self) -> NDArrayDimsProxy<'ctx, '_> {
|
||||
NDArrayDimsProxy(self)
|
||||
}
|
||||
|
||||
/// Returns the double-indirection pointer to the `data` array, as if by calling `getelementptr`
|
||||
/// on the field.
|
||||
pub fn ptr_to_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let var_name = self.name.map(|v| format!("{v}.data.addr")).unwrap_or_default();
|
||||
|
||||
unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.as_base_value(),
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_int(2, true)],
|
||||
var_name.as_str(),
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
/// Stores the array of data elements `data` into this instance.
|
||||
fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
|
||||
ctx.builder.build_store(self.ptr_to_data(ctx), data).unwrap();
|
||||
}
|
||||
|
||||
/// Convenience method for creating a new array storing data elements with the given element
|
||||
/// type `elem_ty` and `size`.
|
||||
pub fn create_data(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
elem_ty: BasicTypeEnum<'ctx>,
|
||||
size: IntValue<'ctx>,
|
||||
) {
|
||||
self.store_data(ctx, ctx.builder.build_array_alloca(elem_ty, size, "").unwrap());
|
||||
}
|
||||
|
||||
/// Returns a proxy object to the field storing the data of this `NDArray`.
|
||||
#[must_use]
|
||||
pub fn data(&self) -> NDArrayDataProxy<'ctx, '_> {
|
||||
NDArrayDataProxy(self)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyValue<'ctx> for NDArrayValue<'ctx> {
|
||||
type Base = PointerValue<'ctx>;
|
||||
type Underlying = StructValue<'ctx>;
|
||||
type Type = NDArrayType<'ctx>;
|
||||
|
||||
fn get_type(&self) -> Self::Type {
|
||||
NDArrayType::from_type(self.as_base_value().get_type(), self.llvm_usize)
|
||||
}
|
||||
|
||||
fn as_base_value(&self) -> Self::Base {
|
||||
self.value
|
||||
}
|
||||
|
||||
fn as_underlying_value(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self::Underlying {
|
||||
ctx.builder
|
||||
.build_load(self.as_base_value(), name.unwrap_or_default())
|
||||
.map(BasicValueEnum::into_struct_value)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<NDArrayValue<'ctx>> for PointerValue<'ctx> {
|
||||
fn from(value: NDArrayValue<'ctx>) -> Self {
|
||||
value.as_base_value()
|
||||
}
|
||||
}
|
||||
|
||||
/// Proxy type for accessing the `dims` array of an `NDArray` instance in LLVM.
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct NDArrayDimsProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
|
||||
|
||||
impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDimsProxy<'ctx, '_> {
|
||||
fn element_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
) -> AnyTypeEnum<'ctx> {
|
||||
self.0.dim_sizes().base_ptr(ctx, generator).get_type().get_element_type()
|
||||
}
|
||||
|
||||
fn base_ptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
_: &G,
|
||||
) -> PointerValue<'ctx> {
|
||||
let var_name = self.0.name.map(|v| format!("{v}.data")).unwrap_or_default();
|
||||
|
||||
ctx.builder
|
||||
.build_load(self.0.ptr_to_dims(ctx), var_name.as_str())
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
fn size<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
_: &G,
|
||||
) -> IntValue<'ctx> {
|
||||
self.0.load_ndims(ctx)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
|
||||
|
||||
unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(self.base_ptr(ctx, generator), &[*idx], var_name.as_str())
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
fn ptr_offset<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let size = self.size(ctx, generator);
|
||||
let in_range = ctx.builder.build_int_compare(IntPredicate::ULT, *idx, size, "").unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
in_range,
|
||||
"0:IndexError",
|
||||
"index {0} is out of bounds for axis 0 with size {1}",
|
||||
[Some(*idx), Some(self.0.load_ndims(ctx)), None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {}
|
||||
impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {}
|
||||
|
||||
impl<'ctx> TypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {
|
||||
fn downcast_to_type(
|
||||
&self,
|
||||
_: &mut CodeGenContext<'ctx, '_>,
|
||||
value: BasicValueEnum<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
value.into_int_value()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> TypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDimsProxy<'ctx, '_> {
|
||||
fn upcast_from_type(
|
||||
&self,
|
||||
_: &mut CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
value.into()
|
||||
}
|
||||
}
|
||||
|
||||
/// Proxy type for accessing the `data` array of an `NDArray` instance in LLVM.
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct NDArrayDataProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
|
||||
|
||||
impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
|
||||
fn element_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
) -> AnyTypeEnum<'ctx> {
|
||||
self.0.data().base_ptr(ctx, generator).get_type().get_element_type()
|
||||
}
|
||||
|
||||
fn base_ptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
_: &G,
|
||||
) -> PointerValue<'ctx> {
|
||||
let var_name = self.0.name.map(|v| format!("{v}.data")).unwrap_or_default();
|
||||
|
||||
ctx.builder
|
||||
.build_load(self.0.ptr_to_data(ctx), var_name.as_str())
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
fn size<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
) -> IntValue<'ctx> {
|
||||
call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.base_ptr(ctx, generator),
|
||||
&[*idx],
|
||||
name.unwrap_or_default(),
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
fn ptr_offset<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let data_sz = self.size(ctx, generator);
|
||||
let in_range = ctx.builder.build_int_compare(IntPredicate::ULT, *idx, data_sz, "").unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
in_range,
|
||||
"0:IndexError",
|
||||
"index {0} is out of bounds with size {1}",
|
||||
[Some(*idx), Some(self.0.load_ndims(ctx)), None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
|
||||
impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
|
||||
|
||||
impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
|
||||
for NDArrayDataProxy<'ctx, '_>
|
||||
{
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
indices: &Index,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let indices_elem_ty = indices
|
||||
.ptr_offset(ctx, generator, &llvm_usize.const_zero(), None)
|
||||
.get_type()
|
||||
.get_element_type();
|
||||
let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
|
||||
panic!("Expected list[int32] but got {indices_elem_ty}")
|
||||
};
|
||||
assert_eq!(
|
||||
indices_elem_ty.get_bit_width(),
|
||||
32,
|
||||
"Expected list[int32] but got list[int{}]",
|
||||
indices_elem_ty.get_bit_width()
|
||||
);
|
||||
|
||||
let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
|
||||
|
||||
unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.base_ptr(ctx, generator),
|
||||
&[index],
|
||||
name.unwrap_or_default(),
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
fn ptr_offset<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
indices: &Index,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let indices_size = indices.size(ctx, generator);
|
||||
let nidx_leq_ndims = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::SLE, indices_size, self.0.load_ndims(ctx), "")
|
||||
.unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
nidx_leq_ndims,
|
||||
"0:IndexError",
|
||||
"invalid index to scalar variable",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
let indices_len = indices.size(ctx, generator);
|
||||
let ndarray_len = self.0.load_ndims(ctx);
|
||||
let len = call_int_umin(ctx, indices_len, ndarray_len, None);
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(len, false),
|
||||
|generator, ctx, _, i| {
|
||||
let (dim_idx, dim_sz) = unsafe {
|
||||
(
|
||||
indices.get_unchecked(ctx, generator, &i, None).into_int_value(),
|
||||
self.0.dim_sizes().get_typed_unchecked(ctx, generator, &i, None),
|
||||
)
|
||||
};
|
||||
let dim_idx = ctx
|
||||
.builder
|
||||
.build_int_z_extend_or_bit_cast(dim_idx, dim_sz.get_type(), "")
|
||||
.unwrap();
|
||||
|
||||
let dim_lt =
|
||||
ctx.builder.build_int_compare(IntPredicate::SLT, dim_idx, dim_sz, "").unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
dim_lt,
|
||||
"0:IndexError",
|
||||
"index {0} is out of bounds for axis 0 with size {1}",
|
||||
[Some(dim_idx), Some(dim_sz), None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
unsafe { self.ptr_offset_unchecked(ctx, generator, indices, name) }
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeAccessor<'ctx, Index>
|
||||
for NDArrayDataProxy<'ctx, '_>
|
||||
{
|
||||
}
|
||||
impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeMutator<'ctx, Index>
|
||||
for NDArrayDataProxy<'ctx, '_>
|
||||
{
|
||||
}
|
||||
|
|
|
|||
|
|
@ -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
|
|
@ -1,7 +1,5 @@
|
|||
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 crate::codegen::CodeGenContext;
|
||||
|
|
|
|||
|
|
@ -1,17 +1,15 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicTypeEnum, IntType},
|
||||
values::{BasicValueEnum, IntValue, PointerValue},
|
||||
};
|
||||
|
||||
use nac3parser::ast::{Expr, Stmt, StrRef};
|
||||
|
||||
use crate::{
|
||||
codegen::{bool_to_i1, bool_to_i8, classes::ArraySliceValue, expr::*, stmt::*, CodeGenContext},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{DefinitionId, TopLevelDef},
|
||||
typecheck::typedef::{FunSignature, Type},
|
||||
};
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicTypeEnum, IntType},
|
||||
values::{BasicValueEnum, IntValue, PointerValue},
|
||||
};
|
||||
use nac3parser::ast::{Expr, Stmt, StrRef};
|
||||
|
||||
pub trait CodeGenerator {
|
||||
/// Return the module name for the code generator.
|
||||
|
|
|
|||
|
|
@ -1,30 +1,29 @@
|
|||
use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
|
||||
|
||||
use super::{
|
||||
classes::{ArrayLikeValue, ListValue},
|
||||
model::*,
|
||||
object::{
|
||||
list::List,
|
||||
ndarray::{broadcast::ShapeEntry, indexing::NDIndex, nditer::NDIter, NDArray},
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
use function::CallFunction;
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
context::Context,
|
||||
memory_buffer::MemoryBuffer,
|
||||
module::Module,
|
||||
types::{BasicTypeEnum, IntType},
|
||||
types::BasicTypeEnum,
|
||||
values::{BasicValue, BasicValueEnum, CallSiteValue, FloatValue, IntValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
use itertools::Either;
|
||||
|
||||
use nac3parser::ast::Expr;
|
||||
|
||||
use super::{
|
||||
classes::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
|
||||
TypedArrayLikeAccessor, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
|
||||
},
|
||||
llvm_intrinsics,
|
||||
macros::codegen_unreachable,
|
||||
stmt::gen_for_callback_incrementing,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
|
||||
|
||||
#[must_use]
|
||||
pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver) -> Module<'ctx> {
|
||||
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 +39,6 @@ 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 +56,7 @@ 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(|| {
|
||||
|
|
@ -462,7 +442,7 @@ 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()
|
||||
}
|
||||
|
|
@ -584,369 +564,324 @@ pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> Flo
|
|||
.unwrap()
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_size`. Returns an [`IntValue`] representing the
|
||||
/// calculated total size.
|
||||
///
|
||||
/// * `dims` - An [`ArrayLikeIndexer`] containing the size of each dimension.
|
||||
/// * `range` - The dimension index to begin and end (exclusively) calculating the dimensions for,
|
||||
/// or [`None`] if starting from the first dimension and ending at the last dimension
|
||||
/// respectively.
|
||||
pub fn call_ndarray_calc_size<'ctx, G, Dims>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dims: &Dims,
|
||||
(begin, end): (Option<IntValue<'ctx>>, Option<IntValue<'ctx>>),
|
||||
) -> IntValue<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Dims: ArrayLikeIndexer<'ctx>,
|
||||
{
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
/// 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 ndarray_calc_size_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_size",
|
||||
64 => "__nac3_ndarray_calc_size64",
|
||||
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_calc_size_fn_t = llvm_usize.fn_type(
|
||||
&[llvm_pusize.into(), llvm_usize.into(), llvm_usize.into(), llvm_usize.into()],
|
||||
false,
|
||||
);
|
||||
let ndarray_calc_size_fn =
|
||||
ctx.module.get_function(ndarray_calc_size_fn_name).unwrap_or_else(|| {
|
||||
ctx.module.add_function(ndarray_calc_size_fn_name, ndarray_calc_size_fn_t, None)
|
||||
let 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 = module.get_global(irrt_name).unwrap_or_else(|| {
|
||||
panic!("Exception symbol name '{irrt_name}' should exist in the IRRT LLVM module")
|
||||
});
|
||||
|
||||
let begin = begin.unwrap_or_else(|| llvm_usize.const_zero());
|
||||
let end = end.unwrap_or_else(|| dims.size(ctx, generator));
|
||||
ctx.builder
|
||||
.build_call(
|
||||
ndarray_calc_size_fn,
|
||||
&[
|
||||
dims.base_ptr(ctx, generator).into(),
|
||||
dims.size(ctx, generator).into(),
|
||||
begin.into(),
|
||||
end.into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(|v| v.map_left(BasicValueEnum::into_int_value))
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap()
|
||||
global.set_initializer(&exn_id);
|
||||
}
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_nd_indices`. Returns a [`TypeArrayLikeAdpater`]
|
||||
/// containing `i32` indices of the flattened index.
|
||||
///
|
||||
/// * `index` - The index to compute the multidimensional index for.
|
||||
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
|
||||
/// `NDArray`.
|
||||
pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
index: IntValue<'ctx>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
|
||||
let llvm_void = ctx.ctx.void_type();
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
let ndarray_calc_nd_indices_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_nd_indices",
|
||||
64 => "__nac3_ndarray_calc_nd_indices64",
|
||||
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_calc_nd_indices_fn =
|
||||
ctx.module.get_function(ndarray_calc_nd_indices_fn_name).unwrap_or_else(|| {
|
||||
let fn_type = llvm_void.fn_type(
|
||||
&[llvm_usize.into(), llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into()],
|
||||
false,
|
||||
);
|
||||
|
||||
ctx.module.add_function(ndarray_calc_nd_indices_fn_name, fn_type, None)
|
||||
});
|
||||
|
||||
let ndarray_num_dims = ndarray.load_ndims(ctx);
|
||||
let ndarray_dims = ndarray.dim_sizes();
|
||||
|
||||
let indices = ctx.builder.build_array_alloca(llvm_i32, ndarray_num_dims, "").unwrap();
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
ndarray_calc_nd_indices_fn,
|
||||
&[
|
||||
index.into(),
|
||||
ndarray_dims.base_ptr(ctx, generator).into(),
|
||||
ndarray_num_dims.into(),
|
||||
indices.into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
TypedArrayLikeAdapter::from(
|
||||
ArraySliceValue::from_ptr_val(indices, ndarray_num_dims, None),
|
||||
Box::new(|_, v| v.into_int_value()),
|
||||
Box::new(|_, v| v.into()),
|
||||
)
|
||||
// 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
|
||||
}
|
||||
|
||||
fn call_ndarray_flatten_index_impl<'ctx, G, Indices>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
indices: &Indices,
|
||||
) -> IntValue<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Indices: ArrayLikeIndexer<'ctx>,
|
||||
{
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
debug_assert_eq!(
|
||||
IntType::try_from(indices.element_type(ctx, generator))
|
||||
.map(IntType::get_bit_width)
|
||||
.unwrap_or_default(),
|
||||
llvm_i32.get_bit_width(),
|
||||
"Expected i32 value for argument `indices` to `call_ndarray_flatten_index_impl`"
|
||||
);
|
||||
debug_assert_eq!(
|
||||
indices.size(ctx, generator).get_type().get_bit_width(),
|
||||
llvm_usize.get_bit_width(),
|
||||
"Expected usize integer value for argument `indices_size` to `call_ndarray_flatten_index_impl`"
|
||||
);
|
||||
|
||||
let ndarray_flatten_index_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_flatten_index",
|
||||
64 => "__nac3_ndarray_flatten_index64",
|
||||
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_flatten_index_fn =
|
||||
ctx.module.get_function(ndarray_flatten_index_fn_name).unwrap_or_else(|| {
|
||||
let fn_type = llvm_usize.fn_type(
|
||||
&[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_usize.into()],
|
||||
false,
|
||||
);
|
||||
|
||||
ctx.module.add_function(ndarray_flatten_index_fn_name, fn_type, None)
|
||||
});
|
||||
|
||||
let ndarray_num_dims = ndarray.load_ndims(ctx);
|
||||
let ndarray_dims = ndarray.dim_sizes();
|
||||
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_call(
|
||||
ndarray_flatten_index_fn,
|
||||
&[
|
||||
ndarray_dims.base_ptr(ctx, generator).into(),
|
||||
ndarray_num_dims.into(),
|
||||
indices.base_ptr(ctx, generator).into(),
|
||||
indices.size(ctx, generator).into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(|v| v.map_left(BasicValueEnum::into_int_value))
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap();
|
||||
|
||||
index
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_flatten_index`. Returns the flattened index for the
|
||||
/// multidimensional index.
|
||||
///
|
||||
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
|
||||
/// `NDArray`.
|
||||
/// * `indices` - The multidimensional index to compute the flattened index for.
|
||||
pub fn call_ndarray_flatten_index<'ctx, G, Index>(
|
||||
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
indices: &Index,
|
||||
) -> IntValue<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Index: ArrayLikeIndexer<'ctx>,
|
||||
{
|
||||
call_ndarray_flatten_index_impl(generator, ctx, ndarray, indices)
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_broadcast`. Returns a tuple containing the number of
|
||||
/// dimension and size of each dimension of the resultant `ndarray`.
|
||||
pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
lhs: NDArrayValue<'ctx>,
|
||||
rhs: NDArrayValue<'ctx>,
|
||||
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_broadcast",
|
||||
64 => "__nac3_ndarray_calc_broadcast64",
|
||||
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_calc_broadcast_fn =
|
||||
ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
|
||||
let fn_type = llvm_usize.fn_type(
|
||||
&[
|
||||
llvm_pusize.into(),
|
||||
llvm_usize.into(),
|
||||
llvm_pusize.into(),
|
||||
llvm_usize.into(),
|
||||
llvm_pusize.into(),
|
||||
],
|
||||
false,
|
||||
);
|
||||
|
||||
ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
|
||||
});
|
||||
|
||||
let lhs_ndims = lhs.load_ndims(ctx);
|
||||
let rhs_ndims = rhs.load_ndims(ctx);
|
||||
let min_ndims = llvm_intrinsics::call_int_umin(ctx, lhs_ndims, rhs_ndims, None);
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
ndims: Instance<'ctx, Int<SizeT>>,
|
||||
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(min_ndims, false),
|
||||
|generator, ctx, _, idx| {
|
||||
let idx = ctx.builder.build_int_sub(min_ndims, idx, "").unwrap();
|
||||
let (lhs_dim_sz, rhs_dim_sz) = unsafe {
|
||||
(
|
||||
lhs.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None),
|
||||
rhs.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None),
|
||||
)
|
||||
};
|
||||
|
||||
let llvm_usize_const_one = llvm_usize.const_int(1, false);
|
||||
let lhs_eqz = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::EQ, lhs_dim_sz, llvm_usize_const_one, "")
|
||||
.unwrap();
|
||||
let rhs_eqz = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::EQ, rhs_dim_sz, llvm_usize_const_one, "")
|
||||
.unwrap();
|
||||
let lhs_or_rhs_eqz = ctx.builder.build_or(lhs_eqz, rhs_eqz, "").unwrap();
|
||||
|
||||
let lhs_eq_rhs = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::EQ, lhs_dim_sz, rhs_dim_sz, "")
|
||||
.unwrap();
|
||||
|
||||
let is_compatible = ctx.builder.build_or(lhs_or_rhs_eqz, lhs_eq_rhs, "").unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
is_compatible,
|
||||
"0:ValueError",
|
||||
"operands could not be broadcast together",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
|
||||
let lhs_dims = lhs.dim_sizes().base_ptr(ctx, generator);
|
||||
let lhs_ndims = lhs.load_ndims(ctx);
|
||||
let rhs_dims = rhs.dim_sizes().base_ptr(ctx, generator);
|
||||
let rhs_ndims = rhs.load_ndims(ctx);
|
||||
let out_dims = ctx.builder.build_array_alloca(llvm_usize, max_ndims, "").unwrap();
|
||||
let out_dims = ArraySliceValue::from_ptr_val(out_dims, max_ndims, None);
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
ndarray_calc_broadcast_fn,
|
||||
&[
|
||||
lhs_dims.into(),
|
||||
lhs_ndims.into(),
|
||||
rhs_dims.into(),
|
||||
rhs_ndims.into(),
|
||||
out_dims.base_ptr(ctx, generator).into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
TypedArrayLikeAdapter::from(
|
||||
out_dims,
|
||||
Box::new(|_, v| v.into_int_value()),
|
||||
Box::new(|_, v| v.into()),
|
||||
)
|
||||
"__nac3_ndarray_util_assert_shape_no_negative",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name).arg(ndims).arg(shape).returning_void();
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
|
||||
/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
|
||||
/// array `broadcast_idx`.
|
||||
pub fn call_ndarray_calc_broadcast_index<
|
||||
'ctx,
|
||||
G: CodeGenerator + ?Sized,
|
||||
BroadcastIdx: UntypedArrayLikeAccessor<'ctx>,
|
||||
>(
|
||||
pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
array: NDArrayValue<'ctx>,
|
||||
broadcast_idx: &BroadcastIdx,
|
||||
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_broadcast_idx",
|
||||
64 => "__nac3_ndarray_calc_broadcast_idx64",
|
||||
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_calc_broadcast_fn =
|
||||
ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
|
||||
let fn_type = llvm_usize.fn_type(
|
||||
&[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_pi32.into()],
|
||||
false,
|
||||
);
|
||||
|
||||
ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
|
||||
});
|
||||
|
||||
let broadcast_size = broadcast_idx.size(ctx, generator);
|
||||
let out_idx = ctx.builder.build_array_alloca(llvm_i32, broadcast_size, "").unwrap();
|
||||
|
||||
let array_dims = array.dim_sizes().base_ptr(ctx, generator);
|
||||
let array_ndims = array.load_ndims(ctx);
|
||||
let broadcast_idx_ptr = unsafe {
|
||||
broadcast_idx.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
|
||||
};
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
ndarray_calc_broadcast_fn,
|
||||
&[array_dims.into(), array_ndims.into(), broadcast_idx_ptr.into(), out_idx.into()],
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
TypedArrayLikeAdapter::from(
|
||||
ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None),
|
||||
Box::new(|_, v| v.into_int_value()),
|
||||
Box::new(|_, v| v.into()),
|
||||
)
|
||||
ndarray_ndims: Instance<'ctx, Int<SizeT>>,
|
||||
ndarray_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
output_ndims: Instance<'ctx, Int<SizeT>>,
|
||||
output_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
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, '_>,
|
||||
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
) -> Instance<'ctx, Int<SizeT>> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
|
||||
CallFunction::begin(generator, ctx, &name).arg(ndarray).returning_auto("size")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
) -> Instance<'ctx, Int<SizeT>> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
|
||||
CallFunction::begin(generator, ctx, &name).arg(ndarray).returning_auto("nbytes")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
) -> Instance<'ctx, Int<SizeT>> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
|
||||
CallFunction::begin(generator, ctx, &name).arg(ndarray).returning_auto("len")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
) -> Instance<'ctx, Int<Bool>> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
|
||||
CallFunction::begin(generator, ctx, &name).arg(ndarray).returning_auto("is_c_contiguous")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
index: Instance<'ctx, Int<SizeT>>,
|
||||
) -> Instance<'ctx, Ptr<Int<Byte>>> {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
|
||||
CallFunction::begin(generator, ctx, &name).arg(ndarray).arg(index).returning_auto("pelement")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) -> Instance<'ctx, Ptr<Int<Byte>>> {
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_pelement_by_indices");
|
||||
CallFunction::begin(generator, ctx, &name).arg(ndarray).arg(indices).returning_auto("pelement")
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
) {
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
|
||||
CallFunction::begin(generator, ctx, &name).arg(ndarray).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
|
||||
CallFunction::begin(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
|
||||
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_initialize");
|
||||
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: Instance<'ctx, Ptr<Struct<NDIter>>>,
|
||||
) -> Instance<'ctx, Int<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: Instance<'ctx, Ptr<Struct<NDIter>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_next");
|
||||
CallFunction::begin(generator, ctx, &name).arg(iter).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
num_indices: Instance<'ctx, Int<SizeT>>,
|
||||
indices: Instance<'ctx, Ptr<Struct<NDIndex>>>,
|
||||
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(num_indices)
|
||||
.arg(indices)
|
||||
.arg(src_ndarray)
|
||||
.arg(dst_ndarray)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
|
||||
ndims: Instance<'ctx, Int<SizeT>>,
|
||||
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_array_set_and_validate_list_shape",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name).arg(list).arg(ndims).arg(shape).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
|
||||
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_array_write_list_to_array",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name).arg(list).arg(ndarray).returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
size: Instance<'ctx, Int<SizeT>>,
|
||||
new_ndims: Instance<'ctx, Int<SizeT>>,
|
||||
new_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_reshape_resolve_and_check_new_shape",
|
||||
);
|
||||
CallFunction::begin(generator, ctx, &name)
|
||||
.arg(size)
|
||||
.arg(new_ndims)
|
||||
.arg(new_shape)
|
||||
.returning_void();
|
||||
}
|
||||
|
||||
pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
|
||||
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: Instance<'ctx, Int<SizeT>>,
|
||||
shape_entries: Instance<'ctx, Ptr<Struct<ShapeEntry>>>,
|
||||
dst_ndims: Instance<'ctx, Int<SizeT>>,
|
||||
dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
|
||||
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_transpose<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
num_axes: Instance<'ctx, Int<SizeT>>,
|
||||
axes: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
|
||||
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: Instance<'ctx, Int<SizeT>>,
|
||||
a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
b_ndims: Instance<'ctx, Int<SizeT>>,
|
||||
b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
final_ndims: Instance<'ctx, Int<SizeT>>,
|
||||
new_a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
new_b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let name =
|
||||
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
|
||||
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();
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,13 +1,11 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
intrinsics::Intrinsic,
|
||||
types::{AnyTypeEnum::IntType, FloatType},
|
||||
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use itertools::Either;
|
||||
|
||||
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;
|
||||
|
||||
/// Returns the string representation for the floating-point type `ft` when used in intrinsic
|
||||
/// functions.
|
||||
|
|
@ -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()
|
||||
};
|
||||
|
|
|
|||
|
|
@ -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},
|
||||
|
|
@ -24,19 +24,16 @@ use inkwell::{
|
|||
AddressSpace, IntPredicate, OptimizationLevel,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
use parking_lot::{Condvar, Mutex};
|
||||
|
||||
use model::*;
|
||||
use nac3parser::ast::{Location, Stmt, StrRef};
|
||||
|
||||
use crate::{
|
||||
codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
|
||||
symbol_resolver::{StaticValue, SymbolResolver},
|
||||
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
|
||||
typecheck::{
|
||||
type_inferencer::{CodeLocation, PrimitiveStore},
|
||||
typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
|
||||
},
|
||||
use object::ndarray::NDArray;
|
||||
use parking_lot::{Condvar, Mutex};
|
||||
use std::collections::{HashMap, HashSet};
|
||||
use std::sync::{
|
||||
atomic::{AtomicBool, Ordering},
|
||||
Arc,
|
||||
};
|
||||
use std::thread;
|
||||
|
||||
pub mod builtin_fns;
|
||||
pub mod classes;
|
||||
|
|
@ -46,7 +43,9 @@ pub mod extern_fns;
|
|||
mod generator;
|
||||
pub mod irrt;
|
||||
pub mod llvm_intrinsics;
|
||||
pub mod model;
|
||||
pub mod numpy;
|
||||
pub mod object;
|
||||
pub mod stmt;
|
||||
|
||||
#[cfg(test)]
|
||||
|
|
@ -55,22 +54,6 @@ mod test;
|
|||
use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
|
||||
pub use generator::{CodeGenerator, DefaultCodeGenerator};
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
#[derive(Default)]
|
||||
pub struct StaticValueStore {
|
||||
pub lookup: HashMap<Vec<(usize, u64)>, usize>,
|
||||
|
|
@ -510,12 +493,7 @@ 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()
|
||||
Ptr(Struct(NDArray)).get_type(generator, ctx).as_basic_type_enum()
|
||||
}
|
||||
|
||||
_ => unreachable!(
|
||||
|
|
@ -853,9 +831,10 @@ 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 +1,42 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum},
|
||||
values::BasicValueEnum,
|
||||
};
|
||||
|
||||
use crate::codegen::CodeGenerator;
|
||||
|
||||
use super::*;
|
||||
|
||||
/// A [`Model`] of any [`BasicTypeEnum`].
|
||||
///
|
||||
/// Use this when it is infeasible to use model abstractions.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct Any<'ctx>(pub BasicTypeEnum<'ctx>);
|
||||
|
||||
impl<'ctx> Model<'ctx> for Any<'ctx> {
|
||||
type Value = BasicValueEnum<'ctx>;
|
||||
type Type = BasicTypeEnum<'ctx>;
|
||||
|
||||
fn 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 +1,143 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{ArrayType, BasicType, BasicTypeEnum},
|
||||
values::{ArrayValue, IntValue},
|
||||
};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
/// Trait for Rust structs identifying length values for [`Array`].
|
||||
pub trait LenKind: fmt::Debug + Clone + Copy {
|
||||
fn get_length(&self) -> u32;
|
||||
}
|
||||
|
||||
/// A statically known length.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Len<const N: u32>;
|
||||
|
||||
/// A dynamically known length.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyLen(pub u32);
|
||||
|
||||
impl<const N: u32> LenKind for Len<N> {
|
||||
fn get_length(&self) -> u32 {
|
||||
N
|
||||
}
|
||||
}
|
||||
|
||||
impl LenKind for AnyLen {
|
||||
fn get_length(&self) -> u32 {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
||||
/// A Model for an [`ArrayType`].
|
||||
///
|
||||
/// `Len` should be of a [`LenKind`] and `Item` should be a of [`Model`].
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Array<Len, Item> {
|
||||
/// Length of this array.
|
||||
pub len: Len,
|
||||
/// [`Model`] of the array items.
|
||||
pub item: Item,
|
||||
}
|
||||
|
||||
impl<'ctx, Len: LenKind, Item: Model<'ctx>> Model<'ctx> for Array<Len, Item> {
|
||||
type Value = ArrayValue<'ctx>;
|
||||
type Type = ArrayType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
self.item.get_type(generator, ctx).array_type(self.len.get_length())
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
let BasicTypeEnum::ArrayType(ty) = ty else {
|
||||
return Err(ModelError(format!("Expecting ArrayType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
if ty.len() != self.len.get_length() {
|
||||
return Err(ModelError(format!(
|
||||
"Expecting ArrayType with size {}, but got an ArrayType with size {}",
|
||||
ty.len(),
|
||||
self.len.get_length()
|
||||
)));
|
||||
}
|
||||
|
||||
self.item
|
||||
.check_type(generator, ctx, ty.get_element_type())
|
||||
.map_err(|err| err.under_context("an ArrayType"))?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, Len: LenKind, Item: Model<'ctx>> Instance<'ctx, Ptr<Array<Len, Item>>> {
|
||||
/// Get the pointer to the `i`-th (0-based) array element.
|
||||
pub fn gep(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Ptr<Item>> {
|
||||
let zero = ctx.ctx.i32_type().const_zero();
|
||||
let ptr = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[zero, i], "").unwrap() };
|
||||
|
||||
Ptr(self.model.0.item).believe_value(ptr)
|
||||
}
|
||||
|
||||
/// Like `gep` but `i` is a constant.
|
||||
pub fn gep_const(&self, ctx: &CodeGenContext<'ctx, '_>, i: u64) -> Instance<'ctx, Ptr<Item>> {
|
||||
assert!(
|
||||
i < u64::from(self.model.0.len.get_length()),
|
||||
"Index {i} is out of bounds. Array length = {}",
|
||||
self.model.0.len.get_length()
|
||||
);
|
||||
|
||||
let i = ctx.ctx.i32_type().const_int(i, false);
|
||||
self.gep(ctx, i)
|
||||
}
|
||||
|
||||
/// Convenience function equivalent to `.gep(...).load(...)`.
|
||||
pub fn get<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Item> {
|
||||
self.gep(ctx, i).load(generator, ctx)
|
||||
}
|
||||
|
||||
/// Like `get` but `i` is a constant.
|
||||
pub fn get_const<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: u64,
|
||||
) -> Instance<'ctx, Item> {
|
||||
self.gep_const(ctx, i).load(generator, ctx)
|
||||
}
|
||||
|
||||
/// Convenience function equivalent to `.gep(...).store(...)`.
|
||||
pub fn set(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
i: IntValue<'ctx>,
|
||||
value: Instance<'ctx, Item>,
|
||||
) {
|
||||
self.gep(ctx, i).store(ctx, value);
|
||||
}
|
||||
|
||||
/// Like `set` but `i` is a constant.
|
||||
pub fn set_const(&self, ctx: &CodeGenContext<'ctx, '_>, i: u64, value: Instance<'ctx, Item>) {
|
||||
self.gep_const(ctx, i).store(ctx, value);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,202 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{context::Context, types::*, values::*};
|
||||
use itertools::Itertools;
|
||||
|
||||
use super::*;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
/// A error type for reporting any [`Model`]-related error (e.g., a [`BasicType`] mismatch).
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ModelError(pub String);
|
||||
|
||||
impl ModelError {
|
||||
// Append a context message to the error.
|
||||
pub(super) fn under_context(mut self, context: &str) -> Self {
|
||||
self.0.push_str(" ... in ");
|
||||
self.0.push_str(context);
|
||||
self
|
||||
}
|
||||
}
|
||||
|
||||
/// Trait for Rust structs identifying [`BasicType`]s in the context of a known [`CodeGenerator`] and [`CodeGenContext`].
|
||||
///
|
||||
/// For instance,
|
||||
/// - [`Int<Int32>`] identifies an [`IntType`] with 32-bits.
|
||||
/// - [`Int<SizeT>`] identifies an [`IntType`] with bit-width [`CodeGenerator::get_size_type`].
|
||||
/// - [`Ptr<Int<SizeT>>`] identifies a [`PointerType`] that points to an [`IntType`] with bit-width [`CodeGenerator::get_size_type`].
|
||||
/// - [`Int<AnyInt>`] identifies an [`IntType`] with bit-width of whatever is set in the [`AnyInt`] object.
|
||||
/// - [`Any`] identifies a [`BasicType`] set in the [`Any`] object itself.
|
||||
///
|
||||
/// You can get the [`BasicType`] out of a model with [`Model::get_type`].
|
||||
///
|
||||
/// Furthermore, [`Instance<'ctx, M>`] is a simple structure that carries a [`BasicValue`] with [`BasicType`] identified by model `M`.
|
||||
///
|
||||
/// The main purpose of this abstraction is to have a more Rust type-safe way to use Inkwell and give type-hints for programmers.
|
||||
///
|
||||
/// ### Notes on `Default` trait
|
||||
///
|
||||
/// For some models like [`Int<Int32>`] or [`Int<SizeT>`], they have a [`Default`] trait since just by looking at their types, it is possible
|
||||
/// to tell the [`BasicType`]s they are identifying.
|
||||
///
|
||||
/// This can be used to create strongly-typed interfaces accepting only values of a specific [`BasicType`] without having to worry about
|
||||
/// writing debug assertions to check, for example, if the programmer has passed in an [`IntValue`] with the wrong bit-width.
|
||||
/// ```ignore
|
||||
/// fn give_me_i32_and_get_a_size_t_back<'ctx>(i32: Instance<'ctx, Int<Int32>>) -> Instance<'ctx, Int<SizeT>> {
|
||||
/// // code...
|
||||
/// }
|
||||
/// ```
|
||||
///
|
||||
/// ### Notes on converting between Inkwell and model.
|
||||
///
|
||||
/// Suppose you have an [`IntValue`], and you want to pass it into a function that takes a [`Instance<'ctx, Int<Int32>>`]. You can do use
|
||||
/// [`Model::check_value`] or [`Model::believe_value`].
|
||||
/// ```ignore
|
||||
/// let my_value: IntValue<'ctx>;
|
||||
///
|
||||
/// let my_value = Int(Int32).check_value(my_value).unwrap(); // Panics if `my_value` is not 32-bit with a descriptive error message.
|
||||
///
|
||||
/// // or, if you are absolutely certain that `my_value` is 32-bit and doing extra checks is a waste of time:
|
||||
/// let my_value = Int(Int32).believe_value(my_value);
|
||||
/// ```
|
||||
pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
|
||||
/// The [`BasicType`] *variant* this model is identifying.
|
||||
type Type: BasicType<'ctx>;
|
||||
|
||||
/// The [`BasicValue`] type of the [`BasicType`] of this model.
|
||||
type Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>>;
|
||||
|
||||
/// Return the [`BasicType`] of this model.
|
||||
#[must_use]
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type;
|
||||
|
||||
/// Get the number of bytes of the [`BasicType`] of this model.
|
||||
fn sizeof<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntValue<'ctx> {
|
||||
self.get_type(generator, ctx).size_of().unwrap()
|
||||
}
|
||||
|
||||
/// Check if a [`BasicType`] matches the [`BasicType`] of this model.
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError>;
|
||||
|
||||
/// Create an instance from a value.
|
||||
///
|
||||
/// 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 the [`BasicValue`] into an [`Instance`] if it is.
|
||||
fn check_value<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
value: V,
|
||||
) -> Result<Instance<'ctx, Self>, ModelError> {
|
||||
let value = value.as_basic_value_enum();
|
||||
self.check_type(generator, ctx, value.get_type())
|
||||
.map_err(|err| err.under_context(format!("the value {value:?}").as_str()))?;
|
||||
|
||||
let Ok(value) = Self::Value::try_from(value) else {
|
||||
unreachable!("check_type() has bad implementation")
|
||||
};
|
||||
Ok(self.believe_value(value))
|
||||
}
|
||||
|
||||
// Allocate a value on the stack and return its pointer.
|
||||
fn alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Ptr<Self>> {
|
||||
let p = ctx.builder.build_alloca(self.get_type(generator, ctx.ctx), "").unwrap();
|
||||
Ptr(*self).believe_value(p)
|
||||
}
|
||||
|
||||
// Allocate an array on the stack and return its pointer.
|
||||
fn array_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
len: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Ptr<Self>> {
|
||||
let p = ctx.builder.build_array_alloca(self.get_type(generator, ctx.ctx), len, "").unwrap();
|
||||
Ptr(*self).believe_value(p)
|
||||
}
|
||||
|
||||
fn var_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&str>,
|
||||
) -> Result<Instance<'ctx, Ptr<Self>>, String> {
|
||||
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
|
||||
let p = generator.gen_var_alloc(ctx, ty, name)?;
|
||||
Ok(Ptr(*self).believe_value(p))
|
||||
}
|
||||
|
||||
fn array_var_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
len: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Result<Instance<'ctx, Ptr<Self>>, String> {
|
||||
// TODO: Remove ArraySliceValue
|
||||
let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
|
||||
let p = generator.gen_array_var_alloc(ctx, ty, len, name)?;
|
||||
Ok(Ptr(*self).believe_value(PointerValue::from(p)))
|
||||
}
|
||||
|
||||
/// Allocate a constant array.
|
||||
fn const_array<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
values: &[Instance<'ctx, Self>],
|
||||
) -> Instance<'ctx, Array<AnyLen, Self>> {
|
||||
macro_rules! make {
|
||||
($t:expr, $into_value:expr) => {
|
||||
$t.const_array(
|
||||
&values
|
||||
.iter()
|
||||
.map(|x| $into_value(x.value.as_basic_value_enum()))
|
||||
.collect_vec(),
|
||||
)
|
||||
};
|
||||
}
|
||||
|
||||
let value = match self.get_type(generator, ctx).as_basic_type_enum() {
|
||||
BasicTypeEnum::ArrayType(t) => make!(t, BasicValueEnum::into_array_value),
|
||||
BasicTypeEnum::IntType(t) => make!(t, BasicValueEnum::into_int_value),
|
||||
BasicTypeEnum::FloatType(t) => make!(t, BasicValueEnum::into_float_value),
|
||||
BasicTypeEnum::PointerType(t) => make!(t, BasicValueEnum::into_pointer_value),
|
||||
BasicTypeEnum::StructType(t) => make!(t, BasicValueEnum::into_struct_value),
|
||||
BasicTypeEnum::VectorType(t) => make!(t, BasicValueEnum::into_vector_value),
|
||||
};
|
||||
|
||||
Array { len: AnyLen(values.len() as u32), item: *self }
|
||||
.check_value(generator, ctx, value)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct Instance<'ctx, M: Model<'ctx>> {
|
||||
/// The model of this instance.
|
||||
pub model: M,
|
||||
/// The value of this instance.
|
||||
///
|
||||
/// It is guaranteed the [`BasicType`] of `value` is consistent with that of `model`.
|
||||
pub value: M::Value,
|
||||
}
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, FloatType},
|
||||
values::FloatValue,
|
||||
};
|
||||
|
||||
use crate::codegen::CodeGenerator;
|
||||
|
||||
use super::*;
|
||||
|
||||
pub trait FloatKind<'ctx>: fmt::Debug + Clone + Copy {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx>;
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Float32;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Float64;
|
||||
|
||||
impl<'ctx> FloatKind<'ctx> for Float32 {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx> {
|
||||
ctx.f32_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> FloatKind<'ctx> for Float64 {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx> {
|
||||
ctx.f64_type()
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyFloat<'ctx>(FloatType<'ctx>);
|
||||
|
||||
impl<'ctx> FloatKind<'ctx> for AnyFloat<'ctx> {
|
||||
fn get_float_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
_ctx: &'ctx Context,
|
||||
) -> FloatType<'ctx> {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Float<N>(pub N);
|
||||
|
||||
impl<'ctx, N: FloatKind<'ctx>> Model<'ctx> for Float<N> {
|
||||
type Value = FloatValue<'ctx>;
|
||||
type Type = FloatType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
self.0.get_float_type(generator, ctx)
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
let Ok(ty) = FloatType::try_from(ty) else {
|
||||
return Err(ModelError(format!("Expecting FloatType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
let exp_ty = self.0.get_float_type(generator, ctx);
|
||||
|
||||
// TODO: Inkwell does not have get_bit_width for FloatType?
|
||||
if ty != exp_ty {
|
||||
return Err(ModelError(format!("Expecting {exp_ty:?}, but got {ty:?}")));
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,122 @@
|
|||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
types::{BasicMetadataTypeEnum, BasicType, FunctionType},
|
||||
values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
struct Arg<'ctx> {
|
||||
ty: BasicMetadataTypeEnum<'ctx>,
|
||||
val: BasicMetadataValueEnum<'ctx>,
|
||||
}
|
||||
|
||||
/// A convenience structure to construct & call an LLVM function.
|
||||
///
|
||||
/// ### Usage
|
||||
///
|
||||
/// The syntax is like this:
|
||||
/// ```ignore
|
||||
/// let result = CallFunction::begin("my_function_name")
|
||||
/// .attrs(...)
|
||||
/// .arg(arg1)
|
||||
/// .arg(arg2)
|
||||
/// .arg(arg3)
|
||||
/// .returning("my_function_result", Int32);
|
||||
/// ```
|
||||
///
|
||||
/// The function `my_function_name` is called when `.returning()` (or its variants) is called, returning
|
||||
/// the result as an `Instance<'ctx, Int<Int32>>`.
|
||||
///
|
||||
/// If `my_function_name` has not been declared in `ctx.module`, once `.returning()` is called, a function
|
||||
/// declaration of `my_function_name` is added to `ctx.module`, where the [`FunctionType`] is deduced from
|
||||
/// the argument types and returning type.
|
||||
pub struct 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.call(|tys| ret_ty.fn_type(tys, false), name);
|
||||
let ret = BasicValueEnum::try_from(ret.as_any_value_enum()).unwrap(); // Must work
|
||||
let ret = return_model.check_value(self.generator, self.ctx.ctx, ret).unwrap(); // Must work
|
||||
ret
|
||||
}
|
||||
|
||||
/// Like [`CallFunction::returning_`] but `return_model` is automatically inferred.
|
||||
#[must_use]
|
||||
pub fn returning_auto<M: Model<'ctx> + Default>(self, name: &str) -> Instance<'ctx, M> {
|
||||
self.returning(name, M::default())
|
||||
}
|
||||
|
||||
/// Call the function and expect the function to return a void-type.
|
||||
pub fn returning_void(self) {
|
||||
let ret_ty = self.ctx.ctx.void_type();
|
||||
|
||||
let _ = self.call(|tys| ret_ty.fn_type(tys, false), "");
|
||||
}
|
||||
|
||||
fn call<F>(&self, make_fn_type: F, return_value_name: &str) -> CallSiteValue<'ctx>
|
||||
where
|
||||
F: FnOnce(&[BasicMetadataTypeEnum<'ctx>]) -> FunctionType<'ctx>,
|
||||
{
|
||||
// Get the LLVM function.
|
||||
let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
|
||||
// Declare the function if it doesn't exist.
|
||||
let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
|
||||
|
||||
let func_type = make_fn_type(&tys);
|
||||
let func = self.ctx.module.add_function(self.name, func_type, None);
|
||||
|
||||
for attr in &self.attrs {
|
||||
func.add_attribute(
|
||||
AttributeLoc::Function,
|
||||
self.ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
|
||||
);
|
||||
}
|
||||
|
||||
func
|
||||
});
|
||||
|
||||
let vals = self.args.iter().map(|arg| arg.val).collect_vec();
|
||||
self.ctx.builder.build_call(func, &vals, return_value_name).unwrap()
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,417 @@
|
|||
use std::{cmp::Ordering, fmt};
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, IntType},
|
||||
values::IntValue,
|
||||
IntPredicate,
|
||||
};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
pub trait IntKind<'ctx>: fmt::Debug + Clone + Copy {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx>;
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Bool;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Byte;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Int32;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Int64;
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct SizeT;
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Bool {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.bool_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Byte {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i8_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Int32 {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i32_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for Int64 {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
ctx.i64_type()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for SizeT {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
generator.get_size_type(ctx)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyInt<'ctx>(pub IntType<'ctx>);
|
||||
|
||||
impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
|
||||
fn get_int_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
_generator: &G,
|
||||
_ctx: &'ctx Context,
|
||||
) -> IntType<'ctx> {
|
||||
self.0
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Int<N>(pub N);
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for Int<N> {
|
||||
type Value = IntValue<'ctx>;
|
||||
type Type = IntType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
self.0.get_int_type(generator, ctx)
|
||||
}
|
||||
|
||||
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
ty: T,
|
||||
) -> Result<(), ModelError> {
|
||||
let ty = ty.as_basic_type_enum();
|
||||
let Ok(ty) = IntType::try_from(ty) else {
|
||||
return Err(ModelError(format!("Expecting IntType, but got {ty:?}")));
|
||||
};
|
||||
|
||||
let exp_ty = self.0.get_int_type(generator, ctx);
|
||||
if ty.get_bit_width() != exp_ty.get_bit_width() {
|
||||
return Err(ModelError(format!(
|
||||
"Expecting IntType to have {} bit(s), but got {} bit(s)",
|
||||
exp_ty.get_bit_width(),
|
||||
ty.get_bit_width()
|
||||
)));
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> Int<N> {
|
||||
pub fn const_int<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
value: u64,
|
||||
) -> Instance<'ctx, Self> {
|
||||
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,
|
||||
) -> Instance<'ctx, Self> {
|
||||
let value = self.get_type(generator, ctx).const_zero();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn const_1<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Instance<'ctx, Self> {
|
||||
self.const_int(generator, ctx, 1)
|
||||
}
|
||||
|
||||
pub fn const_all_ones<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Instance<'ctx, Self> {
|
||||
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>,
|
||||
) -> Instance<'ctx, Self> {
|
||||
assert!(
|
||||
value.get_type().get_bit_width()
|
||||
<= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
|
||||
);
|
||||
let value = ctx
|
||||
.builder
|
||||
.build_int_s_extend_or_bit_cast(value, self.get_type(generator, ctx.ctx), "")
|
||||
.unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn s_extend<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Self> {
|
||||
assert!(
|
||||
value.get_type().get_bit_width()
|
||||
< self.0.get_int_type(generator, ctx.ctx).get_bit_width()
|
||||
);
|
||||
let value =
|
||||
ctx.builder.build_int_s_extend(value, self.get_type(generator, ctx.ctx), "").unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn z_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Self> {
|
||||
assert!(
|
||||
value.get_type().get_bit_width()
|
||||
<= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
|
||||
);
|
||||
let value = ctx
|
||||
.builder
|
||||
.build_int_z_extend_or_bit_cast(value, self.get_type(generator, ctx.ctx), "")
|
||||
.unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn z_extend<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Self> {
|
||||
assert!(
|
||||
value.get_type().get_bit_width()
|
||||
< self.0.get_int_type(generator, ctx.ctx).get_bit_width()
|
||||
);
|
||||
let value =
|
||||
ctx.builder.build_int_z_extend(value, self.get_type(generator, ctx.ctx), "").unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn truncate_or_bit_cast<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Self> {
|
||||
assert!(
|
||||
value.get_type().get_bit_width()
|
||||
>= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
|
||||
);
|
||||
let value = ctx
|
||||
.builder
|
||||
.build_int_truncate_or_bit_cast(value, self.get_type(generator, ctx.ctx), "")
|
||||
.unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn truncate<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Self> {
|
||||
assert!(
|
||||
value.get_type().get_bit_width()
|
||||
> self.0.get_int_type(generator, ctx.ctx).get_bit_width()
|
||||
);
|
||||
let value =
|
||||
ctx.builder.build_int_truncate(value, self.get_type(generator, ctx.ctx), "").unwrap();
|
||||
self.believe_value(value)
|
||||
}
|
||||
|
||||
/// `sext` or `trunc` an int to this model's int type. Does nothing if equal bit-widths.
|
||||
pub fn s_extend_or_truncate<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Self> {
|
||||
let their_width = value.get_type().get_bit_width();
|
||||
let our_width = self.0.get_int_type(generator, ctx.ctx).get_bit_width();
|
||||
match their_width.cmp(&our_width) {
|
||||
Ordering::Less => self.s_extend(generator, ctx, value),
|
||||
Ordering::Equal => self.believe_value(value),
|
||||
Ordering::Greater => self.truncate(generator, ctx, value),
|
||||
}
|
||||
}
|
||||
|
||||
/// `zext` or `trunc` an int to this model's int type. Does nothing if equal bit-widths.
|
||||
pub fn z_extend_or_truncate<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Self> {
|
||||
let their_width = value.get_type().get_bit_width();
|
||||
let our_width = self.0.get_int_type(generator, ctx.ctx).get_bit_width();
|
||||
match their_width.cmp(&our_width) {
|
||||
Ordering::Less => self.z_extend(generator, ctx, value),
|
||||
Ordering::Equal => self.believe_value(value),
|
||||
Ordering::Greater => self.truncate(generator, ctx, value),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Int<Bool> {
|
||||
#[must_use]
|
||||
pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Instance<'ctx, Self> {
|
||||
self.const_int(generator, ctx, 0)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Instance<'ctx, Self> {
|
||||
self.const_int(generator, ctx, 1)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> Instance<'ctx, Int<N>> {
|
||||
pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
) -> Instance<'ctx, Int<NewN>> {
|
||||
Int(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
pub fn s_extend<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
) -> Instance<'ctx, Int<NewN>> {
|
||||
Int(to_int_kind).s_extend(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
pub fn z_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
) -> Instance<'ctx, Int<NewN>> {
|
||||
Int(to_int_kind).z_extend_or_bit_cast(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
pub fn z_extend<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
) -> Instance<'ctx, Int<NewN>> {
|
||||
Int(to_int_kind).z_extend(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
pub fn truncate_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
) -> Instance<'ctx, Int<NewN>> {
|
||||
Int(to_int_kind).truncate_or_bit_cast(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
) -> Instance<'ctx, Int<NewN>> {
|
||||
Int(to_int_kind).truncate(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
pub fn s_extend_or_truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
) -> Instance<'ctx, Int<NewN>> {
|
||||
Int(to_int_kind).s_extend_or_truncate(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
pub fn z_extend_or_truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
to_int_kind: NewN,
|
||||
) -> Instance<'ctx, Int<NewN>> {
|
||||
Int(to_int_kind).z_extend_or_truncate(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn add(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
|
||||
let value = ctx.builder.build_int_add(self.value, other.value, "").unwrap();
|
||||
self.model.believe_value(value)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn sub(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
|
||||
let value = ctx.builder.build_int_sub(self.value, other.value, "").unwrap();
|
||||
self.model.believe_value(value)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn mul(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
|
||||
let value = ctx.builder.build_int_mul(self.value, other.value, "").unwrap();
|
||||
self.model.believe_value(value)
|
||||
}
|
||||
|
||||
pub fn compare(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
op: IntPredicate,
|
||||
other: Self,
|
||||
) -> Instance<'ctx, Int<Bool>> {
|
||||
let value = ctx.builder.build_int_compare(op, self.value, other.value, "").unwrap();
|
||||
Int(Bool).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 +1,219 @@
|
|||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum, PointerType},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use crate::codegen::{llvm_intrinsics::call_memcpy_generic, CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
/// A model for [`PointerType`].
|
||||
///
|
||||
/// `Item` is the element type this pointer is pointing to, and should be of a [`Model`].
|
||||
///
|
||||
// TODO: LLVM 15: `Item` is a Rust type-hint for the LLVM type of value the `.store()/.load()` family
|
||||
// of functions return. If a truly opaque pointer is needed, tell the programmer to use `OpaquePtr`.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Ptr<Item>(pub Item);
|
||||
|
||||
/// An opaque pointer. Like [`Ptr`] but without any Rust type-hints about its element type.
|
||||
///
|
||||
/// `.load()/.store()` is not available for [`Instance`]s of opaque pointers.
|
||||
pub type OpaquePtr = Ptr<()>;
|
||||
|
||||
// TODO: LLVM 15: `Item: Model<'ctx>` don't even need to be a model anymore. It will only be
|
||||
// a type-hint for the `.load()/.store()` functions for the `pointee_ty`.
|
||||
//
|
||||
// See https://thedan64.github.io/inkwell/inkwell/builder/struct.Builder.html#method.build_load.
|
||||
impl<'ctx, Item: Model<'ctx>> Model<'ctx> for Ptr<Item> {
|
||||
type Value = PointerValue<'ctx>;
|
||||
type Type = PointerType<'ctx>;
|
||||
|
||||
fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
|
||||
// TODO: LLVM 15: ctx.ptr_type(AddressSpace::default())
|
||||
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, Item: Model<'ctx>> Ptr<Item> {
|
||||
/// Return a ***constant*** nullptr.
|
||||
pub fn nullptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Instance<'ctx, Ptr<Item>> {
|
||||
let ptr = self.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>,
|
||||
) -> Instance<'ctx, Ptr<Item>> {
|
||||
// TODO: LLVM 15: Write in an impl where `Item` does not have to be `Model<'ctx>`.
|
||||
// TODO: LLVM 15: This function will only have to be:
|
||||
// ```
|
||||
// return self.believe_value(ptr);
|
||||
// ```
|
||||
let t = self.get_type(generator, ctx.ctx);
|
||||
let ptr = ctx.builder.build_pointer_cast(ptr, t, "").unwrap();
|
||||
self.believe_value(ptr)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Item>> {
|
||||
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
|
||||
#[must_use]
|
||||
pub fn offset(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
offset: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Ptr<Item>> {
|
||||
let p = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], "").unwrap() };
|
||||
self.model.believe_value(p)
|
||||
}
|
||||
|
||||
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`] by a constant offset.
|
||||
#[must_use]
|
||||
pub fn offset_const(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
offset: u64,
|
||||
) -> Instance<'ctx, Ptr<Item>> {
|
||||
let offset = ctx.ctx.i32_type().const_int(offset, false);
|
||||
self.offset(ctx, offset)
|
||||
}
|
||||
|
||||
pub fn set_index(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
index: IntValue<'ctx>,
|
||||
value: Instance<'ctx, Item>,
|
||||
) {
|
||||
self.offset(ctx, index).store(ctx, value);
|
||||
}
|
||||
|
||||
pub fn set_index_const(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
index: u64,
|
||||
value: Instance<'ctx, Item>,
|
||||
) {
|
||||
self.offset_const(ctx, index).store(ctx, value);
|
||||
}
|
||||
|
||||
pub fn get_index<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
index: IntValue<'ctx>,
|
||||
) -> Instance<'ctx, Item> {
|
||||
self.offset(ctx, index).load(generator, ctx)
|
||||
}
|
||||
|
||||
pub fn get_index_const<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
index: u64,
|
||||
) -> Instance<'ctx, Item> {
|
||||
self.offset_const(ctx, index).load(generator, ctx)
|
||||
}
|
||||
|
||||
/// Load the value with [`inkwell::builder::Builder::build_load`].
|
||||
pub fn load<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Item> {
|
||||
let value = ctx.builder.build_load(self.value, "").unwrap();
|
||||
self.model.0.check_value(generator, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
|
||||
}
|
||||
|
||||
/// Store a value with [`inkwell::builder::Builder::build_store`].
|
||||
pub fn store(&self, ctx: &CodeGenContext<'ctx, '_>, value: Instance<'ctx, Item>) {
|
||||
ctx.builder.build_store(self.value, value.value).unwrap();
|
||||
}
|
||||
|
||||
/// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
|
||||
pub fn pointer_cast<NewItem: Model<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
new_item: NewItem,
|
||||
) -> Instance<'ctx, Ptr<NewItem>> {
|
||||
// TODO: LLVM 15: Write in an impl where `Item` does not have to be `Model<'ctx>`.
|
||||
Ptr(new_item).pointer_cast(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Cast this pointer to `uint8_t*`
|
||||
pub fn cast_to_pi8<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Ptr<Int<Byte>>> {
|
||||
Ptr(Int(Byte)).pointer_cast(generator, ctx, self.value)
|
||||
}
|
||||
|
||||
/// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
|
||||
pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
|
||||
let value = ctx.builder.build_is_null(self.value, "").unwrap();
|
||||
Int(Bool).believe_value(value)
|
||||
}
|
||||
|
||||
/// Check if the pointer is not null with [`inkwell::builder::Builder::build_is_not_null`].
|
||||
pub fn is_not_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
|
||||
let value = ctx.builder.build_is_not_null(self.value, "").unwrap();
|
||||
Int(Bool).believe_value(value)
|
||||
}
|
||||
|
||||
/// `memcpy` from another pointer.
|
||||
pub fn copy_from<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
source: Self,
|
||||
num_items: IntValue<'ctx>,
|
||||
) {
|
||||
// Force extend `num_items` and `itemsize` to `i64` so their types would match.
|
||||
let itemsize = self.model.sizeof(generator, ctx.ctx);
|
||||
let itemsize = Int(Int64).z_extend_or_truncate(generator, ctx, itemsize);
|
||||
let num_items = Int(Int64).z_extend_or_truncate(generator, ctx, num_items);
|
||||
let totalsize = itemsize.mul(ctx, num_items);
|
||||
|
||||
let is_volatile = ctx.ctx.bool_type().const_zero(); // is_volatile = false
|
||||
call_memcpy_generic(ctx, self.value, source.value, totalsize.value, is_volatile);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,359 @@
|
|||
use std::fmt;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum, StructType},
|
||||
values::{BasicValueEnum, StructValue},
|
||||
};
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
use super::*;
|
||||
|
||||
/// A traveral that traverses a Rust `struct` that is used to declare an LLVM's struct's field types.
|
||||
pub trait FieldTraversal<'ctx> {
|
||||
/// Output type of [`FieldTraversal::add`].
|
||||
type Out<M>;
|
||||
|
||||
/// Traverse through the type of a declared field and do something with it.
|
||||
///
|
||||
/// * `name` - The cosmetic name of the LLVM field. Used for debugging.
|
||||
/// * `model` - The [`Model`] representing the LLVM type of this field.
|
||||
fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M>;
|
||||
|
||||
/// Like [`FieldTraversal::add`] but [`Model`] is automatically inferred from its [`Default`] trait.
|
||||
fn add_auto<M: Model<'ctx> + Default>(&mut self, name: &'static str) -> Self::Out<M> {
|
||||
self.add(name, M::default())
|
||||
}
|
||||
}
|
||||
|
||||
/// Descriptor of an LLVM struct field.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct GepField<M> {
|
||||
/// The GEP index of this field. This is the index to use with `build_gep`.
|
||||
pub gep_index: u64,
|
||||
/// The cosmetic name of this field.
|
||||
pub name: &'static str,
|
||||
/// The [`Model`] of this field's type.
|
||||
pub model: M,
|
||||
}
|
||||
|
||||
/// A traversal to calculate the GEP index of fields.
|
||||
pub struct GepFieldTraversal {
|
||||
/// The current GEP index.
|
||||
gep_index_counter: 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 }
|
||||
}
|
||||
}
|
||||
|
||||
/// A traversal to collect the field types of a struct.
|
||||
///
|
||||
/// This is used to collect field types and construct the LLVM struct type with [`Context::struct_type`].
|
||||
struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
|
||||
generator: &'a G,
|
||||
ctx: &'ctx Context,
|
||||
/// The collected field types so far in exact order.
|
||||
field_types: Vec<BasicTypeEnum<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx, 'a, G> {
|
||||
type Out<M> = (); // Checking types return nothing.
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
/// A traversal to check the types of fields.
|
||||
struct CheckTypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
|
||||
generator: &'a mut G,
|
||||
ctx: &'ctx Context,
|
||||
/// The current GEP index, so we can tell the index of the field we are checking
|
||||
/// and report the GEP index.
|
||||
gep_index_counter: u32,
|
||||
/// The [`StructType`] to check.
|
||||
scrutinee: StructType<'ctx>,
|
||||
/// The list of collected errors so far.
|
||||
errors: Vec<ModelError>,
|
||||
}
|
||||
|
||||
impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
|
||||
for CheckTypeFieldTraversal<'ctx, 'a, G>
|
||||
{
|
||||
type Out<M> = (); // Checking types return nothing.
|
||||
|
||||
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;
|
||||
|
||||
if let Some(t) = self.scrutinee.get_field_type_at_index(gep_index) {
|
||||
if let Err(err) = model.check_type(self.generator, self.ctx, t) {
|
||||
self.errors
|
||||
.push(err.under_context(format!("field #{gep_index} '{name}'").as_str()));
|
||||
}
|
||||
} // Otherwise, it will be caught by Struct's `check_type`.
|
||||
}
|
||||
}
|
||||
|
||||
/// A trait for Rust structs identifying LLVM structures.
|
||||
///
|
||||
/// ### Example
|
||||
///
|
||||
/// Suppose you want to define this structure:
|
||||
/// ```c
|
||||
/// template <typename T>
|
||||
/// struct ContiguousNDArray {
|
||||
/// size_t ndims;
|
||||
/// size_t* shape;
|
||||
/// T* data;
|
||||
/// }
|
||||
/// ```
|
||||
///
|
||||
/// This is how it should be done:
|
||||
/// ```ignore
|
||||
/// pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
|
||||
/// pub ndims: F::Out<Int<SizeT>>,
|
||||
/// pub shape: F::Out<Ptr<Int<SizeT>>>,
|
||||
/// pub data: F::Out<Ptr<Item>>,
|
||||
/// }
|
||||
///
|
||||
/// /// An ndarray without strides and non-opaque `data` field in NAC3.
|
||||
/// #[derive(Debug, Clone, Copy)]
|
||||
/// pub struct ContiguousNDArray<M> {
|
||||
/// /// [`Model`] of the items.
|
||||
/// pub item: M,
|
||||
/// }
|
||||
///
|
||||
/// impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
|
||||
/// type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
|
||||
///
|
||||
/// fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
/// // The order of `traversal.add*` is important
|
||||
/// Self::Fields {
|
||||
/// ndims: traversal.add_auto("ndims"),
|
||||
/// shape: traversal.add_auto("shape"),
|
||||
/// data: traversal.add("data", Ptr(self.item)),
|
||||
/// }
|
||||
/// }
|
||||
/// }
|
||||
/// ```
|
||||
///
|
||||
/// The [`FieldTraversal`] here is a mechanism to allow the fields of `ContiguousNDArrayFields` to be
|
||||
/// traversed to do useful work such as:
|
||||
///
|
||||
/// - To create the [`StructType`] of `ContiguousNDArray` by collecting [`BasicType`]s of the fields.
|
||||
/// - To enable the `.gep(ctx, |f| f.ndims).store(ctx, ...)` syntax.
|
||||
///
|
||||
/// Suppose now that you have defined `ContiguousNDArray` and you want to allocate a `ContiguousNDArray`
|
||||
/// with dtype `float64` in LLVM, this is how you do it:
|
||||
/// ```ignore
|
||||
/// type F64NDArray = Struct<ContiguousNDArray<Float<Float64>>>; // Type alias for leaner documentation
|
||||
/// let model: F64NDArray = Struct(ContigousNDArray { item: Float(Float64) });
|
||||
/// let ndarray: Instance<'ctx, Ptr<F64NDArray>> = model.alloca(generator, ctx);
|
||||
/// ```
|
||||
///
|
||||
/// ...and here is how you may manipulate/access `ndarray`:
|
||||
///
|
||||
/// (NOTE: some arguments have been omitted)
|
||||
///
|
||||
/// ```ignore
|
||||
/// // Get `&ndarray->data`
|
||||
/// ndarray.gep(|f| f.data); // type: Instance<'ctx, Ptr<Float<Float64>>>
|
||||
///
|
||||
/// // Get `ndarray->ndims`
|
||||
/// ndarray.get(|f| f.ndims); // type: Instance<'ctx, Int<SizeT>>
|
||||
///
|
||||
/// // Get `&ndarray->ndims`
|
||||
/// ndarray.gep(|f| f.ndims); // type: Instance<'ctx, Ptr<Int<SizeT>>>
|
||||
///
|
||||
/// // Get `ndarray->shape[0]`
|
||||
/// ndarray.get(|f| f.shape).get_index_const(0); // Instance<'ctx, Int<SizeT>>
|
||||
///
|
||||
/// // Get `&ndarray->shape[2]`
|
||||
/// ndarray.get(|f| f.shape).offset_const(2); // Instance<'ctx, Ptr<Int<SizeT>>>
|
||||
///
|
||||
/// // Do `ndarray->ndims = 3;`
|
||||
/// let num_3 = Int(SizeT).const_int(3);
|
||||
/// ndarray.set(|f| f.ndims, num_3);
|
||||
/// ```
|
||||
pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy {
|
||||
/// The associated fields of this struct.
|
||||
type Fields<F: FieldTraversal<'ctx>>;
|
||||
|
||||
/// Traverse through all fields of this [`StructKind`].
|
||||
///
|
||||
/// Only used internally in this module for implementing other components.
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F>;
|
||||
|
||||
/// Get a convenience structure to get a struct field's GEP index through its corresponding Rust field.
|
||||
///
|
||||
/// Only used internally in this module for implementing other components.
|
||||
fn fields(&self) -> Self::Fields<GepFieldTraversal> {
|
||||
self.traverse_fields(&mut GepFieldTraversal { gep_index_counter: 0 })
|
||||
}
|
||||
|
||||
/// Get the LLVM [`StructType`] of this [`StructKind`].
|
||||
fn get_struct_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
) -> StructType<'ctx> {
|
||||
let mut traversal = TypeFieldTraversal { generator, ctx, field_types: Vec::new() };
|
||||
self.traverse_fields(&mut traversal);
|
||||
|
||||
ctx.struct_type(&traversal.field_types, false)
|
||||
}
|
||||
}
|
||||
|
||||
/// A model for LLVM struct.
|
||||
///
|
||||
/// `S` should be of a [`StructKind`].
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct Struct<S>(pub S);
|
||||
|
||||
impl<'ctx, S: StructKind<'ctx>> Struct<S> {
|
||||
/// Create a constant struct value from its fields.
|
||||
///
|
||||
/// This function also validates `fields` and panic when there is something wrong.
|
||||
pub fn const_struct<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
fields: &[BasicValueEnum<'ctx>],
|
||||
) -> Instance<'ctx, Self> {
|
||||
// NOTE: There *could* have been a functor `F<M> = Instance<'ctx, M>` for `S::Fields<F>`
|
||||
// to create a more user-friendly interface, but Rust's type system is not sophisticated enough
|
||||
// and if you try doing that Rust would force you put lifetimes everywhere.
|
||||
let val = ctx.const_struct(fields, false);
|
||||
self.check_value(generator, ctx, val).unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for Struct<S> {
|
||||
type Value = StructValue<'ctx>;
|
||||
type Type = StructType<'ctx>;
|
||||
|
||||
fn 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:?}")));
|
||||
};
|
||||
|
||||
// Check each field individually.
|
||||
let mut traversal = CheckTypeFieldTraversal {
|
||||
generator,
|
||||
ctx,
|
||||
gep_index_counter: 0,
|
||||
errors: Vec::new(),
|
||||
scrutinee: ty,
|
||||
};
|
||||
self.0.traverse_fields(&mut traversal);
|
||||
|
||||
// Check the number of fields.
|
||||
let exp_num_fields = traversal.gep_index_counter;
|
||||
let got_num_fields = u32::try_from(ty.get_field_types().len()).unwrap();
|
||||
if exp_num_fields != got_num_fields {
|
||||
return Err(ModelError(format!(
|
||||
"Expecting StructType with {exp_num_fields} field(s), but got {got_num_fields}"
|
||||
)));
|
||||
}
|
||||
|
||||
if !traversal.errors.is_empty() {
|
||||
// Currently, only the first error is reported.
|
||||
return Err(traversal.errors[0].clone());
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, S: StructKind<'ctx>> Instance<'ctx, Struct<S>> {
|
||||
/// Get a field with [`StructValue::get_field_at_index`].
|
||||
pub fn get_field<G: CodeGenerator + ?Sized, M, GetField>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
get_field: GetField,
|
||||
) -> Instance<'ctx, M>
|
||||
where
|
||||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
let field = get_field(self.model.0.fields());
|
||||
let val = self.value.get_field_at_index(field.gep_index as u32).unwrap();
|
||||
field.model.check_value(generator, ctx, val).unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, S: StructKind<'ctx>> Instance<'ctx, Ptr<Struct<S>>> {
|
||||
/// Get a pointer to a field with [`Builder::build_in_bounds_gep`].
|
||||
pub fn gep<M, GetField>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
get_field: GetField,
|
||||
) -> Instance<'ctx, Ptr<M>>
|
||||
where
|
||||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
let field = get_field(self.model.0 .0.fields());
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
|
||||
let ptr = unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.value,
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_int(field.gep_index, false)],
|
||||
field.name,
|
||||
)
|
||||
.unwrap()
|
||||
};
|
||||
|
||||
Ptr(field.model).believe_value(ptr)
|
||||
}
|
||||
|
||||
/// Convenience function equivalent to `.gep(...).load(...)`.
|
||||
pub fn get<M, GetField, G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
get_field: GetField,
|
||||
) -> Instance<'ctx, M>
|
||||
where
|
||||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
self.gep(ctx, get_field).load(generator, ctx)
|
||||
}
|
||||
|
||||
/// Convenience function equivalent to `.gep(...).store(...)`.
|
||||
pub fn set<M, GetField>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
get_field: GetField,
|
||||
value: Instance<'ctx, M>,
|
||||
) where
|
||||
M: Model<'ctx>,
|
||||
GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
|
||||
{
|
||||
self.gep(ctx, get_field).store(ctx, value);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
use crate::codegen::{
|
||||
stmt::{gen_for_callback_incrementing, BreakContinueHooks},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::*;
|
||||
|
||||
/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
|
||||
///
|
||||
/// `stop` is not included.
|
||||
pub fn gen_for_model<'ctx, 'a, G, F, N>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
start: Instance<'ctx, Int<N>>,
|
||||
stop: Instance<'ctx, Int<N>>,
|
||||
step: Instance<'ctx, Int<N>>,
|
||||
body: F,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
Instance<'ctx, Int<N>>,
|
||||
) -> Result<(), String>,
|
||||
N: IntKind<'ctx> + Default,
|
||||
{
|
||||
let int_model = Int(N::default());
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
start.value,
|
||||
(stop.value, false),
|
||||
|g, ctx, hooks, i| {
|
||||
let i = int_model.believe_value(i);
|
||||
body(g, ctx, hooks, i)
|
||||
},
|
||||
step.value,
|
||||
)
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,12 @@
|
|||
use inkwell::values::BasicValueEnum;
|
||||
|
||||
use crate::typecheck::typedef::Type;
|
||||
|
||||
/// A NAC3 LLVM Python object of any type.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct AnyObject<'ctx> {
|
||||
/// Typechecker type of the object.
|
||||
pub ty: Type,
|
||||
/// LLVM value of the object.
|
||||
pub value: BasicValueEnum<'ctx>,
|
||||
}
|
||||
|
|
@ -0,0 +1,87 @@
|
|||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{iter_type_vars, Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::any::AnyObject;
|
||||
|
||||
/// Fields of [`List`]
|
||||
pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
|
||||
/// Array pointer to content
|
||||
pub items: F::Out<Ptr<Item>>,
|
||||
/// Number of items in the array
|
||||
pub len: F::Out<Int<SizeT>>,
|
||||
}
|
||||
|
||||
/// A list in NAC3.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct List<Item> {
|
||||
/// Model of the list items
|
||||
pub item: Item,
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for List<Item> {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
items: traversal.add("items", Ptr(self.item)),
|
||||
len: traversal.add_auto("len"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Struct<List<Item>>>> {
|
||||
/// Cast the items pointer to `uint8_t*`.
|
||||
pub fn with_pi8_items<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>> {
|
||||
self.pointer_cast(generator, ctx, Struct(List { item: Int(Byte) }))
|
||||
}
|
||||
}
|
||||
|
||||
/// A NAC3 Python List object.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct ListObject<'ctx> {
|
||||
/// Typechecker type of the list items
|
||||
pub item_type: Type,
|
||||
pub instance: Instance<'ctx, Ptr<Struct<List<Any<'ctx>>>>>,
|
||||
}
|
||||
|
||||
impl<'ctx> ListObject<'ctx> {
|
||||
/// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
|
||||
pub fn from_object<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> Self {
|
||||
// Check typechecker type and extract `item_type`
|
||||
let item_type = match &*ctx.unifier.get_ty(object.ty) {
|
||||
TypeEnum::TObj { obj_id, params, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
iter_type_vars(params).next().unwrap().ty // Extract `item_type`
|
||||
}
|
||||
_ => {
|
||||
panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(object.ty))
|
||||
}
|
||||
};
|
||||
|
||||
let plist = Ptr(Struct(List { item: Any(ctx.get_llvm_type(generator, item_type)) }));
|
||||
|
||||
// Create object
|
||||
let value = plist.check_value(generator, ctx.ctx, object.value).unwrap();
|
||||
ListObject { item_type, instance: value }
|
||||
}
|
||||
|
||||
/// Get the `len()` of this list.
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Int<SizeT>> {
|
||||
self.instance.get(generator, ctx, |f| f.len)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
pub mod any;
|
||||
pub mod list;
|
||||
pub mod ndarray;
|
||||
pub mod tuple;
|
||||
pub mod utils;
|
||||
|
|
@ -0,0 +1,184 @@
|
|||
use super::NDArrayObject;
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{
|
||||
call_nac3_ndarray_array_set_and_validate_list_shape,
|
||||
call_nac3_ndarray_array_write_list_to_array,
|
||||
},
|
||||
model::*,
|
||||
object::{any::AnyObject, list::ListObject},
|
||||
stmt::gen_if_else_expr_callback,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
toplevel::helper::{arraylike_flatten_element_type, arraylike_get_ndims},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
/// Get the expected `dtype` and `ndims` of the ndarray returned by `np_array(list)`.
|
||||
fn get_list_object_dtype_and_ndims<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
) -> (Type, u64) {
|
||||
let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list.item_type);
|
||||
|
||||
let ndims = arraylike_get_ndims(&mut ctx.unifier, list.item_type);
|
||||
let ndims = ndims + 1; // To count `list` itself.
|
||||
|
||||
(dtype, ndims)
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Implementation of `np_array(<list>, copy=True)`
|
||||
fn make_np_array_list_copy_true_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
) -> Self {
|
||||
let (dtype, ndims_int) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
let list_value = list.instance.with_pi8_items(generator, ctx);
|
||||
|
||||
// Validate `list` has a consistent shape.
|
||||
// Raise an exception if `list` is something abnormal like `[[1, 2], [3]]`.
|
||||
// If `list` has a consistent shape, deduce the shape and write it to `shape`.
|
||||
let ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims_int);
|
||||
let shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
|
||||
call_nac3_ndarray_array_set_and_validate_list_shape(
|
||||
generator, ctx, list_value, ndims, shape,
|
||||
);
|
||||
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims_int);
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape);
|
||||
ndarray.create_data(generator, ctx);
|
||||
|
||||
// Copy all contents from the list.
|
||||
call_nac3_ndarray_array_write_list_to_array(generator, ctx, list_value, ndarray.instance);
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Implementation of `np_array(<list>, copy=None)`
|
||||
fn make_np_array_list_copy_none_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
) -> Self {
|
||||
// np_array without copying is only possible `list` is not nested.
|
||||
//
|
||||
// If `list` is `list[T]`, we can create an ndarray with `data` set
|
||||
// to the array pointer of `list`.
|
||||
//
|
||||
// If `list` is `list[list[T]]` or worse, copy.
|
||||
|
||||
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
if ndims == 1 {
|
||||
// `list` is not nested
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, 1);
|
||||
|
||||
// Set data
|
||||
let data = list.instance.get(generator, ctx, |f| f.items).cast_to_pi8(generator, ctx);
|
||||
ndarray.instance.set(ctx, |f| f.data, data);
|
||||
|
||||
// ndarray->shape[0] = list->len;
|
||||
let shape = ndarray.instance.get(generator, ctx, |f| f.shape);
|
||||
let list_len = list.instance.get(generator, ctx, |f| f.len);
|
||||
shape.set_index_const(ctx, 0, list_len);
|
||||
|
||||
// Set strides, the `data` is contiguous
|
||||
ndarray.set_strides_contiguous(generator, ctx);
|
||||
|
||||
ndarray
|
||||
} else {
|
||||
// `list` is nested, copy
|
||||
NDArrayObject::make_np_array_list_copy_true_impl(generator, ctx, list)
|
||||
}
|
||||
}
|
||||
|
||||
/// Implementation of `np_array(<list>, copy=copy)`
|
||||
fn make_np_array_list_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list: ListObject<'ctx>,
|
||||
copy: Instance<'ctx, Int<Bool>>,
|
||||
) -> Self {
|
||||
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
|
||||
|
||||
let ndarray = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_generator, _ctx| Ok(copy.value),
|
||||
|generator, ctx| {
|
||||
let ndarray =
|
||||
NDArrayObject::make_np_array_list_copy_true_impl(generator, ctx, list);
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
|generator, ctx| {
|
||||
let ndarray =
|
||||
NDArrayObject::make_np_array_list_copy_none_impl(generator, ctx, list);
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
.unwrap();
|
||||
|
||||
NDArrayObject::from_value_and_unpacked_types(generator, ctx, ndarray, dtype, ndims)
|
||||
}
|
||||
|
||||
/// Implementation of `np_array(<ndarray>, copy=copy)`.
|
||||
pub fn make_np_array_ndarray_impl<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayObject<'ctx>,
|
||||
copy: Instance<'ctx, Int<Bool>>,
|
||||
) -> Self {
|
||||
let ndarray_val = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_generator, _ctx| Ok(copy.value),
|
||||
|generator, ctx| {
|
||||
let ndarray = ndarray.make_copy(generator, ctx); // Force copy
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
|_generator, _ctx| {
|
||||
// No need to copy. Return `ndarray` itself.
|
||||
Ok(Some(ndarray.instance.value))
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
.unwrap();
|
||||
|
||||
NDArrayObject::from_value_and_unpacked_types(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray_val,
|
||||
ndarray.dtype,
|
||||
ndarray.ndims,
|
||||
)
|
||||
}
|
||||
|
||||
/// Create a new ndarray like `np.array()`.
|
||||
///
|
||||
/// NOTE: The `ndmin` argument is not here. You may want to
|
||||
/// do [`NDArrayObject::atleast_nd`] to achieve that.
|
||||
pub fn make_np_array<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
copy: Instance<'ctx, Int<Bool>>,
|
||||
) -> Self {
|
||||
match &*ctx.unifier.get_ty(object.ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let list = ListObject::from_object(generator, ctx, object);
|
||||
NDArrayObject::make_np_array_list_impl(generator, ctx, list, copy)
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, object);
|
||||
NDArrayObject::make_np_array_ndarray_impl(generator, ctx, ndarray, copy)
|
||||
}
|
||||
_ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object.ty)), // Typechecker ensures this
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,135 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::codegen::{
|
||||
irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
|
||||
model::*,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
/// Fields of [`ShapeEntry`]
|
||||
pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub ndims: F::Out<Int<SizeT>>,
|
||||
pub shape: F::Out<Ptr<Int<SizeT>>>,
|
||||
}
|
||||
|
||||
/// An IRRT structure used in broadcasting.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct ShapeEntry;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for ShapeEntry {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ShapeEntryFields<'ctx, F>;
|
||||
|
||||
fn 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: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) -> Self {
|
||||
let broadcast_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, target_ndims);
|
||||
broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
|
||||
|
||||
call_nac3_ndarray_broadcast_to(generator, ctx, self.instance, broadcast_ndarray.instance);
|
||||
broadcast_ndarray
|
||||
}
|
||||
}
|
||||
/// A result produced by [`broadcast_all_ndarrays`]
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct BroadcastAllResult<'ctx> {
|
||||
/// The statically known `ndims` of the broadcast result.
|
||||
pub ndims: u64,
|
||||
/// The broadcasting shape.
|
||||
pub shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
/// Broadcasted views on the inputs.
|
||||
///
|
||||
/// All of them will have `shape` [`BroadcastAllResult::shape`] and
|
||||
/// `ndims` [`BroadcastAllResult::ndims`]. The length of the vector
|
||||
/// is the same as the input.
|
||||
pub ndarrays: Vec<NDArrayObject<'ctx>>,
|
||||
}
|
||||
|
||||
/// Helper function to call `call_nac3_ndarray_broadcast_shapes`
|
||||
fn broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
in_shape_entries: &[(Instance<'ctx, Ptr<Int<SizeT>>>, u64)], // (shape, shape's length/ndims)
|
||||
broadcast_ndims: u64,
|
||||
broadcast_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
// Prepare input shape entries to be passed to `call_nac3_ndarray_broadcast_shapes`.
|
||||
let num_shape_entries =
|
||||
Int(SizeT).const_int(generator, ctx.ctx, u64::try_from(in_shape_entries.len()).unwrap());
|
||||
let shape_entries = Struct(ShapeEntry).array_alloca(generator, ctx, num_shape_entries.value);
|
||||
for (i, (in_shape, in_ndims)) in in_shape_entries.iter().enumerate() {
|
||||
let pshape_entry = shape_entries.offset_const(ctx, i as u64);
|
||||
|
||||
let in_ndims = Int(SizeT).const_int(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 = Int(SizeT).const_int(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> {
|
||||
/// Broadcast all ndarrays according to `np.broadcast()` and return a [`BroadcastAllResult`]
|
||||
/// containing all the information of the result of the broadcast operation.
|
||||
pub fn broadcast<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarrays: &[Self],
|
||||
) -> BroadcastAllResult<'ctx> {
|
||||
assert!(!ndarrays.is_empty());
|
||||
|
||||
// Infer the broadcast output ndims.
|
||||
let broadcast_ndims_int = ndarrays.iter().map(|ndarray| ndarray.ndims).max().unwrap();
|
||||
|
||||
let broadcast_ndims = Int(SizeT).const_int(generator, ctx.ctx, broadcast_ndims_int);
|
||||
let broadcast_shape = Int(SizeT).array_alloca(generator, ctx, broadcast_ndims.value);
|
||||
|
||||
let shape_entries = ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| (ndarray.instance.get(generator, ctx, |f| f.shape), ndarray.ndims))
|
||||
.collect_vec();
|
||||
broadcast_shapes(generator, ctx, &shape_entries, broadcast_ndims_int, broadcast_shape);
|
||||
|
||||
// Broadcast all the inputs to shape `dst_shape`.
|
||||
let broadcast_ndarrays: Vec<_> = ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| {
|
||||
ndarray.broadcast_to(generator, ctx, broadcast_ndims_int, broadcast_shape)
|
||||
})
|
||||
.collect_vec();
|
||||
|
||||
BroadcastAllResult {
|
||||
ndims: broadcast_ndims_int,
|
||||
shape: broadcast_shape,
|
||||
ndarrays: broadcast_ndarrays,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,134 @@
|
|||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
/// Fields of [`ContiguousNDArray`]
|
||||
pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
|
||||
pub ndims: F::Out<Int<SizeT>>,
|
||||
pub shape: F::Out<Ptr<Int<SizeT>>>,
|
||||
pub data: F::Out<Ptr<Item>>,
|
||||
}
|
||||
|
||||
/// An ndarray without strides and non-opaque `data` field in NAC3.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct ContiguousNDArray<M> {
|
||||
/// [`Model`] of the items.
|
||||
pub item: M,
|
||||
}
|
||||
|
||||
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
|
||||
type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
ndims: traversal.add_auto("ndims"),
|
||||
shape: traversal.add_auto("shape"),
|
||||
data: traversal.add("data", Ptr(self.item)),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create a [`ContiguousNDArray`] from the contents of this ndarray.
|
||||
///
|
||||
/// This function may or may not be expensive depending on if this ndarray has contiguous data.
|
||||
///
|
||||
/// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
|
||||
/// the returned [`ContiguousNDArray`] and copy contents of this ndarray to there.
|
||||
///
|
||||
/// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`ContiguousNDArray`]
|
||||
/// will share memory with this ndarray.
|
||||
///
|
||||
/// The `item_model` sets the [`Model`] of the returned [`ContiguousNDArray`]'s `Item` model for type-safety, and
|
||||
/// should match the `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics. Use model [`Any`]
|
||||
/// if you don't care/cannot know the [`Model`] in advance.
|
||||
pub fn make_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
item_model: Item,
|
||||
) -> Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>> {
|
||||
// Sanity check on `self.dtype` and `item_model`.
|
||||
let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
|
||||
item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
|
||||
|
||||
let cdarray_model = Struct(ContiguousNDArray { item: item_model });
|
||||
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
|
||||
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
|
||||
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
|
||||
|
||||
// Allocate and setup the resulting [`ContiguousNDArray`].
|
||||
let result = cdarray_model.alloca(generator, ctx);
|
||||
|
||||
// Set ndims and shape.
|
||||
let ndims = self.ndims_llvm(generator, ctx.ctx);
|
||||
result.set(ctx, |f| f.ndims, ndims);
|
||||
|
||||
let shape = self.instance.get(generator, ctx, |f| f.shape);
|
||||
result.set(ctx, |f| f.shape, shape);
|
||||
|
||||
let is_contiguous = self.is_c_contiguous(generator, ctx);
|
||||
ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
|
||||
|
||||
// Inserting into then_bb; This ndarray is contiguous.
|
||||
ctx.builder.position_at_end(then_bb);
|
||||
let data = self.instance.get(generator, ctx, |f| f.data);
|
||||
let data = data.pointer_cast(generator, ctx, item_model);
|
||||
result.set(ctx, |f| f.data, data);
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
|
||||
// `make_copy` produces an ndarray with contiguous `data`.
|
||||
ctx.builder.position_at_end(else_bb);
|
||||
let copied_ndarray = self.make_copy(generator, ctx);
|
||||
let data = copied_ndarray.instance.get(generator, ctx, |f| f.data);
|
||||
let data = data.pointer_cast(generator, ctx, item_model);
|
||||
result.set(ctx, |f| f.data, data);
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Reposition to end_bb for continuation
|
||||
ctx.builder.position_at_end(end_bb);
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
/// Create an [`NDArrayObject`] from a [`ContiguousNDArray`].
|
||||
///
|
||||
/// The operation is super cheap. The newly created [`NDArrayObject`] will share the
|
||||
/// same memory as the [`ContiguousNDArray`].
|
||||
///
|
||||
/// `ndims` has to be provided as [`NDArrayObject`] requires a statically known `ndims` value, despite
|
||||
/// the fact that the information should be contained within the [`ContiguousNDArray`].
|
||||
pub fn from_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
carray: Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
) -> Self {
|
||||
// Sanity check on `dtype` and `contiguous_array`'s `Item` model.
|
||||
let dtype_llvm = ctx.get_llvm_type(generator, dtype);
|
||||
carray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
|
||||
|
||||
// TODO: Debug assert `ndims == carray.ndims` to catch bugs.
|
||||
|
||||
// Allocate the resulting ndarray.
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
|
||||
|
||||
// Copy shape and update strides
|
||||
let shape = carray.get(generator, ctx, |f| f.shape);
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape);
|
||||
ndarray.set_strides_contiguous(generator, ctx);
|
||||
|
||||
// Share data
|
||||
let data = carray.get(generator, ctx, |f| f.data).pointer_cast(generator, ctx, Int(Byte));
|
||||
ndarray.instance.set(ctx, |f| f.data, data);
|
||||
|
||||
ndarray
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,176 @@
|
|||
use inkwell::{values::BasicValueEnum, IntPredicate};
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, CodeGenContext,
|
||||
CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
/// Get the zero value in `np.zeros()` of a `dtype`.
|
||||
fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
ctx.ctx.i32_type().const_zero().into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
ctx.ctx.i64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "").into()
|
||||
} else {
|
||||
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the one value in `np.ones()` of a `dtype`.
|
||||
fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int32);
|
||||
ctx.ctx.i32_type().const_int(1, is_signed).into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int64);
|
||||
ctx.ctx.i64_type().const_int(1, is_signed).into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_float(1.0).into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_int(1, false).into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "1").into()
|
||||
} else {
|
||||
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Create an ndarray like `np.empty`.
|
||||
pub fn make_np_empty<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) -> Self {
|
||||
// Validate `shape`
|
||||
let ndims_llvm = Int(SizeT).const_int(generator, ctx.ctx, ndims);
|
||||
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims_llvm, shape);
|
||||
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape);
|
||||
ndarray.create_data(generator, ctx);
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.full`.
|
||||
pub fn make_np_full<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
fill_value: BasicValueEnum<'ctx>,
|
||||
) -> Self {
|
||||
let ndarray = NDArrayObject::make_np_empty(generator, ctx, dtype, ndims, shape);
|
||||
ndarray.fill(generator, ctx, fill_value);
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.zero`.
|
||||
pub fn make_np_zeros<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) -> Self {
|
||||
let fill_value = ndarray_zero_value(generator, ctx, dtype);
|
||||
NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.ones`.
|
||||
pub fn make_np_ones<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) -> Self {
|
||||
let fill_value = ndarray_one_value(generator, ctx, dtype);
|
||||
NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.eye`.
|
||||
pub fn make_np_eye<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
nrows: Instance<'ctx, Int<SizeT>>,
|
||||
ncols: Instance<'ctx, Int<SizeT>>,
|
||||
offset: Instance<'ctx, Int<SizeT>>,
|
||||
) -> Self {
|
||||
let ndzero = ndarray_zero_value(generator, ctx, dtype);
|
||||
let ndone = ndarray_one_value(generator, ctx, dtype);
|
||||
|
||||
let ndarray = NDArrayObject::alloca_dynamic_shape(generator, ctx, dtype, &[nrows, ncols]);
|
||||
|
||||
// Create data and make the matrix like look np.eye()
|
||||
ndarray.create_data(generator, ctx);
|
||||
ndarray
|
||||
.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
|
||||
// NOTE: rows and cols can never be zero here, since this ndarray's `np.size` would be zero
|
||||
// and this loop would not execute.
|
||||
|
||||
// Load up `row_i` and `col_i` from indices.
|
||||
let row_i = nditer.get_indices().get_index_const(generator, ctx, 0);
|
||||
let col_i = nditer.get_indices().get_index_const(generator, ctx, 1);
|
||||
|
||||
let be_one = row_i.add(ctx, offset).compare(ctx, IntPredicate::EQ, col_i);
|
||||
let value = ctx.builder.build_select(be_one.value, ndone, ndzero, "value").unwrap();
|
||||
|
||||
let p = nditer.get_pointer(generator, ctx);
|
||||
ctx.builder.build_store(p, value).unwrap();
|
||||
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like `np.identity`.
|
||||
pub fn make_np_identity<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
size: Instance<'ctx, Int<SizeT>>,
|
||||
) -> Self {
|
||||
// Convenient implementation
|
||||
let offset = Int(SizeT).const_0(generator, ctx.ctx);
|
||||
NDArrayObject::make_np_eye(generator, ctx, dtype, size, size, offset)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,226 @@
|
|||
use crate::codegen::{
|
||||
irrt::call_nac3_ndarray_index,
|
||||
model::*,
|
||||
object::utils::slice::{RustSlice, Slice},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
pub type NDIndexType = Byte;
|
||||
|
||||
/// Fields of [`NDIndex`]
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub type_: F::Out<Int<NDIndexType>>, // Defined to be uint8_t in IRRT
|
||||
pub data: F::Out<Ptr<Int<Byte>>>,
|
||||
}
|
||||
|
||||
/// An IRRT representation of an ndarray subscript index.
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct NDIndex;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NDIndex {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NDIndexFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields { type_: traversal.add_auto("type"), data: traversal.add_auto("data") }
|
||||
}
|
||||
}
|
||||
|
||||
// A convenience enum representing a [`NDIndex`].
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum RustNDIndex<'ctx> {
|
||||
SingleElement(Instance<'ctx, Int<Int32>>),
|
||||
Slice(RustSlice<'ctx, Int32>),
|
||||
NewAxis,
|
||||
Ellipsis,
|
||||
}
|
||||
|
||||
impl<'ctx> RustNDIndex<'ctx> {
|
||||
/// Get the value to set `NDIndex::type` for this variant.
|
||||
fn get_type_id(&self) -> u64 {
|
||||
// Defined in IRRT, must be in sync
|
||||
match self {
|
||||
RustNDIndex::SingleElement(_) => 0,
|
||||
RustNDIndex::Slice(_) => 1,
|
||||
RustNDIndex::NewAxis => 2,
|
||||
RustNDIndex::Ellipsis => 3,
|
||||
}
|
||||
}
|
||||
|
||||
/// Write the contents to an LLVM [`NDIndex`].
|
||||
fn write_to_ndindex<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_ndindex_ptr: Instance<'ctx, Ptr<Struct<NDIndex>>>,
|
||||
) {
|
||||
// Set `dst_ndindex_ptr->type`
|
||||
dst_ndindex_ptr.gep(ctx, |f| f.type_).store(
|
||||
ctx,
|
||||
Int(NDIndexType::default()).const_int(generator, ctx.ctx, self.get_type_id()),
|
||||
);
|
||||
|
||||
// Set `dst_ndindex_ptr->data`
|
||||
match self {
|
||||
RustNDIndex::SingleElement(in_index) => {
|
||||
let index_ptr = Int(Int32).alloca(generator, ctx);
|
||||
index_ptr.store(ctx, *in_index);
|
||||
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.data)
|
||||
.store(ctx, index_ptr.pointer_cast(generator, ctx, Int(Byte)));
|
||||
}
|
||||
RustNDIndex::Slice(in_rust_slice) => {
|
||||
let user_slice_ptr = Struct(Slice(Int32)).alloca(generator, ctx);
|
||||
in_rust_slice.write_to_slice(generator, ctx, user_slice_ptr);
|
||||
|
||||
dst_ndindex_ptr
|
||||
.gep(ctx, |f| f.data)
|
||||
.store(ctx, user_slice_ptr.pointer_cast(generator, ctx, Int(Byte)));
|
||||
}
|
||||
RustNDIndex::NewAxis | RustNDIndex::Ellipsis => {}
|
||||
}
|
||||
}
|
||||
|
||||
/// Allocate an array of `NDIndex`es on the stack and return the array pointer.
|
||||
pub fn make_ndindices<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
in_ndindices: &[RustNDIndex<'ctx>],
|
||||
) -> (Instance<'ctx, Int<SizeT>>, Instance<'ctx, Ptr<Struct<NDIndex>>>) {
|
||||
let ndindex_model = Struct(NDIndex);
|
||||
|
||||
let num_ndindices = Int(SizeT).const_int(generator, ctx.ctx, in_ndindices.len() as u64);
|
||||
let ndindices = ndindex_model.array_alloca(generator, ctx, num_ndindices.value);
|
||||
for (i, in_ndindex) in in_ndindices.iter().enumerate() {
|
||||
let pndindex = ndindices.offset_const(ctx, i as u64);
|
||||
in_ndindex.write_to_ndindex(generator, ctx, pndindex);
|
||||
}
|
||||
|
||||
(num_ndindices, ndindices)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Get the expected `ndims` after indexing with `indices`.
|
||||
#[must_use]
|
||||
fn deduce_ndims_after_indexing_with(&self, indices: &[RustNDIndex<'ctx>]) -> u64 {
|
||||
let mut ndims = self.ndims;
|
||||
for index in indices {
|
||||
match index {
|
||||
RustNDIndex::SingleElement(_) => {
|
||||
ndims -= 1; // Single elements decrements ndims
|
||||
}
|
||||
RustNDIndex::NewAxis => {
|
||||
ndims += 1; // `np.newaxis` / `none` adds a new axis
|
||||
}
|
||||
RustNDIndex::Ellipsis | RustNDIndex::Slice(_) => {}
|
||||
}
|
||||
}
|
||||
ndims
|
||||
}
|
||||
|
||||
/// Index into the ndarray, and return a newly-allocated view on this ndarray.
|
||||
///
|
||||
/// This function behaves like NumPy's ndarray indexing, but if the indices index
|
||||
/// into a single element, an unsized ndarray is returned.
|
||||
#[must_use]
|
||||
pub fn index<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
indices: &[RustNDIndex<'ctx>],
|
||||
) -> Self {
|
||||
let dst_ndims = self.deduce_ndims_after_indexing_with(indices);
|
||||
let dst_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, dst_ndims);
|
||||
|
||||
let (num_indices, indices) = RustNDIndex::make_ndindices(generator, ctx, indices);
|
||||
call_nac3_ndarray_index(
|
||||
generator,
|
||||
ctx,
|
||||
num_indices,
|
||||
indices,
|
||||
self.instance,
|
||||
dst_ndarray.instance,
|
||||
);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
}
|
||||
|
||||
pub mod util {
|
||||
use itertools::Itertools;
|
||||
use nac3parser::ast::{Expr, ExprKind};
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
expr::gen_slice, model::*, object::utils::slice::RustSlice, CodeGenContext,
|
||||
CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::RustNDIndex;
|
||||
|
||||
/// Generate LLVM code to transform an ndarray subscript expression to
|
||||
/// its list of [`RustNDIndex`]
|
||||
///
|
||||
/// i.e.,
|
||||
/// ```python
|
||||
/// my_ndarray[::3, 1, :2:]
|
||||
/// ^^^^^^^^^^^ Then these into a three `RustNDIndex`es
|
||||
/// ```
|
||||
pub fn gen_ndarray_subscript_ndindices<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
subscript: &Expr<Option<Type>>,
|
||||
) -> Result<Vec<RustNDIndex<'ctx>>, String> {
|
||||
// TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools
|
||||
|
||||
// Annoying notes about `slice`
|
||||
// - `my_array[5]`
|
||||
// - slice is a `Constant`
|
||||
// - `my_array[:5]`
|
||||
// - slice is a `Slice`
|
||||
// - `my_array[:]`
|
||||
// - slice is a `Slice`, but lower upper step would all be `Option::None`
|
||||
// - `my_array[:, :]`
|
||||
// - slice is now a `Tuple` of two `Slice`-s
|
||||
//
|
||||
// In summary:
|
||||
// - when there is a comma "," within [], `slice` will be a `Tuple` of the entries.
|
||||
// - when there is not comma "," within [] (i.e., just a single entry), `slice` will be that entry itself.
|
||||
//
|
||||
// So we first "flatten" out the slice expression
|
||||
let index_exprs = match &subscript.node {
|
||||
ExprKind::Tuple { elts, .. } => elts.iter().collect_vec(),
|
||||
_ => vec![subscript],
|
||||
};
|
||||
|
||||
// Process all index expressions
|
||||
let mut rust_ndindices: Vec<RustNDIndex> = Vec::with_capacity(index_exprs.len()); // Not using iterators here because `?` is used here.
|
||||
for index_expr in index_exprs {
|
||||
// NOTE: Currently nac3core's slices do not have an object representation,
|
||||
// so the code/implementation looks awkward - we have to do pattern matching on the expression
|
||||
let ndindex = if let ExprKind::Slice { lower, upper, step } = &index_expr.node {
|
||||
// Handle slices
|
||||
let (lower, upper, step) = gen_slice(generator, ctx, lower, upper, step)?;
|
||||
RustNDIndex::Slice(RustSlice { int_kind: Int32, start: lower, stop: upper, step })
|
||||
} else {
|
||||
// Treat and handle everything else as a single element index.
|
||||
let index = generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.primitives.int32, // Must be int32, this checks for illegal values
|
||||
)?;
|
||||
let index = Int(Int32).check_value(generator, ctx.ctx, index).unwrap();
|
||||
|
||||
RustNDIndex::SingleElement(index)
|
||||
};
|
||||
rust_ndindices.push(ndindex);
|
||||
}
|
||||
Ok(rust_ndindices)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,220 @@
|
|||
use inkwell::values::BasicValueEnum;
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
object::ndarray::{AnyObject, NDArrayObject},
|
||||
stmt::gen_for_callback,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::{nditer::NDIterHandle, NDArrayOut, ScalarOrNDArray};
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Generate LLVM IR to broadcast `ndarray`s together, and starmap through them with `mapping` elementwise.
|
||||
///
|
||||
/// `mapping` is an LLVM IR generator. The input of `mapping` is the list of elements when iterating through
|
||||
/// the input `ndarrays` after broadcasting. The output of `mapping` is the result of the elementwise operation.
|
||||
///
|
||||
/// `out` specifies whether the result should be a new ndarray or to be written an existing ndarray.
|
||||
pub fn broadcast_starmap<'a, G, MappingFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ndarrays: &[Self],
|
||||
out: NDArrayOut<'ctx>,
|
||||
mapping: MappingFn,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
MappingFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
&[BasicValueEnum<'ctx>],
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
// Broadcast inputs
|
||||
let broadcast_result = NDArrayObject::broadcast(generator, ctx, ndarrays);
|
||||
|
||||
let out_ndarray = match out {
|
||||
NDArrayOut::NewNDArray { dtype } => {
|
||||
// Create a new ndarray based on the broadcast shape.
|
||||
let result_ndarray =
|
||||
NDArrayObject::alloca(generator, ctx, dtype, broadcast_result.ndims);
|
||||
result_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
|
||||
result_ndarray.create_data(generator, ctx);
|
||||
result_ndarray
|
||||
}
|
||||
NDArrayOut::WriteToNDArray { ndarray: result_ndarray } => {
|
||||
// Use an existing ndarray.
|
||||
|
||||
// Check that its shape is compatible with the broadcast shape.
|
||||
result_ndarray.assert_can_be_written_by_out(
|
||||
generator,
|
||||
ctx,
|
||||
broadcast_result.ndims,
|
||||
broadcast_result.shape,
|
||||
);
|
||||
result_ndarray
|
||||
}
|
||||
};
|
||||
|
||||
// Map element-wise and store results into `mapped_ndarray`.
|
||||
let nditer = NDIterHandle::new(generator, ctx, out_ndarray);
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
Some("broadcast_starmap"),
|
||||
|generator, ctx| {
|
||||
// Create NDIters for all broadcasted input ndarrays.
|
||||
let other_nditers = broadcast_result
|
||||
.ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| NDIterHandle::new(generator, ctx, *ndarray))
|
||||
.collect_vec();
|
||||
Ok((nditer, other_nditers))
|
||||
},
|
||||
|generator, ctx, (out_nditer, _in_nditers)| {
|
||||
// We can simply use `out_nditer`'s `has_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).value)
|
||||
.collect_vec();
|
||||
|
||||
let result = mapping(generator, ctx, &in_scalars)?;
|
||||
|
||||
let p = out_nditer.get_pointer(generator, ctx);
|
||||
ctx.builder.build_store(p, result).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|generator, ctx, (out_nditer, in_nditers)| {
|
||||
// Advance all iterators
|
||||
out_nditer.next(generator, ctx);
|
||||
in_nditers.iter().for_each(|nditer| nditer.next(generator, ctx));
|
||||
Ok(())
|
||||
},
|
||||
)?;
|
||||
|
||||
Ok(out_ndarray)
|
||||
}
|
||||
|
||||
/// Map through this ndarray with an elementwise function.
|
||||
pub fn map<'a, G, Mapping>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
out: NDArrayOut<'ctx>,
|
||||
mapping: Mapping,
|
||||
) -> Result<Self, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Mapping: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BasicValueEnum<'ctx>,
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
NDArrayObject::broadcast_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&[*self],
|
||||
out,
|
||||
|generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
/// Starmap through a list of inputs using `mapping`, where an input could be an ndarray, a scalar.
|
||||
///
|
||||
/// This function is very helpful when implementing NumPy functions that takes on either scalars or ndarrays or a mix of them
|
||||
/// as their inputs and produces either an ndarray with broadcast, or a scalar if all its inputs are all scalars.
|
||||
///
|
||||
/// For example ,this function can be used to implement `np.add`, which has the following behaviors:
|
||||
/// - `np.add(3, 4) = 7` # (scalar, scalar) -> scalar
|
||||
/// - `np.add(3, np.array([4, 5, 6]))` # (scalar, ndarray) -> ndarray; the first `scalar` is converted into an ndarray and broadcasted.
|
||||
/// - `np.add(np.array([[1], [2], [3]]), np.array([[4, 5, 6]]))` # (ndarray, ndarray) -> ndarray; there is broadcasting.
|
||||
///
|
||||
/// ## Details:
|
||||
///
|
||||
/// If `inputs` are all [`ScalarOrNDArray::Scalar`], the output will be a [`ScalarOrNDArray::Scalar`] with type `ret_dtype`.
|
||||
///
|
||||
/// Otherwise (if there are any [`ScalarOrNDArray::NDArray`] in `inputs`), all inputs will be 'as-ndarray'-ed into ndarrays,
|
||||
/// then all inputs (now all ndarrays) will be passed to [`NDArrayObject::broadcasting_starmap`] and **create** a new ndarray
|
||||
/// with dtype `ret_dtype`.
|
||||
pub fn broadcasting_starmap<'a, G, MappingFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
inputs: &[ScalarOrNDArray<'ctx>],
|
||||
ret_dtype: Type,
|
||||
mapping: MappingFn,
|
||||
) -> Result<ScalarOrNDArray<'ctx>, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
MappingFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
&[BasicValueEnum<'ctx>],
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
// Check if all inputs are Scalars
|
||||
let all_scalars: Option<Vec<_>> = inputs.iter().map(AnyObject::try_from).try_collect().ok();
|
||||
|
||||
if let Some(scalars) = all_scalars {
|
||||
let scalars = scalars.iter().map(|scalar| scalar.value).collect_vec();
|
||||
let value = mapping(generator, ctx, &scalars)?;
|
||||
|
||||
Ok(ScalarOrNDArray::Scalar(AnyObject { ty: ret_dtype, value }))
|
||||
} else {
|
||||
// Promote all input to ndarrays and map through them.
|
||||
let inputs = inputs.iter().map(|input| input.to_ndarray(generator, ctx)).collect_vec();
|
||||
let ndarray = NDArrayObject::broadcast_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&inputs,
|
||||
NDArrayOut::NewNDArray { dtype: ret_dtype },
|
||||
mapping,
|
||||
)?;
|
||||
Ok(ScalarOrNDArray::NDArray(ndarray))
|
||||
}
|
||||
}
|
||||
|
||||
/// Map through this [`ScalarOrNDArray`] with an elementwise function.
|
||||
///
|
||||
/// If this is a scalar, `mapping` will directly act on the scalar. This function will return a [`ScalarOrNDArray::Scalar`] of that result.
|
||||
///
|
||||
/// If this is an ndarray, `mapping` will be applied to the elements of the ndarray. A new ndarray of the results will be created and
|
||||
/// returned as a [`ScalarOrNDArray::NDArray`].
|
||||
pub fn map<'a, G, Mapping>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ret_dtype: Type,
|
||||
mapping: Mapping,
|
||||
) -> Result<ScalarOrNDArray<'ctx>, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Mapping: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BasicValueEnum<'ctx>,
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
ScalarOrNDArray::broadcasting_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&[*self],
|
||||
ret_dtype,
|
||||
|generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,218 @@
|
|||
use std::cmp::max;
|
||||
|
||||
use nac3parser::ast::Operator;
|
||||
use util::gen_for_model;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
expr::gen_binop_expr_with_values, irrt::call_nac3_ndarray_matmul_calculate_shapes,
|
||||
model::*, object::ndarray::indexing::RustNDIndex, CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::{magic_methods::Binop, typedef::Type},
|
||||
};
|
||||
|
||||
use super::{NDArrayObject, NDArrayOut};
|
||||
|
||||
/// Perform `np.einsum("...ij,...jk->...ik", in_a, in_b)`.
|
||||
///
|
||||
/// `dst_dtype` defines the dtype of the returned ndarray.
|
||||
fn matmul_at_least_2d<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dst_dtype: Type,
|
||||
in_a: NDArrayObject<'ctx>,
|
||||
in_b: NDArrayObject<'ctx>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
assert!(in_a.ndims >= 2);
|
||||
assert!(in_b.ndims >= 2);
|
||||
|
||||
// Deduce ndims of the result of matmul.
|
||||
let ndims_int = max(in_a.ndims, in_b.ndims);
|
||||
let ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims_int);
|
||||
|
||||
let num_0 = Int(SizeT).const_int(generator, ctx.ctx, 0);
|
||||
let num_1 = Int(SizeT).const_int(generator, ctx.ctx, 1);
|
||||
|
||||
// Broadcasts `in_a.shape[:-2]` and `in_b.shape[:-2]` together and allocate the
|
||||
// destination ndarray to store the result of matmul.
|
||||
let (lhs, rhs, dst) = {
|
||||
let in_lhs_ndims = in_a.ndims_llvm(generator, ctx.ctx);
|
||||
let in_lhs_shape = in_a.instance.get(generator, ctx, |f| f.shape);
|
||||
let in_rhs_ndims = in_b.ndims_llvm(generator, ctx.ctx);
|
||||
let in_rhs_shape = in_b.instance.get(generator, ctx, |f| f.shape);
|
||||
let lhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
|
||||
let rhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
|
||||
let dst_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
|
||||
|
||||
// Matmul dimension compatibility is checked here.
|
||||
call_nac3_ndarray_matmul_calculate_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
in_lhs_ndims,
|
||||
in_lhs_shape,
|
||||
in_rhs_ndims,
|
||||
in_rhs_shape,
|
||||
ndims,
|
||||
lhs_shape,
|
||||
rhs_shape,
|
||||
dst_shape,
|
||||
);
|
||||
|
||||
let lhs = in_a.broadcast_to(generator, ctx, ndims_int, lhs_shape);
|
||||
let rhs = in_b.broadcast_to(generator, ctx, ndims_int, rhs_shape);
|
||||
|
||||
let dst = NDArrayObject::alloca(generator, ctx, dst_dtype, ndims_int);
|
||||
dst.copy_shape_from_array(generator, ctx, dst_shape);
|
||||
dst.create_data(generator, ctx);
|
||||
|
||||
(lhs, rhs, dst)
|
||||
};
|
||||
|
||||
let len = lhs.instance.get(generator, ctx, |f| f.shape).get_index_const(
|
||||
generator,
|
||||
ctx,
|
||||
ndims_int - 1,
|
||||
);
|
||||
|
||||
let at_row = ndims_int - 2;
|
||||
let at_col = ndims_int - 1;
|
||||
|
||||
let dst_dtype_llvm = ctx.get_llvm_type(generator, dst_dtype);
|
||||
let dst_zero = dst_dtype_llvm.const_zero();
|
||||
|
||||
dst.foreach(generator, ctx, |generator, ctx, _, hdl| {
|
||||
let pdst_ij = hdl.get_pointer(generator, ctx);
|
||||
|
||||
ctx.builder.build_store(pdst_ij, dst_zero).unwrap();
|
||||
|
||||
let indices = hdl.get_indices();
|
||||
let i = indices.get_index_const(generator, ctx, at_row);
|
||||
let j = indices.get_index_const(generator, ctx, at_col);
|
||||
|
||||
gen_for_model(generator, ctx, num_0, len, num_1, |generator, ctx, _, k| {
|
||||
// `indices` is modified to index into `a` and `b`, and restored.
|
||||
indices.set_index_const(ctx, at_row, i);
|
||||
indices.set_index_const(ctx, at_col, k);
|
||||
let a_ik = lhs.get_scalar_by_indices(generator, ctx, indices);
|
||||
|
||||
indices.set_index_const(ctx, at_row, k);
|
||||
indices.set_index_const(ctx, at_col, j);
|
||||
let b_kj = rhs.get_scalar_by_indices(generator, ctx, indices);
|
||||
|
||||
// Restore `indices`.
|
||||
indices.set_index_const(ctx, at_row, i);
|
||||
indices.set_index_const(ctx, at_col, j);
|
||||
|
||||
// x = a_[...]ik * b_[...]kj
|
||||
let x = gen_binop_expr_with_values(
|
||||
generator,
|
||||
ctx,
|
||||
(&Some(lhs.dtype), a_ik.value),
|
||||
Binop::normal(Operator::Mult),
|
||||
(&Some(rhs.dtype), b_kj.value),
|
||||
ctx.current_loc,
|
||||
)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, dst_dtype)?;
|
||||
|
||||
// dst_[...]ij += x
|
||||
let dst_ij = ctx.builder.build_load(pdst_ij, "").unwrap();
|
||||
let dst_ij = gen_binop_expr_with_values(
|
||||
generator,
|
||||
ctx,
|
||||
(&Some(dst_dtype), dst_ij),
|
||||
Binop::normal(Operator::Add),
|
||||
(&Some(dst_dtype), x),
|
||||
ctx.current_loc,
|
||||
)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, dst_dtype)?;
|
||||
ctx.builder.build_store(pdst_ij, dst_ij).unwrap();
|
||||
|
||||
Ok(())
|
||||
})
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
dst
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Perform `np.matmul` according to the rules in
|
||||
/// <https://numpy.org/doc/stable/reference/generated/numpy.matmul.html>.
|
||||
///
|
||||
/// This function always return an [`NDArrayObject`]. You may want to use [`NDArrayObject::split_unsized`]
|
||||
/// to handle when the output could be a scalar.
|
||||
///
|
||||
/// `dst_dtype` defines the dtype of the returned ndarray.
|
||||
pub fn matmul<G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
a: Self,
|
||||
b: Self,
|
||||
out: NDArrayOut<'ctx>,
|
||||
) -> Self {
|
||||
// Sanity check, but type inference should prevent this.
|
||||
assert!(a.ndims > 0 && b.ndims > 0, "np.matmul disallows scalar input");
|
||||
|
||||
/*
|
||||
If both arguments are 2-D they are multiplied like conventional matrices.
|
||||
If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indices and broadcast accordingly.
|
||||
If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
|
||||
If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.
|
||||
*/
|
||||
|
||||
let new_a = if a.ndims == 1 {
|
||||
// Prepend 1 to its dimensions
|
||||
a.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis])
|
||||
} else {
|
||||
a
|
||||
};
|
||||
|
||||
let new_b = if b.ndims == 1 {
|
||||
// Append 1 to its dimensions
|
||||
b.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis])
|
||||
} else {
|
||||
b
|
||||
};
|
||||
|
||||
// NOTE: `result` will always be a newly allocated ndarray.
|
||||
// Current implementation cannot do in-place matrix muliplication.
|
||||
let mut result = matmul_at_least_2d(generator, ctx, out.get_dtype(), new_a, new_b);
|
||||
|
||||
// Postprocessing on the result to remove prepended/appended axes.
|
||||
let mut postindices = vec![];
|
||||
let zero = Int(Int32).const_0(generator, ctx.ctx);
|
||||
|
||||
if a.ndims == 1 {
|
||||
// Remove the prepended 1
|
||||
postindices.push(RustNDIndex::SingleElement(zero));
|
||||
}
|
||||
|
||||
if b.ndims == 1 {
|
||||
// Remove the appended 1
|
||||
postindices.push(RustNDIndex::Ellipsis);
|
||||
postindices.push(RustNDIndex::SingleElement(zero));
|
||||
}
|
||||
|
||||
if !postindices.is_empty() {
|
||||
result = result.index(generator, ctx, &postindices);
|
||||
}
|
||||
|
||||
match out {
|
||||
NDArrayOut::NewNDArray { .. } => result,
|
||||
NDArrayOut::WriteToNDArray { ndarray: out_ndarray } => {
|
||||
let result_shape = result.instance.get(generator, ctx, |f| f.shape);
|
||||
out_ndarray.assert_can_be_written_by_out(
|
||||
generator,
|
||||
ctx,
|
||||
result.ndims,
|
||||
result_shape,
|
||||
);
|
||||
|
||||
out_ndarray.copy_data_from(generator, ctx, result);
|
||||
out_ndarray
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,668 @@
|
|||
pub mod array;
|
||||
pub mod broadcast;
|
||||
pub mod contiguous;
|
||||
pub mod factory;
|
||||
pub mod indexing;
|
||||
pub mod map;
|
||||
pub mod matmul;
|
||||
pub mod nditer;
|
||||
pub mod shape_util;
|
||||
pub mod view;
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::BasicType,
|
||||
values::{BasicValue, BasicValueEnum, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt::{
|
||||
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
|
||||
call_nac3_ndarray_get_pelement_by_indices, call_nac3_ndarray_is_c_contiguous,
|
||||
call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
|
||||
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
|
||||
call_nac3_ndarray_util_assert_output_shape_same,
|
||||
},
|
||||
model::*,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
toplevel::{
|
||||
helper::{create_ndims, extract_ndims},
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::{any::AnyObject, tuple::TupleObject};
|
||||
|
||||
/// Fields of [`NDArray`]
|
||||
pub struct NDArrayFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub data: F::Out<Ptr<Int<Byte>>>,
|
||||
pub itemsize: F::Out<Int<SizeT>>,
|
||||
pub ndims: F::Out<Int<SizeT>>,
|
||||
pub shape: F::Out<Ptr<Int<SizeT>>>,
|
||||
pub strides: F::Out<Ptr<Int<SizeT>>>,
|
||||
}
|
||||
|
||||
/// A strided ndarray in NAC3.
|
||||
///
|
||||
/// See IRRT implementation for details about its fields.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NDArray;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NDArray {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NDArrayFields<'ctx, F>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
data: traversal.add_auto("data"),
|
||||
itemsize: traversal.add_auto("itemsize"),
|
||||
ndims: traversal.add_auto("ndims"),
|
||||
shape: traversal.add_auto("shape"),
|
||||
strides: traversal.add_auto("strides"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A NAC3 Python ndarray object.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub struct NDArrayObject<'ctx> {
|
||||
pub dtype: Type,
|
||||
pub ndims: u64,
|
||||
pub instance: Instance<'ctx, Ptr<Struct<NDArray>>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Attempt to convert an [`AnyObject`] into an [`NDArrayObject`].
|
||||
pub fn from_object<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
|
||||
}
|
||||
|
||||
/// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
|
||||
/// `dtype` and `ndims`.
|
||||
pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: V,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
) -> Self {
|
||||
let value = Ptr(Struct(NDArray)).check_value(generator, ctx.ctx, value).unwrap();
|
||||
NDArrayObject { dtype, ndims, instance: value }
|
||||
}
|
||||
|
||||
/// Get this ndarray's `ndims` as an LLVM constant.
|
||||
pub fn ndims_llvm<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &'ctx Context,
|
||||
) -> Instance<'ctx, Int<SizeT>> {
|
||||
Int(SizeT).const_int(generator, ctx, self.ndims)
|
||||
}
|
||||
|
||||
/// Get the typechecker ndarray type of this [`NDArrayObject`].
|
||||
pub fn get_type(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Type {
|
||||
let ndims = create_ndims(&mut ctx.unifier, self.ndims);
|
||||
make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(self.dtype), Some(ndims))
|
||||
}
|
||||
|
||||
/// Forget that this is an ndarray and convert into an [`AnyObject`].
|
||||
pub fn to_any(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
|
||||
let ty = self.get_type(ctx);
|
||||
AnyObject { value: self.instance.value.as_basic_value_enum(), ty }
|
||||
}
|
||||
|
||||
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
|
||||
///
|
||||
/// `shape` and `strides` will be automatically allocated onto the stack.
|
||||
///
|
||||
/// The returned ndarray's content will be:
|
||||
/// - `data`: uninitialized.
|
||||
/// - `itemsize`: set to the `sizeof()` of `dtype`.
|
||||
/// - `ndims`: set to the value of `ndims`.
|
||||
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
|
||||
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
|
||||
pub fn alloca<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
ndims: u64,
|
||||
) -> Self {
|
||||
let ndarray = Struct(NDArray).alloca(generator, ctx);
|
||||
|
||||
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
|
||||
let itemsize = Int(SizeT).z_extend_or_truncate(generator, ctx, itemsize);
|
||||
ndarray.set(ctx, |f| f.itemsize, itemsize);
|
||||
|
||||
let ndims_val = Int(SizeT).const_int(generator, ctx.ctx, ndims);
|
||||
ndarray.set(ctx, |f| f.ndims, ndims_val);
|
||||
|
||||
let shape = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
|
||||
ndarray.set(ctx, |f| f.shape, shape);
|
||||
|
||||
let strides = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
|
||||
ndarray.set(ctx, |f| f.strides, strides);
|
||||
|
||||
NDArrayObject { dtype, ndims, instance: ndarray }
|
||||
}
|
||||
|
||||
/// Convenience function. Allocate an [`NDArrayObject`] with a statically known shape.
|
||||
///
|
||||
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
|
||||
pub fn alloca_constant_shape<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
shape: &[u64],
|
||||
) -> Self {
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
|
||||
|
||||
// Write shape
|
||||
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
|
||||
for (i, dim) in shape.iter().enumerate() {
|
||||
let dim = Int(SizeT).const_int(generator, ctx.ctx, *dim);
|
||||
dst_shape.offset_const(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: &[Instance<'ctx, Int<SizeT>>],
|
||||
) -> Self {
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
|
||||
|
||||
// Write shape
|
||||
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
|
||||
for (i, dim) in shape.iter().enumerate() {
|
||||
dst_shape.offset_const(ctx, i as u64).store(ctx, *dim);
|
||||
}
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
|
||||
/// The allocated data buffer is considered to be *owned* by the ndarray.
|
||||
///
|
||||
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
|
||||
///
|
||||
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
|
||||
pub fn create_data<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
let nbytes = self.nbytes(generator, ctx);
|
||||
|
||||
let data = Int(Byte).array_alloca(generator, ctx, nbytes.value);
|
||||
self.instance.set(ctx, |f| f.data, data);
|
||||
|
||||
self.set_strides_contiguous(generator, ctx);
|
||||
}
|
||||
|
||||
/// Copy shape dimensions from an array.
|
||||
pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let num_items = self.ndims_llvm(generator, ctx.ctx).value;
|
||||
self.instance.get(generator, ctx, |f| f.shape).copy_from(generator, ctx, shape, num_items);
|
||||
}
|
||||
|
||||
/// Copy shape dimensions from an ndarray.
|
||||
/// Panics if `ndims` mismatches.
|
||||
pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert_eq!(self.ndims, src_ndarray.ndims);
|
||||
let src_shape = src_ndarray.instance.get(generator, ctx, |f| f.shape);
|
||||
self.copy_shape_from_array(generator, ctx, src_shape);
|
||||
}
|
||||
|
||||
/// Copy strides dimensions from an array.
|
||||
pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
strides: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let num_items = self.ndims_llvm(generator, ctx.ctx).value;
|
||||
self.instance
|
||||
.get(generator, ctx, |f| f.strides)
|
||||
.copy_from(generator, ctx, strides, num_items);
|
||||
}
|
||||
|
||||
/// Copy strides dimensions from an ndarray.
|
||||
/// Panics if `ndims` mismatches.
|
||||
pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert_eq!(self.ndims, src_ndarray.ndims);
|
||||
let src_strides = src_ndarray.instance.get(generator, ctx, |f| f.strides);
|
||||
self.copy_strides_from_array(generator, ctx, src_strides);
|
||||
}
|
||||
|
||||
/// Get the `np.size()` of this ndarray.
|
||||
pub fn size<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Int<SizeT>> {
|
||||
call_nac3_ndarray_size(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
/// Get the `ndarray.nbytes` of this ndarray.
|
||||
pub fn nbytes<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Int<SizeT>> {
|
||||
call_nac3_ndarray_nbytes(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
/// Get the `len()` of this ndarray.
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Int<SizeT>> {
|
||||
call_nac3_ndarray_len(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
/// Check if this ndarray is C-contiguous.
|
||||
///
|
||||
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
|
||||
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Int<Bool>> {
|
||||
call_nac3_ndarray_is_c_contiguous(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
/// Get the pointer to the n-th (0-based) element.
|
||||
///
|
||||
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
|
||||
pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Instance<'ctx, Int<SizeT>>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
|
||||
|
||||
let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.instance, nth);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Get the n-th (0-based) scalar.
|
||||
pub fn get_nth_scalar<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
nth: Instance<'ctx, Int<SizeT>>,
|
||||
) -> AnyObject<'ctx> {
|
||||
let ptr = self.get_nth_pelement(generator, ctx, nth);
|
||||
let value = ctx.builder.build_load(ptr, "").unwrap();
|
||||
AnyObject { ty: self.dtype, value }
|
||||
}
|
||||
|
||||
/// Get the pointer to the element indexed by `indices`.
|
||||
///
|
||||
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
|
||||
pub fn get_pelement_by_indices<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
|
||||
|
||||
let p = call_nac3_ndarray_get_pelement_by_indices(generator, ctx, self.instance, indices);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Get the scalar indexed by `indices`.
|
||||
pub fn get_scalar_by_indices<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) -> AnyObject<'ctx> {
|
||||
let ptr = self.get_pelement_by_indices(generator, ctx, indices);
|
||||
let value = ctx.builder.build_load(ptr, "").unwrap();
|
||||
AnyObject { ty: self.dtype, value }
|
||||
}
|
||||
|
||||
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
|
||||
///
|
||||
/// Update the ndarray's strides to make the ndarray contiguous.
|
||||
pub fn set_strides_contiguous<G: CodeGenerator + ?Sized>(
|
||||
self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.instance);
|
||||
}
|
||||
|
||||
/// Clone/Copy this ndarray - Allocate a new ndarray with the same shape as this ndarray and copy the contents over.
|
||||
///
|
||||
/// The new ndarray will own its data and will be C-contiguous.
|
||||
#[must_use]
|
||||
pub fn make_copy<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Self {
|
||||
let clone = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
|
||||
|
||||
let shape = self.instance.gep(ctx, |f| f.shape).load(generator, ctx);
|
||||
clone.copy_shape_from_array(generator, ctx, shape);
|
||||
clone.create_data(generator, ctx);
|
||||
clone.copy_data_from(generator, ctx, *self);
|
||||
clone
|
||||
}
|
||||
|
||||
/// Copy data from another ndarray.
|
||||
///
|
||||
/// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
|
||||
/// do not matter. The copying order is determined by how their flattened views look.
|
||||
///
|
||||
/// Panics if the `dtype`s of ndarrays are different.
|
||||
pub fn copy_data_from<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
src: NDArrayObject<'ctx>,
|
||||
) {
|
||||
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
|
||||
call_nac3_ndarray_copy_data(generator, ctx, src.instance, self.instance);
|
||||
}
|
||||
|
||||
/// Returns true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
|
||||
#[must_use]
|
||||
pub fn is_unsized(&self) -> bool {
|
||||
self.ndims == 0
|
||||
}
|
||||
|
||||
/// If this ndarray is unsized, return its sole value as an [`AnyObject`].
|
||||
/// Otherwise, do nothing and return the ndarray itself.
|
||||
pub fn split_unsized<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
if self.is_unsized() {
|
||||
// NOTE: `np.size(self) == 0` here is never possible.
|
||||
let zero = Int(SizeT).const_0(generator, ctx.ctx);
|
||||
let value = self.get_nth_scalar(generator, ctx, zero).value;
|
||||
|
||||
ScalarOrNDArray::Scalar(AnyObject { ty: self.dtype, value })
|
||||
} else {
|
||||
ScalarOrNDArray::NDArray(*self)
|
||||
}
|
||||
}
|
||||
|
||||
/// Fill the ndarray with a scalar.
|
||||
///
|
||||
/// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
|
||||
pub fn fill<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: BasicValueEnum<'ctx>,
|
||||
) {
|
||||
self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
|
||||
let p = nditer.get_pointer(generator, ctx);
|
||||
ctx.builder.build_store(p, value).unwrap();
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
}
|
||||
|
||||
/// Create the shape tuple of this ndarray like `np.shape(<ndarray>)`.
|
||||
///
|
||||
/// The returned integers in the tuple are in int32.
|
||||
pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> TupleObject<'ctx> {
|
||||
// TODO: Return a tuple of SizeT
|
||||
|
||||
let mut objects = Vec::with_capacity(self.ndims as usize);
|
||||
|
||||
for i in 0..self.ndims {
|
||||
let dim = self
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.shape)
|
||||
.get_index_const(generator, ctx, i)
|
||||
.truncate_or_bit_cast(generator, ctx, Int32);
|
||||
|
||||
objects.push(AnyObject {
|
||||
ty: ctx.primitives.int32,
|
||||
value: dim.value.as_basic_value_enum(),
|
||||
});
|
||||
}
|
||||
|
||||
TupleObject::from_objects(generator, ctx, objects)
|
||||
}
|
||||
|
||||
/// Create the strides tuple of this ndarray like `<ndarray>.strides`.
|
||||
///
|
||||
/// The returned integers in the tuple are in int32.
|
||||
pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> TupleObject<'ctx> {
|
||||
// TODO: Return a tuple of SizeT.
|
||||
|
||||
let mut objects = Vec::with_capacity(self.ndims as usize);
|
||||
|
||||
for i in 0..self.ndims {
|
||||
let dim = self
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.strides)
|
||||
.get_index_const(generator, ctx, i)
|
||||
.truncate_or_bit_cast(generator, ctx, Int32);
|
||||
|
||||
objects.push(AnyObject {
|
||||
ty: ctx.primitives.int32,
|
||||
value: dim.value.as_basic_value_enum(),
|
||||
});
|
||||
}
|
||||
|
||||
TupleObject::from_objects(generator, ctx, objects)
|
||||
}
|
||||
|
||||
/// Create an unsized ndarray to contain `object`.
|
||||
pub fn make_unsized<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
// We have to put the value on the stack to get a data pointer.
|
||||
let data = ctx.builder.build_alloca(object.value.get_type(), "make_unsized").unwrap();
|
||||
ctx.builder.build_store(data, object.value).unwrap();
|
||||
let data = Ptr(Int(Byte)).pointer_cast(generator, ctx, data);
|
||||
|
||||
let ndarray = NDArrayObject::alloca(generator, ctx, object.ty, 0);
|
||||
ndarray.instance.set(ctx, |f| f.data, data);
|
||||
ndarray
|
||||
}
|
||||
/// Check if this `NDArray` can be used as an `out` ndarray for an operation.
|
||||
///
|
||||
/// Raise an exception if the shapes do not match.
|
||||
pub fn assert_can_be_written_by_out<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
out_ndims: u64,
|
||||
out_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) {
|
||||
let ndarray_ndims = self.ndims_llvm(generator, ctx.ctx);
|
||||
let ndarray_shape = self.instance.get(generator, ctx, |f| f.shape);
|
||||
|
||||
let output_ndims = Int(SizeT).const_int(generator, ctx.ctx, out_ndims);
|
||||
let output_shape = out_shape;
|
||||
|
||||
call_nac3_ndarray_util_assert_output_shape_same(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray_ndims,
|
||||
ndarray_shape,
|
||||
output_ndims,
|
||||
output_shape,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/// A convenience enum for implementing functions that acts on scalars or ndarrays or both.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum ScalarOrNDArray<'ctx> {
|
||||
Scalar(AnyObject<'ctx>),
|
||||
NDArray(NDArrayObject<'ctx>),
|
||||
}
|
||||
|
||||
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for AnyObject<'ctx> {
|
||||
type Error = ();
|
||||
|
||||
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
|
||||
match value {
|
||||
ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
|
||||
ScalarOrNDArray::NDArray(_ndarray) => Err(()),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
|
||||
type Error = ();
|
||||
|
||||
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
|
||||
match value {
|
||||
ScalarOrNDArray::Scalar(_scalar) => Err(()),
|
||||
ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
/// Split on `object` either into a scalar or an ndarray.
|
||||
///
|
||||
/// If `object` is an ndarray, [`ScalarOrNDArray::NDArray`].
|
||||
///
|
||||
/// For everything else, it is wrapped with [`ScalarOrNDArray::Scalar`].
|
||||
pub fn split_object<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
object: AnyObject<'ctx>,
|
||||
) -> ScalarOrNDArray<'ctx> {
|
||||
match &*ctx.unifier.get_ty(object.ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, object);
|
||||
ScalarOrNDArray::NDArray(ndarray)
|
||||
}
|
||||
_ => ScalarOrNDArray::Scalar(object),
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
|
||||
#[must_use]
|
||||
pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.value,
|
||||
ScalarOrNDArray::NDArray(ndarray) => ndarray.instance.value.as_basic_value_enum(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
|
||||
/// - If this is an ndarray, the ndarray is returned.
|
||||
/// - If this is a scalar, this function returns new ndarray created with [`NDArrayObject::make_unsized`].
|
||||
pub fn to_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> NDArrayObject<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
|
||||
ScalarOrNDArray::Scalar(scalar) => NDArrayObject::make_unsized(generator, ctx, *scalar),
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the dtype of the ndarray created if this were called with [`ScalarOrNDArray::to_ndarray`].
|
||||
#[must_use]
|
||||
pub fn get_dtype(&self) -> Type {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.ty,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// An helper enum specifying how a function should produce its output.
|
||||
///
|
||||
/// Many functions in NumPy has an optional `out` parameter (e.g., `matmul`). If `out` is specified
|
||||
/// with an ndarray, the result of a function will be written to `out`. If `out` is not specified, a function will
|
||||
/// create a new ndarray and store the result in it.
|
||||
#[derive(Debug, Clone, Copy)]
|
||||
pub enum NDArrayOut<'ctx> {
|
||||
/// Tell a function should create a new ndarray with the expected element type `dtype`.
|
||||
NewNDArray { dtype: Type },
|
||||
/// Tell a function to write the result to `ndarray`.
|
||||
WriteToNDArray { ndarray: NDArrayObject<'ctx> },
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayOut<'ctx> {
|
||||
/// Get the dtype of this output.
|
||||
#[must_use]
|
||||
pub fn get_dtype(&self) -> Type {
|
||||
match self {
|
||||
NDArrayOut::NewNDArray { dtype } => *dtype,
|
||||
NDArrayOut::WriteToNDArray { ndarray } => ndarray.dtype,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A version of [`call_nac3_ndarray_set_strides_by_shape`] in Rust.
|
||||
///
|
||||
/// This function is used generating strides for globally defined contiguous ndarrays.
|
||||
#[must_use]
|
||||
pub fn make_contiguous_strides(itemsize: u64, ndims: u64, shape: &[u64]) -> Vec<u64> {
|
||||
let mut strides = Vec::with_capacity(ndims as usize);
|
||||
let mut stride_product = 1u64;
|
||||
for i in 0..ndims {
|
||||
let axis = ndims - i - 1;
|
||||
strides[axis as usize] = stride_product * itemsize;
|
||||
stride_product *= shape[axis as usize];
|
||||
}
|
||||
strides
|
||||
}
|
||||
|
|
@ -0,0 +1,177 @@
|
|||
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::any::AnyObject,
|
||||
stmt::{gen_for_callback, BreakContinueHooks},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::NDArrayObject;
|
||||
|
||||
/// Fields of [`NDIter`]
|
||||
pub struct NDIterFields<'ctx, F: FieldTraversal<'ctx>> {
|
||||
pub ndims: F::Out<Int<SizeT>>,
|
||||
pub shape: F::Out<Ptr<Int<SizeT>>>,
|
||||
pub strides: F::Out<Ptr<Int<SizeT>>>,
|
||||
|
||||
pub indices: F::Out<Ptr<Int<SizeT>>>,
|
||||
pub nth: F::Out<Int<SizeT>>,
|
||||
pub element: F::Out<Ptr<Int<Byte>>>,
|
||||
|
||||
pub size: F::Out<Int<SizeT>>,
|
||||
}
|
||||
|
||||
/// An IRRT helper structure used to iterate through an ndarray.
|
||||
#[derive(Debug, Clone, Copy, Default)]
|
||||
pub struct NDIter;
|
||||
|
||||
impl<'ctx> StructKind<'ctx> for NDIter {
|
||||
type Fields<F: FieldTraversal<'ctx>> = NDIterFields<'ctx, F>;
|
||||
|
||||
fn 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 helper structure with a convenient interface to interact with [`NDIter`].
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct NDIterHandle<'ctx> {
|
||||
instance: Instance<'ctx, Ptr<Struct<NDIter>>>,
|
||||
/// The ndarray this [`NDIter`] to iterating over.
|
||||
ndarray: NDArrayObject<'ctx>,
|
||||
/// The current indices of [`NDIter`].
|
||||
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDIterHandle<'ctx> {
|
||||
/// Allocate an [`NDIter`] that iterates through an ndarray.
|
||||
pub fn new<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayObject<'ctx>,
|
||||
) -> Self {
|
||||
let nditer = Struct(NDIter).alloca(generator, ctx);
|
||||
let ndims = ndarray.ndims_llvm(generator, ctx.ctx);
|
||||
|
||||
// The caller has the responsibility to allocate 'indices' for `NDIter`.
|
||||
let indices = Int(SizeT).array_alloca(generator, ctx, ndims.value);
|
||||
call_nac3_nditer_initialize(generator, ctx, nditer, ndarray.instance, indices);
|
||||
|
||||
NDIterHandle { ndarray, instance: nditer, indices }
|
||||
}
|
||||
|
||||
/// Is there a next element?
|
||||
///
|
||||
/// If `ndarray` is unsized, this returns true only for the first iteration.
|
||||
/// If `ndarray` is 0-sized, this always returns false.
|
||||
#[must_use]
|
||||
pub fn has_next<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Int<Bool>> {
|
||||
call_nac3_nditer_has_next(generator, ctx, self.instance)
|
||||
}
|
||||
|
||||
/// Go to the next element. If `has_next()` is false, then this has undefined behavior.
|
||||
///
|
||||
/// If `ndarray` is unsized, this can only be called once.
|
||||
/// If `ndarray` is 0-sized, this can never be called.
|
||||
pub fn next<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
call_nac3_nditer_next(generator, ctx, self.instance);
|
||||
}
|
||||
|
||||
/// Get pointer to the current element.
|
||||
#[must_use]
|
||||
pub fn get_pointer<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let elem_ty = ctx.get_llvm_type(generator, self.ndarray.dtype);
|
||||
|
||||
let p = self.instance.get(generator, ctx, |f| f.element);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "element")
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Get the value of the current element.
|
||||
#[must_use]
|
||||
pub fn get_scalar<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> AnyObject<'ctx> {
|
||||
let p = self.get_pointer(generator, ctx);
|
||||
let value = ctx.builder.build_load(p, "value").unwrap();
|
||||
AnyObject { ty: self.ndarray.dtype, value }
|
||||
}
|
||||
|
||||
/// Get the index of the current element if this ndarray were a flat ndarray.
|
||||
#[must_use]
|
||||
pub fn get_index<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Int<SizeT>> {
|
||||
self.instance.get(generator, ctx, |f| f.nth)
|
||||
}
|
||||
|
||||
/// Get the indices of the current element.
|
||||
#[must_use]
|
||||
pub fn get_indices(&self) -> Instance<'ctx, Ptr<Int<SizeT>>> {
|
||||
self.indices
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Iterate through every element in the ndarray.
|
||||
///
|
||||
/// `body` has access to [`BreakContinueHooks`] to short-circuit and [`NDIterHandle`] to
|
||||
/// get properties of the current iteration (e.g., the current element, indices, etc.)
|
||||
pub fn foreach<'a, G, F>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
body: F,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
F: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
BreakContinueHooks<'ctx>,
|
||||
NDIterHandle<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
Some("ndarray_foreach"),
|
||||
|generator, ctx| Ok(NDIterHandle::new(generator, ctx, *self)),
|
||||
|generator, ctx, nditer| Ok(nditer.has_next(generator, ctx).value),
|
||||
|generator, ctx, hooks, nditer| body(generator, ctx, hooks, nditer),
|
||||
|generator, ctx, nditer| {
|
||||
nditer.next(generator, ctx);
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,105 @@
|
|||
use util::gen_for_model;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
model::*,
|
||||
object::{any::AnyObject, list::ListObject, tuple::TupleObject},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::TypeEnum,
|
||||
};
|
||||
|
||||
/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
|
||||
///
|
||||
/// * `sequence` - The `sequence` parameter.
|
||||
/// * `sequence_ty` - The typechecker type of `sequence`
|
||||
///
|
||||
/// The `sequence` argument type may only be one of the following:
|
||||
/// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
|
||||
/// 2. A tuple of `int32`; e.g., `np.empty((600, 800, 3))`
|
||||
/// 3. A scalar `int32`; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
|
||||
///
|
||||
/// All `int32` values will be sign-extended to `SizeT`.
|
||||
pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
input_sequence: AnyObject<'ctx>,
|
||||
) -> (Instance<'ctx, Int<SizeT>>, Instance<'ctx, Ptr<Int<SizeT>>>) {
|
||||
let zero = Int(SizeT).const_0(generator, ctx.ctx);
|
||||
let one = Int(SizeT).const_1(generator, ctx.ctx);
|
||||
|
||||
// The result `list` to return.
|
||||
match &*ctx.unifier.get_ty(input_sequence.ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
|
||||
|
||||
// Check `input_sequence`
|
||||
let input_sequence = ListObject::from_object(generator, ctx, input_sequence);
|
||||
|
||||
let len = input_sequence.instance.get(generator, ctx, |f| f.len);
|
||||
let result = Int(SizeT).array_alloca(generator, ctx, len.value);
|
||||
|
||||
// Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
|
||||
gen_for_model(generator, ctx, zero, len, one, |generator, ctx, _hooks, i| {
|
||||
// Load the i-th int32 in the input sequence
|
||||
let int = input_sequence
|
||||
.instance
|
||||
.get(generator, ctx, |f| f.items)
|
||||
.get_index(generator, ctx, i.value)
|
||||
.value
|
||||
.into_int_value();
|
||||
|
||||
// Cast to SizeT
|
||||
let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, int);
|
||||
|
||||
// Store
|
||||
result.set_index(ctx, i.value, int);
|
||||
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
(len, result)
|
||||
}
|
||||
TypeEnum::TTuple { .. } => {
|
||||
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
|
||||
|
||||
let input_sequence = TupleObject::from_object(ctx, input_sequence);
|
||||
|
||||
let len = input_sequence.len(generator, ctx);
|
||||
|
||||
let result = Int(SizeT).array_alloca(generator, ctx, len.value);
|
||||
|
||||
for i in 0..input_sequence.num_elements() {
|
||||
// Get the i-th element off of the tuple and load it into `result`.
|
||||
let int = input_sequence.index(ctx, i).value.into_int_value();
|
||||
let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, int);
|
||||
|
||||
result.set_index_const(ctx, i as u64, int);
|
||||
}
|
||||
|
||||
(len, result)
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
|
||||
let input_int = input_sequence.value.into_int_value();
|
||||
|
||||
let len = Int(SizeT).const_1(generator, ctx.ctx);
|
||||
let result = Int(SizeT).array_alloca(generator, ctx, len.value);
|
||||
let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, input_int);
|
||||
|
||||
// Storing into result[0]
|
||||
result.store(ctx, int);
|
||||
|
||||
(len, result)
|
||||
}
|
||||
_ => panic!(
|
||||
"encountered unknown sequence type: {}",
|
||||
ctx.unifier.stringify(input_sequence.ty)
|
||||
),
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,119 @@
|
|||
use crate::codegen::{
|
||||
irrt::{call_nac3_ndarray_reshape_resolve_and_check_new_shape, call_nac3_ndarray_transpose},
|
||||
model::*,
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
use super::{indexing::RustNDIndex, NDArrayObject};
|
||||
|
||||
impl<'ctx> NDArrayObject<'ctx> {
|
||||
/// Make sure the ndarray is at least `ndmin`-dimensional.
|
||||
///
|
||||
/// If this ndarray's `ndims` is less than `ndmin`, a view is created on this with 1s prepended to the shape.
|
||||
/// If this ndarray's `ndims` is not less than `ndmin`, this function does nothing and return this ndarray.
|
||||
#[must_use]
|
||||
pub fn atleast_nd<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndmin: u64,
|
||||
) -> Self {
|
||||
if self.ndims < ndmin {
|
||||
// Extend the dimensions with np.newaxis.
|
||||
let mut indices = vec![];
|
||||
for _ in self.ndims..ndmin {
|
||||
indices.push(RustNDIndex::NewAxis);
|
||||
}
|
||||
indices.push(RustNDIndex::Ellipsis);
|
||||
self.index(generator, ctx, &indices)
|
||||
} else {
|
||||
*self
|
||||
}
|
||||
}
|
||||
|
||||
/// Create a reshaped view on this ndarray like `np.reshape()`.
|
||||
///
|
||||
/// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be modified as a result.
|
||||
///
|
||||
/// If reshape without copying is impossible, this function will allocate a new ndarray and copy contents.
|
||||
///
|
||||
/// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
|
||||
/// * `new_shape` - The target shape to do `np.reshape()`.
|
||||
#[must_use]
|
||||
pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
new_ndims: u64,
|
||||
new_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
|
||||
) -> Self {
|
||||
// TODO: The current criterion for whether to do a full copy or not is by checking `is_c_contiguous`,
|
||||
// but this is not optimal - there are cases when the ndarray is not contiguous but could be reshaped
|
||||
// without copying data. Look into how numpy does it.
|
||||
|
||||
let current_bb = ctx.builder.get_insert_block().unwrap();
|
||||
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
|
||||
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
|
||||
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
|
||||
|
||||
let dst_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, new_ndims);
|
||||
dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
|
||||
|
||||
// Reolsve negative indices
|
||||
let size = self.size(generator, ctx);
|
||||
let dst_ndims = dst_ndarray.ndims_llvm(generator, ctx.ctx);
|
||||
let dst_shape = dst_ndarray.instance.get(generator, ctx, |f| f.shape);
|
||||
call_nac3_ndarray_reshape_resolve_and_check_new_shape(
|
||||
generator, ctx, size, dst_ndims, dst_shape,
|
||||
);
|
||||
|
||||
let is_c_contiguous = self.is_c_contiguous(generator, ctx);
|
||||
ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
|
||||
|
||||
// Inserting into then_bb: reshape is possible without copying
|
||||
ctx.builder.position_at_end(then_bb);
|
||||
dst_ndarray.set_strides_contiguous(generator, ctx);
|
||||
dst_ndarray.instance.set(ctx, |f| f.data, self.instance.get(generator, ctx, |f| f.data));
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Inserting into else_bb: reshape is impossible without copying
|
||||
ctx.builder.position_at_end(else_bb);
|
||||
dst_ndarray.create_data(generator, ctx);
|
||||
dst_ndarray.copy_data_from(generator, ctx, *self);
|
||||
ctx.builder.build_unconditional_branch(end_bb).unwrap();
|
||||
|
||||
// Reposition for continuation
|
||||
ctx.builder.position_at_end(end_bb);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
|
||||
/// Create a transposed view on this ndarray like `np.transpose(<ndarray>, <axes> = None)`.
|
||||
/// * `axes` - If specified, should be an array of the permutation (negative indices are **allowed**).
|
||||
#[must_use]
|
||||
pub fn transpose<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
axes: Option<Instance<'ctx, Ptr<Int<SizeT>>>>,
|
||||
) -> Self {
|
||||
// Define models
|
||||
let transposed_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
|
||||
|
||||
let num_axes = self.ndims_llvm(generator, ctx.ctx);
|
||||
|
||||
// `axes = nullptr` if `axes` is unspecified.
|
||||
let axes = axes.unwrap_or_else(|| Ptr(Int(SizeT)).nullptr(generator, ctx.ctx));
|
||||
|
||||
call_nac3_ndarray_transpose(
|
||||
generator,
|
||||
ctx,
|
||||
self.instance,
|
||||
transposed_ndarray.instance,
|
||||
num_axes,
|
||||
axes,
|
||||
);
|
||||
|
||||
transposed_ndarray
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,99 @@
|
|||
use inkwell::values::StructValue;
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
use super::any::AnyObject;
|
||||
|
||||
/// A NAC3 tuple object.
|
||||
///
|
||||
/// NOTE: This struct has no copy trait.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TupleObject<'ctx> {
|
||||
/// The type of the tuple.
|
||||
pub tys: Vec<Type>,
|
||||
/// The underlying LLVM struct value of this tuple.
|
||||
pub value: StructValue<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> TupleObject<'ctx> {
|
||||
pub fn from_object(ctx: &mut CodeGenContext<'ctx, '_>, object: AnyObject<'ctx>) -> Self {
|
||||
// TODO: Keep `is_vararg_ctx` from TTuple?
|
||||
|
||||
// Sanity check on object type.
|
||||
let TypeEnum::TTuple { ty: tys, .. } = &*ctx.unifier.get_ty(object.ty) else {
|
||||
panic!(
|
||||
"Expected type to be a TypeEnum::TTuple, got {}",
|
||||
ctx.unifier.stringify(object.ty)
|
||||
);
|
||||
};
|
||||
|
||||
// Check number of fields
|
||||
let value = object.value.into_struct_value();
|
||||
let value_num_fields = value.get_type().count_fields() as usize;
|
||||
assert!(
|
||||
value_num_fields == tys.len(),
|
||||
"Tuple type has {} item(s), but the LLVM struct value has {} field(s)",
|
||||
tys.len(),
|
||||
value_num_fields
|
||||
);
|
||||
|
||||
TupleObject { tys: tys.clone(), value }
|
||||
}
|
||||
|
||||
/// Convenience function. Create a [`TupleObject`] from an iterator of objects.
|
||||
pub fn from_objects<I, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
objects: I,
|
||||
) -> Self
|
||||
where
|
||||
I: IntoIterator<Item = AnyObject<'ctx>>,
|
||||
{
|
||||
let (values, tys): (Vec<_>, Vec<_>) =
|
||||
objects.into_iter().map(|object| (object.value, object.ty)).unzip();
|
||||
|
||||
let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
|
||||
let llvm_tuple_ty = ctx.ctx.struct_type(&llvm_tys, false);
|
||||
|
||||
let pllvm_tuple = ctx.builder.build_alloca(llvm_tuple_ty, "tuple").unwrap();
|
||||
for (i, val) in values.into_iter().enumerate() {
|
||||
let pval = ctx.builder.build_struct_gep(pllvm_tuple, i as u32, "value").unwrap();
|
||||
ctx.builder.build_store(pval, val).unwrap();
|
||||
}
|
||||
|
||||
let value = ctx.builder.build_load(pllvm_tuple, "").unwrap().into_struct_value();
|
||||
TupleObject { tys, value }
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn num_elements(&self) -> usize {
|
||||
self.tys.len()
|
||||
}
|
||||
|
||||
/// Get the `len()` of this tuple.
|
||||
#[must_use]
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Instance<'ctx, Int<SizeT>> {
|
||||
Int(SizeT).const_int(generator, ctx.ctx, self.num_elements() as u64)
|
||||
}
|
||||
|
||||
/// Get the `i`-th (0-based) object in this tuple.
|
||||
pub fn index(&self, ctx: &mut CodeGenContext<'ctx, '_>, i: usize) -> AnyObject<'ctx> {
|
||||
assert!(
|
||||
i < self.num_elements(),
|
||||
"Tuple object with length {} have index {i}",
|
||||
self.num_elements()
|
||||
);
|
||||
|
||||
let value = ctx.builder.build_extract_value(self.value, i as u32, "tuple[{i}]").unwrap();
|
||||
let ty = self.tys[i];
|
||||
AnyObject { ty, value }
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1 @@
|
|||
pub mod slice;
|
||||
|
|
@ -0,0 +1,125 @@
|
|||
use crate::codegen::{model::*, CodeGenContext, CodeGenerator};
|
||||
|
||||
/// Fields of [`Slice`]
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct SliceFields<'ctx, F: FieldTraversal<'ctx>, N: IntKind<'ctx>> {
|
||||
pub start_defined: F::Out<Int<Bool>>,
|
||||
pub start: F::Out<Int<N>>,
|
||||
pub stop_defined: F::Out<Int<Bool>>,
|
||||
pub stop: F::Out<Int<N>>,
|
||||
pub step_defined: F::Out<Int<Bool>>,
|
||||
pub step: F::Out<Int<N>>,
|
||||
}
|
||||
|
||||
/// An IRRT representation of an (unresolved) slice.
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct Slice<N>(pub N);
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> StructKind<'ctx> for Slice<N> {
|
||||
type Fields<F: FieldTraversal<'ctx>> = SliceFields<'ctx, F, N>;
|
||||
|
||||
fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
|
||||
Self::Fields {
|
||||
start_defined: traversal.add_auto("start_defined"),
|
||||
start: traversal.add("start", Int(self.0)),
|
||||
stop_defined: traversal.add_auto("stop_defined"),
|
||||
stop: traversal.add("stop", Int(self.0)),
|
||||
step_defined: traversal.add_auto("step_defined"),
|
||||
step: traversal.add("step", Int(self.0)),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// A Rust structure that has [`Slice`] utilities and looks like a [`Slice`] but
|
||||
/// `start`, `stop` and `step` are held by LLVM registers only and possibly
|
||||
/// [`Option::None`] if unspecified.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct RustSlice<'ctx, N: IntKind<'ctx>> {
|
||||
// It is possible that `start`, `stop`, and `step` are all `None`.
|
||||
// We need to know the `int_kind` even when that is the case.
|
||||
pub int_kind: N,
|
||||
pub start: Option<Instance<'ctx, Int<N>>>,
|
||||
pub stop: Option<Instance<'ctx, Int<N>>>,
|
||||
pub step: Option<Instance<'ctx, Int<N>>>,
|
||||
}
|
||||
|
||||
impl<'ctx, N: IntKind<'ctx>> RustSlice<'ctx, N> {
|
||||
/// Write the contents to an LLVM [`Slice`].
|
||||
pub fn write_to_slice<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dst_slice_ptr: Instance<'ctx, Ptr<Struct<Slice<N>>>>,
|
||||
) {
|
||||
let false_ = Int(Bool).const_false(generator, ctx.ctx);
|
||||
let true_ = Int(Bool).const_true(generator, ctx.ctx);
|
||||
|
||||
match self.start {
|
||||
Some(start) => {
|
||||
dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, true_);
|
||||
dst_slice_ptr.gep(ctx, |f| f.start).store(ctx, start);
|
||||
}
|
||||
None => dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, false_),
|
||||
}
|
||||
|
||||
match self.stop {
|
||||
Some(stop) => {
|
||||
dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, true_);
|
||||
dst_slice_ptr.gep(ctx, |f| f.stop).store(ctx, stop);
|
||||
}
|
||||
None => dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, false_),
|
||||
}
|
||||
|
||||
match self.step {
|
||||
Some(step) => {
|
||||
dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, true_);
|
||||
dst_slice_ptr.gep(ctx, |f| f.step).store(ctx, step);
|
||||
}
|
||||
None => dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, false_),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub mod util {
|
||||
use nac3parser::ast::Expr;
|
||||
|
||||
use crate::{
|
||||
codegen::{model::*, CodeGenContext, CodeGenerator},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
use super::RustSlice;
|
||||
|
||||
/// Generate LLVM IR for an [`ExprKind::Slice`] and convert it into a [`RustSlice`].
|
||||
#[allow(clippy::type_complexity)]
|
||||
pub fn gen_slice<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
lower: &Option<Box<Expr<Option<Type>>>>,
|
||||
upper: &Option<Box<Expr<Option<Type>>>>,
|
||||
step: &Option<Box<Expr<Option<Type>>>>,
|
||||
) -> Result<RustSlice<'ctx, Int32>, String> {
|
||||
let mut help = |value_expr: &Option<Box<Expr<Option<Type>>>>| -> Result<_, String> {
|
||||
Ok(match value_expr {
|
||||
None => None,
|
||||
Some(value_expr) => {
|
||||
let value_expr = generator
|
||||
.gen_expr(ctx, value_expr)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, ctx.primitives.int32)?;
|
||||
|
||||
let value_expr =
|
||||
Int(Int32).check_value(generator, ctx.ctx, value_expr).unwrap();
|
||||
|
||||
Some(value_expr)
|
||||
}
|
||||
})
|
||||
};
|
||||
|
||||
let start = help(lower)?;
|
||||
let stop = help(upper)?;
|
||||
let step = help(step)?;
|
||||
|
||||
Ok(RustSlice { int_kind: Int32, start, stop, step })
|
||||
}
|
||||
}
|
||||
|
|
@ -1,3 +1,27 @@
|
|||
use super::{
|
||||
super::symbol_resolver::ValueEnum,
|
||||
expr::destructure_range,
|
||||
irrt::{handle_slice_indices, list_slice_assignment},
|
||||
object::{
|
||||
any::AnyObject,
|
||||
ndarray::{
|
||||
indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject, ScalarOrNDArray,
|
||||
},
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
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 +407,47 @@ 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_ndindices(generator, ctx, key)?;
|
||||
|
||||
// Process value
|
||||
let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
|
||||
let value = AnyObject { value, ty: value_ty };
|
||||
|
||||
/*
|
||||
Reference code:
|
||||
```python
|
||||
target = target[key]
|
||||
value = np.asarray(value)
|
||||
|
||||
shape = np.broadcast_shape((target, value))
|
||||
|
||||
target = np.broadcast_to(target, shape)
|
||||
value = np.broadcast_to(value, shape)
|
||||
|
||||
...and finally copy 1-1 from value to target.
|
||||
```
|
||||
*/
|
||||
|
||||
let target = NDArrayObject::from_object(generator, ctx, target);
|
||||
let target = target.index(generator, ctx, &key);
|
||||
|
||||
let value =
|
||||
ScalarOrNDArray::split_object(generator, ctx, value).to_ndarray(generator, ctx);
|
||||
|
||||
let broadcast_result = NDArrayObject::broadcast(generator, ctx, &[target, value]);
|
||||
|
||||
let target = broadcast_result.ndarrays[0];
|
||||
let value = broadcast_result.ndarrays[1];
|
||||
|
||||
target.copy_data_from(generator, ctx, value);
|
||||
}
|
||||
_ => {
|
||||
panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
|
||||
|
|
@ -426,9 +462,7 @@ 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 +504,7 @@ 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);
|
||||
|
||||
|
|
@ -913,7 +947,7 @@ 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 +977,7 @@ 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 +1125,7 @@ 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 +1248,11 @@ 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();
|
||||
|
|
@ -1326,7 +1360,7 @@ 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 +1437,7 @@ 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 +1709,6 @@ 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 +1719,25 @@ 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();
|
||||
}
|
||||
|
|
@ -1770,30 +1806,7 @@ 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(());
|
||||
|
|
@ -1828,37 +1841,6 @@ pub fn gen_stmt<G: CodeGenerator>(
|
|||
stmt.location,
|
||||
);
|
||||
}
|
||||
StmtKind::Global { names, .. } => {
|
||||
let registered_globals = ctx
|
||||
.top_level
|
||||
.definitions
|
||||
.read()
|
||||
.iter()
|
||||
.filter_map(|def| {
|
||||
if let TopLevelDef::Variable { simple_name, ty, .. } = &*def.read() {
|
||||
Some((*simple_name, *ty))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
})
|
||||
.collect_vec();
|
||||
|
||||
for id in names {
|
||||
let Some((_, ty)) = registered_globals.iter().find(|(name, _)| name == id) else {
|
||||
return Err(format!("{id} is not a global at {}", stmt.location));
|
||||
};
|
||||
|
||||
let resolver = ctx.resolver.clone();
|
||||
let ptr = resolver
|
||||
.get_symbol_value(*id, ctx, generator)
|
||||
.map(|val| val.to_basic_value_enum(ctx, generator, *ty))
|
||||
.transpose()?
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
|
||||
ctx.var_assignment.insert(*id, (ptr, None, 0));
|
||||
}
|
||||
}
|
||||
_ => unimplemented!(),
|
||||
};
|
||||
Ok(())
|
||||
|
|
|
|||
|
|
@ -1,23 +1,6 @@
|
|||
use std::{
|
||||
collections::{HashMap, HashSet},
|
||||
sync::Arc,
|
||||
};
|
||||
|
||||
use indexmap::IndexMap;
|
||||
use indoc::indoc;
|
||||
use inkwell::{
|
||||
targets::{InitializationConfig, Target},
|
||||
OptimizationLevel,
|
||||
};
|
||||
use nac3parser::{
|
||||
ast::{fold::Fold, FileName, StrRef},
|
||||
parser::parse_program,
|
||||
};
|
||||
use parking_lot::RwLock;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{ListType, NDArrayType, ProxyType, RangeType},
|
||||
classes::{ListType, ProxyType, RangeType},
|
||||
concrete_type::ConcreteTypeStore,
|
||||
CodeGenContext, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask,
|
||||
CodeGenerator, DefaultCodeGenerator, WithCall, WorkerRegistry,
|
||||
|
|
@ -28,10 +11,24 @@ use crate::{
|
|||
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,
|
||||
|
|
@ -462,15 +456,3 @@ fn test_classes_range_type_new() {
|
|||
let llvm_range = RangeType::new(&ctx);
|
||||
assert!(RangeType::is_type(llvm_range.as_base_type()).is_ok());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_classes_ndarray_type_new() {
|
||||
let ctx = inkwell::context::Context::create();
|
||||
let generator = DefaultCodeGenerator::new(String::new(), 64);
|
||||
|
||||
let llvm_i32 = ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(&ctx);
|
||||
|
||||
let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into());
|
||||
assert!(NDArrayType::is_type(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
|
||||
}
|
||||
|
|
|
|||
|
|
@ -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>;
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
use std::iter::once;
|
||||
|
||||
use helper::{debug_assert_prim_is_allowed, extract_ndims, make_exception_fields, PrimDefDetails};
|
||||
use indexmap::IndexMap;
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
|
|
@ -8,17 +9,19 @@ use inkwell::{
|
|||
IntPredicate,
|
||||
};
|
||||
use itertools::Either;
|
||||
use numpy::unpack_ndarray_var_tys;
|
||||
use strum::IntoEnumIterator;
|
||||
|
||||
use super::{
|
||||
helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDefDetails},
|
||||
*,
|
||||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
builtin_fns,
|
||||
classes::{ProxyValue, RangeValue},
|
||||
model::*,
|
||||
numpy::*,
|
||||
object::{
|
||||
any::AnyObject,
|
||||
ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
|
||||
},
|
||||
stmt::exn_constructor,
|
||||
},
|
||||
symbol_resolver::SymbolValue,
|
||||
|
|
@ -26,6 +29,8 @@ use crate::{
|
|||
typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
|
||||
};
|
||||
|
||||
use super::*;
|
||||
|
||||
type BuiltinInfo = Vec<(Arc<RwLock<TopLevelDef>>, Option<Stmt>)>;
|
||||
|
||||
pub fn get_exn_constructor(
|
||||
|
|
@ -512,6 +517,14 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
| PrimDef::FunNpEye
|
||||
| PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
|
||||
|
||||
PrimDef::FunNpSize | PrimDef::FunNpShape | PrimDef::FunNpStrides => {
|
||||
self.build_ndarray_property_getter_function(prim)
|
||||
}
|
||||
|
||||
PrimDef::FunNpBroadcastTo | PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
|
||||
self.build_ndarray_view_function(prim)
|
||||
}
|
||||
|
||||
PrimDef::FunStr => self.build_str_function(),
|
||||
|
||||
PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
|
||||
|
|
@ -577,10 +590,6 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
| PrimDef::FunNpHypot
|
||||
| PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
|
||||
|
||||
PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
|
||||
self.build_np_sp_ndarray_function(prim)
|
||||
}
|
||||
|
||||
PrimDef::FunNpDot
|
||||
| PrimDef::FunNpLinalgCholesky
|
||||
| PrimDef::FunNpLinalgQr
|
||||
|
|
@ -1386,6 +1395,171 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
}
|
||||
}
|
||||
|
||||
fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(
|
||||
prim,
|
||||
&[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
|
||||
);
|
||||
|
||||
let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
|
||||
&[self.primitives.ndarray],
|
||||
Some("T".into()),
|
||||
None,
|
||||
);
|
||||
|
||||
match prim {
|
||||
PrimDef::FunNpSize => create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&into_var_map([in_ndarray_ty]),
|
||||
prim.name(),
|
||||
self.primitives.int32,
|
||||
&[(in_ndarray_ty.ty, "a")],
|
||||
Box::new(|ctx, obj, fun, args, generator| {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray =
|
||||
args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
|
||||
let size =
|
||||
ndarray.size(generator, ctx).truncate_or_bit_cast(generator, ctx, Int32);
|
||||
Ok(Some(size.value.as_basic_value_enum()))
|
||||
}),
|
||||
),
|
||||
PrimDef::FunNpShape | PrimDef::FunNpStrides => {
|
||||
// The function signatures of `np_shape` an `np_size` are the same.
|
||||
// Mixed together for convenience.
|
||||
|
||||
// The return type is a tuple of variable length depending on the ndims of the input ndarray.
|
||||
let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special folding
|
||||
|
||||
create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&into_var_map([in_ndarray_ty]),
|
||||
prim.name(),
|
||||
ret_ty,
|
||||
&[(in_ndarray_ty.ty, "a")],
|
||||
Box::new(move |ctx, obj, fun, args, generator| {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray =
|
||||
args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
|
||||
let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
|
||||
let result_tuple = match prim {
|
||||
PrimDef::FunNpShape => ndarray.make_shape_tuple(generator, ctx),
|
||||
PrimDef::FunNpStrides => ndarray.make_strides_tuple(generator, ctx),
|
||||
_ => unreachable!(),
|
||||
};
|
||||
|
||||
Ok(Some(result_tuple.value.as_basic_value_enum()))
|
||||
}),
|
||||
)
|
||||
}
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Build np/sp functions that take as input `NDArray` only
|
||||
fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(
|
||||
prim,
|
||||
&[PrimDef::FunNpBroadcastTo, PrimDef::FunNpTranspose, PrimDef::FunNpReshape],
|
||||
);
|
||||
|
||||
let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
|
||||
&[self.primitives.ndarray],
|
||||
Some("T".into()),
|
||||
None,
|
||||
);
|
||||
|
||||
match prim {
|
||||
PrimDef::FunNpTranspose => {
|
||||
create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&into_var_map([in_ndarray_ty]),
|
||||
prim.name(),
|
||||
in_ndarray_ty.ty,
|
||||
&[(in_ndarray_ty.ty, "x")],
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let arg_ty = fun.0.args[0].ty;
|
||||
let arg_val =
|
||||
args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
|
||||
|
||||
let arg = AnyObject { ty: arg_ty, value: arg_val };
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, arg);
|
||||
|
||||
let ndarray = ndarray.transpose(generator, ctx, None); // TODO: Add axes argument
|
||||
Ok(Some(ndarray.instance.value.as_basic_value_enum()))
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
|
||||
// the `param_ty` for `create_fn_by_codegen`.
|
||||
//
|
||||
// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
|
||||
// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
|
||||
// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
|
||||
PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape => {
|
||||
// These two functions have the same function signature.
|
||||
// Mixed together for convenience.
|
||||
|
||||
let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special holding
|
||||
|
||||
create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&VarMap::new(),
|
||||
prim.name(),
|
||||
ret_ty,
|
||||
&[
|
||||
(in_ndarray_ty.ty, "x"),
|
||||
(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"), // Handled by special folding
|
||||
],
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray_val =
|
||||
args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
|
||||
let shape_ty = fun.0.args[1].ty;
|
||||
let shape_val =
|
||||
args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
|
||||
|
||||
let ndarray = AnyObject { value: ndarray_val, ty: ndarray_ty };
|
||||
let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
|
||||
|
||||
let shape = AnyObject { value: shape_val, ty: shape_ty };
|
||||
let (_, shape) = parse_numpy_int_sequence(generator, ctx, shape);
|
||||
|
||||
// The ndims after reshaping is gotten from the return type of the call.
|
||||
let (_, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
|
||||
let new_ndarray = match prim {
|
||||
PrimDef::FunNpBroadcastTo => {
|
||||
ndarray.broadcast_to(generator, ctx, ndims, shape)
|
||||
}
|
||||
PrimDef::FunNpReshape => {
|
||||
ndarray.reshape_or_copy(generator, ctx, ndims, shape)
|
||||
}
|
||||
_ => unreachable!(),
|
||||
};
|
||||
Ok(Some(new_ndarray.instance.value.as_basic_value_enum()))
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Build the `str()` function.
|
||||
fn build_str_function(&mut self) -> TopLevelDef {
|
||||
let prim = PrimDef::FunStr;
|
||||
|
|
@ -1873,57 +2047,6 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
}
|
||||
}
|
||||
|
||||
/// Build np/sp functions that take as input `NDArray` only
|
||||
fn build_np_sp_ndarray_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
|
||||
|
||||
match prim {
|
||||
PrimDef::FunNpTranspose => {
|
||||
let ndarray_ty = self.unifier.get_fresh_var_with_range(
|
||||
&[self.ndarray_num_ty],
|
||||
Some("T".into()),
|
||||
None,
|
||||
);
|
||||
create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&into_var_map([ndarray_ty]),
|
||||
prim.name(),
|
||||
ndarray_ty.ty,
|
||||
&[(ndarray_ty.ty, "x")],
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let arg_ty = fun.0.args[0].ty;
|
||||
let arg_val =
|
||||
args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
|
||||
Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
|
||||
// the `param_ty` for `create_fn_by_codegen`.
|
||||
//
|
||||
// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
|
||||
// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
|
||||
// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
|
||||
PrimDef::FunNpReshape => create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&VarMap::new(),
|
||||
prim.name(),
|
||||
self.ndarray_num_ty,
|
||||
&[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let x1_ty = fun.0.args[0].ty;
|
||||
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
|
||||
let x2_ty = fun.0.args[1].ty;
|
||||
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
|
||||
Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
|
||||
}),
|
||||
),
|
||||
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Build `np_linalg` and `sp_linalg` functions
|
||||
///
|
||||
/// The input to these functions must be floating point `NDArray`
|
||||
|
|
@ -1955,10 +2078,12 @@ impl<'a> BuiltinBuilder<'a> {
|
|||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let x1_ty = fun.0.args[0].ty;
|
||||
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
|
||||
|
||||
let x2_ty = fun.0.args[1].ty;
|
||||
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
|
||||
|
||||
Ok(Some(ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
|
||||
let result = ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?;
|
||||
Ok(Some(result))
|
||||
}),
|
||||
),
|
||||
|
||||
|
|
|
|||
|
|
@ -1,17 +1,17 @@
|
|||
use nac3parser::ast::fold::Fold;
|
||||
use std::rc::Rc;
|
||||
|
||||
use nac3parser::ast::{fold::Fold, ExprKind};
|
||||
|
||||
use super::*;
|
||||
use crate::{
|
||||
codegen::{expr::get_subst_key, stmt::exn_constructor},
|
||||
symbol_resolver::SymbolValue,
|
||||
typecheck::{
|
||||
type_inferencer::{FunctionData, 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();
|
||||
|
||||
|
|
@ -382,58 +381,8 @@ impl TopLevelComposer {
|
|||
))
|
||||
}
|
||||
|
||||
ast::StmtKind::AnnAssign { target, annotation, .. } => {
|
||||
let ExprKind::Name { id: name, .. } = target.node else {
|
||||
return Err(format!(
|
||||
"global variable declaration must be an identifier (at {})",
|
||||
ast.location
|
||||
));
|
||||
};
|
||||
|
||||
if self.keyword_list.contains(&name) {
|
||||
return Err(format!(
|
||||
"cannot use keyword `{}` as a class name (at {})",
|
||||
name,
|
||||
ast.location
|
||||
));
|
||||
}
|
||||
|
||||
let global_var_name = if mod_path.is_empty() {
|
||||
name.to_string()
|
||||
} else {
|
||||
format!("{mod_path}.{name}")
|
||||
};
|
||||
if !defined_names.insert(global_var_name.clone()) {
|
||||
return Err(format!(
|
||||
"global variable `{}` defined twice (at {})",
|
||||
global_var_name,
|
||||
ast.location
|
||||
));
|
||||
}
|
||||
|
||||
let ty_to_be_unified = self.unifier.get_dummy_var().ty;
|
||||
self.definition_ast_list.push((
|
||||
RwLock::new(Self::make_top_level_variable_def(
|
||||
global_var_name,
|
||||
name,
|
||||
// dummy here, unify with correct type later,
|
||||
ty_to_be_unified,
|
||||
*(annotation.clone()),
|
||||
resolver,
|
||||
Some(ast.location),
|
||||
)).into(),
|
||||
None,
|
||||
));
|
||||
|
||||
Ok((
|
||||
name,
|
||||
DefinitionId(self.definition_ast_list.len() - 1),
|
||||
Some(ty_to_be_unified),
|
||||
))
|
||||
}
|
||||
|
||||
_ => Err(format!(
|
||||
"registrations of constructs other than top level classes/functions/variables are not supported (at {})",
|
||||
"registrations of constructs other than top level classes/functions are not supported (at {})",
|
||||
ast.location
|
||||
)),
|
||||
}
|
||||
|
|
@ -447,7 +396,6 @@ impl TopLevelComposer {
|
|||
if inference {
|
||||
self.analyze_function_instance()?;
|
||||
}
|
||||
self.analyze_top_level_variables()?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
|
|
@ -552,7 +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() {
|
||||
|
|
@ -906,6 +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;
|
||||
|
|
@ -1180,8 +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;
|
||||
|
|
@ -1756,6 +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 {
|
||||
|
|
@ -1875,12 +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([
|
||||
|
|
@ -1898,8 +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;
|
||||
|
|
@ -1949,8 +1889,7 @@ impl TopLevelComposer {
|
|||
} = &mut *function_def
|
||||
{
|
||||
let signature_ty_enum = unifier.get_ty(*signature);
|
||||
let TypeEnum::TFunc(FunSignature { args, ret, vars, .. }) =
|
||||
signature_ty_enum.as_ref()
|
||||
let TypeEnum::TFunc(FunSignature { args, ret, vars }) = signature_ty_enum.as_ref()
|
||||
else {
|
||||
unreachable!("must be typeenum::tfunc")
|
||||
};
|
||||
|
|
@ -2058,12 +1997,11 @@ impl TopLevelComposer {
|
|||
})
|
||||
};
|
||||
let mut identifiers = {
|
||||
let mut result = HashMap::new();
|
||||
let mut result: HashSet<_> = HashSet::new();
|
||||
if self_type.is_some() {
|
||||
result.insert("self".into(), IdentifierInfo::default());
|
||||
result.insert("self".into());
|
||||
}
|
||||
result
|
||||
.extend(inst_args.iter().map(|x| (x.name, IdentifierInfo::default())));
|
||||
result.extend(inst_args.iter().map(|x| x.name));
|
||||
result
|
||||
};
|
||||
let mut calls: HashMap<CodeLocation, CallId> = HashMap::new();
|
||||
|
|
@ -2114,16 +2052,6 @@ impl TopLevelComposer {
|
|||
instance_to_symbol.insert(String::new(), simple_name.to_string());
|
||||
continue;
|
||||
}
|
||||
if !decorator_list.is_empty() {
|
||||
if let ast::ExprKind::Call { func, .. } = &decorator_list[0].node {
|
||||
if matches!(&func.node,
|
||||
ast::ExprKind::Name{ id, .. } if id == &"rpc".into())
|
||||
{
|
||||
instance_to_symbol.insert(String::new(), simple_name.to_string());
|
||||
continue;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let fun_body = body
|
||||
.into_iter()
|
||||
|
|
@ -2228,57 +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 variable_def = &mut *variable_def.write();
|
||||
|
||||
let TopLevelDef::Variable { ty: dummy_ty, ty_decl, resolver, loc, .. } = variable_def
|
||||
else {
|
||||
// not top level variable def, skip
|
||||
return Ok(());
|
||||
};
|
||||
|
||||
let resolver = &**resolver.as_ref().unwrap();
|
||||
|
||||
let ty_annotation = parse_ast_to_type_annotation_kinds(
|
||||
resolver,
|
||||
&temp_def_list,
|
||||
unifier,
|
||||
primitives_store,
|
||||
ty_decl,
|
||||
HashMap::new(),
|
||||
)?;
|
||||
let ty_from_ty_annotation = get_type_from_type_annotation_kinds(
|
||||
&temp_def_list,
|
||||
unifier,
|
||||
primitives_store,
|
||||
&ty_annotation,
|
||||
&mut None,
|
||||
)?;
|
||||
|
||||
unifier.unify(*dummy_ty, ty_from_ty_annotation).map_err(|e| {
|
||||
HashSet::from([e.at(Some(loc.unwrap())).to_display(unifier).to_string()])
|
||||
})?;
|
||||
Ok(())
|
||||
};
|
||||
|
||||
let mut errors = HashSet::new();
|
||||
for (variable_def, _) in def_list.iter().skip(self.builtin_num) {
|
||||
if let Err(e) = analyze(variable_def) {
|
||||
errors.extend(e);
|
||||
}
|
||||
}
|
||||
if !errors.is_empty() {
|
||||
return Err(errors);
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,17 +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 ast::ExprKind;
|
||||
use nac3parser::ast::{Constant, Location};
|
||||
|
||||
use super::*;
|
||||
use crate::{
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::numpy::unpack_ndarray_var_tys,
|
||||
typecheck::typedef::{into_var_map, iter_type_vars, Mapping, TypeVarId, VarMap},
|
||||
};
|
||||
|
||||
/// All primitive types and functions in nac3core.
|
||||
#[derive(Clone, Copy, Debug, EnumIter, PartialEq, Eq)]
|
||||
|
|
@ -56,6 +52,16 @@ pub enum PrimDef {
|
|||
FunNpEye,
|
||||
FunNpIdentity,
|
||||
|
||||
// NumPy ndarray property getters
|
||||
FunNpSize,
|
||||
FunNpShape,
|
||||
FunNpStrides,
|
||||
|
||||
// NumPy ndarray view functions
|
||||
FunNpBroadcastTo,
|
||||
FunNpTranspose,
|
||||
FunNpReshape,
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
FunNpRound,
|
||||
FunNpFloor,
|
||||
|
|
@ -103,8 +109,6 @@ pub enum PrimDef {
|
|||
FunNpLdExp,
|
||||
FunNpHypot,
|
||||
FunNpNextAfter,
|
||||
FunNpTranspose,
|
||||
FunNpReshape,
|
||||
|
||||
// Linalg functions
|
||||
FunNpDot,
|
||||
|
|
@ -242,6 +246,16 @@ impl PrimDef {
|
|||
PrimDef::FunNpEye => fun("np_eye", None),
|
||||
PrimDef::FunNpIdentity => fun("np_identity", None),
|
||||
|
||||
// NumPy NDArray property getters,
|
||||
PrimDef::FunNpSize => fun("np_size", None),
|
||||
PrimDef::FunNpShape => fun("np_shape", None),
|
||||
PrimDef::FunNpStrides => fun("np_strides", None),
|
||||
|
||||
// NumPy NDArray view functions
|
||||
PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
|
||||
PrimDef::FunNpTranspose => fun("np_transpose", None),
|
||||
PrimDef::FunNpReshape => fun("np_reshape", None),
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
PrimDef::FunNpRound => fun("np_round", None),
|
||||
PrimDef::FunNpFloor => fun("np_floor", None),
|
||||
|
|
@ -289,8 +303,6 @@ 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),
|
||||
|
|
@ -391,9 +403,6 @@ impl TopLevelDef {
|
|||
r
|
||||
}
|
||||
),
|
||||
TopLevelDef::Variable { name, ty, .. } => {
|
||||
format!("Variable {{ name: {name:?}, ty: {:?} }}", unifier.stringify(*ty),)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -595,18 +604,6 @@ impl TopLevelComposer {
|
|||
}
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn make_top_level_variable_def(
|
||||
name: String,
|
||||
simple_name: StrRef,
|
||||
ty: Type,
|
||||
ty_decl: Expr,
|
||||
resolver: Option<Arc<dyn SymbolResolver + Send + Sync>>,
|
||||
loc: Option<Location>,
|
||||
) -> TopLevelDef {
|
||||
TopLevelDef::Variable { name, simple_name, ty, ty_decl, resolver, loc }
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn make_class_method_name(mut class_name: String, method_name: &str) -> String {
|
||||
class_name.push('.');
|
||||
|
|
@ -752,16 +749,7 @@ 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 {
|
||||
|
|
@ -797,138 +785,30 @@ 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!()
|
||||
|
|
@ -1136,3 +1016,23 @@ 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)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -6,23 +6,23 @@ 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 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);
|
||||
|
|
@ -148,25 +148,6 @@ 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.
|
||||
ty_decl: Expr,
|
||||
|
||||
/// Symbol resolver of the module defined the class.
|
||||
resolver: Option<Arc<dyn SymbolResolver + Send + Sync>>,
|
||||
|
||||
/// Definition location.
|
||||
loc: Option<Location>,
|
||||
},
|
||||
}
|
||||
|
||||
pub struct TopLevelContext {
|
||||
|
|
|
|||
|
|
@ -1,5 +1,3 @@
|
|||
use itertools::Itertools;
|
||||
|
||||
use crate::{
|
||||
toplevel::helper::PrimDef,
|
||||
typecheck::{
|
||||
|
|
@ -7,6 +5,7 @@ use crate::{
|
|||
typedef::{Type, TypeEnum, TypeVarId, Unifier, VarMap},
|
||||
},
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
/// Creates a `ndarray` [`Type`] with the given type arguments.
|
||||
///
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ expression: res_vec
|
|||
[
|
||||
"Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
|
||||
"Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(241)]\n}\n",
|
||||
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [\"aa\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ expression: res_vec
|
|||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B[typevar230]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar230\"]\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B[typevar246]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar246\"]\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",
|
||||
|
|
|
|||
|
|
@ -5,8 +5,8 @@ expression: res_vec
|
|||
[
|
||||
"Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(243)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(259)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(264)]\n}\n",
|
||||
"Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
|
|||
expression: res_vec
|
||||
---
|
||||
[
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[typevar229, typevar230]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar229\", \"typevar230\"]\n}\n",
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[typevar245, typevar246]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar245\", \"typevar246\"]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",
|
||||
|
|
|
|||
|
|
@ -6,12 +6,12 @@ expression: res_vec
|
|||
"Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(249)]\n}\n",
|
||||
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(265)]\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
|
||||
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(273)]\n}\n",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -1,24 +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::*;
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
use crate::typecheck::typedef::into_var_map;
|
||||
use crate::{
|
||||
codegen::CodeGenContext,
|
||||
symbol_resolver::{SymbolResolver, ValueEnum},
|
||||
toplevel::{helper::PrimDef, DefinitionId},
|
||||
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>>,
|
||||
|
|
@ -65,7 +62,6 @@ impl SymbolResolver for Resolver {
|
|||
&self,
|
||||
_: StrRef,
|
||||
_: &mut CodeGenContext<'ctx, '_>,
|
||||
_: &mut dyn CodeGenerator,
|
||||
) -> Option<ValueEnum<'ctx>> {
|
||||
unimplemented!()
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,13 +1,9 @@
|
|||
use strum::IntoEnumIterator;
|
||||
|
||||
use nac3parser::ast::Constant;
|
||||
|
||||
use super::*;
|
||||
use crate::{
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::helper::{PrimDef, PrimDefDetails},
|
||||
typecheck::typedef::VarMap,
|
||||
};
|
||||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::helper::{PrimDef, PrimDefDetails};
|
||||
use crate::typecheck::typedef::VarMap;
|
||||
use nac3parser::ast::Constant;
|
||||
use strum::IntoEnumIterator;
|
||||
|
||||
#[derive(Clone, Debug)]
|
||||
pub enum TypeAnnotation {
|
||||
|
|
|
|||
|
|
@ -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::{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>> {
|
||||
|
|
@ -27,15 +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 !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(())
|
||||
|
|
@ -75,7 +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 {
|
||||
|
|
@ -96,7 +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(),
|
||||
|
|
@ -104,7 +98,7 @@ impl<'a> Inferencer<'a> {
|
|||
*id,
|
||||
) {
|
||||
Ok(_) => {
|
||||
self.defined_identifiers.insert(*id, IdentifierInfo::default());
|
||||
self.defined_identifiers.insert(*id);
|
||||
}
|
||||
Err(e) => {
|
||||
return Err(HashSet::from([format!(
|
||||
|
|
@ -177,7 +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)?;
|
||||
}
|
||||
|
|
@ -240,7 +236,7 @@ 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, .. } => {
|
||||
|
|
@ -266,11 +262,9 @@ 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)
|
||||
|
|
@ -301,7 +295,7 @@ 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)?;
|
||||
}
|
||||
|
|
@ -365,40 +359,6 @@ 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.is_global {
|
||||
return Err(HashSet::from([format!(
|
||||
"name '{id}' is assigned to before global declaration at {}",
|
||||
stmt.location,
|
||||
)]));
|
||||
}
|
||||
|
||||
continue;
|
||||
}
|
||||
|
||||
match self.function_data.resolver.get_symbol_type(
|
||||
self.unifier,
|
||||
&self.top_level.definitions.read(),
|
||||
self.primitives,
|
||||
*id,
|
||||
) {
|
||||
Ok(_) => {
|
||||
self.defined_identifiers
|
||||
.insert(*id, IdentifierInfo { is_global: true });
|
||||
}
|
||||
Err(e) => {
|
||||
return Err(HashSet::from([format!(
|
||||
"type error at identifier `{}` ({}) at {}",
|
||||
id, e, stmt.location
|
||||
)]))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Ok(false)
|
||||
}
|
||||
// break, raise, etc.
|
||||
_ => Ok(false),
|
||||
}
|
||||
|
|
@ -407,7 +367,7 @@ 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 +1,19 @@
|
|||
use std::{cmp::max, collections::HashMap, rc::Rc};
|
||||
|
||||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::helper::{extract_ndims, PrimDef};
|
||||
use crate::toplevel::numpy::{make_ndarray_ty, unpack_ndarray_var_tys};
|
||||
use crate::typecheck::{
|
||||
type_inferencer::*,
|
||||
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
|
||||
};
|
||||
use itertools::{iproduct, Itertools};
|
||||
use nac3parser::ast::StrRef;
|
||||
use nac3parser::ast::{Cmpop, Operator, Unaryop};
|
||||
use std::cmp::max;
|
||||
use std::collections::HashMap;
|
||||
use std::rc::Rc;
|
||||
use strum::IntoEnumIterator;
|
||||
|
||||
use nac3parser::ast::{Cmpop, Operator, StrRef, Unaryop};
|
||||
|
||||
use crate::{
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::{
|
||||
helper::PrimDef,
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
},
|
||||
typecheck::{
|
||||
type_inferencer::*,
|
||||
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
|
||||
},
|
||||
};
|
||||
use super::typedef::into_var_map;
|
||||
|
||||
/// The variant of a binary operator.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
|
|
@ -175,19 +173,8 @@ pub fn impl_binop(
|
|||
ops: &[Operator],
|
||||
) {
|
||||
with_fields(unifier, ty, |unifier, fields| {
|
||||
let (other_ty, other_var_id) = if other_ty.len() == 1 {
|
||||
(other_ty[0], None)
|
||||
} else {
|
||||
let tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
|
||||
(tvar.ty, Some(tvar.id))
|
||||
};
|
||||
|
||||
let function_vars = if let Some(var_id) = other_var_id {
|
||||
vec![(var_id, other_ty)].into_iter().collect::<VarMap>()
|
||||
} else {
|
||||
VarMap::new()
|
||||
};
|
||||
|
||||
let other_tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
|
||||
let function_vars = into_var_map([other_tvar]);
|
||||
let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).ty);
|
||||
|
||||
for (base_op, variant) in iproduct!(ops, [BinopVariant::Normal, BinopVariant::AugAssign]) {
|
||||
|
|
@ -198,7 +185,7 @@ pub fn impl_binop(
|
|||
ret: ret_ty,
|
||||
vars: function_vars.clone(),
|
||||
args: vec![FuncArg {
|
||||
ty: other_ty,
|
||||
ty: other_tvar.ty,
|
||||
default_value: None,
|
||||
name: "other".into(),
|
||||
is_vararg: false,
|
||||
|
|
@ -524,53 +511,41 @@ 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, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
|
||||
let lhs_ndims = extract_ndims(unifier, lhs_ndims);
|
||||
|
||||
let (rhs_dtype, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
|
||||
let rhs_ndims = extract_ndims(unifier, rhs_ndims);
|
||||
|
||||
if !(unifier.unioned(lhs_dtype, primitives.float)
|
||||
&& unifier.unioned(rhs_dtype, primitives.float))
|
||||
{
|
||||
return Err(format!(
|
||||
"ndarray.__matmul__ only supports float64 operations, but LHS has type {} and RHS has type {}",
|
||||
unifier.stringify(lhs),
|
||||
unifier.stringify(rhs)
|
||||
));
|
||||
}
|
||||
|
||||
let (_, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
|
||||
let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
|
||||
TypeEnum::TLiteral { values, .. } => {
|
||||
assert_eq!(values.len(), 1);
|
||||
u64::try_from(values[0].clone()).unwrap()
|
||||
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))
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -773,7 +748,7 @@ 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], None);
|
||||
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);
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
use std::{collections::HashMap, fmt::Display};
|
||||
use std::collections::HashMap;
|
||||
use std::fmt::Display;
|
||||
|
||||
use itertools::Itertools;
|
||||
|
||||
use nac3parser::ast::{Cmpop, Location, StrRef};
|
||||
use crate::typecheck::{magic_methods::HasOpInfo, typedef::TypeEnum};
|
||||
|
||||
use super::{
|
||||
magic_methods::Binop,
|
||||
typedef::{RecordKey, Type, Unifier},
|
||||
};
|
||||
use crate::typecheck::{magic_methods::HasOpInfo, typedef::TypeEnum};
|
||||
use itertools::Itertools;
|
||||
use nac3parser::ast::{Cmpop, Location, StrRef};
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum TypeErrorKind {
|
||||
|
|
|
|||
|
|
@ -1,25 +1,14 @@
|
|||
use std::{
|
||||
cell::RefCell,
|
||||
cmp::max,
|
||||
collections::{HashMap, HashSet},
|
||||
convert::{From, TryInto},
|
||||
iter::once,
|
||||
sync::Arc,
|
||||
};
|
||||
|
||||
use itertools::{izip, Itertools};
|
||||
|
||||
use nac3parser::ast::{
|
||||
self,
|
||||
fold::{self, Fold},
|
||||
Arguments, Comprehension, ExprContext, ExprKind, Located, Location, StrRef,
|
||||
};
|
||||
use std::cmp::max;
|
||||
use std::collections::{HashMap, HashSet};
|
||||
use std::convert::{From, TryInto};
|
||||
use std::iter::{self, once};
|
||||
use std::{cell::RefCell, sync::Arc};
|
||||
|
||||
use super::{
|
||||
magic_methods::*,
|
||||
type_error::{TypeError, TypeErrorKind},
|
||||
typedef::{
|
||||
into_var_map, iter_type_vars, Call, CallId, FunSignature, FuncArg, Mapping, OperatorInfo,
|
||||
into_var_map, iter_type_vars, Call, CallId, FunSignature, FuncArg, OperatorInfo,
|
||||
RecordField, RecordKey, Type, TypeEnum, TypeVar, Unifier, VarMap,
|
||||
},
|
||||
};
|
||||
|
|
@ -28,9 +17,15 @@ use crate::{
|
|||
toplevel::{
|
||||
helper::{arraylike_flatten_element_type, arraylike_get_ndims, PrimDef},
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
type_annotation::TypeAnnotation,
|
||||
TopLevelContext, TopLevelDef,
|
||||
},
|
||||
typecheck::typedef::Mapping,
|
||||
};
|
||||
use itertools::{izip, Itertools};
|
||||
use nac3parser::ast::{
|
||||
self,
|
||||
fold::{self, Fold},
|
||||
Arguments, Comprehension, ExprContext, ExprKind, Located, Location, StrRef,
|
||||
};
|
||||
|
||||
#[cfg(test)]
|
||||
|
|
@ -88,20 +83,6 @@ impl PrimitiveStore {
|
|||
}
|
||||
}
|
||||
|
||||
/// Information regarding a defined identifier.
|
||||
#[derive(Clone, Copy, Debug, Default)]
|
||||
pub struct IdentifierInfo {
|
||||
/// Whether this identifier refers to a global variable.
|
||||
pub is_global: bool,
|
||||
}
|
||||
|
||||
impl IdentifierInfo {
|
||||
#[must_use]
|
||||
pub fn new() -> IdentifierInfo {
|
||||
IdentifierInfo::default()
|
||||
}
|
||||
}
|
||||
|
||||
pub struct FunctionData {
|
||||
pub resolver: Arc<dyn SymbolResolver + Send + Sync>,
|
||||
pub return_type: Option<Type>,
|
||||
|
|
@ -110,7 +91,7 @@ pub struct FunctionData {
|
|||
|
||||
pub struct Inferencer<'a> {
|
||||
pub top_level: &'a TopLevelContext,
|
||||
pub defined_identifiers: HashMap<StrRef, IdentifierInfo>,
|
||||
pub defined_identifiers: HashSet<StrRef>,
|
||||
pub function_data: &'a mut FunctionData,
|
||||
pub unifier: &'a mut Unifier,
|
||||
pub primitives: &'a PrimitiveStore,
|
||||
|
|
@ -121,7 +102,6 @@ pub struct Inferencer<'a> {
|
|||
}
|
||||
|
||||
type InferenceError = HashSet<String>;
|
||||
type OverrideResult = Result<Option<ast::Expr<Option<Type>>>, InferenceError>;
|
||||
|
||||
struct NaiveFolder();
|
||||
impl Fold<()> for NaiveFolder {
|
||||
|
|
@ -242,7 +222,9 @@ impl<'a> Fold<()> for Inferencer<'a> {
|
|||
handler.location,
|
||||
));
|
||||
if let Some(name) = name {
|
||||
self.defined_identifiers.entry(name).or_default();
|
||||
if !self.defined_identifiers.contains(&name) {
|
||||
self.defined_identifiers.insert(name);
|
||||
}
|
||||
if let Some(old_typ) = self.variable_mapping.insert(name, typ) {
|
||||
let loc = handler.location;
|
||||
self.unifier.unify(old_typ, typ).map_err(|e| {
|
||||
|
|
@ -394,7 +376,6 @@ impl<'a> Fold<()> for Inferencer<'a> {
|
|||
| ast::StmtKind::Continue { .. }
|
||||
| ast::StmtKind::Expr { .. }
|
||||
| ast::StmtKind::For { .. }
|
||||
| ast::StmtKind::Global { .. }
|
||||
| ast::StmtKind::Pass { .. }
|
||||
| ast::StmtKind::Try { .. } => {}
|
||||
ast::StmtKind::If { test, .. } | ast::StmtKind::While { test, .. } => {
|
||||
|
|
@ -566,7 +547,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
|
|||
unreachable!("must be tobj")
|
||||
}
|
||||
} else {
|
||||
if !self.defined_identifiers.contains_key(id) {
|
||||
if !self.defined_identifiers.contains(id) {
|
||||
match self.function_data.resolver.get_symbol_type(
|
||||
self.unifier,
|
||||
&self.top_level.definitions.read(),
|
||||
|
|
@ -574,7 +555,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
|
|||
*id,
|
||||
) {
|
||||
Ok(_) => {
|
||||
self.defined_identifiers.insert(*id, IdentifierInfo::default());
|
||||
self.defined_identifiers.insert(*id);
|
||||
}
|
||||
Err(e) => {
|
||||
return report_error(
|
||||
|
|
@ -639,8 +620,8 @@ impl<'a> Inferencer<'a> {
|
|||
fn infer_pattern<T>(&mut self, pattern: &ast::Expr<T>) -> Result<(), InferenceError> {
|
||||
match &pattern.node {
|
||||
ExprKind::Name { id, .. } => {
|
||||
if !self.defined_identifiers.contains_key(id) {
|
||||
self.defined_identifiers.insert(*id, IdentifierInfo::default());
|
||||
if !self.defined_identifiers.contains(id) {
|
||||
self.defined_identifiers.insert(*id);
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
|
@ -749,8 +730,8 @@ impl<'a> Inferencer<'a> {
|
|||
let mut defined_identifiers = self.defined_identifiers.clone();
|
||||
for arg in &args.args {
|
||||
let name = &arg.node.arg;
|
||||
if !defined_identifiers.contains_key(name) {
|
||||
defined_identifiers.insert(*name, IdentifierInfo::default());
|
||||
if !defined_identifiers.contains(name) {
|
||||
defined_identifiers.insert(*name);
|
||||
}
|
||||
}
|
||||
let fn_args: Vec<_> = args
|
||||
|
|
@ -1200,6 +1181,45 @@ impl<'a> Inferencer<'a> {
|
|||
}));
|
||||
}
|
||||
|
||||
if ["np_shape".into(), "np_strides".into()].contains(id) && args.len() == 1 {
|
||||
let ndarray = self.fold_expr(args.remove(0))?;
|
||||
|
||||
let ndims = arraylike_get_ndims(self.unifier, ndarray.custom.unwrap());
|
||||
|
||||
// Make a tuple of size `ndims` full of int32 (TODO: Make it usize)
|
||||
let ret_ty = TypeEnum::TTuple {
|
||||
ty: iter::repeat(self.primitives.int32).take(ndims as usize).collect_vec(),
|
||||
is_vararg_ctx: false,
|
||||
};
|
||||
let ret_ty = self.unifier.add_ty(ret_ty);
|
||||
|
||||
let func_ty = TypeEnum::TFunc(FunSignature {
|
||||
args: vec![FuncArg {
|
||||
name: "a".into(),
|
||||
default_value: None,
|
||||
ty: ndarray.custom.unwrap(),
|
||||
is_vararg: false,
|
||||
}],
|
||||
ret: ret_ty,
|
||||
vars: VarMap::new(),
|
||||
});
|
||||
let func_ty = self.unifier.add_ty(func_ty);
|
||||
|
||||
return Ok(Some(Located {
|
||||
location,
|
||||
custom: Some(ret_ty),
|
||||
node: ExprKind::Call {
|
||||
func: Box::new(Located {
|
||||
custom: Some(func_ty),
|
||||
location: func.location,
|
||||
node: ExprKind::Name { id: *id, ctx: *ctx },
|
||||
}),
|
||||
args: vec![ndarray],
|
||||
keywords: vec![],
|
||||
},
|
||||
}));
|
||||
}
|
||||
|
||||
if id == &"np_dot".into() {
|
||||
let arg0 = self.fold_expr(args.remove(0))?;
|
||||
let arg1 = self.fold_expr(args.remove(0))?;
|
||||
|
|
@ -1521,7 +1541,7 @@ impl<'a> Inferencer<'a> {
|
|||
}));
|
||||
}
|
||||
// 2-argument ndarray n-dimensional factory functions
|
||||
if id == &"np_reshape".into() && args.len() == 2 {
|
||||
if ["np_reshape".into(), "np_broadcast_to".into()].contains(id) && args.len() == 2 {
|
||||
let arg0 = self.fold_expr(args.remove(0))?;
|
||||
|
||||
let shape_expr = args.remove(0);
|
||||
|
|
@ -1567,29 +1587,36 @@ impl<'a> Inferencer<'a> {
|
|||
}
|
||||
// 2-argument ndarray n-dimensional creation functions
|
||||
if id == &"np_full".into() && args.len() == 2 {
|
||||
// Parse arguments
|
||||
let shape_expr = args.remove(0);
|
||||
let (ndims, shape) =
|
||||
self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?; // Special handling for `shape`
|
||||
let ExprKind::List { elts, .. } = &args[0].node else {
|
||||
return report_error(
|
||||
format!(
|
||||
"Expected List literal for first argument of {id}, got {}",
|
||||
args[0].node.name()
|
||||
)
|
||||
.as_str(),
|
||||
args[0].location,
|
||||
);
|
||||
};
|
||||
|
||||
let fill_value = self.fold_expr(args.remove(0))?;
|
||||
let ndims = elts.len() as u64;
|
||||
|
||||
// Build the return type
|
||||
let dtype = fill_value.custom.unwrap();
|
||||
let arg0 = self.fold_expr(args.remove(0))?;
|
||||
let arg1 = self.fold_expr(args.remove(0))?;
|
||||
|
||||
let ty = arg1.custom.unwrap();
|
||||
let ndims = self.unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None);
|
||||
let ret = make_ndarray_ty(self.unifier, self.primitives, Some(dtype), Some(ndims));
|
||||
|
||||
let ret = make_ndarray_ty(self.unifier, self.primitives, Some(ty), Some(ndims));
|
||||
let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
|
||||
args: vec![
|
||||
FuncArg {
|
||||
name: "shape".into(),
|
||||
ty: shape.custom.unwrap(),
|
||||
ty: arg0.custom.unwrap(),
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
},
|
||||
FuncArg {
|
||||
name: "fill_value".into(),
|
||||
ty: fill_value.custom.unwrap(),
|
||||
ty: arg1.custom.unwrap(),
|
||||
default_value: None,
|
||||
is_vararg: false,
|
||||
},
|
||||
|
|
@ -1607,7 +1634,7 @@ impl<'a> Inferencer<'a> {
|
|||
location: func.location,
|
||||
node: ExprKind::Name { id: *id, ctx: *ctx },
|
||||
}),
|
||||
args: vec![shape, fill_value],
|
||||
args: vec![arg0, arg1],
|
||||
keywords: vec![],
|
||||
},
|
||||
}));
|
||||
|
|
@ -1684,86 +1711,6 @@ impl<'a> Inferencer<'a> {
|
|||
Ok(None)
|
||||
}
|
||||
|
||||
/// Checks whether a class method is calling parent function
|
||||
/// Returns [`None`] if its not a call to parent method, otherwise
|
||||
/// returns a new `func` with class name replaced by `self` and method resolved to its `DefinitionID`
|
||||
///
|
||||
/// e.g. A.f1(self, ...) returns Some(self.{DefintionID(f1)})
|
||||
fn check_overriding(&mut self, func: &ast::Expr<()>, args: &[ast::Expr<()>]) -> OverrideResult {
|
||||
// `self` must be first argument for call to parent method
|
||||
if let Some(Located { node: ExprKind::Name { id, .. }, .. }) = &args.first() {
|
||||
if *id != "self".into() {
|
||||
return Ok(None);
|
||||
}
|
||||
} else {
|
||||
return Ok(None);
|
||||
}
|
||||
|
||||
let Located {
|
||||
node: ExprKind::Attribute { value, attr: method_name, ctx }, location, ..
|
||||
} = func
|
||||
else {
|
||||
return Ok(None);
|
||||
};
|
||||
let ExprKind::Name { id: class_name, ctx: class_ctx } = &value.node else {
|
||||
return Ok(None);
|
||||
};
|
||||
let zelf = &self.fold_expr(args[0].clone())?;
|
||||
|
||||
// Check whether the method belongs to class ancestors
|
||||
let def_id = self.unifier.get_ty(zelf.custom.unwrap());
|
||||
let TypeEnum::TObj { obj_id, .. } = def_id.as_ref() else { unreachable!() };
|
||||
let defs = self.top_level.definitions.read();
|
||||
let res = {
|
||||
if let TopLevelDef::Class { ancestors, .. } = &*defs[obj_id.0].read() {
|
||||
let res = ancestors.iter().find_map(|f| {
|
||||
let TypeAnnotation::CustomClass { id, .. } = f else { unreachable!() };
|
||||
let TopLevelDef::Class { name, methods, .. } = &*defs[id.0].read() else {
|
||||
unreachable!()
|
||||
};
|
||||
// Class names are stored as `__module__.class`
|
||||
let name = name.to_string();
|
||||
let (_, name) = name.rsplit_once('.').unwrap();
|
||||
if name == class_name.to_string() {
|
||||
return methods.iter().find_map(|f| {
|
||||
if f.0 == *method_name {
|
||||
return Some(*f);
|
||||
}
|
||||
None
|
||||
});
|
||||
}
|
||||
None
|
||||
});
|
||||
res
|
||||
} else {
|
||||
None
|
||||
}
|
||||
};
|
||||
|
||||
match res {
|
||||
Some(r) => {
|
||||
let mut new_func = func.clone();
|
||||
let mut new_value = value.clone();
|
||||
new_value.node = ExprKind::Name { id: "self".into(), ctx: *class_ctx };
|
||||
new_func.node =
|
||||
ExprKind::Attribute { value: new_value.clone(), attr: *method_name, ctx: *ctx };
|
||||
|
||||
let mut new_func = self.fold_expr(new_func)?;
|
||||
|
||||
let ExprKind::Attribute { value, .. } = new_func.node else { unreachable!() };
|
||||
new_func.node =
|
||||
ExprKind::Attribute { value, attr: r.2 .0.to_string().into(), ctx: *ctx };
|
||||
new_func.custom = Some(r.1);
|
||||
|
||||
Ok(Some(new_func))
|
||||
}
|
||||
None => report_error(
|
||||
format!("Ancestor method [{class_name}.{method_name}] should be defined with same decorator as its overridden version").as_str(),
|
||||
*location,
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
fn fold_call(
|
||||
&mut self,
|
||||
location: Location,
|
||||
|
|
@ -1777,20 +1724,8 @@ impl<'a> Inferencer<'a> {
|
|||
return Ok(spec_call_func);
|
||||
}
|
||||
|
||||
// Check for call to parent method
|
||||
let override_res = self.check_overriding(&func, &args)?;
|
||||
let is_override = override_res.is_some();
|
||||
let func = if is_override { override_res.unwrap() } else { self.fold_expr(func)? };
|
||||
let func = Box::new(func);
|
||||
|
||||
let mut args =
|
||||
args.into_iter().map(|v| self.fold_expr(v)).collect::<Result<Vec<_>, _>>()?;
|
||||
|
||||
// TODO: Handle passing of self to functions to allow runtime lookup of functions to be called
|
||||
// Currently removing `self` and using compile time function definitions
|
||||
if is_override {
|
||||
args.remove(0);
|
||||
}
|
||||
let func = Box::new(self.fold_expr(func)?);
|
||||
let args = args.into_iter().map(|v| self.fold_expr(v)).collect::<Result<Vec<_>, _>>()?;
|
||||
let keywords = keywords
|
||||
.into_iter()
|
||||
.map(|v| fold::fold_keyword(self, v))
|
||||
|
|
|
|||
|
|
@ -1,19 +1,17 @@
|
|||
use std::iter::zip;
|
||||
|
||||
use indexmap::IndexMap;
|
||||
use indoc::indoc;
|
||||
use parking_lot::RwLock;
|
||||
use test_case::test_case;
|
||||
|
||||
use nac3parser::{ast::FileName, parser::parse_program};
|
||||
|
||||
use super::super::{magic_methods::with_fields, typedef::*};
|
||||
use super::*;
|
||||
use crate::{
|
||||
codegen::{CodeGenContext, CodeGenerator},
|
||||
codegen::CodeGenContext,
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{helper::PrimDef, DefinitionId, TopLevelDef},
|
||||
typecheck::{magic_methods::with_fields, typedef::*},
|
||||
};
|
||||
use indexmap::IndexMap;
|
||||
use indoc::indoc;
|
||||
use nac3parser::ast::FileName;
|
||||
use nac3parser::parser::parse_program;
|
||||
use parking_lot::RwLock;
|
||||
use std::iter::zip;
|
||||
use test_case::test_case;
|
||||
|
||||
struct Resolver {
|
||||
id_to_type: HashMap<StrRef, Type>,
|
||||
|
|
@ -43,7 +41,6 @@ impl SymbolResolver for Resolver {
|
|||
&self,
|
||||
_: StrRef,
|
||||
_: &mut CodeGenContext<'ctx, '_>,
|
||||
_: &mut dyn CodeGenerator,
|
||||
) -> Option<ValueEnum<'ctx>> {
|
||||
unimplemented!()
|
||||
}
|
||||
|
|
@ -520,7 +517,7 @@ impl TestEnvironment {
|
|||
primitives: &mut self.primitives,
|
||||
virtual_checks: &mut self.virtual_checks,
|
||||
calls: &mut self.calls,
|
||||
defined_identifiers: HashMap::default(),
|
||||
defined_identifiers: HashSet::default(),
|
||||
in_handler: false,
|
||||
}
|
||||
}
|
||||
|
|
@ -596,9 +593,8 @@ fn test_basic(source: &str, mapping: &HashMap<&str, &str>, virtuals: &[(&str, &s
|
|||
println!("source:\n{source}");
|
||||
let mut env = TestEnvironment::new();
|
||||
let id_to_name = std::mem::take(&mut env.id_to_name);
|
||||
let mut defined_identifiers: HashMap<_, _> =
|
||||
env.identifier_mapping.keys().copied().map(|id| (id, IdentifierInfo::default())).collect();
|
||||
defined_identifiers.insert("virtual".into(), IdentifierInfo::default());
|
||||
let mut defined_identifiers: HashSet<_> = env.identifier_mapping.keys().copied().collect();
|
||||
defined_identifiers.insert("virtual".into());
|
||||
let mut inferencer = env.get_inferencer();
|
||||
inferencer.defined_identifiers.clone_from(&defined_identifiers);
|
||||
let statements = parse_program(source, FileName::default()).unwrap();
|
||||
|
|
@ -743,9 +739,8 @@ fn test_primitive_magic_methods(source: &str, mapping: &HashMap<&str, &str>) {
|
|||
println!("source:\n{source}");
|
||||
let mut env = TestEnvironment::basic_test_env();
|
||||
let id_to_name = std::mem::take(&mut env.id_to_name);
|
||||
let mut defined_identifiers: HashMap<_, _> =
|
||||
env.identifier_mapping.keys().copied().map(|id| (id, IdentifierInfo::default())).collect();
|
||||
defined_identifiers.insert("virtual".into(), IdentifierInfo::default());
|
||||
let mut defined_identifiers: HashSet<_> = env.identifier_mapping.keys().copied().collect();
|
||||
defined_identifiers.insert("virtual".into());
|
||||
let mut inferencer = env.get_inferencer();
|
||||
inferencer.defined_identifiers.clone_from(&defined_identifiers);
|
||||
let statements = parse_program(source, FileName::default()).unwrap();
|
||||
|
|
|
|||
|
|
@ -1,28 +1,21 @@
|
|||
use std::{
|
||||
borrow::Cow,
|
||||
cell::RefCell,
|
||||
collections::{HashMap, HashSet},
|
||||
fmt::{self, Display},
|
||||
iter::{repeat, zip},
|
||||
rc::Rc,
|
||||
sync::{Arc, Mutex},
|
||||
};
|
||||
|
||||
use super::magic_methods::{Binop, HasOpInfo};
|
||||
use super::type_error::{TypeError, TypeErrorKind};
|
||||
use super::unification_table::{UnificationKey, UnificationTable};
|
||||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
use crate::toplevel::{DefinitionId, TopLevelContext, TopLevelDef};
|
||||
use crate::typecheck::magic_methods::OpInfo;
|
||||
use crate::typecheck::type_inferencer::PrimitiveStore;
|
||||
use indexmap::IndexMap;
|
||||
use itertools::{repeat_n, Itertools};
|
||||
|
||||
use nac3parser::ast::{Cmpop, Location, StrRef, Unaryop};
|
||||
|
||||
use super::{
|
||||
magic_methods::{Binop, HasOpInfo},
|
||||
type_error::{TypeError, TypeErrorKind},
|
||||
unification_table::{UnificationKey, UnificationTable},
|
||||
};
|
||||
use crate::{
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::{helper::PrimDef, DefinitionId, TopLevelContext, TopLevelDef},
|
||||
typecheck::{magic_methods::OpInfo, type_inferencer::PrimitiveStore},
|
||||
};
|
||||
use std::cell::RefCell;
|
||||
use std::collections::HashMap;
|
||||
use std::fmt::{self, Display};
|
||||
use std::iter::{repeat, zip};
|
||||
use std::rc::Rc;
|
||||
use std::sync::{Arc, Mutex};
|
||||
use std::{borrow::Cow, collections::HashSet};
|
||||
|
||||
#[cfg(test)]
|
||||
mod test;
|
||||
|
|
@ -677,8 +670,8 @@ impl Unifier {
|
|||
let num_args = posargs.len() + kwargs.len();
|
||||
|
||||
// Now we check the arguments against the parameters,
|
||||
// and depending on what `call_info` is, we might change how `unify_call()` behaves
|
||||
// to improve user error messages when type checking fails.
|
||||
// and depending on what `call_info` is, we might change how the behavior `unify_call()`
|
||||
// in hopes to improve user error messages when type checking fails.
|
||||
match operator_info {
|
||||
Some(OperatorInfo::IsBinaryOp { self_type, operator }) => {
|
||||
// The call is written in the form of (say) `a + b`.
|
||||
|
|
|
|||
|
|
@ -1,12 +1,10 @@
|
|||
use std::collections::HashMap;
|
||||
|
||||
use super::super::magic_methods::with_fields;
|
||||
use super::*;
|
||||
use indoc::indoc;
|
||||
use itertools::Itertools;
|
||||
use std::collections::HashMap;
|
||||
use test_case::test_case;
|
||||
|
||||
use super::*;
|
||||
use crate::typecheck::magic_methods::with_fields;
|
||||
|
||||
impl Unifier {
|
||||
/// Check whether two types are equal.
|
||||
fn eq(&mut self, a: Type, b: Type) -> bool {
|
||||
|
|
|
|||
|
|
@ -21,12 +21,13 @@
|
|||
clippy::wildcard_imports
|
||||
)]
|
||||
|
||||
use std::{collections::HashMap, mem, ptr, slice, str};
|
||||
|
||||
use byteorder::{ByteOrder, LittleEndian};
|
||||
|
||||
use dwarf::*;
|
||||
use elf::*;
|
||||
use std::collections::HashMap;
|
||||
use std::{mem, ptr, slice, str};
|
||||
|
||||
extern crate byteorder;
|
||||
use byteorder::{ByteOrder, LittleEndian};
|
||||
|
||||
mod dwarf;
|
||||
mod elf;
|
||||
|
|
|
|||
|
|
@ -8,15 +8,15 @@ license = "MIT"
|
|||
edition = "2021"
|
||||
|
||||
[build-dependencies]
|
||||
lalrpop = "0.22"
|
||||
lalrpop = "0.20"
|
||||
|
||||
[dependencies]
|
||||
nac3ast = { path = "../nac3ast" }
|
||||
lalrpop-util = "0.22"
|
||||
lalrpop-util = "0.20"
|
||||
log = "0.4"
|
||||
unic-emoji-char = "0.9"
|
||||
unic-ucd-ident = "0.9"
|
||||
unicode_names2 = "1.3"
|
||||
unicode_names2 = "1.2"
|
||||
phf = { version = "0.11", features = ["macros"] }
|
||||
ahash = "0.8"
|
||||
|
||||
|
|
|
|||
|
|
@ -1,10 +1,8 @@
|
|||
use crate::{
|
||||
ast::{Ident, Location},
|
||||
error::*,
|
||||
token::Tok,
|
||||
};
|
||||
use crate::ast::Ident;
|
||||
use crate::ast::Location;
|
||||
use crate::error::*;
|
||||
use crate::token::Tok;
|
||||
use lalrpop_util::ParseError;
|
||||
|
||||
use nac3ast::*;
|
||||
|
||||
pub fn make_config_comment(
|
||||
|
|
|
|||
|
|
@ -1,11 +1,12 @@
|
|||
//! Define internal parse error types
|
||||
//! The goal is to provide a matching and a safe error API, maksing errors from LALR
|
||||
use std::error::Error;
|
||||
use std::fmt;
|
||||
|
||||
use lalrpop_util::ParseError as LalrpopError;
|
||||
|
||||
use crate::{ast::Location, token::Tok};
|
||||
use crate::ast::Location;
|
||||
use crate::token::Tok;
|
||||
|
||||
use std::error::Error;
|
||||
use std::fmt;
|
||||
|
||||
/// Represents an error during lexical scanning.
|
||||
#[derive(Debug, PartialEq)]
|
||||
|
|
|
|||
|
|
@ -1,11 +1,12 @@
|
|||
use std::{iter, mem, str};
|
||||
use std::iter;
|
||||
use std::mem;
|
||||
use std::str;
|
||||
|
||||
use crate::ast::{Constant, ConversionFlag, Expr, ExprKind, Location};
|
||||
use crate::error::{FStringError, FStringErrorType, ParseError};
|
||||
use crate::parser::parse_expression;
|
||||
|
||||
use self::FStringErrorType::*;
|
||||
use crate::{
|
||||
ast::{Constant, ConversionFlag, Expr, ExprKind, Location},
|
||||
error::{FStringError, FStringErrorType, ParseError},
|
||||
parser::parse_expression,
|
||||
};
|
||||
|
||||
struct FStringParser<'a> {
|
||||
chars: iter::Peekable<str::Chars<'a>>,
|
||||
|
|
|
|||
|
|
@ -1,11 +1,8 @@
|
|||
use ahash::RandomState;
|
||||
use std::collections::HashSet;
|
||||
|
||||
use ahash::RandomState;
|
||||
|
||||
use crate::{
|
||||
ast,
|
||||
error::{LexicalError, LexicalErrorType},
|
||||
};
|
||||
use crate::ast;
|
||||
use crate::error::{LexicalError, LexicalErrorType};
|
||||
|
||||
pub struct ArgumentList {
|
||||
pub args: Vec<ast::Expr>,
|
||||
|
|
|
|||
Some files were not shown because too many files have changed in this diff Show More
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Reference in New Issue