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

53 Commits
master ... ndstrides-

Author SHA1 Message Date
lyken 4e9facd457
standalone/ndarray: improve {reshape,broadcast_to,transpose} tests 
Print their shapes and exhaustively print all contents.
 2024-08-30 15:15:21 +08:00
lyken b3a66948fa
standalone/ndarray: add and organize view function tests 2024-08-30 15:15:07 +08:00
lyken 7fb5e10ee2
core/ndstrides: update builtin_fns to use ndarray with strides 2024-08-30 15:14:29 +08:00
lyken 1de3f05734
core/ndstrides: add NDArrayObject::to_any 2024-08-30 15:10:03 +08:00
lyken 3426e0c9c2
core/ndstrides: add ContiguousNDArray 
Currently this is used to interop with nalgebra.
 2024-08-30 15:09:54 +08:00
lyken 4baf1c64ed
core/ndstrides: implement np_dot() for scalars and 1D 2024-08-30 15:07:44 +08:00
lyken c93f05f74b
core/ndstrides: implement general matmul 2024-08-30 15:06:28 +08:00
lyken e86718d3d9
core/ndstrides: implement cmpop 2024-08-30 14:59:34 +08:00
lyken a43ec47530
core/ndstrides: implement unary op 2024-08-30 14:57:26 +08:00
lyken bc88819eaf
core/ndstrides: implement binop 2024-08-30 14:57:11 +08:00
lyken c6d3620431
core/ndstrides: add NDArrayOut, broadcast_map and map 2024-08-30 14:57:02 +08:00
lyken cea512456a
core/ndstrides: implement subscript assignment 
Overlapping is not handled. Currently it has undefined behavior.
 2024-08-30 14:54:53 +08:00
lyken b22f2bc76c
core/ndstrides: add more ScalarOrNDArray and NDArrayObject utils 2024-08-30 14:52:57 +08:00
lyken b9e837109b
core/ndstrides: implement np_transpose() (no axes argument) 
The IRRT implementation knows how to handle axes. But the argument is
not in NAC3 yet.
 2024-08-30 14:50:48 +08:00
lyken d32268fb5d
core/ndstrides: implement broadcasting & np_broadcast_to() 2024-08-30 14:45:43 +08:00
lyken 916a2b4993
core/ndstrides: implement np_reshape() 2024-08-30 14:41:54 +08:00
lyken c7c3cc21a8
core: categorize np_{transpose,reshape} as 'view functions' 2024-08-30 14:41:54 +08:00
lyken d2072d9248
core/ndstrides: implement np_size() 2024-08-30 14:41:00 +08:00
lyken be19165ead
core/ndstrides: implement np_shape() and np_strides() 
These functions are not important, but they are handy for debugging.

`np.strides()` is not an actual NumPy function, but `ndarray.strides` is used.
 2024-08-30 14:41:00 +08:00
lyken ee58cf3fc3
core/ndstrides: implement ndarray.fill() and .copy() 2024-08-30 14:41:00 +08:00
lyken 8fe8ccf200
core/ndstrides: implement np_identity() and np_eye() 2024-08-30 14:41:00 +08:00
lyken d222236492
core/ndstrides: implement np_array() 
It also checks for inconsistent dimensions if the input is a list.
e.g., rejecting `[[1.0, 2.0], [3.0]]`.

However, currently only `np_array(<input>, copy=False)` and `np_array(<input>, copy=True)` are supported. In NumPy, copy could be false, true, or None. Right now, NAC3's `np_array(<input>, copy=False)` behaves like NumPy's `np.array(<input>, copy=None)`.
 2024-08-30 14:40:15 +08:00
lyken 13715dbda9
core/irrt: add List 
Needed for implementing np_array()
 2024-08-30 14:20:19 +08:00
lyken 7910de10a1
core/ndstrides: add NDArrayObject::atleast_nd 2024-08-30 14:18:34 +08:00
lyken 6edc3f895b
core/ndstrides: add NDArrayObject::make_copy 2024-08-30 14:18:17 +08:00
lyken a40cdde8d2
core/ndstrides: implement ndarray indexing 
The functionality for `...` and `np.newaxis` is there in IRRT, but there
is no implementation of them for @kernel Python expressions because of
M-Labs/nac3#486.
 2024-08-30 14:12:54 +08:00
lyken 853fa39537
core/irrt: rename NDIndex to NDIndexInt 
Unfortunately the name `NDIndex` is used in later commits. Renaming this
typedef to `NDIndexInt` to avoid amending. `NDIndexInt` will be removed
anyway when ndarray strides is completed.
 2024-08-30 14:00:19 +08:00
lyken b6a1880226
core/irrt: add Slice and Range 
Needed for implementing general ndarray indexing.

Currently IRRT slice and range have nothing to do with NAC3's slice
and range. The IRRT slice and range are currently there to implement
ndarray specific features. However, in the future their definitions may
be used to replace that of NAC3's. (NAC3's range is a [i32 x 3], IRRT's
range is a proper struct. NAC3 does not have a slice struct).
 2024-08-30 13:57:10 +08:00
lyken d1c75c7444
core/ndstrides: implement len(ndarray) & refactor len() 2024-08-30 13:45:25 +08:00
lyken 58c5bc56b9
core/ndstrides: implement np_{zeros,ones,full,empty} 2024-08-30 13:44:12 +08:00
lyken ddc0e44c61
core/model: add util::gen_for_model 2024-08-30 13:42:39 +08:00
lyken 549536f72c
core/object: add ListObject and TupleObject 
Needed for implementing other ndarray utils.
 2024-08-30 13:41:31 +08:00
lyken 40c42b571a
core/ndstrides: implement ndarray iterator NDIter 
A necessary utility to iterate through all elements in a possibly strided ndarray.
 2024-08-30 13:39:10 +08:00
lyken 92e7103ec7
core/ndstrides: introduce NDArray 
NDArray with strides.
 2024-08-30 13:24:45 +08:00
lyken 9bc5e96dba
core/irrt: fix exception.hpp C++ castings 2024-08-30 13:15:07 +08:00
lyken 78639b1030
core/toplevel/helper: add {extract,create}_ndims 2024-08-30 13:05:16 +08:00
lyken 9723c17e24
core/object: introduce object 
A small abstraction to simplify implementations.
 2024-08-30 13:04:54 +08:00
lyken d1c7a8ee50
StructKind::{traverse -> iter}_fields 2024-08-30 12:51:17 +08:00
lyken e0524c19eb
Newline "Otherwise, it will be caught..." 2024-08-30 12:51:17 +08:00
lyken 32822f9052
gep_index must be u32 2024-08-30 12:51:17 +08:00
lyken 6283036815
FieldTraversal::{Out -> Output} 2024-08-30 12:51:17 +08:00
lyken f167f5f215
Ptr::copy_from to use SizeT 2024-08-30 12:51:17 +08:00
lyken baf8ee2b3d
Ptr::offset_const offset i64, can be negative 2024-08-30 12:51:17 +08:00
lyken d68760447f
Int::const_int to have sign_extend 2024-08-30 12:51:17 +08:00
lyken fdd194ee2a
FnCall::{begin -> builder} 2024-08-30 12:51:17 +08:00
lyken 5fca81c68e
CallFunction -> FnCall 2024-08-30 12:51:17 +08:00
lyken 0562e9a385
Instance add newline 2024-08-30 12:51:17 +08:00
lyken 36af473816
unsafe Model::believe_value 2024-08-30 12:51:17 +08:00
lyken 7c7e1b3ab8
Model::{sizeof -> size_of} 2024-08-30 12:51:17 +08:00
lyken dbcfc9538a
ArrayLen::{get_length -> length} 2024-08-30 12:51:17 +08:00
lyken 5c4ba09e2f
LenKind -> ArrayLen 2024-08-30 12:51:17 +08:00
lyken eb34b99ee9
core/model: renaming and add notes on upgrading Ptr to LLVM 15 2024-08-30 12:51:17 +08:00
lyken d397b9ceaa
core/model: introduce models 2024-08-30 12:51:17 +08:00
101 changed files with 8546 additions and 3993 deletions
Show Stats Expand all files Collapse all files

8
.pre-commit-config.yaml
View File

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

333
Cargo.lock generated
View File

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checksum = "4fa4d8d74483041a882adaa9a29f633253a66dde85055f0495c121620ac484b2"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.79",
"syn 2.0.76",
]
[[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"
version = "0.1.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ecc2af9a1119c51f12a14607e783cb977bde58bc069ff0c3da1095e635d70654"
dependencies = [
"cpufeatures",
]
[[package]]
name = "lalrpop"
version = "0.22.0"
version = "0.20.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "06093b57658c723a21da679530e061a8c25340fa5a6f98e313b542268c7e2a1f"
checksum = "55cb077ad656299f160924eb2912aa147d7339ea7d69e1b5517326fdcec3c1ca"
dependencies = [
"ascii-canvas",
"bit-set",
"ena",
"itertools",
"itertools 0.11.0",
"lalrpop-util",
"petgraph",
"pico-args",
"regex",
"regex-syntax",
"sha3",
"string_cache",
"term",
"tiny-keccak",
"unicode-xid",
"walkdir",
]
[[package]]
name = "lalrpop-util"
version = "0.22.0"
version = "0.20.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "feee752d43abd0f4807a921958ab4131f692a44d4d599733d4419c5d586176ce"
checksum = "507460a910eb7b32ee961886ff48539633b788a36b65692b95f225b844c82553"
dependencies = [
"regex-automata",
"rustversion",
]
[[package]]
@ -547,9 +510,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
[[package]]
name = "libc"
version = "0.2.159"
version = "0.2.158"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "561d97a539a36e26a9a5fad1ea11a3039a67714694aaa379433e580854bc3dc5"
checksum = "d8adc4bb1803a324070e64a98ae98f38934d91957a99cfb3a43dcbc01bc56439"
[[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 2.0.76",
]
[[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 2.0.76",
]
[[package]]
@ -895,7 +869,7 @@ dependencies = [
"proc-macro2",
"pyo3-build-config",
"quote",
"syn 2.0.79",
"syn 2.0.76",
]
[[package]]
@ -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"
checksum = "2a908a6e00f1fdd0dfd9c0eb08ce85126f6d8bbda50017e74bc4a4b7d4a926a4"
dependencies = [
"bitflags",
]
[[package]]
name = "regex"
version = "1.11.0"
name = "redox_users"
version = "0.4.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "38200e5ee88914975b69f657f0801b6f6dccafd44fd9326302a4aaeecfacb1d8"
checksum = "ba009ff324d1fc1b900bd1fdb31564febe58a8ccc8a6fdbb93b543d33b13ca43"
dependencies = [
"getrandom",
"libredox",
"thiserror",
]
[[package]]
name = "regex"
version = "1.10.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "4219d74c6b67a3654a9fbebc4b419e22126d13d2f3c4a07ee0cb61ff79a79619"
dependencies = [
"aho-corasick",
"memchr",
@ -980,9 +965,9 @@ dependencies = [
[[package]]
name = "regex-automata"
version = "0.4.8"
version = "0.4.7"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "368758f23274712b504848e9d5a6f010445cc8b87a7cdb4d7cbee666c1288da3"
checksum = "38caf58cc5ef2fed281f89292ef23f6365465ed9a41b7a7754eb4e26496c92df"
dependencies = [
"aho-corasick",
"memchr",
@ -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"
checksum = "2b15c43186be67a4fd63bee50d0303afffcef381492ebe2c5d87f324e1b8815c"
checksum = "7a66a03ae7c801facd77a29370b4faec201768915ac14a721ba36f20bc9c209b"
[[package]]
name = "runkernel"
@ -1004,9 +989,9 @@ dependencies = [
[[package]]
name = "rustix"
version = "0.38.37"
version = "0.38.35"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "8acb788b847c24f28525660c4d7758620a7210875711f79e7f663cc152726811"
checksum = "a85d50532239da68e9addb745ba38ff4612a242c1c7ceea689c4bc7c2f43c36f"
dependencies = [
"bitflags",
"errno",
@ -1050,29 +1035,29 @@ checksum = "61697e0a1c7e512e84a621326239844a24d8207b4669b41bc18b32ea5cbf988b"
[[package]]
name = "serde"
version = "1.0.210"
version = "1.0.209"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "c8e3592472072e6e22e0a54d5904d9febf8508f65fb8552499a1abc7d1078c3a"
checksum = "99fce0ffe7310761ca6bf9faf5115afbc19688edd00171d81b1bb1b116c63e09"
dependencies = [
"serde_derive",
]
[[package]]
name = "serde_derive"
version = "1.0.210"
version = "1.0.209"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "243902eda00fad750862fc144cea25caca5e20d615af0a81bee94ca738f1df1f"
checksum = "a5831b979fd7b5439637af1752d535ff49f4860c0f341d1baeb6faf0f4242170"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.79",
"syn 2.0.76",
]
[[package]]
name = "serde_json"
version = "1.0.128"
version = "1.0.127"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "6ff5456707a1de34e7e37f2a6fd3d3f808c318259cbd01ab6377795054b483d8"
checksum = "8043c06d9f82bd7271361ed64f415fe5e12a77fdb52e573e7f06a516dea329ad"
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 2.0.76",
]
[[package]]
@ -1188,9 +1163,9 @@ dependencies = [
[[package]]
name = "syn"
version = "2.0.79"
version = "2.0.76"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "89132cd0bf050864e1d38dc3bbc07a0eb8e7530af26344d3d2bbbef83499f590"
checksum = "578e081a14e0cefc3279b0472138c513f37b41a08d5a3cca9b6e4e8ceb6cd525"
dependencies = [
"proc-macro2",
"quote",
@ -1205,9 +1180,9 @@ checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1"
[[package]]
name = "tempfile"
version = "3.13.0"
version = "3.12.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "f0f2c9fc62d0beef6951ccffd757e241266a2c833136efbe35af6cd2567dca5b"
checksum = "04cbcdd0c794ebb0d4cf35e88edd2f7d2c4c3e9a5a6dab322839b321c6a87a64"
dependencies = [
"cfg-if",
"fastrand",
@ -1218,12 +1193,13 @@ dependencies = [
[[package]]
name = "term"
version = "1.0.0"
version = "0.7.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "4df4175de05129f31b80458c6df371a15e7fc3fd367272e6bf938e5c351c7ea0"
checksum = "c59df8ac95d96ff9bede18eb7300b0fda5e5d8d90960e76f8e14ae765eedbf1f"
dependencies = [
"home",
"windows-sys 0.52.0",
"dirs-next",
"rustversion",
"winapi",
]
[[package]]
@ -1241,29 +1217,32 @@ dependencies = [
[[package]]
name = "thiserror"
version = "1.0.64"
version = "1.0.63"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "d50af8abc119fb8bb6dbabcfa89656f46f84aa0ac7688088608076ad2b459a84"
checksum = "c0342370b38b6a11b6cc11d6a805569958d54cfa061a29969c3b5ce2ea405724"
dependencies = [
"thiserror-impl",
]
[[package]]
name = "thiserror-impl"
version = "1.0.64"
version = "1.0.63"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "08904e7672f5eb876eaaf87e0ce17857500934f4981c4a0ab2b4aa98baac7fc3"
checksum = "a4558b58466b9ad7ca0f102865eccc95938dca1a74a856f2b57b6629050da261"
dependencies = [
"proc-macro2",
"quote",
"syn 2.0.79",
"syn 2.0.76",
]
[[package]]
name = "typenum"
version = "1.17.0"
name = "tiny-keccak"
version = "2.0.2"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825"
checksum = "2c9d3793400a45f954c52e73d068316d76b6f4e36977e3fcebb13a2721e80237"
dependencies = [
"crunchy",
]
[[package]]
name = "unic-char-property"
@ -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 = "0.2.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ebc1c04c71510c7f702b52b7c350734c9ff1295c464a03335b00bb84fc54f853"
checksum = "229730647fbc343e3a80e463c1db7f78f3855d3f3739bee0dda773c9a037c90a"
[[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 2.0.76",
]

6
flake.lock
View File

@ -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": {

8
nac3artiq/Cargo.toml
View File

@ -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"]

13
nac3artiq/demo/min_artiq.py
View File

@ -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."""

101
nac3artiq/src/codegen.rs
View File

@ -1,17 +1,3 @@
use std::{
collections::{hash_map::DefaultHasher, HashMap},
hash::{Hash, Hasher},
iter::once,
mem,
sync::Arc,
};
use itertools::Itertools;
use pyo3::{
types::{PyDict, PyList},
PyObject, PyResult, Python,
};
use nac3core::{
codegen::{
classes::{
@ -24,21 +10,38 @@ use nac3core::{
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 nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef};
use inkwell::{
context::Context,
module::Linkage,
types::{BasicType, IntType},
values::{BasicValueEnum, PointerValue, StructValue},
AddressSpace, IntPredicate, OptimizationLevel,
};
use pyo3::{
types::{PyDict, PyList},
PyObject, PyResult, Python,
};
use crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
use inkwell::values::IntValue;
use itertools::Itertools;
use std::{
collections::{hash_map::DefaultHasher, HashMap},
hash::{Hash, Hasher},
iter::once,
mem,
sync::Arc,
};
/// The parallelism mode within a block.
#[derive(Copy, Clone, Eq, PartialEq)]
enum ParallelMode {
@ -458,8 +461,8 @@ fn format_rpc_arg<'ctx>(
let llvm_usize = generator.get_size_type(ctx.ctx);
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
let llvm_arg_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
let llvm_arg_ty =
NDArrayType::new(generator, ctx.ctx, ctx.get_llvm_type(generator, elem_ty));
let llvm_arg = NDArrayValue::from_ptr_val(arg.into_pointer_value(), llvm_usize, None);
let llvm_usize_sizeof = ctx
@ -469,7 +472,7 @@ fn format_rpc_arg<'ctx>(
let llvm_pdata_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(
llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
llvm_arg_ty.element_type().ptr_type(AddressSpace::default()).size_of(),
llvm_usize,
"",
)
@ -512,7 +515,7 @@ 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()
}
@ -627,7 +630,7 @@ fn format_rpc_ret<'ctx>(
let llvm_pdata_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(
llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
llvm_ret_ty.element_type().size_of().unwrap(),
llvm_usize,
"",
)
@ -659,7 +662,7 @@ fn format_rpc_ret<'ctx>(
.unwrap();
let buffer = ctx
.builder
.build_bit_cast(buffer, llvm_pi8, "")
.build_bitcast(buffer, llvm_pi8, "")
.map(BasicValueEnum::into_pointer_value)
.unwrap();
let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, None);
@ -782,7 +785,7 @@ fn format_rpc_ret<'ctx>(
_ => {
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();
let slotgen = ctx.builder.build_bitcast(slot, llvm_pi8, "rpc.ret.ptr").unwrap();
ctx.builder.build_unconditional_branch(head_bb).unwrap();
ctx.builder.position_at_end(head_bb);
@ -803,7 +806,7 @@ fn format_rpc_ret<'ctx>(
let alloc_ptr =
ctx.builder.build_array_alloca(llvm_pi8, alloc_size, "rpc.alloc").unwrap();
let alloc_ptr =
ctx.builder.build_bit_cast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
ctx.builder.build_bitcast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
ctx.builder.build_unconditional_branch(head_bb).unwrap();
@ -821,7 +824,6 @@ fn rpc_codegen_callback_fn<'ctx>(
fun: (&FunSignature, DefinitionId),
args: Vec<(Option<StrRef>, ValueEnum<'ctx>)>,
generator: &mut dyn CodeGenerator,
is_async: bool,
) -> Result<Option<BasicValueEnum<'ctx>>, String> {
let int8 = ctx.ctx.i8_type();
let int32 = ctx.ctx.i32_type();
@ -930,29 +932,6 @@ 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",
@ -970,15 +949,10 @@ fn rpc_codegen_callback_fn<'ctx>(
ctx.builder
.build_call(rpc_send, &[service_id.into(), tag_ptr.into(), args_ptr.into()], "rpc.send")
.unwrap();
}
// reclaim stack space used by arguments
call_stackrestore(ctx, stackptr);
if is_async {
// async RPCs do not return any values
Ok(None)
} else {
let result = format_rpc_ret(generator, ctx, fun.0.ret);
if !result.is_some_and(|res| res.get_type().is_pointer_type()) {
@ -987,7 +961,6 @@ fn rpc_codegen_callback_fn<'ctx>(
}
Ok(result)
}
}
pub fn attributes_writeback(
@ -1082,7 +1055,7 @@ 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 +1065,9 @@ 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)
})))
}

162
nac3artiq/src/lib.rs
View File

@ -16,30 +16,14 @@
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 tempfile::{self, TempDir};
use nac3core::{
codegen::{
concrete_type::ConcreteTypeStore, gen_func_impl, irrt::load_irrt, CodeGenLLVMOptions,
CodeGenTargetMachineOptions, CodeGenTask, WithCall, WorkerRegistry,
},
inkwell::{
use inkwell::{
context::Context,
memory_buffer::MemoryBuffer,
module::{Linkage, Module},
@ -47,22 +31,33 @@ use nac3core::{
support::is_multithreaded,
targets::*,
OptimizationLevel,
},
nac3parser::{
ast::{Constant, ExprKind, Located, Stmt, StmtKind, StrRef},
};
use itertools::Itertools;
use nac3core::codegen::{gen_func_impl, CodeGenLLVMOptions, CodeGenTargetMachineOptions};
use nac3core::toplevel::builtins::get_exn_constructor;
use nac3core::typecheck::typedef::{into_var_map, TypeEnum, Unifier, VarMap};
use nac3parser::{
ast::{ExprKind, Stmt, StmtKind, StrRef},
parser::parse_program,
},
};
use pyo3::create_exception;
use pyo3::prelude::*;
use pyo3::{exceptions, types::PyBytes, types::PyDict, types::PySet};
use parking_lot::{Mutex, RwLock};
use nac3core::{
codegen::irrt::load_irrt,
codegen::{concrete_type::ConcreteTypeStore, CodeGenTask, WithCall, WorkerRegistry},
symbol_resolver::SymbolResolver,
toplevel::{
builtins::get_exn_constructor,
composer::{BuiltinFuncCreator, BuiltinFuncSpec, ComposerConfig, TopLevelComposer},
DefinitionId, GenCall, TopLevelDef,
},
typecheck::{
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 +65,15 @@ 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 +194,10 @@ 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 +210,9 @@ 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
}
@ -478,25 +478,9 @@ 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 +488,19 @@ 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();
@ -579,7 +556,7 @@ impl Nac3 {
.unwrap();
// Process IRRT
let context = Context::create();
let context = inkwell::context::Context::create();
let irrt = load_irrt(&context, resolver.as_ref());
let fun_signature =
@ -619,12 +596,13 @@ 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 +613,7 @@ 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 +628,6 @@ impl Nac3 {
}
}
}
TopLevelDef::Variable { .. } => {
return Err(CompileError::new_err(String::from(
"Unsupported @rpc annotation on global variable",
)))
}
}
}
}
@ -714,7 +687,7 @@ 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 +706,7 @@ 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 +844,6 @@ 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(),

42
nac3artiq/src/symbol_resolver.rs
View File

@ -1,30 +1,16 @@
use std::{
collections::{HashMap, HashSet},
sync::{
atomic::{AtomicBool, Ordering::Relaxed},
Arc,
},
use crate::PrimitivePythonId;
use inkwell::{
module::Linkage,
types::{BasicType, BasicTypeEnum},
values::BasicValueEnum,
AddressSpace,
};
use itertools::Itertools;
use parking_lot::RwLock;
use pyo3::{
types::{PyDict, PyTuple},
PyAny, PyObject, PyResult, Python,
};
use nac3core::{
codegen::{
classes::{NDArrayType, ProxyType},
CodeGenContext, CodeGenerator,
},
inkwell::{
module::Linkage,
types::{BasicType, BasicTypeEnum},
values::BasicValueEnum,
AddressSpace,
},
nac3parser::ast::{self, StrRef},
symbol_resolver::{StaticValue, SymbolResolver, SymbolValue, ValueEnum},
toplevel::{
helper::PrimDef,
@ -36,8 +22,19 @@ 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::RwLock;
use pyo3::{
types::{PyDict, PyTuple},
PyAny, PyObject, PyResult, Python,
};
use std::{
collections::{HashMap, HashSet},
sync::{
atomic::{AtomicBool, Ordering::Relaxed},
Arc,
},
};
pub enum PrimitiveValue {
I32(i32),
@ -1470,7 +1467,6 @@ 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();

19
nac3artiq/src/timeline.rs
View File

@ -1,12 +1,9 @@
use itertools::Either;
use nac3core::{
codegen::CodeGenContext,
inkwell::{
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();

8
nac3core/Cargo.toml
View File

@ -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"

3
nac3core/build.rs
View File

@ -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);

10
nac3core/irrt/irrt.cpp
View File

@ -3,4 +3,14 @@
#include "irrt/list.hpp"
#include "irrt/math.hpp"
#include "irrt/ndarray.hpp"
#include "irrt/range.hpp"
#include "irrt/slice.hpp"
#include "irrt/ndarray/basic.hpp"
#include "irrt/ndarray/def.hpp"
#include "irrt/ndarray/iter.hpp"
#include "irrt/ndarray/indexing.hpp"
#include "irrt/ndarray/array.hpp"
#include "irrt/ndarray/reshape.hpp"
#include "irrt/ndarray/broadcast.hpp"
#include "irrt/ndarray/transpose.hpp"
#include "irrt/ndarray/matmul.hpp"

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

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

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

@ -1,22 +1,13 @@
#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;
using NDIndexInt = uint32_t;
// The type of an index or a value describing the length of a range/slice is always `int32_t`.
using SliceIndex = int32_t;

15
nac3core/irrt/irrt/list.hpp
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@ -2,6 +2,21 @@
#include "irrt/int_types.hpp"
#include "irrt/math_util.hpp"
#include "irrt/slice.hpp"
namespace {
/**
* @brief A list in NAC3.
*
* The `items` field is opaque. You must rely on external contexts to
* know how to interpret it.
*/
template<typename SizeT>
struct List {
uint8_t* items;
SizeT len;
};
} // namespace
extern "C" {
// Handle list assignment and dropping part of the list when

33
nac3core/irrt/irrt/ndarray.hpp
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@ -2,6 +2,8 @@
#include "irrt/int_types.hpp"
// TODO: To be deleted since NDArray with strides is done.
namespace {
template<typename SizeT>
SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len, SizeT begin_idx, SizeT end_idx) {
@ -17,7 +19,7 @@ SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len, Size
}
template<typename SizeT>
void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT num_dims, NDIndex* idxs) {
void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT num_dims, NDIndexInt* idxs) {
SizeT stride = 1;
for (SizeT dim = 0; dim < num_dims; dim++) {
SizeT i = num_dims - dim - 1;
@ -28,7 +30,10 @@ void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT n
}
template<typename SizeT>
SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims, const NDIndex* indices, SizeT num_indices) {
SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims,
SizeT num_dims,
const NDIndexInt* indices,
SizeT num_indices) {
SizeT idx = 0;
SizeT stride = 1;
for (SizeT i = 0; i < num_dims; ++i) {
@ -75,8 +80,8 @@ void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims,
template<typename SizeT>
void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
SizeT src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx) {
const NDIndexInt* in_idx,
NDIndexInt* out_idx) {
for (SizeT i = 0; i < src_ndims; ++i) {
SizeT src_i = src_ndims - i - 1;
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
@ -94,21 +99,23 @@ __nac3_ndarray_calc_size64(const uint64_t* list_data, uint64_t list_len, uint64_
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) {
void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims, uint32_t num_dims, NDIndexInt* idxs) {
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
}
void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims, uint64_t num_dims, NDIndex* idxs) {
void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims, uint64_t num_dims, NDIndexInt* idxs) {
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
}
uint32_t
__nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims, const NDIndex* indices, uint32_t num_indices) {
__nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims, const NDIndexInt* indices, uint32_t num_indices) {
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
}
uint64_t
__nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims, const NDIndex* indices, uint64_t num_indices) {
uint64_t __nac3_ndarray_flatten_index64(const uint64_t* dims,
uint64_t num_dims,
const NDIndexInt* indices,
uint64_t num_indices) {
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
}
@ -130,15 +137,15 @@ void __nac3_ndarray_calc_broadcast64(const uint64_t* lhs_dims,
void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
uint32_t src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx) {
const NDIndexInt* in_idx,
NDIndexInt* out_idx) {
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
}
void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
uint64_t src_ndims,
const NDIndex* in_idx,
NDIndex* out_idx) {
const NDIndexInt* in_idx,
NDIndexInt* out_idx) {
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
}
}

134
nac3core/irrt/irrt/ndarray/array.hpp Normal file
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@ -0,0 +1,134 @@
#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);
}
}

341
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@ -0,0 +1,341 @@
#pragma once
#include "irrt/debug.hpp"
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/ndarray/def.hpp"
namespace {
namespace ndarray {
namespace basic {
/**
* @brief Assert that `shape` does not contain negative dimensions.
*
* @param ndims Number of dimensions in `shape`
* @param shape The shape to check on
*/
template<typename SizeT>
void assert_shape_no_negative(SizeT ndims, const SizeT* shape) {
for (SizeT axis = 0; axis < ndims; axis++) {
if (shape[axis] < 0) {
raise_exception(SizeT, EXN_VALUE_ERROR,
"negative dimensions are not allowed; axis {0} "
"has dimension {1}",
axis, shape[axis], NO_PARAM);
}
}
}
/**
* @brief Assert that two shapes are the same in the context of writing output to an ndarray.
*/
template<typename SizeT>
void assert_output_shape_same(SizeT ndarray_ndims,
const SizeT* ndarray_shape,
SizeT output_ndims,
const SizeT* output_shape) {
if (ndarray_ndims != output_ndims) {
// There is no corresponding NumPy error message like this.
raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot write output of ndims {0} to an ndarray with ndims {1}",
output_ndims, ndarray_ndims, NO_PARAM);
}
for (SizeT axis = 0; axis < ndarray_ndims; axis++) {
if (ndarray_shape[axis] != output_shape[axis]) {
// There is no corresponding NumPy error message like this.
raise_exception(SizeT, EXN_VALUE_ERROR,
"Mismatched dimensions on axis {0}, output has "
"dimension {1}, but destination ndarray has dimension {2}.",
axis, output_shape[axis], ndarray_shape[axis]);
}
}
}
/**
* @brief Return the number of elements of an ndarray given its shape.
*
* @param ndims Number of dimensions in `shape`
* @param shape The shape of the ndarray
*/
template<typename SizeT>
SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
SizeT size = 1;
for (SizeT axis = 0; axis < ndims; axis++)
size *= shape[axis];
return size;
}
/**
* @brief Compute the array indices of the `nth` (0-based) element of an ndarray given only its shape.
*
* @param ndims Number of elements in `shape` and `indices`
* @param shape The shape of the ndarray
* @param indices The returned indices indexing the ndarray with shape `shape`.
* @param nth The index of the element of interest.
*/
template<typename SizeT>
void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices, SizeT nth) {
for (SizeT i = 0; i < ndims; i++) {
SizeT axis = ndims - i - 1;
SizeT dim = shape[axis];
indices[axis] = nth % dim;
nth /= dim;
}
}
/**
* @brief Return the number of elements of an `ndarray`
*
* This function corresponds to `<an_ndarray>.size`
*/
template<typename SizeT>
SizeT size(const NDArray<SizeT>* ndarray) {
return calc_size_from_shape(ndarray->ndims, ndarray->shape);
}
/**
* @brief Return of the number of its content of an `ndarray`.
*
* This function corresponds to `<an_ndarray>.nbytes`.
*/
template<typename SizeT>
SizeT nbytes(const NDArray<SizeT>* ndarray) {
return size(ndarray) * ndarray->itemsize;
}
/**
* @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
*
* This function corresponds to `<an_ndarray>.__len__`.
*
* @param dst_length The length.
*/
template<typename SizeT>
SizeT len(const NDArray<SizeT>* ndarray) {
// numpy prohibits `__len__` on unsized objects
if (ndarray->ndims == 0) {
raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object", NO_PARAM, NO_PARAM, NO_PARAM);
} else {
return ndarray->shape[0];
}
}
/**
* @brief Return a boolean indicating if `ndarray` is (C-)contiguous.
*
* You may want to see ndarray's rules for C-contiguity:
* https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
*/
template<typename SizeT>
bool is_c_contiguous(const NDArray<SizeT>* ndarray) {
// References:
// - tinynumpy's implementation:
// https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L102
// - ndarray's flags["C_CONTIGUOUS"]:
// https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags
// - ndarray's rules for C-contiguity:
// https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
// From
// https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45:
//
// The traditional rule is that for an array to be flagged as C contiguous,
// the following must hold:
//
// strides[-1] == itemsize
// strides[i] == shape[i+1] * strides[i + 1]
// [...]
// According to these rules, a 0- or 1-dimensional array is either both
// C- and F-contiguous, or neither; and an array with 2+ dimensions
// can be C- or F- contiguous, or neither, but not both. Though there
// there are exceptions for arrays with zero or one item, in the first
// case the check is relaxed up to and including the first dimension
// with shape[i] == 0. In the second case `strides == itemsize` will
// can be true for all dimensions and both flags are set.
if (ndarray->ndims == 0) {
return true;
}
if (ndarray->strides[ndarray->ndims - 1] != ndarray->itemsize) {
return false;
}
for (SizeT i = 1; i < ndarray->ndims; i++) {
SizeT axis_i = ndarray->ndims - i - 1;
if (ndarray->strides[axis_i] != ndarray->shape[axis_i + 1] * ndarray->strides[axis_i + 1]) {
return false;
}
}
return true;
}
/**
* @brief Return the pointer to the element indexed by `indices` along the ndarray's axes.
*
* This function does no bound check.
*/
template<typename SizeT>
uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray, const SizeT* indices) {
uint8_t* element = ndarray->data;
for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
element += indices[dim_i] * ndarray->strides[dim_i];
return element;
}
/**
* @brief Return the pointer to the nth (0-based) element of `ndarray` in flattened view.
*
* This function does no bound check.
*/
template<typename SizeT>
uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
uint8_t* element = ndarray->data;
for (SizeT i = 0; i < ndarray->ndims; i++) {
SizeT axis = ndarray->ndims - i - 1;
SizeT dim = ndarray->shape[axis];
element += ndarray->strides[axis] * (nth % dim);
nth /= dim;
}
return element;
}
/**
* @brief Update the strides of an ndarray given an ndarray `shape` to be contiguous.
*
* You might want to read https://ajcr.net/stride-guide-part-1/.
*/
template<typename SizeT>
void set_strides_by_shape(NDArray<SizeT>* ndarray) {
SizeT stride_product = 1;
for (SizeT i = 0; i < ndarray->ndims; i++) {
SizeT axis = ndarray->ndims - i - 1;
ndarray->strides[axis] = stride_product * ndarray->itemsize;
stride_product *= ndarray->shape[axis];
}
}
/**
* @brief Set an element in `ndarray`.
*
* @param pelement Pointer to the element in `ndarray` to be set.
* @param pvalue Pointer to the value `pelement` will be set to.
*/
template<typename SizeT>
void set_pelement_value(NDArray<SizeT>* ndarray, uint8_t* pelement, const uint8_t* pvalue) {
__builtin_memcpy(pelement, pvalue, ndarray->itemsize);
}
/**
* @brief Copy data from one ndarray to another of the exact same size and itemsize.
*
* Both ndarrays will be viewed in their flatten views when copying the elements.
*/
template<typename SizeT>
void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
// TODO: Make this faster with memcpy when we see a contiguous segment.
// TODO: Handle overlapping.
debug_assert_eq(SizeT, src_ndarray->itemsize, dst_ndarray->itemsize);
for (SizeT i = 0; i < size(src_ndarray); i++) {
auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
ndarray::basic::set_pelement_value(dst_ndarray, dst_element, src_element);
}
}
} // namespace basic
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::basic;
void __nac3_ndarray_util_assert_shape_no_negative(int32_t ndims, int32_t* shape) {
assert_shape_no_negative(ndims, shape);
}
void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims, int64_t* shape) {
assert_shape_no_negative(ndims, shape);
}
void __nac3_ndarray_util_assert_output_shape_same(int32_t ndarray_ndims,
const int32_t* ndarray_shape,
int32_t output_ndims,
const int32_t* output_shape) {
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims, output_shape);
}
void __nac3_ndarray_util_assert_output_shape_same64(int64_t ndarray_ndims,
const int64_t* ndarray_shape,
int64_t output_ndims,
const int64_t* output_shape) {
assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims, output_shape);
}
uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
return size(ndarray);
}
uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
return size(ndarray);
}
uint32_t __nac3_ndarray_nbytes(NDArray<int32_t>* ndarray) {
return nbytes(ndarray);
}
uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t>* ndarray) {
return nbytes(ndarray);
}
int32_t __nac3_ndarray_len(NDArray<int32_t>* ndarray) {
return len(ndarray);
}
int64_t __nac3_ndarray_len64(NDArray<int64_t>* ndarray) {
return len(ndarray);
}
bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t>* ndarray) {
return is_c_contiguous(ndarray);
}
bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
return is_c_contiguous(ndarray);
}
uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
return get_nth_pelement(ndarray, nth);
}
uint8_t* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray, int64_t nth) {
return get_nth_pelement(ndarray, nth);
}
uint8_t* __nac3_ndarray_get_pelement_by_indices(const NDArray<int32_t>* ndarray, int32_t* indices) {
return get_pelement_by_indices(ndarray, indices);
}
uint8_t* __nac3_ndarray_get_pelement_by_indices64(const NDArray<int64_t>* ndarray, int64_t* indices) {
return get_pelement_by_indices(ndarray, indices);
}
void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t>* ndarray) {
set_strides_by_shape(ndarray);
}
void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
set_strides_by_shape(ndarray);
}
void __nac3_ndarray_copy_data(NDArray<int32_t>* src_ndarray, NDArray<int32_t>* dst_ndarray) {
copy_data(src_ndarray, dst_ndarray);
}
void __nac3_ndarray_copy_data64(NDArray<int64_t>* src_ndarray, NDArray<int64_t>* dst_ndarray) {
copy_data(src_ndarray, dst_ndarray);
}
}

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

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

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

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

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

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

145
nac3core/irrt/irrt/ndarray/transpose.hpp Normal file
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@ -0,0 +1,145 @@
#pragma once
#include "irrt/debug.hpp"
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/ndarray/def.hpp"
#include "irrt/slice.hpp"
/*
* Notes on `np.transpose(<array>, <axes>)`
*
* TODO: `axes`, if specified, can actually contain negative indices,
* but it is not documented in numpy.
*
* Supporting it for now.
*/
namespace {
namespace ndarray {
namespace transpose {
/**
* @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
*
* Note that `np.transpose`'s `<axe>` argument is optional. If the argument
* is specified but the user, use this function to do assertions on it.
*
* @param ndims The number of dimensions of `<array>`
* @param num_axes Number of elements in `<axes>` as specified by the user.
* This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
* @param axes The user specified `<axes>`.
*/
template<typename SizeT>
void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
if (ndims != num_axes) {
raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array", NO_PARAM, NO_PARAM, NO_PARAM);
}
// TODO: Optimize this
bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
for (SizeT i = 0; i < ndims; i++)
axe_specified[i] = false;
for (SizeT i = 0; i < ndims; i++) {
SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
if (axis == -1) {
// TODO: numpy actually throws a `numpy.exceptions.AxisError`
raise_exception(SizeT, EXN_VALUE_ERROR, "axis {0} is out of bounds for array of dimension {1}", axis, ndims,
NO_PARAM);
}
if (axe_specified[axis]) {
raise_exception(SizeT, EXN_VALUE_ERROR, "repeated axis in transpose", NO_PARAM, NO_PARAM, NO_PARAM);
}
axe_specified[axis] = true;
}
}
/**
* @brief Create a transpose view of `src_ndarray` and perform proper assertions.
*
* This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
* If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
*
* The transpose view created is returned by modifying `dst_ndarray`.
*
* The caller is responsible for setting up `dst_ndarray` before calling this function.
* Here is what this function expects from `dst_ndarray` when called:
* - `dst_ndarray->data` does not have to be initialized.
* - `dst_ndarray->itemsize` does not have to be initialized.
* - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
* - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
* - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
* When this function call ends:
* - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
* - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
* - `dst_ndarray->ndims` is unchanged
* - `dst_ndarray->shape` is updated according to how `np.transpose` works
* - `dst_ndarray->strides` is updated according to how `np.transpose` works
*
* @param src_ndarray The NDArray to build a transpose view on
* @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
* @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
* @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
*/
template<typename SizeT>
void transpose(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray, SizeT num_axes, const SizeT* axes) {
debug_assert_eq(SizeT, src_ndarray->ndims, dst_ndarray->ndims);
const auto ndims = src_ndarray->ndims;
if (axes != nullptr)
assert_transpose_axes(ndims, num_axes, axes);
dst_ndarray->data = src_ndarray->data;
dst_ndarray->itemsize = src_ndarray->itemsize;
// Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
if (axes == nullptr) {
// `np.transpose(<array>, axes=None)`
/*
* Minor note: `np.transpose(<array>, axes=None)` is equivalent to
* `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
* is reversing the order of strides and shape.
*
* This is a fast implementation to handle this special (but very common) case.
*/
for (SizeT axis = 0; axis < ndims; axis++) {
dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
}
} else {
// `np.transpose(<array>, <axes>)`
// Permute strides and shape according to `axes`, while resolving negative indices in `axes`
for (SizeT axis = 0; axis < ndims; axis++) {
// `i` cannot be OUT_OF_BOUNDS because of assertions
SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
dst_ndarray->shape[axis] = src_ndarray->shape[i];
dst_ndarray->strides[axis] = src_ndarray->strides[i];
}
}
}
} // namespace transpose
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::transpose;
void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
NDArray<int32_t>* dst_ndarray,
int32_t num_axes,
const int32_t* axes) {
transpose(src_ndarray, dst_ndarray, num_axes, axes);
}
void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
NDArray<int64_t>* dst_ndarray,
int64_t num_axes,
const int64_t* axes) {
transpose(src_ndarray, dst_ndarray, num_axes, axes);
}
}

47
nac3core/irrt/irrt/range.hpp Normal file
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@ -0,0 +1,47 @@
#pragma once
#include "irrt/debug.hpp"
#include "irrt/int_types.hpp"
namespace {
namespace range {
template<typename T>
T len(T start, T stop, T step) {
// Reference:
// https://github.com/python/cpython/blob/9dbd12375561a393eaec4b21ee4ac568a407cdb0/Objects/rangeobject.c#L933
if (step > 0 && start < stop)
return 1 + (stop - 1 - start) / step;
else if (step < 0 && start > stop)
return 1 + (start - 1 - stop) / (-step);
else
return 0;
}
} // namespace range
/**
* @brief A Python range.
*/
template<typename T>
struct Range {
T start;
T stop;
T step;
/**
* @brief Calculate the `len()` of this range.
*/
template<typename SizeT>
T len() {
debug_assert(SizeT, step != 0);
return range::len(start, stop, step);
}
};
} // namespace
extern "C" {
using namespace range;
SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step) {
return len(start, end, step);
}
}

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

1700
nac3core/src/codegen/builtin_fns.rs
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File diff suppressed because it is too large Load Diff

19
nac3core/src/codegen/classes.rs
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@ -1,16 +1,17 @@
use inkwell::{
context::Context,
types::{AnyTypeEnum, ArrayType, BasicType, BasicTypeEnum, IntType, PointerType, StructType},
values::{ArrayValue, BasicValue, BasicValueEnum, IntValue, PointerValue, StructValue},
AddressSpace, IntPredicate,
};
use crate::codegen::{
irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
llvm_intrinsics::call_int_umin,
stmt::gen_for_callback_incrementing,
CodeGenContext, CodeGenerator,
};
use inkwell::context::Context;
use inkwell::types::{ArrayType, BasicType, StructType};
use inkwell::values::{ArrayValue, BasicValue, StructValue};
use inkwell::{
types::{AnyTypeEnum, BasicTypeEnum, IntType, PointerType},
values::{BasicValueEnum, IntValue, PointerValue},
AddressSpace, IntPredicate,
};
/// A LLVM type that is used to represent a non-primitive type in NAC3.
pub trait ProxyType<'ctx>: Into<Self::Base> {
@ -1249,13 +1250,11 @@ impl<'ctx> NDArrayType<'ctx> {
/// Returns the element type of this `ndarray` type.
#[must_use]
pub fn element_type(&self) -> AnyTypeEnum<'ctx> {
pub fn element_type(&self) -> BasicTypeEnum<'ctx> {
self.as_base_type()
.get_element_type()
.into_struct_type()
.get_field_type_at_index(2)
.map(BasicTypeEnum::into_pointer_type)
.map(PointerType::get_element_type)
.unwrap()
}
}

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

@ -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>,
}

645
nac3core/src/codegen/expr.rs
View File

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

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

@ -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;

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

@ -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.

323
nac3core/src/codegen/irrt/mod.rs
View File

@ -1,3 +1,21 @@
use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
use super::{
classes::{
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
TypedArrayLikeAccessor, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
},
llvm_intrinsics,
macros::codegen_unreachable,
model::*,
object::{
list::List,
ndarray::{broadcast::ShapeEntry, indexing::NDIndex, nditer::NDIter, NDArray},
},
stmt::gen_for_callback_incrementing,
CodeGenContext, CodeGenerator,
};
use function::FnCall;
use inkwell::{
attributes::{Attribute, AttributeLoc},
context::Context,
@ -8,21 +26,8 @@ use inkwell::{
AddressSpace, IntPredicate,
};
use itertools::Either;
use nac3parser::ast::Expr;
use super::{
classes::{
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
TypedArrayLikeAccessor, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
},
llvm_intrinsics,
macros::codegen_unreachable,
stmt::gen_for_callback_incrementing,
CodeGenContext, CodeGenerator,
};
use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
#[must_use]
pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver) -> Module<'ctx> {
let bitcode_buf = MemoryBuffer::create_from_memory_range(
@ -950,3 +955,295 @@ pub fn call_ndarray_calc_broadcast_index<
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
}
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndims: Instance<'ctx, Int<SizeT>>,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_util_assert_shape_no_negative",
);
FnCall::builder(generator, ctx, &name).arg(ndims).arg(shape).returning_void();
}
pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray_ndims: 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",
);
FnCall::builder(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");
FnCall::builder(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");
FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("nbytes")
}
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) -> Instance<'ctx, Int<SizeT>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("len")
}
pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) -> Instance<'ctx, Int<Bool>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
FnCall::builder(generator, ctx, &name).arg(ndarray).returning_auto("is_c_contiguous")
}
pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
index: Instance<'ctx, Int<SizeT>>,
) -> Instance<'ctx, Ptr<Int<Byte>>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
FnCall::builder(generator, ctx, &name).arg(ndarray).arg(index).returning_auto("pelement")
}
pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Instance<'ctx, Ptr<Int<Byte>>> {
let name =
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_pelement_by_indices");
FnCall::builder(generator, ctx, &name).arg(ndarray).arg(indices).returning_auto("pelement")
}
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name =
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
FnCall::builder(generator, ctx, &name).arg(ndarray).returning_void();
}
pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
FnCall::builder(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
}
pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_initialize");
FnCall::builder(generator, ctx, &name).arg(iter).arg(ndarray).arg(indices).returning_void();
}
pub fn call_nac3_nditer_has_element<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
) -> Instance<'ctx, Int<Bool>> {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_has_element");
FnCall::builder(generator, ctx, &name).arg(iter).returning_auto("has_element")
}
pub fn call_nac3_nditer_next<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
iter: Instance<'ctx, Ptr<Struct<NDIter>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_nditer_next");
FnCall::builder(generator, ctx, &name).arg(iter).returning_void();
}
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
num_indices: Instance<'ctx, Int<SizeT>>,
indices: Instance<'ctx, Ptr<Struct<NDIndex>>>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
FnCall::builder(generator, ctx, &name)
.arg(num_indices)
.arg(indices)
.arg(src_ndarray)
.arg(dst_ndarray)
.returning_void();
}
pub fn call_nac3_ndarray_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
ndims: Instance<'ctx, Int<SizeT>>,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_array_set_and_validate_list_shape",
);
FnCall::builder(generator, ctx, &name).arg(list).arg(ndims).arg(shape).returning_void();
}
pub fn call_nac3_ndarray_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: Instance<'ctx, Ptr<Struct<List<Int<Byte>>>>>,
ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_array_write_list_to_array",
);
FnCall::builder(generator, ctx, &name).arg(list).arg(ndarray).returning_void();
}
pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
size: Instance<'ctx, Int<SizeT>>,
new_ndims: Instance<'ctx, Int<SizeT>>,
new_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(
generator,
ctx,
"__nac3_ndarray_reshape_resolve_and_check_new_shape",
);
FnCall::builder(generator, ctx, &name).arg(size).arg(new_ndims).arg(new_shape).returning_void();
}
pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
FnCall::builder(generator, ctx, &name).arg(src_ndarray).arg(dst_ndarray).returning_void();
}
pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
num_shape_entries: Instance<'ctx, Int<SizeT>>,
shape_entries: Instance<'ctx, Ptr<Struct<ShapeEntry>>>,
dst_ndims: Instance<'ctx, Int<SizeT>>,
dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
FnCall::builder(generator, ctx, &name)
.arg(num_shape_entries)
.arg(shape_entries)
.arg(dst_ndims)
.arg(dst_shape)
.returning_void();
}
pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
num_axes: Instance<'ctx, Int<SizeT>>,
axes: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
FnCall::builder(generator, ctx, &name)
.arg(src_ndarray)
.arg(dst_ndarray)
.arg(num_axes)
.arg(axes)
.returning_void();
}
#[allow(clippy::too_many_arguments)]
pub fn call_nac3_ndarray_matmul_calculate_shapes<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
a_ndims: Instance<'ctx, Int<SizeT>>,
a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
b_ndims: Instance<'ctx, Int<SizeT>>,
b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
final_ndims: Instance<'ctx, Int<SizeT>>,
new_a_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
new_b_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
dst_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let name =
get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
FnCall::builder(generator, ctx, &name)
.arg(a_ndims)
.arg(a_shape)
.arg(b_ndims)
.arg(b_shape)
.arg(final_ndims)
.arg(new_a_shape)
.arg(new_b_shape)
.arg(dst_shape)
.returning_void();
}

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

@ -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()
};

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

@ -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)]
@ -510,12 +509,7 @@ 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)).llvm_type(generator, ctx).as_basic_type_enum()
}
_ => unreachable!(
@ -853,9 +847,10 @@ 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(

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

@ -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 llvm_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> Self::Type {
self.0
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
_generator: &mut G,
_ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
if ty == self.0 {
Ok(())
} else {
Err(ModelError(format!("Expecting {}, but got {}", self.0, ty)))
}
}
}

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

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

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

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

94
nac3core/src/codegen/model/float.rs Normal file
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use std::fmt;
use inkwell::{
context::Context,
types::{BasicType, FloatType},
values::FloatValue,
};
use crate::codegen::CodeGenerator;
use super::*;
pub trait FloatKind<'ctx>: fmt::Debug + Clone + Copy {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> FloatType<'ctx>;
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Float32;
#[derive(Debug, Clone, Copy, Default)]
pub struct Float64;
impl<'ctx> FloatKind<'ctx> for Float32 {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> FloatType<'ctx> {
ctx.f32_type()
}
}
impl<'ctx> FloatKind<'ctx> for Float64 {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> FloatType<'ctx> {
ctx.f64_type()
}
}
#[derive(Debug, Clone, Copy)]
pub struct AnyFloat<'ctx>(FloatType<'ctx>);
impl<'ctx> FloatKind<'ctx> for AnyFloat<'ctx> {
fn get_float_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> FloatType<'ctx> {
self.0
}
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Float<N>(pub N);
impl<'ctx, N: FloatKind<'ctx>> Model<'ctx> for Float<N> {
type Value = FloatValue<'ctx>;
type Type = FloatType<'ctx>;
fn llvm_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
self.0.get_float_type(generator, ctx)
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = FloatType::try_from(ty) else {
return Err(ModelError(format!("Expecting FloatType, but got {ty:?}")));
};
let exp_ty = self.0.get_float_type(generator, ctx);
// TODO: Inkwell does not have get_bit_width for FloatType?
if ty != exp_ty {
return Err(ModelError(format!("Expecting {exp_ty:?}, but got {ty:?}")));
}
Ok(())
}
}

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nac3core/src/codegen/model/function.rs Normal file
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use inkwell::{
attributes::{Attribute, AttributeLoc},
types::{BasicMetadataTypeEnum, BasicType, FunctionType},
values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
};
use itertools::Itertools;
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
#[derive(Debug, Clone, Copy)]
struct Arg<'ctx> {
ty: BasicMetadataTypeEnum<'ctx>,
val: BasicMetadataValueEnum<'ctx>,
}
/// A convenience structure to construct & call an LLVM function.
///
/// ### Usage
///
/// The syntax is like this:
/// ```ignore
/// let result = CallFunction::begin("my_function_name")
/// .attrs(...)
/// .arg(arg1)
/// .arg(arg2)
/// .arg(arg3)
/// .returning("my_function_result", Int32);
/// ```
///
/// The function `my_function_name` is called when `.returning()` (or its variants) is called, returning
/// the result as an `Instance<'ctx, Int<Int32>>`.
///
/// If `my_function_name` has not been declared in `ctx.module`, once `.returning()` is called, a function
/// declaration of `my_function_name` is added to `ctx.module`, where the [`FunctionType`] is deduced from
/// the argument types and returning type.
pub struct FnCall<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> {
generator: &'d mut G,
ctx: &'b CodeGenContext<'ctx, 'a>,
/// Function name
name: &'c str,
/// Call arguments
args: Vec<Arg<'ctx>>,
/// LLVM function Attributes
attrs: Vec<&'static str>,
}
impl<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> FnCall<'ctx, 'a, 'b, 'c, 'd, G> {
pub fn builder(generator: &'d mut G, ctx: &'b CodeGenContext<'ctx, 'a>, name: &'c str) -> Self {
FnCall { generator, ctx, name, args: Vec::new(), attrs: Vec::new() }
}
/// Push a list of LLVM function attributes to the function declaration.
#[must_use]
pub fn attrs(mut self, attrs: Vec<&'static str>) -> Self {
self.attrs = attrs;
self
}
/// Push a call argument to the function call.
#[allow(clippy::needless_pass_by_value)]
#[must_use]
pub fn arg<M: Model<'ctx>>(mut self, arg: Instance<'ctx, M>) -> Self {
let arg = Arg {
ty: arg.model.llvm_type(self.generator, self.ctx.ctx).as_basic_type_enum().into(),
val: arg.value.as_basic_value_enum().into(),
};
self.args.push(arg);
self
}
/// Call the function and expect the function to return a value of type of `return_model`.
#[must_use]
pub fn returning<M: Model<'ctx>>(self, name: &str, return_model: M) -> Instance<'ctx, M> {
let ret_ty = return_model.llvm_type(self.generator, self.ctx.ctx);
let ret = self.call(|tys| ret_ty.fn_type(tys, false), name);
let ret = BasicValueEnum::try_from(ret.as_any_value_enum()).unwrap(); // Must work
let ret = return_model.check_value(self.generator, self.ctx.ctx, ret).unwrap(); // Must work
ret
}
/// Like [`CallFunction::returning_`] but `return_model` is automatically inferred.
#[must_use]
pub fn returning_auto<M: Model<'ctx> + Default>(self, name: &str) -> Instance<'ctx, M> {
self.returning(name, M::default())
}
/// Call the function and expect the function to return a void-type.
pub fn returning_void(self) {
let ret_ty = self.ctx.ctx.void_type();
let _ = self.call(|tys| ret_ty.fn_type(tys, false), "");
}
fn call<F>(&self, make_fn_type: F, return_value_name: &str) -> CallSiteValue<'ctx>
where
F: FnOnce(&[BasicMetadataTypeEnum<'ctx>]) -> FunctionType<'ctx>,
{
// Get the LLVM function.
let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
// Declare the function if it doesn't exist.
let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
let func_type = make_fn_type(&tys);
let func = self.ctx.module.add_function(self.name, func_type, None);
for attr in &self.attrs {
func.add_attribute(
AttributeLoc::Function,
self.ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
);
}
func
});
let vals = self.args.iter().map(|arg| arg.val).collect_vec();
self.ctx.builder.build_call(func, &vals, return_value_name).unwrap()
}
}

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nac3core/src/codegen/model/int.rs Normal file
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use std::{cmp::Ordering, fmt};
use inkwell::{
context::Context,
types::{BasicType, IntType},
values::IntValue,
IntPredicate,
};
use crate::codegen::{CodeGenContext, CodeGenerator};
use super::*;
pub trait IntKind<'ctx>: fmt::Debug + Clone + Copy {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx>;
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Bool;
#[derive(Debug, Clone, Copy, Default)]
pub struct Byte;
#[derive(Debug, Clone, Copy, Default)]
pub struct Int32;
#[derive(Debug, Clone, Copy, Default)]
pub struct Int64;
#[derive(Debug, Clone, Copy, Default)]
pub struct SizeT;
impl<'ctx> IntKind<'ctx> for Bool {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.bool_type()
}
}
impl<'ctx> IntKind<'ctx> for Byte {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i8_type()
}
}
impl<'ctx> IntKind<'ctx> for Int32 {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i32_type()
}
}
impl<'ctx> IntKind<'ctx> for Int64 {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
ctx.i64_type()
}
}
impl<'ctx> IntKind<'ctx> for SizeT {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> IntType<'ctx> {
generator.get_size_type(ctx)
}
}
#[derive(Debug, Clone, Copy)]
pub struct AnyInt<'ctx>(pub IntType<'ctx>);
impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
fn get_int_type<G: CodeGenerator + ?Sized>(
&self,
_generator: &G,
_ctx: &'ctx Context,
) -> IntType<'ctx> {
self.0
}
}
#[derive(Debug, Clone, Copy, Default)]
pub struct Int<N>(pub N);
impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for Int<N> {
type Value = IntValue<'ctx>;
type Type = IntType<'ctx>;
fn llvm_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
self.0.get_int_type(generator, ctx)
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = IntType::try_from(ty) else {
return Err(ModelError(format!("Expecting IntType, but got {ty:?}")));
};
let exp_ty = self.0.get_int_type(generator, ctx);
if ty.get_bit_width() != exp_ty.get_bit_width() {
return Err(ModelError(format!(
"Expecting IntType to have {} bit(s), but got {} bit(s)",
exp_ty.get_bit_width(),
ty.get_bit_width()
)));
}
Ok(())
}
}
impl<'ctx, N: IntKind<'ctx>> Int<N> {
pub fn const_int<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
value: u64,
sign_extend: bool,
) -> Instance<'ctx, Self> {
let value = self.llvm_type(generator, ctx).const_int(value, sign_extend);
unsafe { self.believe_value(value) }
}
pub fn const_0<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
let value = self.llvm_type(generator, ctx).const_zero();
unsafe { self.believe_value(value) }
}
pub fn const_1<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
self.const_int(generator, ctx, 1, false)
}
pub fn const_all_ones<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
let value = self.llvm_type(generator, ctx).const_all_ones();
unsafe { self.believe_value(value) }
}
pub fn s_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
<= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value = ctx
.builder
.build_int_s_extend_or_bit_cast(value, self.llvm_type(generator, ctx.ctx), "")
.unwrap();
unsafe { self.believe_value(value) }
}
pub fn s_extend<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
< self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value =
ctx.builder.build_int_s_extend(value, self.llvm_type(generator, ctx.ctx), "").unwrap();
unsafe { self.believe_value(value) }
}
pub fn z_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
<= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value = ctx
.builder
.build_int_z_extend_or_bit_cast(value, self.llvm_type(generator, ctx.ctx), "")
.unwrap();
unsafe { self.believe_value(value) }
}
pub fn z_extend<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
< self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value =
ctx.builder.build_int_z_extend(value, self.llvm_type(generator, ctx.ctx), "").unwrap();
unsafe { self.believe_value(value) }
}
pub fn truncate_or_bit_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
>= self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value = ctx
.builder
.build_int_truncate_or_bit_cast(value, self.llvm_type(generator, ctx.ctx), "")
.unwrap();
unsafe { self.believe_value(value) }
}
pub fn truncate<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
assert!(
value.get_type().get_bit_width()
> self.0.get_int_type(generator, ctx.ctx).get_bit_width()
);
let value =
ctx.builder.build_int_truncate(value, self.llvm_type(generator, ctx.ctx), "").unwrap();
unsafe { self.believe_value(value) }
}
/// `sext` or `trunc` an int to this model's int type. Does nothing if equal bit-widths.
pub fn s_extend_or_truncate<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
let their_width = value.get_type().get_bit_width();
let our_width = self.0.get_int_type(generator, ctx.ctx).get_bit_width();
match their_width.cmp(&our_width) {
Ordering::Less => self.s_extend(generator, ctx, value),
Ordering::Equal => unsafe { self.believe_value(value) },
Ordering::Greater => self.truncate(generator, ctx, value),
}
}
/// `zext` or `trunc` an int to this model's int type. Does nothing if equal bit-widths.
pub fn z_extend_or_truncate<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
value: IntValue<'ctx>,
) -> Instance<'ctx, Self> {
let their_width = value.get_type().get_bit_width();
let our_width = self.0.get_int_type(generator, ctx.ctx).get_bit_width();
match their_width.cmp(&our_width) {
Ordering::Less => self.z_extend(generator, ctx, value),
Ordering::Equal => unsafe { self.believe_value(value) },
Ordering::Greater => self.truncate(generator, ctx, value),
}
}
}
impl Int<Bool> {
#[must_use]
pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
self.const_int(generator, ctx, 0, false)
}
#[must_use]
pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Self> {
self.const_int(generator, ctx, 1, false)
}
}
impl<'ctx, N: IntKind<'ctx>> Instance<'ctx, Int<N>> {
pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value)
}
pub fn s_extend<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).s_extend(generator, ctx, self.value)
}
pub fn z_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).z_extend_or_bit_cast(generator, ctx, self.value)
}
pub fn z_extend<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).z_extend(generator, ctx, self.value)
}
pub fn truncate_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).truncate_or_bit_cast(generator, ctx, self.value)
}
pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).truncate(generator, ctx, self.value)
}
pub fn s_extend_or_truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).s_extend_or_truncate(generator, ctx, self.value)
}
pub fn z_extend_or_truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
to_int_kind: NewN,
) -> Instance<'ctx, Int<NewN>> {
Int(to_int_kind).z_extend_or_truncate(generator, ctx, self.value)
}
#[must_use]
pub fn add(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
let value = ctx.builder.build_int_add(self.value, other.value, "").unwrap();
unsafe { self.model.believe_value(value) }
}
#[must_use]
pub fn sub(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
let value = ctx.builder.build_int_sub(self.value, other.value, "").unwrap();
unsafe { self.model.believe_value(value) }
}
#[must_use]
pub fn mul(&self, ctx: &CodeGenContext<'ctx, '_>, other: Self) -> Self {
let value = ctx.builder.build_int_mul(self.value, other.value, "").unwrap();
unsafe { self.model.believe_value(value) }
}
pub fn compare(
&self,
ctx: &CodeGenContext<'ctx, '_>,
op: IntPredicate,
other: Self,
) -> Instance<'ctx, Int<Bool>> {
let value = ctx.builder.build_int_compare(op, self.value, other.value, "").unwrap();
unsafe { Int(Bool).believe_value(value) }
}
}

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nac3core/src/codegen/model/mod.rs Normal file
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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::*;

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nac3core/src/codegen/model/ptr.rs Normal file
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use inkwell::{
context::Context,
types::{BasicType, BasicTypeEnum, PointerType},
values::{IntValue, PointerValue},
AddressSpace,
};
use crate::codegen::{llvm_intrinsics::call_memcpy_generic, CodeGenContext, CodeGenerator};
use super::*;
/// A model for [`PointerType`].
///
/// `Item` is the element type this pointer is pointing to, and should be of a [`Model`].
///
// TODO: LLVM 15: `Item` is a Rust type-hint for the LLVM type of value the `.store()/.load()` family
// of functions return. If a truly opaque pointer is needed, tell the programmer to use `OpaquePtr`.
#[derive(Debug, Clone, Copy, Default)]
pub struct Ptr<Item>(pub Item);
/// An opaque pointer. Like [`Ptr`] but without any Rust type-hints about its element type.
///
/// `.load()/.store()` is not available for [`Instance`]s of opaque pointers.
pub type OpaquePtr = Ptr<()>;
// TODO: LLVM 15: `Item: Model<'ctx>` don't even need to be a model anymore. It will only be
// a type-hint for the `.load()/.store()` functions for the `pointee_ty`.
//
// See https://thedan64.github.io/inkwell/inkwell/builder/struct.Builder.html#method.build_load.
impl<'ctx, Item: Model<'ctx>> Model<'ctx> for Ptr<Item> {
type Value = PointerValue<'ctx>;
type Type = PointerType<'ctx>;
fn llvm_type<G: CodeGenerator + ?Sized>(
&self,
generator: &G,
ctx: &'ctx Context,
) -> Self::Type {
// TODO: LLVM 15: ctx.ptr_type(AddressSpace::default())
self.0.llvm_type(generator, ctx).ptr_type(AddressSpace::default())
}
fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
ty: T,
) -> Result<(), ModelError> {
let ty = ty.as_basic_type_enum();
let Ok(ty) = PointerType::try_from(ty) else {
return Err(ModelError(format!("Expecting PointerType, but got {ty:?}")));
};
let elem_ty = ty.get_element_type();
let Ok(elem_ty) = BasicTypeEnum::try_from(elem_ty) else {
return Err(ModelError(format!(
"Expecting pointer element type to be a BasicTypeEnum, but got {elem_ty:?}"
)));
};
// TODO: inkwell `get_element_type()` will be deprecated.
// Remove the check for `get_element_type()` when the time comes.
self.0
.check_type(generator, ctx, elem_ty)
.map_err(|err| err.under_context("a PointerType"))?;
Ok(())
}
}
impl<'ctx, Item: Model<'ctx>> Ptr<Item> {
/// Return a ***constant*** nullptr.
pub fn nullptr<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Ptr<Item>> {
let ptr = self.llvm_type(generator, ctx).const_null();
unsafe { self.believe_value(ptr) }
}
/// Cast a pointer into this model with [`inkwell::builder::Builder::build_pointer_cast`]
pub fn pointer_cast<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
ptr: PointerValue<'ctx>,
) -> Instance<'ctx, Ptr<Item>> {
// TODO: LLVM 15: Write in an impl where `Item` does not have to be `Model<'ctx>`.
// TODO: LLVM 15: This function will only have to be:
// ```
// return self.believe_value(ptr);
// ```
let t = self.llvm_type(generator, ctx.ctx);
let ptr = ctx.builder.build_pointer_cast(ptr, t, "").unwrap();
unsafe { self.believe_value(ptr) }
}
}
impl<'ctx, Item: Model<'ctx>> Instance<'ctx, Ptr<Item>> {
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
#[must_use]
pub fn offset(
&self,
ctx: &CodeGenContext<'ctx, '_>,
offset: IntValue<'ctx>,
) -> Instance<'ctx, Ptr<Item>> {
let p = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], "").unwrap() };
unsafe { self.model.believe_value(p) }
}
/// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`] by a constant offset.
#[must_use]
pub fn offset_const(
&self,
ctx: &CodeGenContext<'ctx, '_>,
offset: i64,
) -> Instance<'ctx, Ptr<Item>> {
let offset = ctx.ctx.i32_type().const_int(offset as u64, true);
self.offset(ctx, offset)
}
pub fn set_index(
&self,
ctx: &CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
value: Instance<'ctx, Item>,
) {
self.offset(ctx, index).store(ctx, value);
}
pub fn set_index_const(
&self,
ctx: &CodeGenContext<'ctx, '_>,
index: i64,
value: Instance<'ctx, Item>,
) {
self.offset_const(ctx, index).store(ctx, value);
}
pub fn get_index<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
) -> Instance<'ctx, Item> {
self.offset(ctx, index).load(generator, ctx)
}
pub fn get_index_const<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
index: i64,
) -> Instance<'ctx, Item> {
self.offset_const(ctx, index).load(generator, ctx)
}
/// Load the value with [`inkwell::builder::Builder::build_load`].
pub fn load<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Item> {
let value = ctx.builder.build_load(self.value, "").unwrap();
self.model.0.check_value(generator, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
}
/// Store a value with [`inkwell::builder::Builder::build_store`].
pub fn store(&self, ctx: &CodeGenContext<'ctx, '_>, value: Instance<'ctx, Item>) {
ctx.builder.build_store(self.value, value.value).unwrap();
}
/// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
pub fn pointer_cast<NewItem: Model<'ctx>, G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
new_item: NewItem,
) -> Instance<'ctx, Ptr<NewItem>> {
// TODO: LLVM 15: Write in an impl where `Item` does not have to be `Model<'ctx>`.
Ptr(new_item).pointer_cast(generator, ctx, self.value)
}
/// Cast this pointer to `uint8_t*`
pub fn cast_to_pi8<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Ptr<Int<Byte>>> {
Ptr(Int(Byte)).pointer_cast(generator, ctx, self.value)
}
/// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
let value = ctx.builder.build_is_null(self.value, "").unwrap();
unsafe { Int(Bool).believe_value(value) }
}
/// Check if the pointer is not null with [`inkwell::builder::Builder::build_is_not_null`].
pub fn is_not_null(&self, ctx: &CodeGenContext<'ctx, '_>) -> Instance<'ctx, Int<Bool>> {
let value = ctx.builder.build_is_not_null(self.value, "").unwrap();
unsafe { Int(Bool).believe_value(value) }
}
/// `memcpy` from another pointer.
pub fn copy_from<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
source: Self,
num_items: IntValue<'ctx>,
) {
// Force extend `num_items` and `itemsize` to `i64` so their types would match.
let itemsize = self.model.size_of(generator, ctx.ctx);
let itemsize = Int(SizeT).z_extend_or_truncate(generator, ctx, itemsize);
let num_items = Int(SizeT).z_extend_or_truncate(generator, ctx, num_items);
let totalsize = itemsize.mul(ctx, num_items);
let is_volatile = ctx.ctx.bool_type().const_zero(); // is_volatile = false
call_memcpy_generic(ctx, self.value, source.value, totalsize.value, is_volatile);
}
}

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

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

1038
nac3core/src/codegen/numpy.rs
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12
nac3core/src/codegen/object/any.rs Normal file
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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>,
}

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

5
nac3core/src/codegen/object/mod.rs Normal file
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pub mod any;
pub mod list;
pub mod ndarray;
pub mod tuple;
pub mod utils;

184
nac3core/src/codegen/object/ndarray/array.rs Normal file
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use super::NDArrayObject;
use crate::{
codegen::{
irrt::{
call_nac3_ndarray_array_set_and_validate_list_shape,
call_nac3_ndarray_array_write_list_to_array,
},
model::*,
object::{any::AnyObject, list::ListObject},
stmt::gen_if_else_expr_callback,
CodeGenContext, CodeGenerator,
},
toplevel::helper::{arraylike_flatten_element_type, arraylike_get_ndims},
typecheck::typedef::{Type, TypeEnum},
};
/// Get the expected `dtype` and `ndims` of the ndarray returned by `np_array(list)`.
fn get_list_object_dtype_and_ndims<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
list: ListObject<'ctx>,
) -> (Type, u64) {
let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list.item_type);
let ndims = arraylike_get_ndims(&mut ctx.unifier, list.item_type);
let ndims = ndims + 1; // To count `list` itself.
(dtype, ndims)
}
impl<'ctx> NDArrayObject<'ctx> {
/// Implementation of `np_array(<list>, copy=True)`
fn make_np_array_list_copy_true_impl<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: ListObject<'ctx>,
) -> Self {
let (dtype, ndims_int) = get_list_object_dtype_and_ndims(ctx, list);
let list_value = list.instance.with_pi8_items(generator, ctx);
// Validate `list` has a consistent shape.
// Raise an exception if `list` is something abnormal like `[[1, 2], [3]]`.
// If `list` has a consistent shape, deduce the shape and write it to `shape`.
let ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims_int, false);
let shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
call_nac3_ndarray_array_set_and_validate_list_shape(
generator, ctx, list_value, ndims, shape,
);
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims_int);
ndarray.copy_shape_from_array(generator, ctx, shape);
ndarray.create_data(generator, ctx);
// Copy all contents from the list.
call_nac3_ndarray_array_write_list_to_array(generator, ctx, list_value, ndarray.instance);
ndarray
}
/// Implementation of `np_array(<list>, copy=None)`
fn make_np_array_list_copy_none_impl<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: ListObject<'ctx>,
) -> Self {
// np_array without copying is only possible `list` is not nested.
//
// If `list` is `list[T]`, we can create an ndarray with `data` set
// to the array pointer of `list`.
//
// If `list` is `list[list[T]]` or worse, copy.
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
if ndims == 1 {
// `list` is not nested
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, 1);
// Set data
let data = list.instance.get(generator, ctx, |f| f.items).cast_to_pi8(generator, ctx);
ndarray.instance.set(ctx, |f| f.data, data);
// ndarray->shape[0] = list->len;
let shape = ndarray.instance.get(generator, ctx, |f| f.shape);
let list_len = list.instance.get(generator, ctx, |f| f.len);
shape.set_index_const(ctx, 0, list_len);
// Set strides, the `data` is contiguous
ndarray.set_strides_contiguous(generator, ctx);
ndarray
} else {
// `list` is nested, copy
NDArrayObject::make_np_array_list_copy_true_impl(generator, ctx, list)
}
}
/// Implementation of `np_array(<list>, copy=copy)`
fn make_np_array_list_impl<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
list: ListObject<'ctx>,
copy: Instance<'ctx, Int<Bool>>,
) -> Self {
let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
let ndarray = gen_if_else_expr_callback(
generator,
ctx,
|_generator, _ctx| Ok(copy.value),
|generator, ctx| {
let ndarray =
NDArrayObject::make_np_array_list_copy_true_impl(generator, ctx, list);
Ok(Some(ndarray.instance.value))
},
|generator, ctx| {
let ndarray =
NDArrayObject::make_np_array_list_copy_none_impl(generator, ctx, list);
Ok(Some(ndarray.instance.value))
},
)
.unwrap()
.unwrap();
NDArrayObject::from_value_and_unpacked_types(generator, ctx, ndarray, dtype, ndims)
}
/// Implementation of `np_array(<ndarray>, copy=copy)`.
pub fn make_np_array_ndarray_impl<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: NDArrayObject<'ctx>,
copy: Instance<'ctx, Int<Bool>>,
) -> Self {
let ndarray_val = gen_if_else_expr_callback(
generator,
ctx,
|_generator, _ctx| Ok(copy.value),
|generator, ctx| {
let ndarray = ndarray.make_copy(generator, ctx); // Force copy
Ok(Some(ndarray.instance.value))
},
|_generator, _ctx| {
// No need to copy. Return `ndarray` itself.
Ok(Some(ndarray.instance.value))
},
)
.unwrap()
.unwrap();
NDArrayObject::from_value_and_unpacked_types(
generator,
ctx,
ndarray_val,
ndarray.dtype,
ndarray.ndims,
)
}
/// Create a new ndarray like `np.array()`.
///
/// NOTE: The `ndmin` argument is not here. You may want to
/// do [`NDArrayObject::atleast_nd`] to achieve that.
pub fn make_np_array<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
copy: Instance<'ctx, Int<Bool>>,
) -> Self {
match &*ctx.unifier.get_ty(object.ty) {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
{
let list = ListObject::from_object(generator, ctx, object);
NDArrayObject::make_np_array_list_impl(generator, ctx, list, copy)
}
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
let ndarray = NDArrayObject::from_object(generator, ctx, object);
NDArrayObject::make_np_array_ndarray_impl(generator, ctx, ndarray, copy)
}
_ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object.ty)), // Typechecker ensures this
}
}
}

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

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nac3core/src/codegen/object/ndarray/contiguous.rs Normal file
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use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::Type,
};
use super::NDArrayObject;
/// Fields of [`ContiguousNDArray`]
pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
pub ndims: F::Output<Int<SizeT>>,
pub shape: F::Output<Ptr<Int<SizeT>>>,
pub data: F::Output<Ptr<Item>>,
}
/// An ndarray without strides and non-opaque `data` field in NAC3.
#[derive(Debug, Clone, Copy)]
pub struct ContiguousNDArray<M> {
/// [`Model`] of the items.
pub item: M,
}
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
data: traversal.add("data", Ptr(self.item)),
}
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create a [`ContiguousNDArray`] from the contents of this ndarray.
///
/// This function may or may not be expensive depending on if this ndarray has contiguous data.
///
/// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
/// the returned [`ContiguousNDArray`] and copy contents of this ndarray to there.
///
/// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`ContiguousNDArray`]
/// will share memory with this ndarray.
///
/// The `item_model` sets the [`Model`] of the returned [`ContiguousNDArray`]'s `Item` model for type-safety, and
/// should match the `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics. Use model [`Any`]
/// if you don't care/cannot know the [`Model`] in advance.
pub fn make_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
item_model: Item,
) -> Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>> {
// Sanity check on `self.dtype` and `item_model`.
let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
let cdarray_model = Struct(ContiguousNDArray { item: item_model });
let current_bb = ctx.builder.get_insert_block().unwrap();
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
// Allocate and setup the resulting [`ContiguousNDArray`].
let result = cdarray_model.alloca(generator, ctx);
// Set ndims and shape.
let ndims = self.ndims_llvm(generator, ctx.ctx);
result.set(ctx, |f| f.ndims, ndims);
let shape = self.instance.get(generator, ctx, |f| f.shape);
result.set(ctx, |f| f.shape, shape);
let is_contiguous = self.is_c_contiguous(generator, ctx);
ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
// Inserting into then_bb; This ndarray is contiguous.
ctx.builder.position_at_end(then_bb);
let data = self.instance.get(generator, ctx, |f| f.data);
let data = data.pointer_cast(generator, ctx, item_model);
result.set(ctx, |f| f.data, data);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
// `make_copy` produces an ndarray with contiguous `data`.
ctx.builder.position_at_end(else_bb);
let copied_ndarray = self.make_copy(generator, ctx);
let data = copied_ndarray.instance.get(generator, ctx, |f| f.data);
let data = data.pointer_cast(generator, ctx, item_model);
result.set(ctx, |f| f.data, data);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Reposition to end_bb for continuation
ctx.builder.position_at_end(end_bb);
result
}
/// Create an [`NDArrayObject`] from a [`ContiguousNDArray`].
///
/// The operation is super cheap. The newly created [`NDArrayObject`] will share the
/// same memory as the [`ContiguousNDArray`].
///
/// `ndims` has to be provided as [`NDArrayObject`] requires a statically known `ndims` value, despite
/// the fact that the information should be contained within the [`ContiguousNDArray`].
pub fn from_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
carray: Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>>,
dtype: Type,
ndims: u64,
) -> Self {
// Sanity check on `dtype` and `contiguous_array`'s `Item` model.
let dtype_llvm = ctx.get_llvm_type(generator, dtype);
carray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
// TODO: Debug assert `ndims == carray.ndims` to catch bugs.
// Allocate the resulting ndarray.
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
// Copy shape and update strides
let shape = carray.get(generator, ctx, |f| f.shape);
ndarray.copy_shape_from_array(generator, ctx, shape);
ndarray.set_strides_contiguous(generator, ctx);
// Share data
let data = carray.get(generator, ctx, |f| f.data).pointer_cast(generator, ctx, Int(Byte));
ndarray.instance.set(ctx, |f| f.data, data);
ndarray
}
}

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nac3core/src/codegen/object/ndarray/factory.rs Normal file
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use inkwell::{values::BasicValueEnum, IntPredicate};
use crate::{
codegen::{
irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, CodeGenContext,
CodeGenerator,
},
typecheck::typedef::Type,
};
use super::NDArrayObject;
/// Get the zero value in `np.zeros()` of a `dtype`.
fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
ctx.ctx.i32_type().const_zero().into()
} else if [ctx.primitives.int64, ctx.primitives.uint64]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
ctx.ctx.i64_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
ctx.ctx.f64_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
ctx.ctx.bool_type().const_zero().into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
ctx.gen_string(generator, "").into()
} else {
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
}
}
/// Get the one value in `np.ones()` of a `dtype`.
fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int32);
ctx.ctx.i32_type().const_int(1, is_signed).into()
} else if [ctx.primitives.int64, ctx.primitives.uint64]
.iter()
.any(|ty| ctx.unifier.unioned(dtype, *ty))
{
let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int64);
ctx.ctx.i64_type().const_int(1, is_signed).into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
ctx.ctx.f64_type().const_float(1.0).into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
ctx.ctx.bool_type().const_int(1, false).into()
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
ctx.gen_string(generator, "1").into()
} else {
panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create an ndarray like `np.empty`.
pub fn make_np_empty<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
// Validate `shape`
let ndims_llvm = Int(SizeT).const_int(generator, ctx.ctx, ndims, false);
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims_llvm, shape);
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
ndarray.copy_shape_from_array(generator, ctx, shape);
ndarray.create_data(generator, ctx);
ndarray
}
/// Create an ndarray like `np.full`.
pub fn make_np_full<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
fill_value: BasicValueEnum<'ctx>,
) -> Self {
let ndarray = NDArrayObject::make_np_empty(generator, ctx, dtype, ndims, shape);
ndarray.fill(generator, ctx, fill_value);
ndarray
}
/// Create an ndarray like `np.zero`.
pub fn make_np_zeros<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
let fill_value = ndarray_zero_value(generator, ctx, dtype);
NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
}
/// Create an ndarray like `np.ones`.
pub fn make_np_ones<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> Self {
let fill_value = ndarray_one_value(generator, ctx, dtype);
NDArrayObject::make_np_full(generator, ctx, dtype, ndims, shape, fill_value)
}
/// Create an ndarray like `np.eye`.
pub fn make_np_eye<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
nrows: Instance<'ctx, Int<SizeT>>,
ncols: Instance<'ctx, Int<SizeT>>,
offset: Instance<'ctx, Int<SizeT>>,
) -> Self {
let ndzero = ndarray_zero_value(generator, ctx, dtype);
let ndone = ndarray_one_value(generator, ctx, dtype);
let ndarray = NDArrayObject::alloca_dynamic_shape(generator, ctx, dtype, &[nrows, ncols]);
// Create data and make the matrix like look np.eye()
ndarray.create_data(generator, ctx);
ndarray
.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
// NOTE: rows and cols can never be zero here, since this ndarray's `np.size` would be zero
// and this loop would not execute.
// Load up `row_i` and `col_i` from indices.
let row_i = nditer.get_indices().get_index_const(generator, ctx, 0);
let col_i = nditer.get_indices().get_index_const(generator, ctx, 1);
let be_one = row_i.add(ctx, offset).compare(ctx, IntPredicate::EQ, col_i);
let value = ctx.builder.build_select(be_one.value, ndone, ndzero, "value").unwrap();
let p = nditer.get_pointer(generator, ctx);
ctx.builder.build_store(p, value).unwrap();
Ok(())
})
.unwrap();
ndarray
}
/// Create an ndarray like `np.identity`.
pub fn make_np_identity<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
size: Instance<'ctx, Int<SizeT>>,
) -> Self {
// Convenient implementation
let offset = Int(SizeT).const_0(generator, ctx.ctx);
NDArrayObject::make_np_eye(generator, ctx, dtype, size, size, offset)
}
}

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

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

218
nac3core/src/codegen/object/ndarray/matmul.rs Normal file
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use std::cmp::max;
use nac3parser::ast::Operator;
use util::gen_for_model;
use crate::{
codegen::{
expr::gen_binop_expr_with_values, irrt::call_nac3_ndarray_matmul_calculate_shapes,
model::*, object::ndarray::indexing::RustNDIndex, CodeGenContext, CodeGenerator,
},
typecheck::{magic_methods::Binop, typedef::Type},
};
use super::{NDArrayObject, NDArrayOut};
/// Perform `np.einsum("...ij,...jk->...ik", in_a, in_b)`.
///
/// `dst_dtype` defines the dtype of the returned ndarray.
fn matmul_at_least_2d<'ctx, G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dst_dtype: Type,
in_a: NDArrayObject<'ctx>,
in_b: NDArrayObject<'ctx>,
) -> NDArrayObject<'ctx> {
assert!(in_a.ndims >= 2);
assert!(in_b.ndims >= 2);
// Deduce ndims of the result of matmul.
let ndims_int = max(in_a.ndims, in_b.ndims);
let ndims = Int(SizeT).const_int(generator, ctx.ctx, ndims_int, false);
// Broadcasts `in_a.shape[:-2]` and `in_b.shape[:-2]` together and allocate the
// destination ndarray to store the result of matmul.
let (lhs, rhs, dst) = {
let in_lhs_ndims = in_a.ndims_llvm(generator, ctx.ctx);
let in_lhs_shape = in_a.instance.get(generator, ctx, |f| f.shape);
let in_rhs_ndims = in_b.ndims_llvm(generator, ctx.ctx);
let in_rhs_shape = in_b.instance.get(generator, ctx, |f| f.shape);
let lhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
let rhs_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
let dst_shape = Int(SizeT).array_alloca(generator, ctx, ndims.value);
// Matmul dimension compatibility is checked here.
call_nac3_ndarray_matmul_calculate_shapes(
generator,
ctx,
in_lhs_ndims,
in_lhs_shape,
in_rhs_ndims,
in_rhs_shape,
ndims,
lhs_shape,
rhs_shape,
dst_shape,
);
let lhs = in_a.broadcast_to(generator, ctx, ndims_int, lhs_shape);
let rhs = in_b.broadcast_to(generator, ctx, ndims_int, rhs_shape);
let dst = NDArrayObject::alloca(generator, ctx, dst_dtype, ndims_int);
dst.copy_shape_from_array(generator, ctx, dst_shape);
dst.create_data(generator, ctx);
(lhs, rhs, dst)
};
let len = lhs.instance.get(generator, ctx, |f| f.shape).get_index_const(
generator,
ctx,
i64::try_from(ndims_int - 1).unwrap(),
);
let at_row = i64::try_from(ndims_int - 2).unwrap();
let at_col = i64::try_from(ndims_int - 1).unwrap();
let dst_dtype_llvm = ctx.get_llvm_type(generator, dst_dtype);
let dst_zero = dst_dtype_llvm.const_zero();
dst.foreach(generator, ctx, |generator, ctx, _, hdl| {
let pdst_ij = hdl.get_pointer(generator, ctx);
ctx.builder.build_store(pdst_ij, dst_zero).unwrap();
let indices = hdl.get_indices();
let i = indices.get_index_const(generator, ctx, at_row);
let j = indices.get_index_const(generator, ctx, at_col);
let num_0 = Int(SizeT).const_int(generator, ctx.ctx, 0, false);
let num_1 = Int(SizeT).const_int(generator, ctx.ctx, 1, false);
gen_for_model(generator, ctx, num_0, len, num_1, |generator, ctx, _, k| {
// `indices` is modified to index into `a` and `b`, and restored.
indices.set_index_const(ctx, at_row, i);
indices.set_index_const(ctx, at_col, k);
let a_ik = lhs.get_scalar_by_indices(generator, ctx, indices);
indices.set_index_const(ctx, at_row, k);
indices.set_index_const(ctx, at_col, j);
let b_kj = rhs.get_scalar_by_indices(generator, ctx, indices);
// Restore `indices`.
indices.set_index_const(ctx, at_row, i);
indices.set_index_const(ctx, at_col, j);
// x = a_[...]ik * b_[...]kj
let x = gen_binop_expr_with_values(
generator,
ctx,
(&Some(lhs.dtype), a_ik.value),
Binop::normal(Operator::Mult),
(&Some(rhs.dtype), b_kj.value),
ctx.current_loc,
)?
.unwrap()
.to_basic_value_enum(ctx, generator, dst_dtype)?;
// dst_[...]ij += x
let dst_ij = ctx.builder.build_load(pdst_ij, "").unwrap();
let dst_ij = gen_binop_expr_with_values(
generator,
ctx,
(&Some(dst_dtype), dst_ij),
Binop::normal(Operator::Add),
(&Some(dst_dtype), x),
ctx.current_loc,
)?
.unwrap()
.to_basic_value_enum(ctx, generator, dst_dtype)?;
ctx.builder.build_store(pdst_ij, dst_ij).unwrap();
Ok(())
})
})
.unwrap();
dst
}
impl<'ctx> NDArrayObject<'ctx> {
/// Perform `np.matmul` according to the rules in
/// <https://numpy.org/doc/stable/reference/generated/numpy.matmul.html>.
///
/// This function always return an [`NDArrayObject`]. You may want to use [`NDArrayObject::split_unsized`]
/// to handle when the output could be a scalar.
///
/// `dst_dtype` defines the dtype of the returned ndarray.
pub fn matmul<G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
a: Self,
b: Self,
out: NDArrayOut<'ctx>,
) -> Self {
// Sanity check, but type inference should prevent this.
assert!(a.ndims > 0 && b.ndims > 0, "np.matmul disallows scalar input");
/*
If both arguments are 2-D they are multiplied like conventional matrices.
If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indices and broadcast accordingly.
If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.
*/
let new_a = if a.ndims == 1 {
// Prepend 1 to its dimensions
a.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis])
} else {
a
};
let new_b = if b.ndims == 1 {
// Append 1 to its dimensions
b.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis])
} else {
b
};
// NOTE: `result` will always be a newly allocated ndarray.
// Current implementation cannot do in-place matrix muliplication.
let mut result = matmul_at_least_2d(generator, ctx, out.get_dtype(), new_a, new_b);
// Postprocessing on the result to remove prepended/appended axes.
let mut postindices = vec![];
let zero = Int(Int32).const_0(generator, ctx.ctx);
if a.ndims == 1 {
// Remove the prepended 1
postindices.push(RustNDIndex::SingleElement(zero));
}
if b.ndims == 1 {
// Remove the appended 1
postindices.push(RustNDIndex::Ellipsis);
postindices.push(RustNDIndex::SingleElement(zero));
}
if !postindices.is_empty() {
result = result.index(generator, ctx, &postindices);
}
match out {
NDArrayOut::NewNDArray { .. } => result,
NDArrayOut::WriteToNDArray { ndarray: out_ndarray } => {
let result_shape = result.instance.get(generator, ctx, |f| f.shape);
out_ndarray.assert_can_be_written_by_out(
generator,
ctx,
result.ndims,
result_shape,
);
out_ndarray.copy_data_from(generator, ctx, result);
out_ndarray
}
}
}
}

655
nac3core/src/codegen/object/ndarray/mod.rs Normal file
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pub mod array;
pub mod broadcast;
pub mod contiguous;
pub mod factory;
pub mod indexing;
pub mod map;
pub mod matmul;
pub mod nditer;
pub mod shape_util;
pub mod view;
use inkwell::{
context::Context,
types::BasicType,
values::{BasicValue, BasicValueEnum, PointerValue},
AddressSpace,
};
use crate::{
codegen::{
irrt::{
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
call_nac3_ndarray_get_pelement_by_indices, call_nac3_ndarray_is_c_contiguous,
call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
call_nac3_ndarray_util_assert_output_shape_same,
},
model::*,
CodeGenContext, CodeGenerator,
},
toplevel::{
helper::{create_ndims, extract_ndims},
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
},
typecheck::typedef::{Type, TypeEnum},
};
use super::{any::AnyObject, tuple::TupleObject};
/// Fields of [`NDArray`]
pub struct NDArrayFields<'ctx, F: FieldTraversal<'ctx>> {
pub data: F::Output<Ptr<Int<Byte>>>,
pub itemsize: F::Output<Int<SizeT>>,
pub ndims: F::Output<Int<SizeT>>,
pub shape: F::Output<Ptr<Int<SizeT>>>,
pub strides: F::Output<Ptr<Int<SizeT>>>,
}
/// A strided ndarray in NAC3.
///
/// See IRRT implementation for details about its fields.
#[derive(Debug, Clone, Copy, Default)]
pub struct NDArray;
impl<'ctx> StructKind<'ctx> for NDArray {
type Fields<F: FieldTraversal<'ctx>> = NDArrayFields<'ctx, F>;
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
data: traversal.add_auto("data"),
itemsize: traversal.add_auto("itemsize"),
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
strides: traversal.add_auto("strides"),
}
}
}
/// A NAC3 Python ndarray object.
#[derive(Debug, Clone, Copy)]
pub struct NDArrayObject<'ctx> {
pub dtype: Type,
pub ndims: u64,
pub instance: Instance<'ctx, Ptr<Struct<NDArray>>>,
}
impl<'ctx> NDArrayObject<'ctx> {
/// Attempt to convert an [`AnyObject`] into an [`NDArrayObject`].
pub fn from_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> NDArrayObject<'ctx> {
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
let ndims = extract_ndims(&ctx.unifier, ndims);
Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
}
/// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
/// `dtype` and `ndims`.
pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
value: V,
dtype: Type,
ndims: u64,
) -> Self {
let value = Ptr(Struct(NDArray)).check_value(generator, ctx.ctx, value).unwrap();
NDArrayObject { dtype, ndims, instance: value }
}
/// Get this ndarray's `ndims` as an LLVM constant.
pub fn ndims_llvm<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Int<SizeT>> {
Int(SizeT).const_int(generator, ctx, self.ndims, false)
}
/// Get the typechecker ndarray type of this [`NDArrayObject`].
pub fn get_type(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Type {
let ndims = create_ndims(&mut ctx.unifier, self.ndims);
make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(self.dtype), Some(ndims))
}
/// Forget that this is an ndarray and convert into an [`AnyObject`].
pub fn to_any(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
let ty = self.get_type(ctx);
AnyObject { value: self.instance.value.as_basic_value_enum(), ty }
}
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
///
/// `shape` and `strides` will be automatically allocated onto the stack.
///
/// The returned ndarray's content will be:
/// - `data`: uninitialized.
/// - `itemsize`: set to the `sizeof()` of `dtype`.
/// - `ndims`: set to the value of `ndims`.
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
pub fn alloca<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
) -> Self {
let ndarray = Struct(NDArray).alloca(generator, ctx);
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
let itemsize = Int(SizeT).z_extend_or_truncate(generator, ctx, itemsize);
ndarray.set(ctx, |f| f.itemsize, itemsize);
let ndims_val = Int(SizeT).const_int(generator, ctx.ctx, ndims, false);
ndarray.set(ctx, |f| f.ndims, ndims_val);
let shape = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
ndarray.set(ctx, |f| f.shape, shape);
let strides = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
ndarray.set(ctx, |f| f.strides, strides);
NDArrayObject { dtype, ndims, instance: ndarray }
}
/// Convenience function. Allocate an [`NDArrayObject`] with a statically known shape.
///
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
pub fn alloca_constant_shape<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
shape: &[u64],
) -> Self {
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
// Write shape
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
for (i, dim) in shape.iter().enumerate() {
let dim = Int(SizeT).const_int(generator, ctx.ctx, *dim, false);
dst_shape.offset_const(ctx, i64::try_from(i).unwrap()).store(ctx, dim);
}
ndarray
}
/// Convenience function. Allocate an [`NDArrayObject`] with a dynamically known shape.
///
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
pub fn alloca_dynamic_shape<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
shape: &[Instance<'ctx, Int<SizeT>>],
) -> Self {
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
// Write shape
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
for (i, dim) in shape.iter().enumerate() {
dst_shape.offset_const(ctx, i64::try_from(i).unwrap()).store(ctx, *dim);
}
ndarray
}
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
/// The allocated data buffer is considered to be *owned* by the ndarray.
///
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
///
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
pub fn create_data<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
let nbytes = self.nbytes(generator, ctx);
let data = Int(Byte).array_alloca(generator, ctx, nbytes.value);
self.instance.set(ctx, |f| f.data, data);
self.set_strides_contiguous(generator, ctx);
}
/// Copy shape dimensions from an array.
pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let num_items = self.ndims_llvm(generator, ctx.ctx).value;
self.instance.get(generator, ctx, |f| f.shape).copy_from(generator, ctx, shape, num_items);
}
/// Copy shape dimensions from an ndarray.
/// Panics if `ndims` mismatches.
pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: NDArrayObject<'ctx>,
) {
assert_eq!(self.ndims, src_ndarray.ndims);
let src_shape = src_ndarray.instance.get(generator, ctx, |f| f.shape);
self.copy_shape_from_array(generator, ctx, src_shape);
}
/// Copy strides dimensions from an array.
pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
strides: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let num_items = self.ndims_llvm(generator, ctx.ctx).value;
self.instance
.get(generator, ctx, |f| f.strides)
.copy_from(generator, ctx, strides, num_items);
}
/// Copy strides dimensions from an ndarray.
/// Panics if `ndims` mismatches.
pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: NDArrayObject<'ctx>,
) {
assert_eq!(self.ndims, src_ndarray.ndims);
let src_strides = src_ndarray.instance.get(generator, ctx, |f| f.strides);
self.copy_strides_from_array(generator, ctx, src_strides);
}
/// Get the `np.size()` of this ndarray.
pub fn size<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_size(generator, ctx, self.instance)
}
/// Get the `ndarray.nbytes` of this ndarray.
pub fn nbytes<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_nbytes(generator, ctx, self.instance)
}
/// Get the `len()` of this ndarray.
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_len(generator, ctx, self.instance)
}
/// Check if this ndarray is C-contiguous.
///
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<Bool>> {
call_nac3_ndarray_is_c_contiguous(generator, ctx, self.instance)
}
/// Get the pointer to the n-th (0-based) element.
///
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Instance<'ctx, Int<SizeT>>,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.instance, nth);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
.unwrap()
}
/// Get the n-th (0-based) scalar.
pub fn get_nth_scalar<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Instance<'ctx, Int<SizeT>>,
) -> AnyObject<'ctx> {
let ptr = self.get_nth_pelement(generator, ctx, nth);
let value = ctx.builder.build_load(ptr, "").unwrap();
AnyObject { ty: self.dtype, value }
}
/// Get the pointer to the element indexed by `indices`.
///
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
pub fn get_pelement_by_indices<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
let p = call_nac3_ndarray_get_pelement_by_indices(generator, ctx, self.instance, indices);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
.unwrap()
}
/// Get the scalar indexed by `indices`.
pub fn get_scalar_by_indices<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> AnyObject<'ctx> {
let ptr = self.get_pelement_by_indices(generator, ctx, indices);
let value = ctx.builder.build_load(ptr, "").unwrap();
AnyObject { ty: self.dtype, value }
}
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
///
/// Update the ndarray's strides to make the ndarray contiguous.
pub fn set_strides_contiguous<G: CodeGenerator + ?Sized>(
self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.instance);
}
/// Clone/Copy this ndarray - Allocate a new ndarray with the same shape as this ndarray and copy the contents over.
///
/// The new ndarray will own its data and will be C-contiguous.
#[must_use]
pub fn make_copy<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Self {
let clone = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
let shape = self.instance.gep(ctx, |f| f.shape).load(generator, ctx);
clone.copy_shape_from_array(generator, ctx, shape);
clone.create_data(generator, ctx);
clone.copy_data_from(generator, ctx, *self);
clone
}
/// Copy data from another ndarray.
///
/// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
/// do not matter. The copying order is determined by how their flattened views look.
///
/// Panics if the `dtype`s of ndarrays are different.
pub fn copy_data_from<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src: NDArrayObject<'ctx>,
) {
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
call_nac3_ndarray_copy_data(generator, ctx, src.instance, self.instance);
}
/// Returns true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
#[must_use]
pub fn is_unsized(&self) -> bool {
self.ndims == 0
}
/// If this ndarray is unsized, return its sole value as an [`AnyObject`].
/// Otherwise, do nothing and return the ndarray itself.
pub fn split_unsized<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> ScalarOrNDArray<'ctx> {
if self.is_unsized() {
// NOTE: `np.size(self) == 0` here is never possible.
let zero = Int(SizeT).const_0(generator, ctx.ctx);
let value = self.get_nth_scalar(generator, ctx, zero).value;
ScalarOrNDArray::Scalar(AnyObject { ty: self.dtype, value })
} else {
ScalarOrNDArray::NDArray(*self)
}
}
/// Fill the ndarray with a scalar.
///
/// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
pub fn fill<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
value: BasicValueEnum<'ctx>,
) {
// TODO: It is possible to optimize this by exploiting contiguous strides with memset.
// Probably best to implement in IRRT.
self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
let p = nditer.get_pointer(generator, ctx);
ctx.builder.build_store(p, value).unwrap();
Ok(())
})
.unwrap();
}
/// Create the shape tuple of this ndarray like `np.shape(<ndarray>)`.
///
/// The returned integers in the tuple are in int32.
pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> TupleObject<'ctx> {
// TODO: Return a tuple of SizeT
let mut objects = Vec::with_capacity(self.ndims as usize);
for i in 0..self.ndims {
let dim = self
.instance
.get(generator, ctx, |f| f.shape)
.get_index_const(generator, ctx, i64::try_from(i).unwrap())
.truncate_or_bit_cast(generator, ctx, Int32);
objects.push(AnyObject {
ty: ctx.primitives.int32,
value: dim.value.as_basic_value_enum(),
});
}
TupleObject::from_objects(generator, ctx, objects)
}
/// Create the strides tuple of this ndarray like `<ndarray>.strides`.
///
/// The returned integers in the tuple are in int32.
pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> TupleObject<'ctx> {
// TODO: Return a tuple of SizeT.
let mut objects = Vec::with_capacity(self.ndims as usize);
for i in 0..self.ndims {
let dim = self
.instance
.get(generator, ctx, |f| f.strides)
.get_index_const(generator, ctx, i64::try_from(i).unwrap())
.truncate_or_bit_cast(generator, ctx, Int32);
objects.push(AnyObject {
ty: ctx.primitives.int32,
value: dim.value.as_basic_value_enum(),
});
}
TupleObject::from_objects(generator, ctx, objects)
}
/// Create an unsized ndarray to contain `object`.
pub fn make_unsized<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> NDArrayObject<'ctx> {
// We have to put the value on the stack to get a data pointer.
let data = ctx.builder.build_alloca(object.value.get_type(), "make_unsized").unwrap();
ctx.builder.build_store(data, object.value).unwrap();
let data = Ptr(Int(Byte)).pointer_cast(generator, ctx, data);
let ndarray = NDArrayObject::alloca(generator, ctx, object.ty, 0);
ndarray.instance.set(ctx, |f| f.data, data);
ndarray
}
/// Check if this `NDArray` can be used as an `out` ndarray for an operation.
///
/// Raise an exception if the shapes do not match.
pub fn assert_can_be_written_by_out<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
out_ndims: u64,
out_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let ndarray_ndims = self.ndims_llvm(generator, ctx.ctx);
let ndarray_shape = self.instance.get(generator, ctx, |f| f.shape);
let output_ndims = Int(SizeT).const_int(generator, ctx.ctx, out_ndims, false);
let output_shape = out_shape;
call_nac3_ndarray_util_assert_output_shape_same(
generator,
ctx,
ndarray_ndims,
ndarray_shape,
output_ndims,
output_shape,
);
}
}
/// A convenience enum for implementing functions that acts on scalars or ndarrays or both.
#[derive(Debug, Clone, Copy)]
pub enum ScalarOrNDArray<'ctx> {
Scalar(AnyObject<'ctx>),
NDArray(NDArrayObject<'ctx>),
}
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for AnyObject<'ctx> {
type Error = ();
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
match value {
ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
ScalarOrNDArray::NDArray(_ndarray) => Err(()),
}
}
}
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
type Error = ();
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
match value {
ScalarOrNDArray::Scalar(_scalar) => Err(()),
ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
}
}
}
impl<'ctx> ScalarOrNDArray<'ctx> {
/// Split on `object` either into a scalar or an ndarray.
///
/// If `object` is an ndarray, [`ScalarOrNDArray::NDArray`].
///
/// For everything else, it is wrapped with [`ScalarOrNDArray::Scalar`].
pub fn split_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> ScalarOrNDArray<'ctx> {
match &*ctx.unifier.get_ty(object.ty) {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
let ndarray = NDArrayObject::from_object(generator, ctx, object);
ScalarOrNDArray::NDArray(ndarray)
}
_ => ScalarOrNDArray::Scalar(object),
}
}
/// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
#[must_use]
pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
match self {
ScalarOrNDArray::Scalar(scalar) => scalar.value,
ScalarOrNDArray::NDArray(ndarray) => ndarray.instance.value.as_basic_value_enum(),
}
}
/// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
/// - If this is an ndarray, the ndarray is returned.
/// - If this is a scalar, this function returns new ndarray created with [`NDArrayObject::make_unsized`].
pub fn to_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> NDArrayObject<'ctx> {
match self {
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
ScalarOrNDArray::Scalar(scalar) => NDArrayObject::make_unsized(generator, ctx, *scalar),
}
}
/// Get the dtype of the ndarray created if this were called with [`ScalarOrNDArray::to_ndarray`].
#[must_use]
pub fn get_dtype(&self) -> Type {
match self {
ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
ScalarOrNDArray::Scalar(scalar) => scalar.ty,
}
}
}
/// An helper enum specifying how a function should produce its output.
///
/// Many functions in NumPy has an optional `out` parameter (e.g., `matmul`). If `out` is specified
/// with an ndarray, the result of a function will be written to `out`. If `out` is not specified, a function will
/// create a new ndarray and store the result in it.
#[derive(Debug, Clone, Copy)]
pub enum NDArrayOut<'ctx> {
/// Tell a function should create a new ndarray with the expected element type `dtype`.
NewNDArray { dtype: Type },
/// Tell a function to write the result to `ndarray`.
WriteToNDArray { ndarray: NDArrayObject<'ctx> },
}
impl<'ctx> NDArrayOut<'ctx> {
/// Get the dtype of this output.
#[must_use]
pub fn get_dtype(&self) -> Type {
match self {
NDArrayOut::NewNDArray { dtype } => *dtype,
NDArrayOut::WriteToNDArray { ndarray } => ndarray.dtype,
}
}
}

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

105
nac3core/src/codegen/object/ndarray/shape_util.rs Normal file
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@ -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, i64::try_from(i).unwrap(), int);
}
(len, result)
}
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
{
// 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
let input_int = input_sequence.value.into_int_value();
let len = Int(SizeT).const_1(generator, ctx.ctx);
let result = Int(SizeT).array_alloca(generator, ctx, len.value);
let int = Int(SizeT).s_extend_or_bit_cast(generator, ctx, input_int);
// Storing into result[0]
result.store(ctx, int);
(len, result)
}
_ => panic!(
"encountered unknown sequence type: {}",
ctx.unifier.stringify(input_sequence.ty)
),
}
}

119
nac3core/src/codegen/object/ndarray/view.rs Normal file
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@ -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
}
}

99
nac3core/src/codegen/object/tuple.rs Normal file
View File

@ -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, false)
}
/// Get the `i`-th (0-based) object in this tuple.
pub fn index(&self, ctx: &mut CodeGenContext<'ctx, '_>, i: usize) -> AnyObject<'ctx> {
assert!(
i < self.num_elements(),
"Tuple object with length {} have index {i}",
self.num_elements()
);
let value = ctx.builder.build_extract_value(self.value, i as u32, "tuple[{i}]").unwrap();
let ty = self.tys[i];
AnyObject { ty, value }
}
}

1
nac3core/src/codegen/object/utils/mod.rs Normal file
View File

@ -0,0 +1 @@
pub mod slice;

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

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

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

@ -1,22 +1,15 @@
use inkwell::{
attributes::{Attribute, AttributeLoc},
basic_block::BasicBlock,
types::{BasicType, BasicTypeEnum},
values::{BasicValue, BasicValueEnum, FunctionValue, IntValue, PointerValue},
IntPredicate,
};
use itertools::{izip, Itertools};
use nac3parser::ast::{
Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
};
use super::{
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
expr::{destructure_range, gen_binop_expr},
gen_in_range_check,
irrt::{handle_slice_indices, list_slice_assignment},
macros::codegen_unreachable,
object::{
any::AnyObject,
ndarray::{
indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject, ScalarOrNDArray,
},
},
CodeGenContext, CodeGenerator,
};
use crate::{
@ -27,6 +20,17 @@ use crate::{
typedef::{iter_type_vars, FunSignature, Type, TypeEnum},
},
};
use inkwell::{
attributes::{Attribute, AttributeLoc},
basic_block::BasicBlock,
types::{BasicType, BasicTypeEnum},
values::{BasicValue, BasicValueEnum, FunctionValue, IntValue, PointerValue},
IntPredicate,
};
use itertools::{izip, Itertools};
use nac3parser::ast::{
Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
};
/// See [`CodeGenerator::gen_var_alloc`].
pub fn gen_var<'ctx>(
@ -411,7 +415,47 @@ 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));
@ -1828,37 +1872,6 @@ 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(())

40
nac3core/src/codegen/test.rs
View File

@ -1,20 +1,3 @@
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},
@ -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,

4
nac3core/src/lib.rs
View File

@ -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;

21
nac3core/src/symbol_resolver.rs
View File

@ -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>;

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

@ -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))
}),
),

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

@ -1,17 +1,17 @@
use nac3parser::ast::fold::Fold;
use std::rc::Rc;
use nac3parser::ast::{fold::Fold, ExprKind};
use super::*;
use crate::{
codegen::{expr::get_subst_key, stmt::exn_constructor},
symbol_resolver::SymbolValue,
typecheck::{
type_inferencer::{FunctionData, 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,
@ -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 {
@ -1898,8 +1845,6 @@ 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 +1894,7 @@ 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 +2002,11 @@ 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 +2057,6 @@ 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 +2161,4 @@ 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(())
}
}

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

@ -1,17 +1,14 @@
use std::convert::TryInto;
use crate::symbol_resolver::SymbolValue;
use crate::toplevel::numpy::unpack_ndarray_var_tys;
use crate::typecheck::typedef::{into_var_map, iter_type_vars, Mapping, TypeVarId, VarMap};
use ast::ExprKind;
use nac3parser::ast::{Constant, Location};
use strum::IntoEnumIterator;
use strum_macros::EnumIter;
use ast::ExprKind;
use nac3parser::ast::{Constant, Location};
use super::*;
use crate::{
symbol_resolver::SymbolValue,
toplevel::numpy::unpack_ndarray_var_tys,
typecheck::typedef::{into_var_map, iter_type_vars, Mapping, TypeVarId, VarMap},
};
/// All primitive types and functions in nac3core.
#[derive(Clone, Copy, Debug, EnumIter, PartialEq, Eq)]
@ -56,6 +53,16 @@ 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 +110,6 @@ pub enum PrimDef {
FunNpLdExp,
FunNpHypot,
FunNpNextAfter,
FunNpTranspose,
FunNpReshape,
// Linalg functions
FunNpDot,
@ -242,6 +247,16 @@ 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 +304,6 @@ 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 +404,6 @@ impl TopLevelDef {
r
}
),
TopLevelDef::Variable { name, ty, .. } => {
format!("Variable {{ name: {name:?}, ty: {:?} }}", unifier.stringify(*ty),)
}
}
}
}
@ -595,18 +605,6 @@ 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('.');
@ -1136,3 +1134,23 @@ 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)
}

43
nac3core/src/toplevel/mod.rs
View File

@ -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 {

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

@ -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.
///

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

@ -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!()
}

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

@ -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 {

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

@ -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 {

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

@ -1,21 +1,19 @@
use std::{cmp::max, collections::HashMap, rc::Rc};
use itertools::{iproduct, Itertools};
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::{
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 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,
@ -541,36 +528,43 @@ pub fn typeof_binop(
}
}
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 (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)
));
}
_ => 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()
// Deduce the ndims of the resulting ndarray.
// If this is 0 (an unsized ndarray), matmul returns a scalar just like NumPy.
let result_ndims = match (lhs_ndims, rhs_ndims) {
(0, _) | (_, 0) => {
return Err(
"ndarray.__matmul__ does not allow unsized ndarray input".to_string()
)
}
_ => unreachable!(),
(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 +767,7 @@ 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);

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

@ -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 {

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

@ -1,36 +1,32 @@
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,
},
};
use crate::toplevel::type_annotation::TypeAnnotation;
use crate::{
symbol_resolver::{SymbolResolver, SymbolValue},
toplevel::{
helper::{arraylike_flatten_element_type, arraylike_get_ndims, PrimDef},
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
type_annotation::TypeAnnotation,
TopLevelContext, TopLevelDef,
},
typecheck::typedef::Mapping,
};
use itertools::{izip, Itertools};
use nac3parser::ast::{
self,
fold::{self, Fold},
Arguments, Comprehension, ExprContext, ExprKind, Located, Location, StrRef,
};
#[cfg(test)]
@ -88,20 +84,6 @@ 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 +92,7 @@ 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,
@ -242,7 +224,9 @@ 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 +378,6 @@ 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 +549,7 @@ 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 +557,7 @@ 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 +622,8 @@ 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 +732,8 @@ 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 +1183,45 @@ 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 +1543,7 @@ 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 +1589,36 @@ 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 +1636,7 @@ 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![],
},
}));

33
nac3core/src/typecheck/type_inferencer/test.rs
View File

@ -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();

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

@ -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`.

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

@ -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 {

9
nac3ld/src/lib.rs
View File

@ -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;

6
nac3parser/Cargo.toml
View File

@ -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"

10
nac3parser/src/config_comment_helper.rs
View File

@ -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(

9
nac3parser/src/error.rs
View File

@ -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)]

13
nac3parser/src/fstring.rs
View File

@ -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>>,

9
nac3parser/src/function.rs
View File

@ -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>,

16
nac3parser/src/lexer.rs
View File

@ -1,16 +1,16 @@
//! This module takes care of lexing python source text.
//!
//! This means source code is translated into separate tokens.
use std::{char, cmp::Ordering, num::IntErrorKind, str::FromStr};
use unic_emoji_char::is_emoji_presentation;
use unic_ucd_ident::{is_xid_continue, is_xid_start};
pub use super::token::Tok;
use crate::{
ast::{FileName, Location},
error::{LexicalError, LexicalErrorType},
};
use crate::ast::{FileName, Location};
use crate::error::{LexicalError, LexicalErrorType};
use std::char;
use std::cmp::Ordering;
use std::num::IntErrorKind;
use std::str::FromStr;
use unic_emoji_char::is_emoji_presentation;
use unic_ucd_ident::{is_xid_continue, is_xid_start};
#[derive(Clone, Copy, PartialEq, Debug, Default)]
struct IndentationLevel {

12
nac3parser/src/parser.rs
View File

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

3
nac3parser/src/token.rs
View File

@ -1,8 +1,7 @@
//! Different token definitions.
//! Loosely based on token.h from CPython source:
use std::fmt::{self, Write};
use crate::ast;
use std::fmt::{self, Write};
/// Python source code can be tokenized in a sequence of these tokens.
#[derive(Clone, Debug, PartialEq)]

6
nac3standalone/Cargo.toml
View File

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

12
nac3standalone/demo/interpret_demo.py
View File

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

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

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

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

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

58
nac3standalone/src/basic_symbol_resolver.rs
View File

@ -1,14 +1,5 @@
use std::{
collections::{HashMap, HashSet},
sync::Arc,
};
use parking_lot::{Mutex, RwLock};
use nac3core::{
codegen::{CodeGenContext, CodeGenerator},
inkwell::{module::Linkage, values::BasicValue},
nac3parser::ast::{self, StrRef},
codegen::CodeGenContext,
symbol_resolver::{SymbolResolver, SymbolValue, ValueEnum},
toplevel::{DefinitionId, TopLevelDef},
typecheck::{
@ -16,6 +7,10 @@ use nac3core::{
typedef::{Type, Unifier},
},
};
use nac3parser::ast::{self, StrRef};
use parking_lot::{Mutex, RwLock};
use std::collections::HashSet;
use std::{collections::HashMap, sync::Arc};
pub struct ResolverInternal {
pub id_to_type: Mutex<HashMap<StrRef, Type>>,
@ -50,51 +45,20 @@ impl SymbolResolver for Resolver {
fn get_symbol_type(
&self,
unifier: &mut Unifier,
_: &mut Unifier,
_: &[Arc<RwLock<TopLevelDef>>],
primitives: &PrimitiveStore,
_: &PrimitiveStore,
str: StrRef,
) -> Result<Type, String> {
self.0
.id_to_type
.lock()
.get(&str)
.copied()
.or_else(|| {
self.0
.module_globals
.lock()
.get(&str)
.cloned()
.map(|v| v.get_type(primitives, unifier))
})
.ok_or(format!("cannot get type of {str}"))
self.0.id_to_type.lock().get(&str).copied().ok_or(format!("cannot get type of {str}"))
}
fn get_symbol_value<'ctx>(
&self,
str: StrRef,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut dyn CodeGenerator,
_: StrRef,
_: &mut CodeGenContext<'ctx, '_>,
) -> Option<ValueEnum<'ctx>> {
self.0.module_globals.lock().get(&str).cloned().map(|v| {
ctx.module
.get_global(&str.to_string())
.unwrap_or_else(|| {
let ty = v.get_type(&ctx.primitives, &mut ctx.unifier);
let init_val = ctx.gen_symbol_val(generator, &v, ty);
let llvm_ty = init_val.get_type();
let global = ctx.module.add_global(llvm_ty, None, &str.to_string());
global.set_linkage(Linkage::LinkOnceAny);
global.set_initializer(&init_val);
global
})
.as_basic_value_enum()
.into()
})
unimplemented!()
}
fn get_identifier_def(&self, id: StrRef) -> Result<DefinitionId, HashSet<String>> {

107
nac3standalone/src/main.rs
View File

@ -8,30 +8,17 @@
#![warn(clippy::pedantic)]
#![allow(clippy::too_many_lines, clippy::wildcard_imports)]
use std::{
collections::{HashMap, HashSet},
fs,
num::NonZeroUsize,
path::Path,
sync::Arc,
};
use clap::Parser;
use parking_lot::{Mutex, RwLock};
use inkwell::context::Context;
use inkwell::{
memory_buffer::MemoryBuffer, passes::PassBuilderOptions, support::is_multithreaded, targets::*,
OptimizationLevel,
};
use nac3core::{
codegen::{
concrete_type::ConcreteTypeStore, irrt::load_irrt, CodeGenLLVMOptions,
CodeGenTargetMachineOptions, CodeGenTask, DefaultCodeGenerator, WithCall, WorkerRegistry,
},
inkwell::{
memory_buffer::MemoryBuffer, module::Linkage, passes::PassBuilderOptions,
support::is_multithreaded, targets::*, OptimizationLevel,
},
nac3parser::{
ast::{Constant, Expr, ExprKind, StmtKind, StrRef},
parser,
},
symbol_resolver::SymbolResolver,
toplevel::{
composer::{ComposerConfig, TopLevelComposer},
@ -44,6 +31,14 @@ use nac3core::{
typedef::{FunSignature, Type, Unifier, VarMap},
},
};
use nac3parser::{
ast::{Constant, Expr, ExprKind, StmtKind, StrRef},
parser,
};
use parking_lot::{Mutex, RwLock};
use std::collections::HashSet;
use std::num::NonZeroUsize;
use std::{collections::HashMap, fs, path::Path, sync::Arc};
mod basic_symbol_resolver;
use basic_symbol_resolver::*;
@ -245,34 +240,6 @@ fn handle_assignment_pattern(
}
}
fn handle_global_var(
target: &Expr,
value: Option<&Expr>,
resolver: &(dyn SymbolResolver + Send + Sync),
internal_resolver: &ResolverInternal,
) -> Result<(), String> {
let ExprKind::Name { id, .. } = target.node else {
return Err(format!(
"global variable declaration must be an identifier (at {})",
target.location,
));
};
let Some(value) = value else {
return Err(format!("global variable `{id}` must be initialized in its definition"));
};
if let Ok(val) = parse_parameter_default_value(value, resolver) {
internal_resolver.add_module_global(id, val);
Ok(())
} else {
Err(format!(
"failed to evaluate this expression `{:?}` as a constant at {}",
target.node, target.location,
))
}
}
fn main() {
let cli = CommandLineArgs::parse();
let CommandLineArgs { file_name, threads, opt_level, emit_llvm, triple, mcpu, target_features } =
@ -313,19 +280,22 @@ fn main() {
reloc_mode: RelocMode::PIC,
..host_target_machine
};
let target_machine = target_machine_options
let size_t = Context::create()
.ptr_sized_int_type(
&target_machine_options
.create_target_machine(opt_level)
.expect("couldn't create target machine");
let context = nac3core::inkwell::context::Context::create();
let size_t =
context.ptr_sized_int_type(&target_machine.get_target_data(), None).get_bit_width();
.map(|tm| tm.get_target_data())
.unwrap(),
None,
)
.get_bit_width();
let program = match fs::read_to_string(file_name.clone()) {
Ok(program) => program,
Err(err) => {
panic!("Cannot open input file: {err}");
println!("Cannot open input file: {err}");
return;
}
};
@ -343,6 +313,8 @@ fn main() {
let resolver =
Arc::new(Resolver(internal_resolver.clone())) as Arc<dyn SymbolResolver + Send + Sync>;
let context = inkwell::context::Context::create();
// Process IRRT
let irrt = load_irrt(&context, resolver.as_ref());
if emit_llvm {
@ -367,26 +339,10 @@ fn main() {
unifier,
primitives,
) {
panic!("{err}");
eprintln!("{err}");
return;
}
}
StmtKind::AnnAssign { target, value, .. } => {
if let Err(err) = handle_global_var(
target,
value.as_ref().map(Box::as_ref),
resolver.as_ref(),
internal_resolver.as_ref(),
) {
panic!("{err}");
}
let (name, def_id, _) = composer
.register_top_level(stmt, Some(resolver.clone()), "__main__", true)
.unwrap();
internal_resolver.add_id_def(name, def_id);
}
// allow (and ignore) "from __future__ import annotations"
StmtKind::ImportFrom { module, names, .. }
if module == &Some("__future__".into())
@ -497,12 +453,17 @@ fn main() {
let mut function_iter = main.get_first_function();
while let Some(func) = function_iter {
if func.count_basic_blocks() > 0 && func.get_name().to_str().unwrap() != "run" {
func.set_linkage(Linkage::Private);
func.set_linkage(inkwell::module::Linkage::Private);
}
function_iter = func.get_next_function();
}
// Optimize `main`
let target_machine = llvm_options
.target
.create_target_machine(llvm_options.opt_level)
.expect("couldn't create target machine");
let pass_options = PassBuilderOptions::create();
pass_options.set_merge_functions(true);
let passes = format!("default<O{}>", opt_level as u32);

4
nix/windows/PKGBUILD
View File

@ -21,6 +21,6 @@ build() {
}
package() {
mkdir -p $pkgdir/clang64/lib/python3.12/site-packages
cp ${srcdir}/nac3artiq.pyd $pkgdir/clang64/lib/python3.12/site-packages
mkdir -p $pkgdir/clang64/lib/python3.11/site-packages
cp ${srcdir}/nac3artiq.pyd $pkgdir/clang64/lib/python3.11/site-packages
}

4
nix/windows/default.nix
View File

@ -21,10 +21,10 @@ let
text =
''
implementation=CPython
version=3.12
version=3.11
shared=true
abi3=false
lib_name=python3.12
lib_name=python3.11
lib_dir=${msys2-env}/clang64/lib
pointer_width=64
build_flags=WITH_THREAD

4
nix/windows/make_msys2_packages.sh
View File

@ -6,11 +6,11 @@ cd $(dirname $0)
MSYS2DIR=`pwd`/msys2
mkdir -p $MSYS2DIR/var/lib/pacman $MSYS2DIR/msys/etc
curl -L https://repo.msys2.org/msys/x86_64/pacman-mirrors-20240523-1-any.pkg.tar.zst | tar xvf - -C $MSYS2DIR --zstd
curl -L https://mirror.msys2.org/msys/x86_64/pacman-mirrors-20220205-1-any.pkg.tar.zst | tar xvf - -C $MSYS2DIR --zstd
curl -L https://raw.githubusercontent.com/msys2/MSYS2-packages/master/pacman/pacman.conf | sed "s|SigLevel = Required|SigLevel = Never|g" | sed "s|/etc/pacman.d|$MSYS2DIR/etc/pacman.d|g" > $MSYS2DIR/etc/pacman.conf
fakeroot pacman --root $MSYS2DIR --config $MSYS2DIR/etc/pacman.conf -Syy
pacman --root $MSYS2DIR --config $MSYS2DIR/etc/pacman.conf --cachedir $MSYS2DIR/msys/cache -Sp mingw-w64-clang-x86_64-rust mingw-w64-clang-x86_64-cmake mingw-w64-clang-x86_64-ninja mingw-w64-clang-x86_64-python-numpy mingw-w64-clang-x86_64-python-setuptools > $MSYS2DIR/packages.txt
pacman --root $MSYS2DIR --config $MSYS2DIR/etc/pacman.conf --cachedir $MSYS2DIR/msys/cache -Sp mingw-w64-clang-x86_64-rust mingw-w64-clang-x86_64-cmake mingw-w64-clang-x86_64-ninja mingw-w64-clang-x86_64-python3.11 mingw-w64-clang-x86_64-python-numpy mingw-w64-clang-x86_64-python-setuptools > $MSYS2DIR/packages.txt
echo "{ pkgs } : [" > msys2_packages.nix
while read package; do

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