Compare commits
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misc/impl-
| Author | SHA1 | Date | |
|---|---|---|---|
| 78a070e8e0 |
111
Cargo.lock
generated
111
Cargo.lock
generated
@ -126,9 +126,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dependencies = [
|
||||
"shlex",
|
||||
]
|
||||
@ -170,7 +170,7 @@ dependencies = [
|
||||
"heck 0.5.0",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.94",
|
||||
"syn 2.0.90",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -187,14 +187,14 @@ checksum = "5b63caa9aa9397e2d9480a9b13673856c78d8ac123288526c37d7839f2a86990"
|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"encode_unicode",
|
||||
"lazy_static",
|
||||
"libc",
|
||||
"once_cell",
|
||||
"windows-sys 0.59.0",
|
||||
"windows-sys 0.52.0",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -221,18 +221,18 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "crossbeam-channel"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
dependencies = [
|
||||
"crossbeam-utils",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "crossbeam-deque"
|
||||
version = "0.8.6"
|
||||
version = "0.8.5"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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|
||||
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|
||||
dependencies = [
|
||||
"crossbeam-epoch",
|
||||
"crossbeam-utils",
|
||||
@ -249,18 +249,18 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "crossbeam-queue"
|
||||
version = "0.3.12"
|
||||
version = "0.3.11"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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|
||||
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|
||||
dependencies = [
|
||||
"crossbeam-utils",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "crossbeam-utils"
|
||||
version = "0.8.21"
|
||||
version = "0.8.20"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "d0a5c400df2834b80a4c3327b3aad3a4c4cd4de0629063962b03235697506a28"
|
||||
checksum = "22ec99545bb0ed0ea7bb9b8e1e9122ea386ff8a48c0922e43f36d45ab09e0e80"
|
||||
|
||||
[[package]]
|
||||
name = "crypto-common"
|
||||
@ -305,9 +305,9 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "encode_unicode"
|
||||
version = "1.0.0"
|
||||
version = "0.3.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "equivalent"
|
||||
@ -378,9 +378,9 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "glob"
|
||||
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|
||||
version = "0.3.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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||||
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||||
|
||||
[[package]]
|
||||
name = "hashbrown"
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||||
@ -417,11 +417,11 @@ checksum = "2304e00983f87ffb38b55b444b5e3b60a884b5d30c0fca7d82fe33449bbe55ea"
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||||
|
||||
[[package]]
|
||||
name = "home"
|
||||
version = "0.5.11"
|
||||
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|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
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||||
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||||
checksum = "e3d1354bf6b7235cb4a0576c2619fd4ed18183f689b12b006a0ee7329eeff9a5"
|
||||
dependencies = [
|
||||
"windows-sys 0.59.0",
|
||||
"windows-sys 0.52.0",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -472,7 +472,7 @@ checksum = "9dd28cfd4cfba665d47d31c08a6ba637eed16770abca2eccbbc3ca831fef1e44"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.94",
|
||||
"syn 2.0.90",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -559,9 +559,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
|
||||
|
||||
[[package]]
|
||||
name = "libc"
|
||||
version = "0.2.169"
|
||||
version = "0.2.167"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
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||||
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||||
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|
||||
|
||||
[[package]]
|
||||
name = "libloading"
|
||||
@ -678,7 +678,7 @@ dependencies = [
|
||||
"proc-macro-error",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.94",
|
||||
"syn 2.0.90",
|
||||
"trybuild",
|
||||
]
|
||||
|
||||
@ -799,7 +799,7 @@ dependencies = [
|
||||
"phf_shared 0.11.2",
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.94",
|
||||
"syn 2.0.90",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -927,7 +927,7 @@ dependencies = [
|
||||
"proc-macro2",
|
||||
"pyo3-macros-backend",
|
||||
"quote",
|
||||
"syn 2.0.94",
|
||||
"syn 2.0.90",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -940,14 +940,14 @@ dependencies = [
|
||||
"proc-macro2",
|
||||
"pyo3-build-config",
|
||||
"quote",
|
||||
"syn 2.0.94",
|
||||
"syn 2.0.90",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "quote"
|
||||
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|
||||
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|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
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||||
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||||
checksum = "b5b9d34b8991d19d98081b46eacdd8eb58c6f2b201139f7c5f643cc155a633af"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
]
|
||||
@ -1004,9 +1004,9 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "redox_syscall"
|
||||
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|
||||
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|
||||
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||||
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||||
checksum = "9b6dfecf2c74bce2466cabf93f6664d6998a69eb21e39f4207930065b27b771f"
|
||||
dependencies = [
|
||||
"bitflags",
|
||||
]
|
||||
@ -1062,9 +1062,9 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "rustversion"
|
||||
version = "1.0.19"
|
||||
version = "1.0.18"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
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||||
checksum = "0e819f2bc632f285be6d7cd36e25940d45b2391dd6d9b939e79de557f7014248"
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||||
|
||||
[[package]]
|
||||
name = "ryu"
|
||||
@ -1089,35 +1089,35 @@ checksum = "94143f37725109f92c262ed2cf5e59bce7498c01bcc1502d7b9afe439a4e9f49"
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||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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||||
|
||||
[[package]]
|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
dependencies = [
|
||||
"serde_derive",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "serde_derive"
|
||||
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|
||||
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|
||||
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checksum = "5a9bf7cf98d04a2b28aead066b7496853d4779c9cc183c440dbac457641e19a0"
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||||
checksum = "ad1e866f866923f252f05c889987993144fb74e722403468a4ebd70c3cd756c0"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.94",
|
||||
"syn 2.0.90",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "serde_json"
|
||||
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|
||||
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|
||||
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|
||||
checksum = "d00f4175c42ee48b15416f6193a959ba3a0d67fc699a0db9ad12df9f83991c7d"
|
||||
checksum = "c7fceb2473b9166b2294ef05efcb65a3db80803f0b03ef86a5fc88a2b85ee377"
|
||||
dependencies = [
|
||||
"itoa",
|
||||
"memchr",
|
||||
@ -1226,7 +1226,7 @@ dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"rustversion",
|
||||
"syn 2.0.94",
|
||||
"syn 2.0.90",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -1242,9 +1242,9 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "syn"
|
||||
version = "2.0.94"
|
||||
version = "2.0.90"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "987bc0be1cdea8b10216bd06e2ca407d40b9543468fafd3ddfb02f36e77f71f3"
|
||||
checksum = "919d3b74a5dd0ccd15aeb8f93e7006bd9e14c295087c9896a110f490752bcf31"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
@ -1265,13 +1265,12 @@ checksum = "42a4d50cdb458045afc8131fd91b64904da29548bcb63c7236e0844936c13078"
|
||||
|
||||
[[package]]
|
||||
name = "tempfile"
|
||||
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|
||||
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|
||||
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|
||||
checksum = "9a8a559c81686f576e8cd0290cd2a24a2a9ad80c98b3478856500fcbd7acd704"
|
||||
checksum = "28cce251fcbc87fac86a866eeb0d6c2d536fc16d06f184bb61aeae11aa4cee0c"
|
||||
dependencies = [
|
||||
"cfg-if",
|
||||
"fastrand",
|
||||
"getrandom",
|
||||
"once_cell",
|
||||
"rustix",
|
||||
"windows-sys 0.59.0",
|
||||
@ -1279,9 +1278,9 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "term"
|
||||
version = "1.0.1"
|
||||
version = "1.0.0"
|
||||
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|
||||
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|
||||
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|
||||
dependencies = [
|
||||
"home",
|
||||
"windows-sys 0.52.0",
|
||||
@ -1326,7 +1325,7 @@ checksum = "4fee6c4efc90059e10f81e6d42c60a18f76588c3d74cb83a0b242a2b6c7504c1"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.94",
|
||||
"syn 2.0.90",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -1604,9 +1603,9 @@ checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec"
|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
version = "0.6.20"
|
||||
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|
||||
checksum = "39281189af81c07ec09db316b302a3e67bf9bd7cbf6c820b50e35fee9c2fa980"
|
||||
checksum = "36c1fec1a2bb5866f07c25f68c26e565c4c200aebb96d7e55710c19d3e8ac49b"
|
||||
dependencies = [
|
||||
"memchr",
|
||||
]
|
||||
@ -1638,5 +1637,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
|
||||
dependencies = [
|
||||
"proc-macro2",
|
||||
"quote",
|
||||
"syn 2.0.94",
|
||||
"syn 2.0.90",
|
||||
]
|
||||
|
||||
6
flake.lock
generated
6
flake.lock
generated
@ -2,11 +2,11 @@
|
||||
"nodes": {
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1735834308,
|
||||
"narHash": "sha256-dklw3AXr3OGO4/XT1Tu3Xz9n/we8GctZZ75ZWVqAVhk=",
|
||||
"lastModified": 1731319897,
|
||||
"narHash": "sha256-PbABj4tnbWFMfBp6OcUK5iGy1QY+/Z96ZcLpooIbuEI=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "6df24922a1400241dae323af55f30e4318a6ca65",
|
||||
"rev": "dc460ec76cbff0e66e269457d7b728432263166c",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
|
||||
@ -1,29 +0,0 @@
|
||||
from min_artiq import *
|
||||
import numpy
|
||||
from numpy import int32
|
||||
|
||||
|
||||
@nac3
|
||||
class NumpyBoolDecay:
|
||||
core: KernelInvariant[Core]
|
||||
np_true: KernelInvariant[bool]
|
||||
np_false: KernelInvariant[bool]
|
||||
np_int: KernelInvariant[int32]
|
||||
np_float: KernelInvariant[float]
|
||||
np_str: KernelInvariant[str]
|
||||
|
||||
def __init__(self):
|
||||
self.core = Core()
|
||||
self.np_true = numpy.True_
|
||||
self.np_false = numpy.False_
|
||||
self.np_int = numpy.int32(0)
|
||||
self.np_float = numpy.float64(0.0)
|
||||
self.np_str = numpy.str_("")
|
||||
|
||||
@kernel
|
||||
def run(self):
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
NumpyBoolDecay().run()
|
||||
@ -12,17 +12,16 @@ use pyo3::{
|
||||
PyObject, PyResult, Python,
|
||||
};
|
||||
|
||||
use super::{symbol_resolver::InnerResolver, timeline::TimeFns};
|
||||
use nac3core::{
|
||||
codegen::{
|
||||
expr::{destructure_range, gen_call},
|
||||
llvm_intrinsics::{call_int_smax, call_memcpy, call_stackrestore, call_stacksave},
|
||||
irrt::call_ndarray_calc_size,
|
||||
llvm_intrinsics::{call_int_smax, call_memcpy_generic, call_stackrestore, call_stacksave},
|
||||
stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
|
||||
type_aligned_alloca,
|
||||
types::ndarray::NDArrayType,
|
||||
types::NDArrayType,
|
||||
values::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, ProxyValue, RangeValue,
|
||||
UntypedArrayLikeAccessor,
|
||||
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue, ProxyValue,
|
||||
RangeValue, UntypedArrayLikeAccessor,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
@ -35,14 +34,12 @@ use nac3core::{
|
||||
},
|
||||
nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{
|
||||
helper::{extract_ndims, PrimDef},
|
||||
numpy::unpack_ndarray_var_tys,
|
||||
DefinitionId, GenCall,
|
||||
},
|
||||
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, GenCall},
|
||||
typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
|
||||
};
|
||||
|
||||
use super::{symbol_resolver::InnerResolver, timeline::TimeFns};
|
||||
|
||||
/// The parallelism mode within a block.
|
||||
#[derive(Copy, Clone, Eq, PartialEq)]
|
||||
enum ParallelMode {
|
||||
@ -162,7 +159,7 @@ impl<'a> ArtiqCodeGenerator<'a> {
|
||||
}
|
||||
}
|
||||
|
||||
impl CodeGenerator for ArtiqCodeGenerator<'_> {
|
||||
impl<'b> CodeGenerator for ArtiqCodeGenerator<'b> {
|
||||
fn get_name(&self) -> &str {
|
||||
&self.name
|
||||
}
|
||||
@ -461,49 +458,60 @@ fn format_rpc_arg<'ctx>(
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let (elem_ty, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
let dtype = ctx.get_llvm_type(generator, elem_ty);
|
||||
let ndarray = NDArrayType::new(generator, ctx.ctx, dtype, ndims)
|
||||
.map_value(arg.into_pointer_value(), None);
|
||||
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 = NDArrayValue::from_pointer_value(
|
||||
arg.into_pointer_value(),
|
||||
llvm_elem_ty,
|
||||
llvm_usize,
|
||||
None,
|
||||
);
|
||||
|
||||
let ndims = llvm_usize.const_int(ndims, false);
|
||||
let llvm_usize_sizeof = ctx
|
||||
.builder
|
||||
.build_int_truncate_or_bit_cast(
|
||||
llvm_arg.get_type().size_type().size_of(),
|
||||
llvm_usize,
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let llvm_pdata_sizeof = ctx
|
||||
.builder
|
||||
.build_int_truncate_or_bit_cast(
|
||||
llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
|
||||
llvm_usize,
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
// `ndarray.data` is possibly not contiguous, and we need it to be contiguous for
|
||||
// the reader.
|
||||
// Turning it into a ContiguousNDArray to get a `data` that is contiguous.
|
||||
let carray = ndarray.make_contiguous_ndarray(generator, ctx);
|
||||
let dims_buf_sz =
|
||||
ctx.builder.build_int_mul(llvm_arg.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
|
||||
|
||||
let sizeof_usize = llvm_usize.size_of();
|
||||
let sizeof_usize =
|
||||
ctx.builder.build_int_z_extend_or_bit_cast(sizeof_usize, llvm_usize, "").unwrap();
|
||||
let buffer_size =
|
||||
ctx.builder.build_int_add(dims_buf_sz, llvm_pdata_sizeof, "").unwrap();
|
||||
|
||||
let sizeof_pdata = dtype.ptr_type(AddressSpace::default()).size_of();
|
||||
let sizeof_pdata =
|
||||
ctx.builder.build_int_z_extend_or_bit_cast(sizeof_pdata, llvm_usize, "").unwrap();
|
||||
let buffer = ctx.builder.build_array_alloca(llvm_i8, buffer_size, "rpc.arg").unwrap();
|
||||
let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, Some("rpc.arg"));
|
||||
|
||||
let sizeof_buf_shape = ctx.builder.build_int_mul(sizeof_usize, ndims, "").unwrap();
|
||||
let sizeof_buf = ctx.builder.build_int_add(sizeof_buf_shape, sizeof_pdata, "").unwrap();
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
buffer.base_ptr(ctx, generator),
|
||||
llvm_arg.ptr_to_data(ctx),
|
||||
llvm_pdata_sizeof,
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
// buf = { data: void*, shape: [size_t; ndims]; }
|
||||
let buf = ctx.builder.build_array_alloca(llvm_i8, sizeof_buf, "rpc.arg").unwrap();
|
||||
let buf = ArraySliceValue::from_ptr_val(buf, sizeof_buf, Some("rpc.arg"));
|
||||
let buf_data = buf.base_ptr(ctx, generator);
|
||||
let buf_shape =
|
||||
unsafe { buf.ptr_offset_unchecked(ctx, generator, &sizeof_pdata, None) };
|
||||
let pbuffer_dims_begin =
|
||||
unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
pbuffer_dims_begin,
|
||||
llvm_arg.shape().base_ptr(ctx, generator),
|
||||
dims_buf_sz,
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
// Write to `buf->data`
|
||||
let carray_data = carray.load_data(ctx);
|
||||
let carray_data = ctx.builder.build_pointer_cast(carray_data, llvm_pi8, "").unwrap();
|
||||
call_memcpy(ctx, buf_data, carray_data, sizeof_pdata, llvm_i1.const_zero());
|
||||
|
||||
// Write to `buf->shape`
|
||||
let carray_shape = ndarray.shape().base_ptr(ctx, generator);
|
||||
let carray_shape_i8 =
|
||||
ctx.builder.build_pointer_cast(carray_shape, llvm_pi8, "").unwrap();
|
||||
call_memcpy(ctx, buf_shape, carray_shape_i8, sizeof_buf_shape, llvm_i1.const_zero());
|
||||
|
||||
buf.base_ptr(ctx, generator)
|
||||
buffer.base_ptr(ctx, generator)
|
||||
}
|
||||
|
||||
_ => {
|
||||
@ -544,8 +552,6 @@ fn format_rpc_ret<'ctx>(
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_i8_8 = ctx.ctx.struct_type(&[llvm_i8.array_type(8).into()], false);
|
||||
let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
let rpc_recv = ctx.module.get_function("rpc_recv").unwrap_or_else(|| {
|
||||
ctx.module.add_function("rpc_recv", llvm_i32.fn_type(&[llvm_pi8.into()], false), None)
|
||||
@ -566,7 +572,8 @@ fn format_rpc_ret<'ctx>(
|
||||
|
||||
let result = match &*ctx.unifier.get_ty_immutable(ret_ty) {
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let num_0 = llvm_usize.const_zero();
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
// Round `val` up to its modulo `power_of_two`
|
||||
let round_up = |ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
@ -592,49 +599,79 @@ fn format_rpc_ret<'ctx>(
|
||||
.unwrap()
|
||||
};
|
||||
|
||||
// Setup types
|
||||
let (elem_ty, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
|
||||
let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
|
||||
let llvm_ret_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
|
||||
|
||||
// Allocate the resulting ndarray
|
||||
// A condition after format_rpc_ret ensures this will not be popped this off.
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
|
||||
let dtype_llvm = ctx.get_llvm_type(generator, dtype);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
let ndarray = NDArrayType::new(generator, ctx.ctx, dtype_llvm, ndims)
|
||||
.construct_uninitialized(generator, ctx, None);
|
||||
let ndarray = llvm_ret_ty.alloca(generator, ctx, Some("rpc.result"));
|
||||
|
||||
// NOTE: Current content of `ndarray`:
|
||||
// - * `data` - **NOT YET** allocated.
|
||||
// - * `itemsize` - initialized to be size_of(dtype).
|
||||
// - * `ndims` - initialized.
|
||||
// - * `shape` - allocated; has uninitialized values.
|
||||
// - * `strides` - allocated; has uninitialized values.
|
||||
// Setup ndims
|
||||
let ndims =
|
||||
if let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) {
|
||||
assert_eq!(values.len(), 1);
|
||||
|
||||
let itemsize = ndarray.load_itemsize(ctx); // Same as doing a `ctx.get_llvm_type` on `dtype` and get its `size_of()`.
|
||||
u64::try_from(values[0].clone()).unwrap()
|
||||
} else {
|
||||
unreachable!();
|
||||
};
|
||||
// Set `ndarray.ndims`
|
||||
ndarray.store_ndims(ctx, generator, llvm_usize.const_int(ndims, false));
|
||||
// Allocate `ndarray.shape` [size_t; ndims]
|
||||
ndarray.create_shape(ctx, llvm_usize, ndarray.load_ndims(ctx));
|
||||
|
||||
/*
|
||||
ndarray now:
|
||||
- .ndims: initialized
|
||||
- .shape: allocated but uninitialized .shape
|
||||
- .data: uninitialized
|
||||
*/
|
||||
|
||||
let llvm_usize_sizeof = ctx
|
||||
.builder
|
||||
.build_int_truncate_or_bit_cast(llvm_usize.size_of(), llvm_usize, "")
|
||||
.unwrap();
|
||||
let llvm_pdata_sizeof = ctx
|
||||
.builder
|
||||
.build_int_truncate_or_bit_cast(
|
||||
llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
|
||||
llvm_usize,
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let llvm_elem_sizeof = ctx
|
||||
.builder
|
||||
.build_int_truncate_or_bit_cast(llvm_elem_ty.size_of().unwrap(), llvm_usize, "")
|
||||
.unwrap();
|
||||
|
||||
// Allocates a buffer for the initial RPC'ed object, which is guaranteed to be
|
||||
// (4 + 4 * ndims) bytes with 8-byte alignment
|
||||
let sizeof_usize = llvm_usize.size_of();
|
||||
let sizeof_usize =
|
||||
ctx.builder.build_int_truncate_or_bit_cast(sizeof_usize, llvm_usize, "").unwrap();
|
||||
|
||||
let sizeof_ptr = llvm_i8.ptr_type(AddressSpace::default()).size_of();
|
||||
let sizeof_ptr =
|
||||
ctx.builder.build_int_z_extend_or_bit_cast(sizeof_ptr, llvm_usize, "").unwrap();
|
||||
|
||||
let sizeof_shape =
|
||||
ctx.builder.build_int_mul(ndarray.load_ndims(ctx), sizeof_usize, "").unwrap();
|
||||
|
||||
// Size of the buffer for the initial `rpc_recv()`.
|
||||
let sizeof_dims =
|
||||
ctx.builder.build_int_mul(ndarray.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
|
||||
let unaligned_buffer_size =
|
||||
ctx.builder.build_int_add(sizeof_ptr, sizeof_shape, "").unwrap();
|
||||
ctx.builder.build_int_add(sizeof_dims, llvm_pdata_sizeof, "").unwrap();
|
||||
let buffer_size = round_up(ctx, unaligned_buffer_size, llvm_usize.const_int(8, false));
|
||||
|
||||
let stackptr = call_stacksave(ctx, None);
|
||||
let buffer = type_aligned_alloca(
|
||||
generator,
|
||||
ctx,
|
||||
// Just to be absolutely sure, alloca in [i8 x 8] slices to force 8-byte alignment
|
||||
let buffer = ctx
|
||||
.builder
|
||||
.build_array_alloca(
|
||||
llvm_i8_8,
|
||||
unaligned_buffer_size,
|
||||
Some("rpc.buffer"),
|
||||
);
|
||||
let buffer = ArraySliceValue::from_ptr_val(buffer, unaligned_buffer_size, None);
|
||||
ctx.builder
|
||||
.build_int_unsigned_div(buffer_size, llvm_usize.const_int(8, false), "")
|
||||
.unwrap(),
|
||||
"rpc.buffer",
|
||||
)
|
||||
.unwrap();
|
||||
let buffer = ctx
|
||||
.builder
|
||||
.build_bit_cast(buffer, llvm_pi8, "")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, None);
|
||||
|
||||
// The first call to `rpc_recv` reads the top-level ndarray object: [pdata, shape]
|
||||
//
|
||||
@ -642,7 +679,7 @@ fn format_rpc_ret<'ctx>(
|
||||
let ndarray_nbytes = ctx
|
||||
.build_call_or_invoke(
|
||||
rpc_recv,
|
||||
&[buffer.base_ptr(ctx, generator).into()], // Reads [usize; ndims]
|
||||
&[buffer.base_ptr(ctx, generator).into()], // Reads [usize; ndims]. NOTE: We are allocated [size_t; ndims].
|
||||
"rpc.size.next",
|
||||
)
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
@ -650,14 +687,16 @@ fn format_rpc_ret<'ctx>(
|
||||
|
||||
// debug_assert(ndarray_nbytes > 0)
|
||||
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
|
||||
let cmp = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::UGT, ndarray_nbytes, num_0, "")
|
||||
.unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
cmp,
|
||||
ctx.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::UGT,
|
||||
ndarray_nbytes,
|
||||
ndarray_nbytes.get_type().const_zero(),
|
||||
"",
|
||||
)
|
||||
.unwrap(),
|
||||
"0:AssertionError",
|
||||
"Unexpected RPC termination for ndarray - Expected data buffer next",
|
||||
[None, None, None],
|
||||
@ -666,50 +705,49 @@ fn format_rpc_ret<'ctx>(
|
||||
}
|
||||
|
||||
// Copy shape from the buffer to `ndarray.shape`.
|
||||
// We need to skip the first `sizeof(uint8_t*)` bytes to skip the `pdata` in `[pdata, shape]`.
|
||||
let pbuffer_shape =
|
||||
unsafe { buffer.ptr_offset_unchecked(ctx, generator, &sizeof_ptr, None) };
|
||||
let pbuffer_shape =
|
||||
ctx.builder.build_pointer_cast(pbuffer_shape, llvm_pusize, "").unwrap();
|
||||
|
||||
// Copy shape from buffer to `ndarray.shape`
|
||||
ndarray.copy_shape_from_array(generator, ctx, pbuffer_shape);
|
||||
let pbuffer_dims =
|
||||
unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
|
||||
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
ndarray.shape().base_ptr(ctx, generator),
|
||||
pbuffer_dims,
|
||||
sizeof_dims,
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
// Restore stack from before allocation of buffer
|
||||
call_stackrestore(ctx, stackptr);
|
||||
|
||||
// Allocate `ndarray.data`.
|
||||
// `ndarray.shape` must be initialized beforehand in this implementation
|
||||
// (for ndarray.create_data() to know how many elements to allocate)
|
||||
unsafe { ndarray.create_data(generator, ctx) }; // NOTE: the strides of `ndarray` has also been set to contiguous in `create_data`.
|
||||
let num_elements =
|
||||
call_ndarray_calc_size(generator, ctx, &ndarray.shape(), (None, None));
|
||||
|
||||
// debug_assert(nelems * sizeof(T) >= ndarray_nbytes)
|
||||
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
|
||||
let num_elements = ndarray.size(generator, ctx);
|
||||
|
||||
let expected_ndarray_nbytes =
|
||||
ctx.builder.build_int_mul(num_elements, itemsize, "").unwrap();
|
||||
let cmp = ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::UGE,
|
||||
expected_ndarray_nbytes,
|
||||
ndarray_nbytes,
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let sizeof_data =
|
||||
ctx.builder.build_int_mul(num_elements, llvm_elem_sizeof, "").unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
cmp,
|
||||
ctx.builder.build_int_compare(IntPredicate::UGE,
|
||||
sizeof_data,
|
||||
ndarray_nbytes,
|
||||
"",
|
||||
).unwrap(),
|
||||
"0:AssertionError",
|
||||
"Unexpected allocation size request for ndarray data - Expected up to {0} bytes, got {1} bytes",
|
||||
[Some(expected_ndarray_nbytes), Some(ndarray_nbytes), None],
|
||||
[Some(sizeof_data), Some(ndarray_nbytes), None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
ndarray.create_data(ctx, llvm_elem_ty, num_elements);
|
||||
|
||||
let ndarray_data = ndarray.data().base_ptr(ctx, generator);
|
||||
let ndarray_data_i8 =
|
||||
ctx.builder.build_pointer_cast(ndarray_data, llvm_pi8, "").unwrap();
|
||||
|
||||
// NOTE: Currently on `prehead_bb`
|
||||
ctx.builder.build_unconditional_branch(head_bb).unwrap();
|
||||
@ -718,7 +756,7 @@ fn format_rpc_ret<'ctx>(
|
||||
ctx.builder.position_at_end(head_bb);
|
||||
|
||||
let phi = ctx.builder.build_phi(llvm_pi8, "rpc.ptr").unwrap();
|
||||
phi.add_incoming(&[(&ndarray_data, prehead_bb)]);
|
||||
phi.add_incoming(&[(&ndarray_data_i8, prehead_bb)]);
|
||||
|
||||
let alloc_size = ctx
|
||||
.build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
|
||||
@ -733,13 +771,12 @@ fn format_rpc_ret<'ctx>(
|
||||
|
||||
ctx.builder.position_at_end(alloc_bb);
|
||||
// Align the allocation to sizeof(T)
|
||||
let alloc_size = round_up(ctx, alloc_size, itemsize);
|
||||
// TODO(Derppening): Candidate for refactor into type_aligned_alloca
|
||||
let alloc_size = round_up(ctx, alloc_size, llvm_elem_sizeof);
|
||||
let alloc_ptr = ctx
|
||||
.builder
|
||||
.build_array_alloca(
|
||||
dtype_llvm,
|
||||
ctx.builder.build_int_unsigned_div(alloc_size, itemsize, "").unwrap(),
|
||||
llvm_elem_ty,
|
||||
ctx.builder.build_int_unsigned_div(alloc_size, llvm_elem_sizeof, "").unwrap(),
|
||||
"rpc.alloc",
|
||||
)
|
||||
.unwrap();
|
||||
@ -1338,50 +1375,62 @@ fn polymorphic_print<'ctx>(
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
|
||||
let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
|
||||
|
||||
fmt.push_str("array([");
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
|
||||
let (dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
|
||||
let ndarray = NDArrayType::from_unifier_type(generator, ctx, ty)
|
||||
.map_value(value.into_pointer_value(), None);
|
||||
let val = NDArrayValue::from_pointer_value(
|
||||
value.into_pointer_value(),
|
||||
llvm_elem_ty,
|
||||
llvm_usize,
|
||||
None,
|
||||
);
|
||||
let len = call_ndarray_calc_size(generator, ctx, &val.shape(), (None, None));
|
||||
let last =
|
||||
ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
|
||||
|
||||
let num_0 = llvm_usize.const_zero();
|
||||
|
||||
// Print `ndarray` as a flat list delimited by interspersed with ", \0"
|
||||
ndarray.foreach(generator, ctx, |generator, ctx, _, hdl| {
|
||||
let i = hdl.get_index(ctx);
|
||||
let scalar = hdl.get_scalar(ctx);
|
||||
|
||||
// if (i != 0) puts(", ");
|
||||
gen_if_callback(
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
let not_first = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::NE, i, num_0, "")
|
||||
.unwrap();
|
||||
Ok(not_first)
|
||||
},
|
||||
|generator, ctx| {
|
||||
printf(ctx, generator, ", \0".into(), Vec::default());
|
||||
Ok(())
|
||||
},
|
||||
|_, _| Ok(()),
|
||||
)?;
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(len, false),
|
||||
|generator, ctx, _, i| {
|
||||
let elem = unsafe { val.data().get_unchecked(ctx, generator, &i, None) };
|
||||
|
||||
// Print element
|
||||
polymorphic_print(
|
||||
ctx,
|
||||
generator,
|
||||
&[(dtype, scalar.into())],
|
||||
&[(elem_ty, elem.into())],
|
||||
"",
|
||||
None,
|
||||
true,
|
||||
as_rtio,
|
||||
)?;
|
||||
|
||||
gen_if_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_, ctx| {
|
||||
Ok(ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::ULT, i, last, "")
|
||||
.unwrap())
|
||||
},
|
||||
|generator, ctx| {
|
||||
printf(ctx, generator, ", \0".into(), Vec::default());
|
||||
|
||||
Ok(())
|
||||
})?;
|
||||
},
|
||||
|_, _| Ok(()),
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
|
||||
fmt.push_str(")]");
|
||||
flush(ctx, generator, &mut fmt, &mut args);
|
||||
@ -1505,7 +1554,7 @@ pub fn call_rtio_log_impl<'ctx>(
|
||||
/// Generates a call to `core_log`.
|
||||
pub fn gen_core_log<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: Option<&(Type, ValueEnum<'ctx>)>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
@ -1522,7 +1571,7 @@ pub fn gen_core_log<'ctx>(
|
||||
/// Generates a call to `rtio_log`.
|
||||
pub fn gen_rtio_log<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: Option<&(Type, ValueEnum<'ctx>)>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
|
||||
@ -30,8 +30,9 @@ use tempfile::{self, TempDir};
|
||||
|
||||
use nac3core::{
|
||||
codegen::{
|
||||
concrete_type::ConcreteTypeStore, gen_func_impl, irrt::load_irrt, CodeGenLLVMOptions,
|
||||
CodeGenTargetMachineOptions, CodeGenTask, CodeGenerator, WithCall, WorkerRegistry,
|
||||
concrete_type::ConcreteTypeStore, gen_func_impl, irrt::load_irrt,
|
||||
tracert::TraceRuntimeConfig, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask,
|
||||
CodeGenerator, WithCall, WorkerRegistry,
|
||||
},
|
||||
inkwell::{
|
||||
context::Context,
|
||||
@ -330,7 +331,7 @@ impl Nac3 {
|
||||
vars: into_var_map([arg_ty]),
|
||||
},
|
||||
Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
|
||||
gen_core_log(ctx, obj.as_ref(), fun, &args, generator)?;
|
||||
gen_core_log(ctx, &obj, fun, &args, generator)?;
|
||||
|
||||
Ok(None)
|
||||
}))),
|
||||
@ -360,7 +361,7 @@ impl Nac3 {
|
||||
vars: into_var_map([arg_ty]),
|
||||
},
|
||||
Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
|
||||
gen_rtio_log(ctx, obj.as_ref(), fun, &args, generator)?;
|
||||
gen_rtio_log(ctx, &obj, fun, &args, generator)?;
|
||||
|
||||
Ok(None)
|
||||
}))),
|
||||
@ -716,8 +717,13 @@ impl Nac3 {
|
||||
let membuffer = membuffers.clone();
|
||||
let mut has_return = false;
|
||||
py.allow_threads(|| {
|
||||
let (registry, handles) =
|
||||
WorkerRegistry::create_workers(threads, top_level.clone(), &self.llvm_options, &f);
|
||||
let (registry, handles) = WorkerRegistry::create_workers(
|
||||
threads,
|
||||
top_level.clone(),
|
||||
&self.llvm_options,
|
||||
&TraceRuntimeConfig::default(),
|
||||
&f,
|
||||
);
|
||||
|
||||
let mut generator = ArtiqCodeGenerator::new("main".to_string(), size_t, self.time_fns);
|
||||
let context = Context::create();
|
||||
|
||||
@ -10,14 +10,12 @@ use itertools::Itertools;
|
||||
use parking_lot::RwLock;
|
||||
use pyo3::{
|
||||
types::{PyDict, PyTuple},
|
||||
PyAny, PyErr, PyObject, PyResult, Python,
|
||||
PyAny, PyObject, PyResult, Python,
|
||||
};
|
||||
|
||||
use super::PrimitivePythonId;
|
||||
use nac3core::{
|
||||
codegen::{
|
||||
types::{ndarray::NDArrayType, ProxyType},
|
||||
values::ndarray::make_contiguous_strides,
|
||||
types::{NDArrayType, ProxyType},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
inkwell::{
|
||||
@ -39,6 +37,8 @@ use nac3core::{
|
||||
},
|
||||
};
|
||||
|
||||
use super::PrimitivePythonId;
|
||||
|
||||
pub enum PrimitiveValue {
|
||||
I32(i32),
|
||||
I64(i64),
|
||||
@ -931,13 +931,10 @@ impl InnerResolver {
|
||||
|_| Ok(Ok(extracted_ty)),
|
||||
)
|
||||
} else if unifier.unioned(extracted_ty, primitives.bool) {
|
||||
if obj.extract::<bool>().is_ok()
|
||||
|| obj.call_method("__bool__", (), None)?.extract::<bool>().is_ok()
|
||||
{
|
||||
Ok(Ok(extracted_ty))
|
||||
} else {
|
||||
Ok(Err(format!("{obj} is not in the range of bool")))
|
||||
}
|
||||
obj.extract::<bool>().map_or_else(
|
||||
|_| Ok(Err(format!("{obj} is not in the range of bool"))),
|
||||
|_| Ok(Ok(extracted_ty)),
|
||||
)
|
||||
} else if unifier.unioned(extracted_ty, primitives.float) {
|
||||
obj.extract::<f64>().map_or_else(
|
||||
|_| Ok(Err(format!("{obj} is not in the range of float64"))),
|
||||
@ -977,14 +974,10 @@ impl InnerResolver {
|
||||
let val: u64 = obj.extract().unwrap();
|
||||
self.id_to_primitive.write().insert(id, PrimitiveValue::U64(val));
|
||||
Ok(Some(ctx.ctx.i64_type().const_int(val, false).into()))
|
||||
} else if ty_id == self.primitive_ids.bool {
|
||||
} else if ty_id == self.primitive_ids.bool || ty_id == self.primitive_ids.np_bool_ {
|
||||
let val: bool = obj.extract().unwrap();
|
||||
self.id_to_primitive.write().insert(id, PrimitiveValue::Bool(val));
|
||||
Ok(Some(ctx.ctx.i8_type().const_int(u64::from(val), false).into()))
|
||||
} else if ty_id == self.primitive_ids.np_bool_ {
|
||||
let val: bool = obj.call_method("__bool__", (), None)?.extract().unwrap();
|
||||
self.id_to_primitive.write().insert(id, PrimitiveValue::Bool(val));
|
||||
Ok(Some(ctx.ctx.i8_type().const_int(u64::from(val), false).into()))
|
||||
} else if ty_id == self.primitive_ids.string || ty_id == self.primitive_ids.np_str_ {
|
||||
let val: String = obj.extract().unwrap();
|
||||
self.id_to_primitive.write().insert(id, PrimitiveValue::Str(val.clone()));
|
||||
@ -1092,19 +1085,18 @@ impl InnerResolver {
|
||||
} else {
|
||||
unreachable!("must be ndarray")
|
||||
};
|
||||
let (ndarray_dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
|
||||
let (ndarray_dtype, ndarray_ndims) =
|
||||
unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
|
||||
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_ndarray = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty);
|
||||
let dtype = llvm_ndarray.element_type();
|
||||
let ndarray_dtype_llvm_ty = ctx.get_llvm_type(generator, ndarray_dtype);
|
||||
let ndarray_llvm_ty = NDArrayType::new(generator, ctx.ctx, ndarray_dtype_llvm_ty);
|
||||
|
||||
{
|
||||
if self.global_value_ids.read().contains_key(&id) {
|
||||
let global = ctx.module.get_global(&id_str).unwrap_or_else(|| {
|
||||
ctx.module.add_global(
|
||||
llvm_ndarray.as_base_type().get_element_type().into_struct_type(),
|
||||
ndarray_llvm_ty.as_base_type().get_element_type().into_struct_type(),
|
||||
Some(AddressSpace::default()),
|
||||
&id_str,
|
||||
)
|
||||
@ -1114,44 +1106,40 @@ impl InnerResolver {
|
||||
self.global_value_ids.write().insert(id, obj.into());
|
||||
}
|
||||
|
||||
let ndims = llvm_ndarray.ndims();
|
||||
let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndarray_ndims)
|
||||
else {
|
||||
unreachable!("Expected Literal for ndarray_ndims")
|
||||
};
|
||||
|
||||
let ndarray_ndims = if values.len() == 1 {
|
||||
values[0].clone()
|
||||
} else {
|
||||
todo!("Unpacking literal of more than one element unimplemented")
|
||||
};
|
||||
let Ok(ndarray_ndims) = u64::try_from(ndarray_ndims) else {
|
||||
unreachable!("Expected u64 value for ndarray_ndims")
|
||||
};
|
||||
|
||||
// Obtain the shape of the ndarray
|
||||
let shape_tuple: &PyTuple = obj.getattr("shape")?.downcast()?;
|
||||
assert_eq!(shape_tuple.len(), ndims as usize);
|
||||
|
||||
// The Rust type inferencer cannot figure this out
|
||||
let shape_values = shape_tuple
|
||||
assert_eq!(shape_tuple.len(), ndarray_ndims as usize);
|
||||
let shape_values: Result<Option<Vec<_>>, _> = shape_tuple
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, elem)| {
|
||||
let value = self
|
||||
.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize())
|
||||
.map_err(|e| {
|
||||
super::CompileError::new_err(format!("Error getting element {i}: {e}"))
|
||||
})?
|
||||
.unwrap();
|
||||
let value = ctx
|
||||
.builder
|
||||
.build_int_z_extend(value.into_int_value(), llvm_usize, "")
|
||||
.unwrap();
|
||||
Ok(value)
|
||||
self.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize()).map_err(
|
||||
|e| super::CompileError::new_err(format!("Error getting element {i}: {e}")),
|
||||
)
|
||||
})
|
||||
.collect::<Result<Vec<_>, PyErr>>()?;
|
||||
|
||||
// Also use this opportunity to get the constant values of `shape_values` for calculating strides.
|
||||
let shape_u64s = shape_values
|
||||
.iter()
|
||||
.map(|dim| {
|
||||
assert!(dim.is_const());
|
||||
dim.get_zero_extended_constant().unwrap()
|
||||
})
|
||||
.collect_vec();
|
||||
let shape_values = llvm_usize.const_array(&shape_values);
|
||||
.collect();
|
||||
let shape_values = shape_values?.unwrap();
|
||||
let shape_values = llvm_usize.const_array(
|
||||
&shape_values.into_iter().map(BasicValueEnum::into_int_value).collect_vec(),
|
||||
);
|
||||
|
||||
// create a global for ndarray.shape and initialize it using the shape
|
||||
let shape_global = ctx.module.add_global(
|
||||
llvm_usize.array_type(ndims as u32),
|
||||
llvm_usize.array_type(ndarray_ndims as u32),
|
||||
Some(AddressSpace::default()),
|
||||
&(id_str.clone() + ".shape"),
|
||||
);
|
||||
@ -1159,25 +1147,17 @@ impl InnerResolver {
|
||||
|
||||
// Obtain the (flattened) elements of the ndarray
|
||||
let sz: usize = obj.getattr("size")?.extract()?;
|
||||
let data: Vec<_> = (0..sz)
|
||||
let data: Result<Option<Vec<_>>, _> = (0..sz)
|
||||
.map(|i| {
|
||||
obj.getattr("flat")?.get_item(i).and_then(|elem| {
|
||||
let value = self
|
||||
.get_obj_value(py, elem, ctx, generator, ndarray_dtype)
|
||||
.map_err(|e| {
|
||||
super::CompileError::new_err(format!(
|
||||
"Error getting element {i}: {e}"
|
||||
))
|
||||
})?
|
||||
.unwrap();
|
||||
|
||||
assert_eq!(value.get_type(), dtype);
|
||||
Ok(value)
|
||||
self.get_obj_value(py, elem, ctx, generator, ndarray_dtype).map_err(|e| {
|
||||
super::CompileError::new_err(format!("Error getting element {i}: {e}"))
|
||||
})
|
||||
})
|
||||
.try_collect()?;
|
||||
let data = data.into_iter();
|
||||
let data = match dtype {
|
||||
})
|
||||
.collect();
|
||||
let data = data?.unwrap().into_iter();
|
||||
let data = match ndarray_dtype_llvm_ty {
|
||||
BasicTypeEnum::ArrayType(ty) => {
|
||||
ty.const_array(&data.map(BasicValueEnum::into_array_value).collect_vec())
|
||||
}
|
||||
@ -1202,97 +1182,38 @@ impl InnerResolver {
|
||||
};
|
||||
|
||||
// create a global for ndarray.data and initialize it using the elements
|
||||
//
|
||||
// NOTE: NDArray's `data` is `u8*`. Here, `data_global` is an array of `dtype`.
|
||||
// We will have to cast it to an `u8*` later.
|
||||
let data_global = ctx.module.add_global(
|
||||
dtype.array_type(sz as u32),
|
||||
ndarray_dtype_llvm_ty.array_type(sz as u32),
|
||||
Some(AddressSpace::default()),
|
||||
&(id_str.clone() + ".data"),
|
||||
);
|
||||
data_global.set_initializer(&data);
|
||||
|
||||
// Get the constant itemsize.
|
||||
//
|
||||
// NOTE: dtype.size_of() may return a non-constant, where `TargetData::get_store_size`
|
||||
// will always return a constant size.
|
||||
let itemsize = ctx
|
||||
.registry
|
||||
.llvm_options
|
||||
.create_target_machine()
|
||||
.map(|tm| tm.get_target_data().get_store_size(&dtype))
|
||||
.unwrap();
|
||||
assert_ne!(itemsize, 0);
|
||||
|
||||
// Create the strides needed for ndarray.strides
|
||||
let strides = make_contiguous_strides(itemsize, ndims, &shape_u64s);
|
||||
let strides =
|
||||
strides.into_iter().map(|stride| llvm_usize.const_int(stride, false)).collect_vec();
|
||||
let strides = llvm_usize.const_array(&strides);
|
||||
|
||||
// create a global for ndarray.strides and initialize it
|
||||
let strides_global = ctx.module.add_global(
|
||||
llvm_usize.array_type(ndims as u32),
|
||||
Some(AddressSpace::default()),
|
||||
&format!("${id_str}.strides"),
|
||||
);
|
||||
strides_global.set_initializer(&strides);
|
||||
|
||||
// create a global for the ndarray object and initialize it
|
||||
|
||||
// NOTE: data_global is an array of dtype, we want a `u8*`.
|
||||
let ndarray_data = data_global.as_pointer_value();
|
||||
let ndarray_data = ctx.builder.build_pointer_cast(ndarray_data, llvm_pi8, "").unwrap();
|
||||
|
||||
let ndarray_itemsize = llvm_usize.const_int(itemsize, false);
|
||||
|
||||
let ndarray_ndims = llvm_usize.const_int(ndims, false);
|
||||
|
||||
// calling as_pointer_value on shape and strides returns [i64 x ndims]*
|
||||
// convert into i64* to conform with expected layout of ndarray
|
||||
|
||||
let ndarray_shape = shape_global.as_pointer_value();
|
||||
let ndarray_shape = unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
ndarray_shape,
|
||||
&[llvm_usize.const_zero(), llvm_usize.const_zero()],
|
||||
"",
|
||||
)
|
||||
.unwrap()
|
||||
};
|
||||
|
||||
let ndarray_strides = strides_global.as_pointer_value();
|
||||
let ndarray_strides = unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
ndarray_strides,
|
||||
&[llvm_usize.const_zero(), llvm_usize.const_zero()],
|
||||
"",
|
||||
)
|
||||
.unwrap()
|
||||
};
|
||||
|
||||
let ndarray = llvm_ndarray
|
||||
let value = ndarray_llvm_ty
|
||||
.as_base_type()
|
||||
.get_element_type()
|
||||
.into_struct_type()
|
||||
.const_named_struct(&[
|
||||
ndarray_itemsize.into(),
|
||||
ndarray_ndims.into(),
|
||||
ndarray_shape.into(),
|
||||
ndarray_strides.into(),
|
||||
ndarray_data.into(),
|
||||
llvm_usize.const_int(ndarray_ndims, false).into(),
|
||||
shape_global
|
||||
.as_pointer_value()
|
||||
.const_cast(llvm_usize.ptr_type(AddressSpace::default()))
|
||||
.into(),
|
||||
data_global
|
||||
.as_pointer_value()
|
||||
.const_cast(ndarray_dtype_llvm_ty.ptr_type(AddressSpace::default()))
|
||||
.into(),
|
||||
]);
|
||||
|
||||
let ndarray_global = ctx.module.add_global(
|
||||
llvm_ndarray.as_base_type().get_element_type().into_struct_type(),
|
||||
let ndarray = ctx.module.add_global(
|
||||
ndarray_llvm_ty.as_base_type().get_element_type().into_struct_type(),
|
||||
Some(AddressSpace::default()),
|
||||
&id_str,
|
||||
);
|
||||
ndarray_global.set_initializer(&ndarray);
|
||||
ndarray.set_initializer(&value);
|
||||
|
||||
Ok(Some(ndarray_global.as_pointer_value().into()))
|
||||
Ok(Some(ndarray.as_pointer_value().into()))
|
||||
} else if ty_id == self.primitive_ids.tuple {
|
||||
let expected_ty_enum = ctx.unifier.get_ty_immutable(expected_ty);
|
||||
let TypeEnum::TTuple { ty, is_vararg_ctx: false } = expected_ty_enum.as_ref() else {
|
||||
@ -1452,12 +1373,9 @@ impl InnerResolver {
|
||||
} else if ty_id == self.primitive_ids.uint64 {
|
||||
let val: u64 = obj.extract()?;
|
||||
Ok(SymbolValue::U64(val))
|
||||
} else if ty_id == self.primitive_ids.bool {
|
||||
} else if ty_id == self.primitive_ids.bool || ty_id == self.primitive_ids.np_bool_ {
|
||||
let val: bool = obj.extract()?;
|
||||
Ok(SymbolValue::Bool(val))
|
||||
} else if ty_id == self.primitive_ids.np_bool_ {
|
||||
let val: bool = obj.call_method("__bool__", (), None)?.extract()?;
|
||||
Ok(SymbolValue::Bool(val))
|
||||
} else if ty_id == self.primitive_ids.string || ty_id == self.primitive_ids.np_str_ {
|
||||
let val: String = obj.extract()?;
|
||||
Ok(SymbolValue::Str(val))
|
||||
|
||||
@ -8,6 +8,7 @@ edition = "2021"
|
||||
default = ["derive"]
|
||||
derive = ["dep:nac3core_derive"]
|
||||
no-escape-analysis = []
|
||||
tracing = []
|
||||
|
||||
[dependencies]
|
||||
itertools = "0.13"
|
||||
@ -31,4 +32,5 @@ indoc = "2.0"
|
||||
insta = "=1.11.0"
|
||||
|
||||
[build-dependencies]
|
||||
itertools = "0.13"
|
||||
regex = "1.10"
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
use std::ffi::OsStr;
|
||||
use std::{
|
||||
env,
|
||||
fs::File,
|
||||
@ -6,14 +7,28 @@ use std::{
|
||||
process::{Command, Stdio},
|
||||
};
|
||||
|
||||
use itertools::Itertools;
|
||||
use regex::Regex;
|
||||
|
||||
fn main() {
|
||||
struct IRRTCompilation<'a> {
|
||||
pub file: &'a str,
|
||||
pub gcc_options: Vec<&'a str>,
|
||||
pub cargo_instructions: Vec<&'a str>,
|
||||
}
|
||||
|
||||
/// Extracts the extension-less filename from a [`Path`].
|
||||
fn path_to_extless_filename(path: &Path) -> &str {
|
||||
path.file_name().map(Path::new).and_then(Path::file_stem).and_then(OsStr::to_str).unwrap()
|
||||
}
|
||||
|
||||
/// Compiles a source C file into LLVM bitcode.
|
||||
fn compile_file_to_ir(compile_opts: &IRRTCompilation) {
|
||||
let out_dir = env::var("OUT_DIR").unwrap();
|
||||
let out_dir = Path::new(&out_dir);
|
||||
let out_path = Path::new(&out_dir);
|
||||
let irrt_dir = Path::new("irrt");
|
||||
|
||||
let irrt_cpp_path = irrt_dir.join("irrt.cpp");
|
||||
let path = Path::new(compile_opts.file);
|
||||
let filename_without_ext = path_to_extless_filename(path);
|
||||
|
||||
/*
|
||||
* HACK: Sadly, clang doesn't let us emit generic LLVM bitcode.
|
||||
@ -35,22 +50,22 @@ fn main() {
|
||||
"-",
|
||||
"-I",
|
||||
irrt_dir.to_str().unwrap(),
|
||||
irrt_cpp_path.to_str().unwrap(),
|
||||
];
|
||||
|
||||
// Apply custom flags from IRRTCompilation
|
||||
flags.extend_from_slice(&compile_opts.gcc_options);
|
||||
|
||||
match env::var("PROFILE").as_deref() {
|
||||
Ok("debug") => {
|
||||
flags.push("-O0");
|
||||
flags.push("-DIRRT_DEBUG_ASSERT");
|
||||
}
|
||||
Ok("release") => {
|
||||
flags.push("-O3");
|
||||
}
|
||||
Ok("debug") => flags.extend_from_slice(&["-O0", "-DIRRT_DEBUG_ASSERT"]),
|
||||
Ok("release") => flags.push("-O3"),
|
||||
flavor => panic!("Unknown or missing build flavor {flavor:?}"),
|
||||
}
|
||||
|
||||
// Tell Cargo to rerun if any file under `irrt_dir` (recursive) changes
|
||||
println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
|
||||
flags.push(path.to_str().unwrap());
|
||||
|
||||
// Tell Cargo to rerun if the main IRRT source is changed
|
||||
println!("cargo:rerun-if-changed={}", path.to_str().unwrap());
|
||||
compile_opts.cargo_instructions.iter().for_each(|inst| println!("cargo::{inst}"));
|
||||
|
||||
// Compile IRRT and capture the LLVM IR output
|
||||
let output = Command::new("clang-irrt")
|
||||
@ -61,8 +76,7 @@ fn main() {
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
// https://github.com/rust-lang/regex/issues/244
|
||||
let output = std::str::from_utf8(&output.stdout).unwrap().replace("\r\n", "\n");
|
||||
let output = std::str::from_utf8(&output.stdout).unwrap();
|
||||
let mut filtered_output = String::with_capacity(output.len());
|
||||
|
||||
// Filter out irrelevant IR
|
||||
@ -76,7 +90,7 @@ fn main() {
|
||||
r"(?ms:^define.*?\}$)|(?m:^declare.*?$)|(?m:^%.+?=\s*type\s*\{.+?\}$)|(?m:^@.+?=.+$)",
|
||||
)
|
||||
.unwrap();
|
||||
for f in regex_filter.captures_iter(&output) {
|
||||
for f in regex_filter.captures_iter(output) {
|
||||
assert_eq!(f.len(), 1);
|
||||
filtered_output.push_str(&f[0]);
|
||||
filtered_output.push('\n');
|
||||
@ -90,20 +104,47 @@ fn main() {
|
||||
// Doing `DEBUG_DUMP_IRRT=1 cargo build -p nac3core` dumps the LLVM IR generated
|
||||
const DEBUG_DUMP_IRRT: &str = "DEBUG_DUMP_IRRT";
|
||||
println!("cargo:rerun-if-env-changed={DEBUG_DUMP_IRRT}");
|
||||
if env::var(DEBUG_DUMP_IRRT).is_ok() {
|
||||
let mut file = File::create(out_dir.join("irrt.ll")).unwrap();
|
||||
if env::var("DEBUG_DUMP_IRRT").is_ok() {
|
||||
let mut file = File::create(out_path.join(format!("{filename_without_ext}.ll"))).unwrap();
|
||||
file.write_all(output.as_bytes()).unwrap();
|
||||
|
||||
let mut file = File::create(out_dir.join("irrt-filtered.ll")).unwrap();
|
||||
let mut file =
|
||||
File::create(out_path.join(format!("{filename_without_ext}-filtered.ll"))).unwrap();
|
||||
file.write_all(filtered_output.as_bytes()).unwrap();
|
||||
}
|
||||
|
||||
let mut llvm_as = Command::new("llvm-as-irrt")
|
||||
.stdin(Stdio::piped())
|
||||
.arg("-o")
|
||||
.arg(out_dir.join("irrt.bc"))
|
||||
.arg(out_path.join(format!("{filename_without_ext}.bc")))
|
||||
.spawn()
|
||||
.unwrap();
|
||||
llvm_as.stdin.as_mut().unwrap().write_all(filtered_output.as_bytes()).unwrap();
|
||||
assert!(llvm_as.wait().unwrap().success());
|
||||
}
|
||||
|
||||
fn main() {
|
||||
let irrt_compilations: &[IRRTCompilation] = &[
|
||||
IRRTCompilation {
|
||||
file: "irrt/irrt.cpp",
|
||||
gcc_options: Vec::default(),
|
||||
cargo_instructions: vec!["rerun-if-changed=irrt/irrt"],
|
||||
},
|
||||
IRRTCompilation {
|
||||
file: "irrt/tracert.cpp",
|
||||
gcc_options: Vec::default(),
|
||||
cargo_instructions: Vec::default(),
|
||||
},
|
||||
];
|
||||
|
||||
assert!(irrt_compilations
|
||||
.iter()
|
||||
.map(|comp| comp.file)
|
||||
.map(Path::new)
|
||||
.map(path_to_extless_filename)
|
||||
.all_unique());
|
||||
|
||||
for path in irrt_compilations {
|
||||
compile_file_to_ir(path)
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,15 +1,5 @@
|
||||
#include "irrt/exception.hpp"
|
||||
#include "irrt/list.hpp"
|
||||
#include "irrt/math.hpp"
|
||||
#include "irrt/range.hpp"
|
||||
#include "irrt/ndarray.hpp"
|
||||
#include "irrt/slice.hpp"
|
||||
#include "irrt/string.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"
|
||||
@ -21,5 +21,7 @@ using uint64_t = unsigned _ExtInt(64);
|
||||
|
||||
#endif
|
||||
|
||||
// NDArray indices are always `uint32_t`.
|
||||
using NDIndex = uint32_t;
|
||||
// The type of an index or a value describing the length of a range/slice is always `int32_t`.
|
||||
using SliceIndex = int32_t;
|
||||
|
||||
@ -2,21 +2,6 @@
|
||||
|
||||
#include "irrt/int_types.hpp"
|
||||
#include "irrt/math_util.hpp"
|
||||
#include "irrt/slice.hpp"
|
||||
|
||||
namespace {
|
||||
/**
|
||||
* @brief A list in NAC3.
|
||||
*
|
||||
* The `items` field is opaque. You must rely on external contexts to
|
||||
* know how to interpret it.
|
||||
*/
|
||||
template<typename SizeT>
|
||||
struct List {
|
||||
uint8_t* items;
|
||||
SizeT len;
|
||||
};
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
// Handle list assignment and dropping part of the list when
|
||||
|
||||
@ -1,7 +1,5 @@
|
||||
#pragma once
|
||||
|
||||
#include "irrt/int_types.hpp"
|
||||
|
||||
namespace {
|
||||
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
|
||||
// need to make sure `exp >= 0` before calling this function
|
||||
|
||||
144
nac3core/irrt/irrt/ndarray.hpp
Normal file
144
nac3core/irrt/irrt/ndarray.hpp
Normal file
@ -0,0 +1,144 @@
|
||||
#pragma once
|
||||
|
||||
#include "irrt/int_types.hpp"
|
||||
|
||||
namespace {
|
||||
template<typename SizeT>
|
||||
SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len, SizeT begin_idx, SizeT end_idx) {
|
||||
__builtin_assume(end_idx <= list_len);
|
||||
|
||||
SizeT num_elems = 1;
|
||||
for (SizeT i = begin_idx; i < end_idx; ++i) {
|
||||
SizeT val = list_data[i];
|
||||
__builtin_assume(val > 0);
|
||||
num_elems *= val;
|
||||
}
|
||||
return num_elems;
|
||||
}
|
||||
|
||||
template<typename SizeT>
|
||||
void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT num_dims, NDIndex* idxs) {
|
||||
SizeT stride = 1;
|
||||
for (SizeT dim = 0; dim < num_dims; dim++) {
|
||||
SizeT i = num_dims - dim - 1;
|
||||
__builtin_assume(dims[i] > 0);
|
||||
idxs[i] = (index / stride) % dims[i];
|
||||
stride *= dims[i];
|
||||
}
|
||||
}
|
||||
|
||||
template<typename SizeT>
|
||||
SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims, const NDIndex* indices, SizeT num_indices) {
|
||||
SizeT idx = 0;
|
||||
SizeT stride = 1;
|
||||
for (SizeT i = 0; i < num_dims; ++i) {
|
||||
SizeT ri = num_dims - i - 1;
|
||||
if (ri < num_indices) {
|
||||
idx += stride * indices[ri];
|
||||
}
|
||||
|
||||
__builtin_assume(dims[i] > 0);
|
||||
stride *= dims[ri];
|
||||
}
|
||||
return idx;
|
||||
}
|
||||
|
||||
template<typename SizeT>
|
||||
void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims,
|
||||
SizeT lhs_ndims,
|
||||
const SizeT* rhs_dims,
|
||||
SizeT rhs_ndims,
|
||||
SizeT* out_dims) {
|
||||
SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
|
||||
|
||||
for (SizeT i = 0; i < max_ndims; ++i) {
|
||||
const SizeT* lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
|
||||
const SizeT* rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
|
||||
SizeT* out_dim = &out_dims[max_ndims - i - 1];
|
||||
|
||||
if (lhs_dim_sz == nullptr) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (rhs_dim_sz == nullptr) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == 1) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (*rhs_dim_sz == 1) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == *rhs_dim_sz) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template<typename SizeT>
|
||||
void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
|
||||
SizeT src_ndims,
|
||||
const NDIndex* in_idx,
|
||||
NDIndex* out_idx) {
|
||||
for (SizeT i = 0; i < src_ndims; ++i) {
|
||||
SizeT src_i = src_ndims - i - 1;
|
||||
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
|
||||
}
|
||||
}
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
uint32_t __nac3_ndarray_calc_size(const uint32_t* list_data, uint32_t list_len, uint32_t begin_idx, uint32_t end_idx) {
|
||||
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
|
||||
}
|
||||
|
||||
uint64_t
|
||||
__nac3_ndarray_calc_size64(const uint64_t* list_data, uint64_t list_len, uint64_t begin_idx, uint64_t end_idx) {
|
||||
return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims, uint32_t num_dims, NDIndex* idxs) {
|
||||
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims, uint64_t num_dims, NDIndex* idxs) {
|
||||
__nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
|
||||
}
|
||||
|
||||
uint32_t
|
||||
__nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims, const NDIndex* indices, uint32_t num_indices) {
|
||||
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
|
||||
}
|
||||
|
||||
uint64_t
|
||||
__nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims, const NDIndex* indices, uint64_t num_indices) {
|
||||
return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast(const uint32_t* lhs_dims,
|
||||
uint32_t lhs_ndims,
|
||||
const uint32_t* rhs_dims,
|
||||
uint32_t rhs_ndims,
|
||||
uint32_t* out_dims) {
|
||||
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast64(const uint64_t* lhs_dims,
|
||||
uint64_t lhs_ndims,
|
||||
const uint64_t* rhs_dims,
|
||||
uint64_t rhs_ndims,
|
||||
uint64_t* out_dims) {
|
||||
return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
|
||||
uint32_t src_ndims,
|
||||
const NDIndex* in_idx,
|
||||
NDIndex* out_idx) {
|
||||
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
|
||||
uint64_t src_ndims,
|
||||
const NDIndex* in_idx,
|
||||
NDIndex* out_idx) {
|
||||
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
|
||||
}
|
||||
} // namespace
|
||||
@ -1,132 +0,0 @@
|
||||
#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::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 = static_cast<uint8_t*>(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 ndarray::array
|
||||
} // 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);
|
||||
}
|
||||
}
|
||||
@ -1,340 +0,0 @@
|
||||
#pragma once
|
||||
|
||||
#include "irrt/debug.hpp"
|
||||
#include "irrt/exception.hpp"
|
||||
#include "irrt/int_types.hpp"
|
||||
#include "irrt/ndarray/def.hpp"
|
||||
|
||||
namespace {
|
||||
namespace ndarray::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) {
|
||||
if (ndarray->ndims != 0) {
|
||||
return ndarray->shape[0];
|
||||
}
|
||||
|
||||
// numpy prohibits `__len__` on unsized objects
|
||||
raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object", NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
__builtin_unreachable();
|
||||
}
|
||||
|
||||
/**
|
||||
* @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>
|
||||
void* get_pelement_by_indices(const NDArray<SizeT>* ndarray, const SizeT* indices) {
|
||||
void* element = ndarray->data;
|
||||
for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
|
||||
element = static_cast<uint8_t*>(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>
|
||||
void* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
|
||||
void* element = ndarray->data;
|
||||
for (SizeT i = 0; i < ndarray->ndims; i++) {
|
||||
SizeT axis = ndarray->ndims - i - 1;
|
||||
SizeT dim = ndarray->shape[axis];
|
||||
element = static_cast<uint8_t*>(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, void* pelement, const void* 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 ndarray::basic
|
||||
} // 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);
|
||||
}
|
||||
|
||||
void* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
|
||||
return get_nth_pelement(ndarray, nth);
|
||||
}
|
||||
|
||||
void* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray, int64_t nth) {
|
||||
return get_nth_pelement(ndarray, nth);
|
||||
}
|
||||
|
||||
void* __nac3_ndarray_get_pelement_by_indices(const NDArray<int32_t>* ndarray, int32_t* indices) {
|
||||
return get_pelement_by_indices(ndarray, indices);
|
||||
}
|
||||
|
||||
void* __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);
|
||||
}
|
||||
}
|
||||
@ -1,165 +0,0 @@
|
||||
#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::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);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef IRRT_DEBUG_ASSERT
|
||||
// Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
|
||||
debug_assert_eq(SizeT, max_ndims_found, dst_ndims);
|
||||
#endif
|
||||
}
|
||||
|
||||
/**
|
||||
* @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 ndarray::broadcast
|
||||
} // 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);
|
||||
}
|
||||
}
|
||||
@ -1,51 +0,0 @@
|
||||
#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#pyarrayinterface
|
||||
*
|
||||
* Note that this implementation is based on `PyArrayInterface` rather of `PyArrayObject`. The
|
||||
* difference between `PyArrayInterface` and `PyArrayObject` (relevant to our implementation) is
|
||||
* that `PyArrayInterface` *has* `itemsize` and uses `void*` for its `data`, whereas `PyArrayObject`
|
||||
* does not require `itemsize` (probably using `strides[-1]` instead) and uses `char*` for its
|
||||
* `data`. There are also minor differences in the struct layout.
|
||||
*/
|
||||
template<typename SizeT>
|
||||
struct NDArray {
|
||||
/**
|
||||
* @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;
|
||||
|
||||
/**
|
||||
* @brief The underlying data this `ndarray` is pointing to.
|
||||
*/
|
||||
void* data;
|
||||
};
|
||||
} // namespace
|
||||
@ -1,219 +0,0 @@
|
||||
#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::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 = static_cast<uint8_t*>(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 =
|
||||
static_cast<uint8_t*>(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 ndarray::indexing
|
||||
} // 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);
|
||||
}
|
||||
}
|
||||
@ -1,146 +0,0 @@
|
||||
#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.
|
||||
*/
|
||||
void* 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, void* 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 = static_cast<void*>(reinterpret_cast<uint8_t*>(element) - strides[axis] * (shape[axis] - 1));
|
||||
} else {
|
||||
element = static_cast<void*>(reinterpret_cast<uint8_t*>(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();
|
||||
}
|
||||
}
|
||||
@ -1,98 +0,0 @@
|
||||
#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::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 ndarray::matmul
|
||||
} // 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);
|
||||
}
|
||||
}
|
||||
@ -1,97 +0,0 @@
|
||||
#pragma once
|
||||
|
||||
#include "irrt/exception.hpp"
|
||||
#include "irrt/int_types.hpp"
|
||||
#include "irrt/ndarray/def.hpp"
|
||||
|
||||
namespace {
|
||||
namespace ndarray::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 ndarray::reshape
|
||||
} // 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);
|
||||
}
|
||||
}
|
||||
@ -1,143 +0,0 @@
|
||||
#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::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 ndarray::transpose
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace ndarray::transpose;
|
||||
void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
|
||||
NDArray<int32_t>* dst_ndarray,
|
||||
int32_t num_axes,
|
||||
const int32_t* axes) {
|
||||
transpose(src_ndarray, dst_ndarray, num_axes, axes);
|
||||
}
|
||||
|
||||
void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
|
||||
NDArray<int64_t>* dst_ndarray,
|
||||
int64_t num_axes,
|
||||
const int64_t* axes) {
|
||||
transpose(src_ndarray, dst_ndarray, num_axes, axes);
|
||||
}
|
||||
}
|
||||
@ -1,47 +0,0 @@
|
||||
#pragma once
|
||||
|
||||
#include "irrt/debug.hpp"
|
||||
#include "irrt/int_types.hpp"
|
||||
|
||||
namespace {
|
||||
namespace range {
|
||||
template<typename T>
|
||||
T len(T start, T stop, T step) {
|
||||
// Reference:
|
||||
// https://github.com/python/cpython/blob/9dbd12375561a393eaec4b21ee4ac568a407cdb0/Objects/rangeobject.c#L933
|
||||
if (step > 0 && start < stop)
|
||||
return 1 + (stop - 1 - start) / step;
|
||||
else if (step < 0 && start > stop)
|
||||
return 1 + (start - 1 - stop) / (-step);
|
||||
else
|
||||
return 0;
|
||||
}
|
||||
} // namespace range
|
||||
|
||||
/**
|
||||
* @brief A Python range.
|
||||
*/
|
||||
template<typename T>
|
||||
struct Range {
|
||||
T start;
|
||||
T stop;
|
||||
T step;
|
||||
|
||||
/**
|
||||
* @brief Calculate the `len()` of this range.
|
||||
*/
|
||||
template<typename SizeT>
|
||||
T len() {
|
||||
debug_assert(SizeT, step != 0);
|
||||
return range::len(start, stop, step);
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
using namespace range;
|
||||
|
||||
SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step) {
|
||||
return len(start, end, step);
|
||||
}
|
||||
}
|
||||
@ -1,145 +1,6 @@
|
||||
#pragma once
|
||||
|
||||
#include "irrt/debug.hpp"
|
||||
#include "irrt/exception.hpp"
|
||||
#include "irrt/int_types.hpp"
|
||||
#include "irrt/math_util.hpp"
|
||||
#include "irrt/range.hpp"
|
||||
|
||||
namespace {
|
||||
namespace slice {
|
||||
/**
|
||||
* @brief Resolve a possibly negative index in a list of a known length.
|
||||
*
|
||||
* Returns -1 if the resolved index is out of the list's bounds.
|
||||
*/
|
||||
template<typename T>
|
||||
T resolve_index_in_length(T length, T index) {
|
||||
T resolved = index < 0 ? length + index : index;
|
||||
if (0 <= resolved && resolved < length) {
|
||||
return resolved;
|
||||
} else {
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Resolve a slice as a range.
|
||||
*
|
||||
* This is equivalent to `range(*slice(start, stop, step).indices(length))` in Python.
|
||||
*/
|
||||
template<typename T>
|
||||
void indices(bool start_defined,
|
||||
T start,
|
||||
bool stop_defined,
|
||||
T stop,
|
||||
bool step_defined,
|
||||
T step,
|
||||
T length,
|
||||
T* range_start,
|
||||
T* range_stop,
|
||||
T* range_step) {
|
||||
// Reference: https://github.com/python/cpython/blob/main/Objects/sliceobject.c#L388
|
||||
*range_step = step_defined ? step : 1;
|
||||
bool step_is_negative = *range_step < 0;
|
||||
|
||||
T lower, upper;
|
||||
if (step_is_negative) {
|
||||
lower = -1;
|
||||
upper = length - 1;
|
||||
} else {
|
||||
lower = 0;
|
||||
upper = length;
|
||||
}
|
||||
|
||||
if (start_defined) {
|
||||
*range_start = start < 0 ? max(lower, start + length) : min(upper, start);
|
||||
} else {
|
||||
*range_start = step_is_negative ? upper : lower;
|
||||
}
|
||||
|
||||
if (stop_defined) {
|
||||
*range_stop = stop < 0 ? max(lower, stop + length) : min(upper, stop);
|
||||
} else {
|
||||
*range_stop = step_is_negative ? lower : upper;
|
||||
}
|
||||
}
|
||||
} // namespace slice
|
||||
|
||||
/**
|
||||
* @brief A Python-like slice with **unresolved** indices.
|
||||
*/
|
||||
template<typename T>
|
||||
struct Slice {
|
||||
bool start_defined;
|
||||
T start;
|
||||
|
||||
bool stop_defined;
|
||||
T stop;
|
||||
|
||||
bool step_defined;
|
||||
T step;
|
||||
|
||||
Slice() { this->reset(); }
|
||||
|
||||
void reset() {
|
||||
this->start_defined = false;
|
||||
this->stop_defined = false;
|
||||
this->step_defined = false;
|
||||
}
|
||||
|
||||
void set_start(T start) {
|
||||
this->start_defined = true;
|
||||
this->start = start;
|
||||
}
|
||||
|
||||
void set_stop(T stop) {
|
||||
this->stop_defined = true;
|
||||
this->stop = stop;
|
||||
}
|
||||
|
||||
void set_step(T step) {
|
||||
this->step_defined = true;
|
||||
this->step = step;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Resolve this slice as a range.
|
||||
*
|
||||
* In Python, this would be `range(*slice(start, stop, step).indices(length))`.
|
||||
*/
|
||||
template<typename SizeT>
|
||||
Range<T> indices(T length) {
|
||||
// Reference:
|
||||
// https://github.com/python/cpython/blob/main/Objects/sliceobject.c#L388
|
||||
debug_assert(SizeT, length >= 0);
|
||||
|
||||
Range<T> result;
|
||||
slice::indices(start_defined, start, stop_defined, stop, step_defined, step, length, &result.start,
|
||||
&result.stop, &result.step);
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Like `.indices()` but with assertions.
|
||||
*/
|
||||
template<typename SizeT>
|
||||
Range<T> indices_checked(T length) {
|
||||
// TODO: Switch to `SizeT length`
|
||||
|
||||
if (length < 0) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "length should not be negative, got {0}", length, NO_PARAM,
|
||||
NO_PARAM);
|
||||
}
|
||||
|
||||
if (this->step_defined && this->step == 0) {
|
||||
raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero", NO_PARAM, NO_PARAM, NO_PARAM);
|
||||
}
|
||||
|
||||
return this->indices<SizeT>(length);
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
|
||||
@ -153,4 +14,15 @@ SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
|
||||
}
|
||||
return i;
|
||||
}
|
||||
|
||||
SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step) {
|
||||
SliceIndex diff = end - start;
|
||||
if (diff > 0 && step > 0) {
|
||||
return ((diff - 1) / step) + 1;
|
||||
} else if (diff < 0 && step < 0) {
|
||||
return ((diff + 1) / step) + 1;
|
||||
} else {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
} // namespace
|
||||
@ -1,23 +0,0 @@
|
||||
#pragma once
|
||||
|
||||
#include "irrt/int_types.hpp"
|
||||
|
||||
namespace {
|
||||
template<typename SizeT>
|
||||
bool __nac3_str_eq_impl(const char* str1, SizeT len1, const char* str2, SizeT len2) {
|
||||
if (len1 != len2) {
|
||||
return 0;
|
||||
}
|
||||
return __builtin_memcmp(str1, str2, static_cast<SizeT>(len1)) == 0;
|
||||
}
|
||||
} // namespace
|
||||
|
||||
extern "C" {
|
||||
bool nac3_str_eq(const char* str1, uint32_t len1, const char* str2, uint32_t len2) {
|
||||
return __nac3_str_eq_impl<uint32_t>(str1, len1, str2, len2);
|
||||
}
|
||||
|
||||
bool nac3_str_eq64(const char* str1, uint64_t len1, const char* str2, uint64_t len2) {
|
||||
return __nac3_str_eq_impl<uint64_t>(str1, len1, str2, len2);
|
||||
}
|
||||
}
|
||||
4
nac3core/irrt/tracert.cpp
Normal file
4
nac3core/irrt/tracert.cpp
Normal file
@ -0,0 +1,4 @@
|
||||
extern "C" {
|
||||
// stdio.h
|
||||
int printf(const char *format, ...);
|
||||
} // extern "C"
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,8 +1,11 @@
|
||||
use std::iter::once;
|
||||
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
|
||||
values::{BasicValue, BasicValueEnum, CallSiteValue, FloatValue, IntValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use itertools::Either;
|
||||
use itertools::{Either, Itertools};
|
||||
|
||||
use super::CodeGenContext;
|
||||
|
||||
@ -191,3 +194,49 @@ generate_linalg_extern_fn!(call_np_linalg_det, "np_linalg_det", 2);
|
||||
generate_linalg_extern_fn!(call_sp_linalg_lu, "sp_linalg_lu", 3);
|
||||
generate_linalg_extern_fn!(call_sp_linalg_schur, "sp_linalg_schur", 3);
|
||||
generate_linalg_extern_fn!(call_sp_linalg_hessenberg, "sp_linalg_hessenberg", 3);
|
||||
|
||||
/// Invokes the `printf` function.
|
||||
pub fn call_printf<'ctx>(
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
format: &str,
|
||||
args: &[BasicValueEnum<'ctx>],
|
||||
) -> IntValue<'ctx> {
|
||||
const FN_NAME: &str = "printf";
|
||||
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
|
||||
let extern_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
|
||||
let fn_type = llvm_i32.fn_type(&[llvm_pi8.into()], true);
|
||||
let func = ctx.module.add_function(FN_NAME, fn_type, None);
|
||||
for attr in ["nofree", "nounwind"] {
|
||||
func.add_attribute(
|
||||
AttributeLoc::Function,
|
||||
ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
|
||||
);
|
||||
}
|
||||
|
||||
func
|
||||
});
|
||||
|
||||
let pformat = ctx
|
||||
.builder
|
||||
.build_global_string_ptr(&format!("{format}\0"), "")
|
||||
.map(|v| v.as_basic_value_enum())
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
|
||||
let fn_args = once(&pformat.as_basic_value_enum())
|
||||
.chain(args)
|
||||
.copied()
|
||||
.map(BasicValueEnum::into)
|
||||
.collect_vec();
|
||||
|
||||
ctx.builder
|
||||
.build_call(extern_fn, fn_args.as_slice(), "")
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(|v| v.map_left(BasicValueEnum::into_int_value))
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
@ -17,7 +17,6 @@ pub trait CodeGenerator {
|
||||
/// Return the module name for the code generator.
|
||||
fn get_name(&self) -> &str;
|
||||
|
||||
/// Return an instance of [`IntType`] corresponding to the type of `size_t` for this instance.
|
||||
fn get_size_type<'ctx>(&self, ctx: &'ctx Context) -> IntType<'ctx>;
|
||||
|
||||
/// Generate function call and returns the function return value.
|
||||
|
||||
@ -24,52 +24,42 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
|
||||
src_arr: ListValue<'ctx>,
|
||||
src_idx: (IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>),
|
||||
) {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pi8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
|
||||
assert_eq!(dest_idx.0.get_type(), llvm_i32);
|
||||
assert_eq!(dest_idx.1.get_type(), llvm_i32);
|
||||
assert_eq!(dest_idx.2.get_type(), llvm_i32);
|
||||
assert_eq!(src_idx.0.get_type(), llvm_i32);
|
||||
assert_eq!(src_idx.1.get_type(), llvm_i32);
|
||||
assert_eq!(src_idx.2.get_type(), llvm_i32);
|
||||
|
||||
let (fun_symbol, elem_ptr_type) = ("__nac3_list_slice_assign_var_size", llvm_pi8);
|
||||
let size_ty = generator.get_size_type(ctx.ctx);
|
||||
let int8_ptr = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
|
||||
let int32 = ctx.ctx.i32_type();
|
||||
let (fun_symbol, elem_ptr_type) = ("__nac3_list_slice_assign_var_size", int8_ptr);
|
||||
let slice_assign_fun = {
|
||||
let ty_vec = vec![
|
||||
llvm_i32.into(), // dest start idx
|
||||
llvm_i32.into(), // dest end idx
|
||||
llvm_i32.into(), // dest step
|
||||
int32.into(), // dest start idx
|
||||
int32.into(), // dest end idx
|
||||
int32.into(), // dest step
|
||||
elem_ptr_type.into(), // dest arr ptr
|
||||
llvm_i32.into(), // dest arr len
|
||||
llvm_i32.into(), // src start idx
|
||||
llvm_i32.into(), // src end idx
|
||||
llvm_i32.into(), // src step
|
||||
int32.into(), // dest arr len
|
||||
int32.into(), // src start idx
|
||||
int32.into(), // src end idx
|
||||
int32.into(), // src step
|
||||
elem_ptr_type.into(), // src arr ptr
|
||||
llvm_i32.into(), // src arr len
|
||||
llvm_i32.into(), // size
|
||||
int32.into(), // src arr len
|
||||
int32.into(), // size
|
||||
];
|
||||
ctx.module.get_function(fun_symbol).unwrap_or_else(|| {
|
||||
let fn_t = llvm_i32.fn_type(ty_vec.as_slice(), false);
|
||||
let fn_t = int32.fn_type(ty_vec.as_slice(), false);
|
||||
ctx.module.add_function(fun_symbol, fn_t, None)
|
||||
})
|
||||
};
|
||||
|
||||
let zero = llvm_i32.const_zero();
|
||||
let one = llvm_i32.const_int(1, false);
|
||||
let zero = int32.const_zero();
|
||||
let one = int32.const_int(1, false);
|
||||
let dest_arr_ptr = dest_arr.data().base_ptr(ctx, generator);
|
||||
let dest_arr_ptr =
|
||||
ctx.builder.build_pointer_cast(dest_arr_ptr, elem_ptr_type, "dest_arr_ptr_cast").unwrap();
|
||||
let dest_len = dest_arr.load_size(ctx, Some("dest.len"));
|
||||
let dest_len =
|
||||
ctx.builder.build_int_truncate_or_bit_cast(dest_len, llvm_i32, "srclen32").unwrap();
|
||||
let dest_len = ctx.builder.build_int_truncate_or_bit_cast(dest_len, int32, "srclen32").unwrap();
|
||||
let src_arr_ptr = src_arr.data().base_ptr(ctx, generator);
|
||||
let src_arr_ptr =
|
||||
ctx.builder.build_pointer_cast(src_arr_ptr, elem_ptr_type, "src_arr_ptr_cast").unwrap();
|
||||
let src_len = src_arr.load_size(ctx, Some("src.len"));
|
||||
let src_len =
|
||||
ctx.builder.build_int_truncate_or_bit_cast(src_len, llvm_i32, "srclen32").unwrap();
|
||||
let src_len = ctx.builder.build_int_truncate_or_bit_cast(src_len, int32, "srclen32").unwrap();
|
||||
|
||||
// index in bound and positive should be done
|
||||
// assert if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest), and
|
||||
@ -146,7 +136,7 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
|
||||
BasicTypeEnum::StructType(t) => t.size_of().unwrap(),
|
||||
_ => codegen_unreachable!(ctx),
|
||||
};
|
||||
ctx.builder.build_int_truncate_or_bit_cast(s, llvm_i32, "size").unwrap()
|
||||
ctx.builder.build_int_truncate_or_bit_cast(s, int32, "size").unwrap()
|
||||
}
|
||||
.into(),
|
||||
];
|
||||
@ -157,7 +147,6 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap()
|
||||
};
|
||||
|
||||
// update length
|
||||
let need_update =
|
||||
ctx.builder.build_int_compare(IntPredicate::NE, new_len, dest_len, "need_update").unwrap();
|
||||
@ -166,8 +155,7 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
|
||||
let cont_bb = ctx.ctx.append_basic_block(current, "cont");
|
||||
ctx.builder.build_conditional_branch(need_update, update_bb, cont_bb).unwrap();
|
||||
ctx.builder.position_at_end(update_bb);
|
||||
let new_len =
|
||||
ctx.builder.build_int_z_extend_or_bit_cast(new_len, llvm_usize, "new_len").unwrap();
|
||||
let new_len = ctx.builder.build_int_z_extend_or_bit_cast(new_len, size_ty, "new_len").unwrap();
|
||||
dest_arr.store_size(ctx, generator, new_len);
|
||||
ctx.builder.build_unconditional_branch(cont_bb).unwrap();
|
||||
ctx.builder.position_at_end(cont_bb);
|
||||
|
||||
@ -62,13 +62,8 @@ pub fn call_isinf<'ctx, G: CodeGenerator + ?Sized>(
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
v: FloatValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_f64 = ctx.ctx.f64_type();
|
||||
|
||||
assert_eq!(v.get_type(), llvm_f64);
|
||||
|
||||
let intrinsic_fn = ctx.module.get_function("__nac3_isinf").unwrap_or_else(|| {
|
||||
let fn_type = llvm_i32.fn_type(&[llvm_f64.into()], false);
|
||||
let fn_type = ctx.ctx.i32_type().fn_type(&[ctx.ctx.f64_type().into()], false);
|
||||
ctx.module.add_function("__nac3_isinf", fn_type, None)
|
||||
});
|
||||
|
||||
@ -89,13 +84,8 @@ pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
v: FloatValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_f64 = ctx.ctx.f64_type();
|
||||
|
||||
assert_eq!(v.get_type(), llvm_f64);
|
||||
|
||||
let intrinsic_fn = ctx.module.get_function("__nac3_isnan").unwrap_or_else(|| {
|
||||
let fn_type = llvm_i32.fn_type(&[llvm_f64.into()], false);
|
||||
let fn_type = ctx.ctx.i32_type().fn_type(&[ctx.ctx.f64_type().into()], false);
|
||||
ctx.module.add_function("__nac3_isnan", fn_type, None)
|
||||
});
|
||||
|
||||
@ -114,8 +104,6 @@ pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
|
||||
pub fn call_gamma<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
|
||||
let llvm_f64 = ctx.ctx.f64_type();
|
||||
|
||||
assert_eq!(v.get_type(), llvm_f64);
|
||||
|
||||
let intrinsic_fn = ctx.module.get_function("__nac3_gamma").unwrap_or_else(|| {
|
||||
let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
|
||||
ctx.module.add_function("__nac3_gamma", fn_type, None)
|
||||
@ -133,8 +121,6 @@ pub fn call_gamma<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) ->
|
||||
pub fn call_gammaln<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
|
||||
let llvm_f64 = ctx.ctx.f64_type();
|
||||
|
||||
assert_eq!(v.get_type(), llvm_f64);
|
||||
|
||||
let intrinsic_fn = ctx.module.get_function("__nac3_gammaln").unwrap_or_else(|| {
|
||||
let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
|
||||
ctx.module.add_function("__nac3_gammaln", fn_type, None)
|
||||
@ -152,8 +138,6 @@ pub fn call_gammaln<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -
|
||||
pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
|
||||
let llvm_f64 = ctx.ctx.f64_type();
|
||||
|
||||
assert_eq!(v.get_type(), llvm_f64);
|
||||
|
||||
let intrinsic_fn = ctx.module.get_function("__nac3_j0").unwrap_or_else(|| {
|
||||
let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
|
||||
ctx.module.add_function("__nac3_j0", fn_type, None)
|
||||
|
||||
@ -13,16 +13,13 @@ use super::{CodeGenContext, CodeGenerator};
|
||||
use crate::{symbol_resolver::SymbolResolver, typecheck::typedef::Type};
|
||||
pub use list::*;
|
||||
pub use math::*;
|
||||
pub use range::*;
|
||||
pub use ndarray::*;
|
||||
pub use slice::*;
|
||||
pub use string::*;
|
||||
|
||||
mod list;
|
||||
mod math;
|
||||
pub mod ndarray;
|
||||
mod range;
|
||||
mod ndarray;
|
||||
mod slice;
|
||||
mod string;
|
||||
|
||||
#[must_use]
|
||||
pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver) -> Module<'ctx> {
|
||||
@ -63,27 +60,6 @@ pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver)
|
||||
irrt_mod
|
||||
}
|
||||
|
||||
/// Returns the name of a function which contains variants for 32-bit and 64-bit `size_t`.
|
||||
///
|
||||
/// - 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_usize_dependent_function_name<G: CodeGenerator + ?Sized>(
|
||||
generator: &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
|
||||
}
|
||||
|
||||
/// NOTE: the output value of the end index of this function should be compared ***inclusively***,
|
||||
/// because python allows `a[2::-1]`, whose semantic is `[a[2], a[1], a[0]]`, which is equivalent to
|
||||
/// NO numeric slice in python.
|
||||
@ -132,11 +108,10 @@ pub fn handle_slice_indices<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
length: IntValue<'ctx>,
|
||||
) -> Result<Option<(IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>)>, String> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
|
||||
let zero = llvm_i32.const_zero();
|
||||
let one = llvm_i32.const_int(1, false);
|
||||
let length = ctx.builder.build_int_truncate_or_bit_cast(length, llvm_i32, "leni32").unwrap();
|
||||
let int32 = ctx.ctx.i32_type();
|
||||
let zero = int32.const_zero();
|
||||
let one = int32.const_int(1, false);
|
||||
let length = ctx.builder.build_int_truncate_or_bit_cast(length, int32, "leni32").unwrap();
|
||||
Ok(Some(match (start, end, step) {
|
||||
(s, e, None) => (
|
||||
if let Some(s) = s.as_ref() {
|
||||
@ -145,7 +120,7 @@ pub fn handle_slice_indices<'ctx, G: CodeGenerator>(
|
||||
None => return Ok(None),
|
||||
}
|
||||
} else {
|
||||
llvm_i32.const_zero()
|
||||
int32.const_zero()
|
||||
},
|
||||
{
|
||||
let e = if let Some(s) = e.as_ref() {
|
||||
|
||||
384
nac3core/src/codegen/irrt/ndarray.rs
Normal file
384
nac3core/src/codegen/irrt/ndarray.rs
Normal file
@ -0,0 +1,384 @@
|
||||
use inkwell::{
|
||||
types::IntType,
|
||||
values::{BasicValueEnum, CallSiteValue, IntValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
use itertools::Either;
|
||||
|
||||
use crate::codegen::{
|
||||
llvm_intrinsics,
|
||||
macros::codegen_unreachable,
|
||||
stmt::gen_for_callback_incrementing,
|
||||
values::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, NDArrayValue, TypedArrayLikeAccessor,
|
||||
TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_size`. Returns an [`IntValue`] representing the
|
||||
/// calculated total size.
|
||||
///
|
||||
/// * `dims` - An [`ArrayLikeIndexer`] containing the size of each dimension.
|
||||
/// * `range` - The dimension index to begin and end (exclusively) calculating the dimensions for,
|
||||
/// or [`None`] if starting from the first dimension and ending at the last dimension
|
||||
/// respectively.
|
||||
pub fn call_ndarray_calc_size<'ctx, G, Dims>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dims: &Dims,
|
||||
(begin, end): (Option<IntValue<'ctx>>, Option<IntValue<'ctx>>),
|
||||
) -> IntValue<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Dims: ArrayLikeIndexer<'ctx>,
|
||||
{
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
let ndarray_calc_size_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_size",
|
||||
64 => "__nac3_ndarray_calc_size64",
|
||||
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_calc_size_fn_t = llvm_usize.fn_type(
|
||||
&[llvm_pusize.into(), llvm_usize.into(), llvm_usize.into(), llvm_usize.into()],
|
||||
false,
|
||||
);
|
||||
let ndarray_calc_size_fn =
|
||||
ctx.module.get_function(ndarray_calc_size_fn_name).unwrap_or_else(|| {
|
||||
ctx.module.add_function(ndarray_calc_size_fn_name, ndarray_calc_size_fn_t, None)
|
||||
});
|
||||
|
||||
let begin = begin.unwrap_or_else(|| llvm_usize.const_zero());
|
||||
let end = end.unwrap_or_else(|| dims.size(ctx, generator));
|
||||
ctx.builder
|
||||
.build_call(
|
||||
ndarray_calc_size_fn,
|
||||
&[
|
||||
dims.base_ptr(ctx, generator).into(),
|
||||
dims.size(ctx, generator).into(),
|
||||
begin.into(),
|
||||
end.into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(|v| v.map_left(BasicValueEnum::into_int_value))
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_nd_indices`. Returns a [`TypeArrayLikeAdpater`]
|
||||
/// containing `i32` indices of the flattened index.
|
||||
///
|
||||
/// * `index` - The index to compute the multidimensional index for.
|
||||
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
|
||||
/// `NDArray`.
|
||||
pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
index: IntValue<'ctx>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
|
||||
let llvm_void = ctx.ctx.void_type();
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
let ndarray_calc_nd_indices_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_nd_indices",
|
||||
64 => "__nac3_ndarray_calc_nd_indices64",
|
||||
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_calc_nd_indices_fn =
|
||||
ctx.module.get_function(ndarray_calc_nd_indices_fn_name).unwrap_or_else(|| {
|
||||
let fn_type = llvm_void.fn_type(
|
||||
&[llvm_usize.into(), llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into()],
|
||||
false,
|
||||
);
|
||||
|
||||
ctx.module.add_function(ndarray_calc_nd_indices_fn_name, fn_type, None)
|
||||
});
|
||||
|
||||
let ndarray_num_dims = ndarray.load_ndims(ctx);
|
||||
let ndarray_dims = ndarray.shape();
|
||||
|
||||
let indices = ctx.builder.build_array_alloca(llvm_i32, ndarray_num_dims, "").unwrap();
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
ndarray_calc_nd_indices_fn,
|
||||
&[
|
||||
index.into(),
|
||||
ndarray_dims.base_ptr(ctx, generator).into(),
|
||||
ndarray_num_dims.into(),
|
||||
indices.into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
TypedArrayLikeAdapter::from(
|
||||
ArraySliceValue::from_ptr_val(indices, ndarray_num_dims, None),
|
||||
Box::new(|_, v| v.into_int_value()),
|
||||
Box::new(|_, v| v.into()),
|
||||
)
|
||||
}
|
||||
|
||||
fn call_ndarray_flatten_index_impl<'ctx, G, Indices>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
indices: &Indices,
|
||||
) -> IntValue<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Indices: ArrayLikeIndexer<'ctx>,
|
||||
{
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
debug_assert_eq!(
|
||||
IntType::try_from(indices.element_type(ctx, generator))
|
||||
.map(IntType::get_bit_width)
|
||||
.unwrap_or_default(),
|
||||
llvm_i32.get_bit_width(),
|
||||
"Expected i32 value for argument `indices` to `call_ndarray_flatten_index_impl`"
|
||||
);
|
||||
debug_assert_eq!(
|
||||
indices.size(ctx, generator).get_type().get_bit_width(),
|
||||
llvm_usize.get_bit_width(),
|
||||
"Expected usize integer value for argument `indices_size` to `call_ndarray_flatten_index_impl`"
|
||||
);
|
||||
|
||||
let ndarray_flatten_index_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_flatten_index",
|
||||
64 => "__nac3_ndarray_flatten_index64",
|
||||
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_flatten_index_fn =
|
||||
ctx.module.get_function(ndarray_flatten_index_fn_name).unwrap_or_else(|| {
|
||||
let fn_type = llvm_usize.fn_type(
|
||||
&[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_usize.into()],
|
||||
false,
|
||||
);
|
||||
|
||||
ctx.module.add_function(ndarray_flatten_index_fn_name, fn_type, None)
|
||||
});
|
||||
|
||||
let ndarray_num_dims = ndarray.load_ndims(ctx);
|
||||
let ndarray_dims = ndarray.shape();
|
||||
|
||||
let index = ctx
|
||||
.builder
|
||||
.build_call(
|
||||
ndarray_flatten_index_fn,
|
||||
&[
|
||||
ndarray_dims.base_ptr(ctx, generator).into(),
|
||||
ndarray_num_dims.into(),
|
||||
indices.base_ptr(ctx, generator).into(),
|
||||
indices.size(ctx, generator).into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(|v| v.map_left(BasicValueEnum::into_int_value))
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap();
|
||||
|
||||
index
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_flatten_index`. Returns the flattened index for the
|
||||
/// multidimensional index.
|
||||
///
|
||||
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
|
||||
/// `NDArray`.
|
||||
/// * `indices` - The multidimensional index to compute the flattened index for.
|
||||
pub fn call_ndarray_flatten_index<'ctx, G, Index>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
indices: &Index,
|
||||
) -> IntValue<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Index: ArrayLikeIndexer<'ctx>,
|
||||
{
|
||||
call_ndarray_flatten_index_impl(generator, ctx, ndarray, indices)
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_broadcast`. Returns a tuple containing the number of
|
||||
/// dimension and size of each dimension of the resultant `ndarray`.
|
||||
pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
lhs: NDArrayValue<'ctx>,
|
||||
rhs: NDArrayValue<'ctx>,
|
||||
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_broadcast",
|
||||
64 => "__nac3_ndarray_calc_broadcast64",
|
||||
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_calc_broadcast_fn =
|
||||
ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
|
||||
let fn_type = llvm_usize.fn_type(
|
||||
&[
|
||||
llvm_pusize.into(),
|
||||
llvm_usize.into(),
|
||||
llvm_pusize.into(),
|
||||
llvm_usize.into(),
|
||||
llvm_pusize.into(),
|
||||
],
|
||||
false,
|
||||
);
|
||||
|
||||
ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
|
||||
});
|
||||
|
||||
let lhs_ndims = lhs.load_ndims(ctx);
|
||||
let rhs_ndims = rhs.load_ndims(ctx);
|
||||
let min_ndims = llvm_intrinsics::call_int_umin(ctx, lhs_ndims, rhs_ndims, None);
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
llvm_usize.const_zero(),
|
||||
(min_ndims, false),
|
||||
|generator, ctx, _, idx| {
|
||||
let idx = ctx.builder.build_int_sub(min_ndims, idx, "").unwrap();
|
||||
let (lhs_dim_sz, rhs_dim_sz) = unsafe {
|
||||
(
|
||||
lhs.shape().get_typed_unchecked(ctx, generator, &idx, None),
|
||||
rhs.shape().get_typed_unchecked(ctx, generator, &idx, None),
|
||||
)
|
||||
};
|
||||
|
||||
let llvm_usize_const_one = llvm_usize.const_int(1, false);
|
||||
let lhs_eqz = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::EQ, lhs_dim_sz, llvm_usize_const_one, "")
|
||||
.unwrap();
|
||||
let rhs_eqz = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::EQ, rhs_dim_sz, llvm_usize_const_one, "")
|
||||
.unwrap();
|
||||
let lhs_or_rhs_eqz = ctx.builder.build_or(lhs_eqz, rhs_eqz, "").unwrap();
|
||||
|
||||
let lhs_eq_rhs = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::EQ, lhs_dim_sz, rhs_dim_sz, "")
|
||||
.unwrap();
|
||||
|
||||
let is_compatible = ctx.builder.build_or(lhs_or_rhs_eqz, lhs_eq_rhs, "").unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
is_compatible,
|
||||
"0:ValueError",
|
||||
"operands could not be broadcast together",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
|
||||
let lhs_dims = lhs.shape().base_ptr(ctx, generator);
|
||||
let lhs_ndims = lhs.load_ndims(ctx);
|
||||
let rhs_dims = rhs.shape().base_ptr(ctx, generator);
|
||||
let rhs_ndims = rhs.load_ndims(ctx);
|
||||
let out_dims = ctx.builder.build_array_alloca(llvm_usize, max_ndims, "").unwrap();
|
||||
let out_dims = ArraySliceValue::from_ptr_val(out_dims, max_ndims, None);
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
ndarray_calc_broadcast_fn,
|
||||
&[
|
||||
lhs_dims.into(),
|
||||
lhs_ndims.into(),
|
||||
rhs_dims.into(),
|
||||
rhs_ndims.into(),
|
||||
out_dims.base_ptr(ctx, generator).into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
TypedArrayLikeAdapter::from(
|
||||
out_dims,
|
||||
Box::new(|_, v| v.into_int_value()),
|
||||
Box::new(|_, v| v.into()),
|
||||
)
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
|
||||
/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
|
||||
/// array `broadcast_idx`.
|
||||
pub fn call_ndarray_calc_broadcast_index<
|
||||
'ctx,
|
||||
G: CodeGenerator + ?Sized,
|
||||
BroadcastIdx: UntypedArrayLikeAccessor<'ctx>,
|
||||
>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
array: NDArrayValue<'ctx>,
|
||||
broadcast_idx: &BroadcastIdx,
|
||||
) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_broadcast_idx",
|
||||
64 => "__nac3_ndarray_calc_broadcast_idx64",
|
||||
bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
|
||||
};
|
||||
let ndarray_calc_broadcast_fn =
|
||||
ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
|
||||
let fn_type = llvm_usize.fn_type(
|
||||
&[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_pi32.into()],
|
||||
false,
|
||||
);
|
||||
|
||||
ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
|
||||
});
|
||||
|
||||
let broadcast_size = broadcast_idx.size(ctx, generator);
|
||||
let out_idx = ctx.builder.build_array_alloca(llvm_i32, broadcast_size, "").unwrap();
|
||||
|
||||
let array_dims = array.shape().base_ptr(ctx, generator);
|
||||
let array_ndims = array.load_ndims(ctx);
|
||||
let broadcast_idx_ptr = unsafe {
|
||||
broadcast_idx.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
|
||||
};
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
ndarray_calc_broadcast_fn,
|
||||
&[array_dims.into(), array_ndims.into(), broadcast_idx_ptr.into(), out_idx.into()],
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
TypedArrayLikeAdapter::from(
|
||||
ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None),
|
||||
Box::new(|_, v| v.into_int_value()),
|
||||
Box::new(|_, v| v.into()),
|
||||
)
|
||||
}
|
||||
@ -1,80 +0,0 @@
|
||||
use inkwell::{types::BasicTypeEnum, values::IntValue};
|
||||
|
||||
use crate::codegen::{
|
||||
expr::infer_and_call_function,
|
||||
irrt::get_usize_dependent_function_name,
|
||||
values::{ndarray::NDArrayValue, ListValue, ProxyValue, TypedArrayLikeAccessor},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_array_set_and_validate_list_shape`.
|
||||
///
|
||||
/// Deduces the target shape of the `ndarray` from the provided `list`, raising an exception if
|
||||
/// there is any issue with the resultant `shape`.
|
||||
///
|
||||
/// `shape` must be pre-allocated by the caller of this function to `[usize; ndims]`, and must be
|
||||
/// initialized to all `-1`s.
|
||||
pub fn call_nac3_ndarray_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
list: ListValue<'ctx>,
|
||||
ndims: IntValue<'ctx>,
|
||||
shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
) {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
assert_eq!(list.get_type().element_type().unwrap(), ctx.ctx.i8_type().into());
|
||||
assert_eq!(ndims.get_type(), llvm_usize);
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(shape.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
|
||||
let name = get_usize_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_array_set_and_validate_list_shape",
|
||||
);
|
||||
|
||||
infer_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[list.as_base_value().into(), ndims.into(), shape.base_ptr(ctx, generator).into()],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_array_write_list_to_array`.
|
||||
///
|
||||
/// Copies the contents stored in `list` into `ndarray`.
|
||||
///
|
||||
/// The `ndarray` must fulfill the following preconditions:
|
||||
///
|
||||
/// - `ndarray.itemsize`: Must be initialized.
|
||||
/// - `ndarray.ndims`: Must be initialized.
|
||||
/// - `ndarray.shape`: Must be initialized.
|
||||
/// - `ndarray.data`: Must be allocated and contiguous.
|
||||
pub fn call_nac3_ndarray_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
list: ListValue<'ctx>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
) {
|
||||
assert_eq!(list.get_type().element_type().unwrap(), ctx.ctx.i8_type().into());
|
||||
|
||||
let name = get_usize_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_array_write_list_to_array",
|
||||
);
|
||||
|
||||
infer_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[list.as_base_value().into(), ndarray.as_base_value().into()],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
@ -1,310 +0,0 @@
|
||||
use inkwell::{
|
||||
types::BasicTypeEnum,
|
||||
values::{BasicValueEnum, IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use crate::codegen::{
|
||||
expr::{create_and_call_function, infer_and_call_function},
|
||||
irrt::get_usize_dependent_function_name,
|
||||
types::ProxyType,
|
||||
values::{ndarray::NDArrayValue, ProxyValue, TypedArrayLikeAccessor},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_util_assert_shape_no_negative`.
|
||||
///
|
||||
/// Assets that `shape` does not contain negative dimensions.
|
||||
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
) {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(shape.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
|
||||
let name = get_usize_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_util_assert_shape_no_negative",
|
||||
);
|
||||
|
||||
create_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
Some(llvm_usize.into()),
|
||||
&[
|
||||
(llvm_usize.into(), shape.size(ctx, generator).into()),
|
||||
(llvm_pusize.into(), shape.base_ptr(ctx, generator).into()),
|
||||
],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_util_assert_shape_output_shape_same`.
|
||||
///
|
||||
/// Asserts that `ndarray_shape` and `output_shape` are the same in the context of writing output to
|
||||
/// an `ndarray`.
|
||||
pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
output_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
) {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(ndarray_shape.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(output_shape.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
|
||||
let name = get_usize_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_util_assert_output_shape_same",
|
||||
);
|
||||
|
||||
create_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
Some(llvm_usize.into()),
|
||||
&[
|
||||
(llvm_usize.into(), ndarray_shape.size(ctx, generator).into()),
|
||||
(llvm_pusize.into(), ndarray_shape.base_ptr(ctx, generator).into()),
|
||||
(llvm_usize.into(), output_shape.size(ctx, generator).into()),
|
||||
(llvm_pusize.into(), output_shape.base_ptr(ctx, generator).into()),
|
||||
],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_size`.
|
||||
///
|
||||
/// Returns a [`usize`][CodeGenerator::get_size_type] value of the number of elements of an
|
||||
/// `ndarray`, corresponding to the value of `ndarray.size`.
|
||||
pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_ndarray = ndarray.get_type().as_base_type();
|
||||
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
|
||||
|
||||
create_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
Some(llvm_usize.into()),
|
||||
&[(llvm_ndarray.into(), ndarray.as_base_value().into())],
|
||||
Some("size"),
|
||||
None,
|
||||
)
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_nbytes`.
|
||||
///
|
||||
/// Returns a [`usize`][CodeGenerator::get_size_type] value of the number of bytes consumed by the
|
||||
/// data of the `ndarray`, corresponding to the value of `ndarray.nbytes`.
|
||||
pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_ndarray = ndarray.get_type().as_base_type();
|
||||
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
|
||||
|
||||
create_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
Some(llvm_usize.into()),
|
||||
&[(llvm_ndarray.into(), ndarray.as_base_value().into())],
|
||||
Some("nbytes"),
|
||||
None,
|
||||
)
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_len`.
|
||||
///
|
||||
/// Returns a [`usize`][CodeGenerator::get_size_type] value of the size of the topmost dimension of
|
||||
/// the `ndarray`, corresponding to the value of `ndarray.__len__`.
|
||||
pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_ndarray = ndarray.get_type().as_base_type();
|
||||
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
|
||||
|
||||
create_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
Some(llvm_usize.into()),
|
||||
&[(llvm_ndarray.into(), ndarray.as_base_value().into())],
|
||||
Some("len"),
|
||||
None,
|
||||
)
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_is_c_contiguous`.
|
||||
///
|
||||
/// Returns an `i1` value indicating whether the `ndarray` is C-contiguous.
|
||||
pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
let llvm_ndarray = ndarray.get_type().as_base_type();
|
||||
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
|
||||
|
||||
create_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
Some(llvm_i1.into()),
|
||||
&[(llvm_ndarray.into(), ndarray.as_base_value().into())],
|
||||
Some("is_c_contiguous"),
|
||||
None,
|
||||
)
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_get_nth_pelement`.
|
||||
///
|
||||
/// Returns a [`PointerValue`] to the `index`-th flattened element of the `ndarray`.
|
||||
pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
index: IntValue<'ctx>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_ndarray = ndarray.get_type().as_base_type();
|
||||
|
||||
assert_eq!(index.get_type(), llvm_usize);
|
||||
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
|
||||
|
||||
create_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
Some(llvm_pi8.into()),
|
||||
&[(llvm_ndarray.into(), ndarray.as_base_value().into()), (llvm_usize.into(), index.into())],
|
||||
Some("pelement"),
|
||||
None,
|
||||
)
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_get_pelement_by_indices`.
|
||||
///
|
||||
/// `indices` must have the same number of elements as the number of dimensions in `ndarray`.
|
||||
///
|
||||
/// Returns a [`PointerValue`] to the element indexed by `indices`.
|
||||
pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
indices: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
let llvm_ndarray = ndarray.get_type().as_base_type();
|
||||
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(indices.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
|
||||
let name =
|
||||
get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_get_pelement_by_indices");
|
||||
|
||||
create_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
Some(llvm_pi8.into()),
|
||||
&[
|
||||
(llvm_ndarray.into(), ndarray.as_base_value().into()),
|
||||
(llvm_pusize.into(), indices.base_ptr(ctx, generator).into()),
|
||||
],
|
||||
Some("pelement"),
|
||||
None,
|
||||
)
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_set_strides_by_shape`.
|
||||
///
|
||||
/// Sets `ndarray.strides` assuming that `ndarray.shape` is C-contiguous.
|
||||
pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
) {
|
||||
let llvm_ndarray = ndarray.get_type().as_base_type();
|
||||
|
||||
let name =
|
||||
get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
|
||||
|
||||
create_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[(llvm_ndarray.into(), ndarray.as_base_value().into())],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_copy_data`.
|
||||
///
|
||||
/// Copies all elements from `src_ndarray` to `dst_ndarray` using their flattened views. The number
|
||||
/// of elements in `src_ndarray` must be greater than or equal to the number of elements in
|
||||
/// `dst_ndarray`.
|
||||
pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: NDArrayValue<'ctx>,
|
||||
dst_ndarray: NDArrayValue<'ctx>,
|
||||
) {
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
|
||||
|
||||
infer_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[src_ndarray.as_base_value().into(), dst_ndarray.as_base_value().into()],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
@ -1,82 +0,0 @@
|
||||
use inkwell::values::IntValue;
|
||||
|
||||
use crate::codegen::{
|
||||
expr::infer_and_call_function,
|
||||
irrt::get_usize_dependent_function_name,
|
||||
types::{ndarray::ShapeEntryType, ProxyType},
|
||||
values::{
|
||||
ndarray::NDArrayValue, ArrayLikeValue, ArraySliceValue, ProxyValue, TypedArrayLikeAccessor,
|
||||
TypedArrayLikeMutator,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_broadcast_to`.
|
||||
///
|
||||
/// Attempts to broadcast `src_ndarray` to the new shape defined by `dst_ndarray`.
|
||||
///
|
||||
/// `dst_ndarray` must meet the following preconditions:
|
||||
///
|
||||
/// - `dst_ndarray.ndims` must be initialized and matching the length of `dst_ndarray.shape`.
|
||||
/// - `dst_ndarray.shape` must be initialized and contains the target broadcast shape.
|
||||
/// - `dst_ndarray.strides` must be allocated and may contain uninitialized values.
|
||||
pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: NDArrayValue<'ctx>,
|
||||
dst_ndarray: NDArrayValue<'ctx>,
|
||||
) {
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
|
||||
infer_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[src_ndarray.as_base_value().into(), dst_ndarray.as_base_value().into()],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_broadcast_shapes`.
|
||||
///
|
||||
/// Attempts to calculate the resultant shape from broadcasting all shapes in `shape_entries`,
|
||||
/// writing the result to `dst_shape`.
|
||||
pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G, Shape>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
num_shape_entries: IntValue<'ctx>,
|
||||
shape_entries: ArraySliceValue<'ctx>,
|
||||
dst_ndims: IntValue<'ctx>,
|
||||
dst_shape: &Shape,
|
||||
) where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Shape: TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
|
||||
+ TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>,
|
||||
{
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
assert_eq!(num_shape_entries.get_type(), llvm_usize);
|
||||
assert!(ShapeEntryType::is_type(
|
||||
generator,
|
||||
ctx.ctx,
|
||||
shape_entries.base_ptr(ctx, generator).get_type()
|
||||
)
|
||||
.is_ok());
|
||||
assert_eq!(dst_ndims.get_type(), llvm_usize);
|
||||
assert_eq!(dst_shape.element_type(ctx, generator), llvm_usize.into());
|
||||
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
|
||||
infer_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[
|
||||
num_shape_entries.into(),
|
||||
shape_entries.base_ptr(ctx, generator).into(),
|
||||
dst_ndims.into(),
|
||||
dst_shape.base_ptr(ctx, generator).into(),
|
||||
],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
@ -1,34 +0,0 @@
|
||||
use crate::codegen::{
|
||||
expr::infer_and_call_function,
|
||||
irrt::get_usize_dependent_function_name,
|
||||
values::{ndarray::NDArrayValue, ArrayLikeValue, ArraySliceValue, ProxyValue},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_index`.
|
||||
///
|
||||
/// Performs [basic indexing](https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
|
||||
/// on `src_ndarray` using `indices`, writing the result to `dst_ndarray`, corresponding to the
|
||||
/// operation `dst_ndarray = src_ndarray[indices]`.
|
||||
pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
indices: ArraySliceValue<'ctx>,
|
||||
src_ndarray: NDArrayValue<'ctx>,
|
||||
dst_ndarray: NDArrayValue<'ctx>,
|
||||
) {
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
|
||||
infer_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[
|
||||
indices.size(ctx, generator).into(),
|
||||
indices.base_ptr(ctx, generator).into(),
|
||||
src_ndarray.as_base_value().into(),
|
||||
dst_ndarray.as_base_value().into(),
|
||||
],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
@ -1,86 +0,0 @@
|
||||
use inkwell::{
|
||||
types::BasicTypeEnum,
|
||||
values::{BasicValueEnum, IntValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use crate::codegen::{
|
||||
expr::{create_and_call_function, infer_and_call_function},
|
||||
irrt::get_usize_dependent_function_name,
|
||||
types::ProxyType,
|
||||
values::{
|
||||
ndarray::{NDArrayValue, NDIterValue},
|
||||
ProxyValue, TypedArrayLikeAccessor,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Generates a call to `__nac3_nditer_initialize`.
|
||||
///
|
||||
/// Initializes the `iter` object.
|
||||
pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
iter: NDIterValue<'ctx>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
indices: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
) {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(indices.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_nditer_initialize");
|
||||
|
||||
create_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[
|
||||
(iter.get_type().as_base_type().into(), iter.as_base_value().into()),
|
||||
(ndarray.get_type().as_base_type().into(), ndarray.as_base_value().into()),
|
||||
(llvm_pusize.into(), indices.base_ptr(ctx, generator).into()),
|
||||
],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_nditer_initialize_has_element`.
|
||||
///
|
||||
/// Returns an `i1` value indicating whether there are elements left to traverse for the `iter`
|
||||
/// object.
|
||||
pub fn call_nac3_nditer_has_element<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
iter: NDIterValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_nditer_has_element");
|
||||
|
||||
infer_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
Some(ctx.ctx.bool_type().into()),
|
||||
&[iter.as_base_value().into()],
|
||||
None,
|
||||
None,
|
||||
)
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_nditer_next`.
|
||||
///
|
||||
/// Moves `iter` to point to the next element.
|
||||
pub fn call_nac3_nditer_next<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
iter: NDIterValue<'ctx>,
|
||||
) {
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_nditer_next");
|
||||
|
||||
infer_and_call_function(ctx, &name, None, &[iter.as_base_value().into()], None, None);
|
||||
}
|
||||
@ -1,66 +0,0 @@
|
||||
use inkwell::{types::BasicTypeEnum, values::IntValue};
|
||||
|
||||
use crate::codegen::{
|
||||
expr::infer_and_call_function, irrt::get_usize_dependent_function_name,
|
||||
values::TypedArrayLikeAccessor, CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_matmul_calculate_shapes`.
|
||||
///
|
||||
/// Calculates the broadcasted shapes for `a`, `b`, and the `ndarray` holding the final values of
|
||||
/// `a @ b`.
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub fn call_nac3_ndarray_matmul_calculate_shapes<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
a_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
b_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
final_ndims: IntValue<'ctx>,
|
||||
new_a_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
new_b_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
dst_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
) {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(a_shape.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(b_shape.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(new_a_shape.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(new_b_shape.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
assert_eq!(
|
||||
BasicTypeEnum::try_from(dst_shape.element_type(ctx, generator)).unwrap(),
|
||||
llvm_usize.into()
|
||||
);
|
||||
|
||||
let name =
|
||||
get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
|
||||
|
||||
infer_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[
|
||||
a_shape.size(ctx, generator).into(),
|
||||
a_shape.base_ptr(ctx, generator).into(),
|
||||
b_shape.size(ctx, generator).into(),
|
||||
b_shape.base_ptr(ctx, generator).into(),
|
||||
final_ndims.into(),
|
||||
new_a_shape.base_ptr(ctx, generator).into(),
|
||||
new_b_shape.base_ptr(ctx, generator).into(),
|
||||
dst_shape.base_ptr(ctx, generator).into(),
|
||||
],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
@ -1,17 +0,0 @@
|
||||
pub use array::*;
|
||||
pub use basic::*;
|
||||
pub use broadcast::*;
|
||||
pub use indexing::*;
|
||||
pub use iter::*;
|
||||
pub use matmul::*;
|
||||
pub use reshape::*;
|
||||
pub use transpose::*;
|
||||
|
||||
mod array;
|
||||
mod basic;
|
||||
mod broadcast;
|
||||
mod indexing;
|
||||
mod iter;
|
||||
mod matmul;
|
||||
mod reshape;
|
||||
mod transpose;
|
||||
@ -1,40 +0,0 @@
|
||||
use inkwell::values::IntValue;
|
||||
|
||||
use crate::codegen::{
|
||||
expr::infer_and_call_function,
|
||||
irrt::get_usize_dependent_function_name,
|
||||
values::{ArrayLikeValue, ArraySliceValue},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_reshape_resolve_and_check_new_shape`.
|
||||
///
|
||||
/// Resolves unknown dimensions in `new_shape` for `numpy.reshape(<ndarray>, new_shape)`, raising an
|
||||
/// assertion if multiple dimensions are unknown (`-1`).
|
||||
pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
size: IntValue<'ctx>,
|
||||
new_ndims: IntValue<'ctx>,
|
||||
new_shape: ArraySliceValue<'ctx>,
|
||||
) {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
assert_eq!(size.get_type(), llvm_usize);
|
||||
assert_eq!(new_ndims.get_type(), llvm_usize);
|
||||
assert_eq!(new_shape.element_type(ctx, generator), llvm_usize.into());
|
||||
|
||||
let name = get_usize_dependent_function_name(
|
||||
generator,
|
||||
ctx,
|
||||
"__nac3_ndarray_reshape_resolve_and_check_new_shape",
|
||||
);
|
||||
infer_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[size.into(), new_ndims.into(), new_shape.base_ptr(ctx, generator).into()],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
@ -1,48 +0,0 @@
|
||||
use inkwell::{values::IntValue, AddressSpace};
|
||||
|
||||
use crate::codegen::{
|
||||
expr::infer_and_call_function,
|
||||
irrt::get_usize_dependent_function_name,
|
||||
values::{ndarray::NDArrayValue, ProxyValue, TypedArrayLikeAccessor},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_transpose`.
|
||||
///
|
||||
/// Creates a transpose view of `src_ndarray` and writes the result to `dst_ndarray`.
|
||||
///
|
||||
/// `dst_ndarray` must fulfill the following preconditions:
|
||||
///
|
||||
/// - `dst_ndarray.ndims` must be initialized and must be equal to `src_ndarray.ndims`.
|
||||
/// - `dst_ndarray.shape` must be allocated and may contain uninitialized values.
|
||||
/// - `dst_ndarray.strides` must be allocated and may contain uninitialized values.
|
||||
pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
src_ndarray: NDArrayValue<'ctx>,
|
||||
dst_ndarray: NDArrayValue<'ctx>,
|
||||
axes: Option<&impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>>,
|
||||
) {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
assert!(axes.is_none_or(|axes| axes.size(ctx, generator).get_type() == llvm_usize));
|
||||
assert!(axes.is_none_or(|axes| axes.element_type(ctx, generator) == llvm_usize.into()));
|
||||
|
||||
let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
|
||||
infer_and_call_function(
|
||||
ctx,
|
||||
&name,
|
||||
None,
|
||||
&[
|
||||
src_ndarray.as_base_value().into(),
|
||||
dst_ndarray.as_base_value().into(),
|
||||
axes.map_or(llvm_usize.const_zero(), |axes| axes.size(ctx, generator)).into(),
|
||||
axes.map_or(llvm_usize.ptr_type(AddressSpace::default()).const_null(), |axes| {
|
||||
axes.base_ptr(ctx, generator)
|
||||
})
|
||||
.into(),
|
||||
],
|
||||
None,
|
||||
None,
|
||||
);
|
||||
}
|
||||
@ -1,56 +0,0 @@
|
||||
use inkwell::{
|
||||
values::{BasicValueEnum, CallSiteValue, IntValue},
|
||||
IntPredicate,
|
||||
};
|
||||
use itertools::Either;
|
||||
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
/// Invokes the `__nac3_range_slice_len` in IRRT.
|
||||
///
|
||||
/// - `start`: The `i32` start value for the slice.
|
||||
/// - `end`: The `i32` end value for the slice.
|
||||
/// - `step`: The `i32` step value for the slice.
|
||||
///
|
||||
/// Returns an `i32` value of the length of the slice.
|
||||
pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
start: IntValue<'ctx>,
|
||||
end: IntValue<'ctx>,
|
||||
step: IntValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
const SYMBOL: &str = "__nac3_range_slice_len";
|
||||
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
|
||||
assert_eq!(start.get_type(), llvm_i32);
|
||||
assert_eq!(end.get_type(), llvm_i32);
|
||||
assert_eq!(step.get_type(), llvm_i32);
|
||||
|
||||
let len_func = ctx.module.get_function(SYMBOL).unwrap_or_else(|| {
|
||||
let fn_t = llvm_i32.fn_type(&[llvm_i32.into(), llvm_i32.into(), llvm_i32.into()], false);
|
||||
ctx.module.add_function(SYMBOL, fn_t, None)
|
||||
});
|
||||
|
||||
// assert step != 0, throw exception if not
|
||||
let not_zero = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::NE, step, step.get_type().const_zero(), "range_step_ne")
|
||||
.unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
not_zero,
|
||||
"0:ValueError",
|
||||
"step must not be zero",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
ctx.builder
|
||||
.build_call(len_func, &[start.into(), end.into(), step.into()], "calc_len")
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(|v| v.map_left(BasicValueEnum::into_int_value))
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap()
|
||||
}
|
||||
@ -1,6 +1,8 @@
|
||||
use inkwell::values::{BasicValueEnum, CallSiteValue, IntValue};
|
||||
use inkwell::{
|
||||
values::{BasicValueEnum, CallSiteValue, IntValue},
|
||||
IntPredicate,
|
||||
};
|
||||
use itertools::Either;
|
||||
|
||||
use nac3parser::ast::Expr;
|
||||
|
||||
use crate::{
|
||||
@ -37,3 +39,38 @@ pub fn handle_slice_index_bound<'ctx, G: CodeGenerator>(
|
||||
.unwrap(),
|
||||
))
|
||||
}
|
||||
|
||||
pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
start: IntValue<'ctx>,
|
||||
end: IntValue<'ctx>,
|
||||
step: IntValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
const SYMBOL: &str = "__nac3_range_slice_len";
|
||||
let len_func = ctx.module.get_function(SYMBOL).unwrap_or_else(|| {
|
||||
let i32_t = ctx.ctx.i32_type();
|
||||
let fn_t = i32_t.fn_type(&[i32_t.into(), i32_t.into(), i32_t.into()], false);
|
||||
ctx.module.add_function(SYMBOL, fn_t, None)
|
||||
});
|
||||
|
||||
// assert step != 0, throw exception if not
|
||||
let not_zero = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::NE, step, step.get_type().const_zero(), "range_step_ne")
|
||||
.unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
not_zero,
|
||||
"0:ValueError",
|
||||
"step must not be zero",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
ctx.builder
|
||||
.build_call(len_func, &[start.into(), end.into(), step.into()], "calc_len")
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(|v| v.map_left(BasicValueEnum::into_int_value))
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
@ -1,46 +0,0 @@
|
||||
use inkwell::values::{BasicValueEnum, CallSiteValue, IntValue, PointerValue};
|
||||
use itertools::Either;
|
||||
|
||||
use super::get_usize_dependent_function_name;
|
||||
use crate::codegen::{CodeGenContext, CodeGenerator};
|
||||
|
||||
/// Generates a call to string equality comparison. Returns an `i1` representing whether the strings are equal.
|
||||
pub fn call_string_eq<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
str1_ptr: PointerValue<'ctx>,
|
||||
str1_len: IntValue<'ctx>,
|
||||
str2_ptr: PointerValue<'ctx>,
|
||||
str2_len: IntValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
|
||||
let func_name = get_usize_dependent_function_name(generator, ctx, "nac3_str_eq");
|
||||
|
||||
let func = ctx.module.get_function(&func_name).unwrap_or_else(|| {
|
||||
ctx.module.add_function(
|
||||
&func_name,
|
||||
llvm_i1.fn_type(
|
||||
&[
|
||||
str1_ptr.get_type().into(),
|
||||
str1_len.get_type().into(),
|
||||
str2_ptr.get_type().into(),
|
||||
str2_len.get_type().into(),
|
||||
],
|
||||
false,
|
||||
),
|
||||
None,
|
||||
)
|
||||
});
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
func,
|
||||
&[str1_ptr.into(), str1_len.into(), str2_ptr.into(), str2_len.into()],
|
||||
"str_eq_call",
|
||||
)
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(|v| v.map_left(BasicValueEnum::into_int_value))
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap()
|
||||
}
|
||||
@ -1,6 +1,7 @@
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
intrinsics::Intrinsic,
|
||||
types::AnyTypeEnum::IntType,
|
||||
types::{AnyTypeEnum::IntType, FloatType},
|
||||
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
@ -8,6 +9,34 @@ use itertools::Either;
|
||||
|
||||
use super::CodeGenContext;
|
||||
|
||||
/// Returns the string representation for the floating-point type `ft` when used in intrinsic
|
||||
/// functions.
|
||||
fn get_float_intrinsic_repr(ctx: &Context, ft: FloatType) -> &'static str {
|
||||
// Standard LLVM floating-point types
|
||||
if ft == ctx.f16_type() {
|
||||
return "f16";
|
||||
}
|
||||
if ft == ctx.f32_type() {
|
||||
return "f32";
|
||||
}
|
||||
if ft == ctx.f64_type() {
|
||||
return "f64";
|
||||
}
|
||||
if ft == ctx.f128_type() {
|
||||
return "f128";
|
||||
}
|
||||
|
||||
// Non-standard floating-point types
|
||||
if ft == ctx.x86_f80_type() {
|
||||
return "f80";
|
||||
}
|
||||
if ft == ctx.ppc_f128_type() {
|
||||
return "ppcf128";
|
||||
}
|
||||
|
||||
unreachable!()
|
||||
}
|
||||
|
||||
/// Invokes the [`llvm.va_start`](https://llvm.org/docs/LangRef.html#llvm-va-start-intrinsic)
|
||||
/// intrinsic.
|
||||
pub fn call_va_start<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue<'ctx>) {
|
||||
@ -25,7 +54,7 @@ pub fn call_va_start<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue
|
||||
ctx.builder.build_call(intrinsic_fn, &[arglist.into()], "").unwrap();
|
||||
}
|
||||
|
||||
/// Invokes the [`llvm.va_end`](https://llvm.org/docs/LangRef.html#llvm-va-end-intrinsic)
|
||||
/// Invokes the [`llvm.va_start`](https://llvm.org/docs/LangRef.html#llvm-va-start-intrinsic)
|
||||
/// intrinsic.
|
||||
pub fn call_va_end<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue<'ctx>) {
|
||||
const FN_NAME: &str = "llvm.va_end";
|
||||
@ -172,49 +201,6 @@ pub fn call_memcpy_generic<'ctx>(
|
||||
call_memcpy(ctx, dest, src, len, is_volatile);
|
||||
}
|
||||
|
||||
/// Invokes the `llvm.memcpy` intrinsic.
|
||||
///
|
||||
/// Unlike [`call_memcpy`], this function accepts any type of pointer value. If `dest` or `src` is
|
||||
/// not a pointer to an integer, the pointer(s) will be cast to `i8*` before invoking `memcpy`.
|
||||
/// Moreover, `len` now refers to the number of elements to copy (rather than number of bytes to
|
||||
/// copy).
|
||||
pub fn call_memcpy_generic_array<'ctx>(
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
dest: PointerValue<'ctx>,
|
||||
src: PointerValue<'ctx>,
|
||||
len: IntValue<'ctx>,
|
||||
is_volatile: IntValue<'ctx>,
|
||||
) {
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_p0i8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
let llvm_sizeof_expr_t = llvm_i8.size_of().get_type();
|
||||
|
||||
let dest_elem_t = dest.get_type().get_element_type();
|
||||
let src_elem_t = src.get_type().get_element_type();
|
||||
|
||||
let dest = if matches!(dest_elem_t, IntType(t) if t.get_bit_width() == 8) {
|
||||
dest
|
||||
} else {
|
||||
ctx.builder
|
||||
.build_bit_cast(dest, llvm_p0i8, "")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
};
|
||||
let src = if matches!(src_elem_t, IntType(t) if t.get_bit_width() == 8) {
|
||||
src
|
||||
} else {
|
||||
ctx.builder
|
||||
.build_bit_cast(src, llvm_p0i8, "")
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
};
|
||||
|
||||
let len = ctx.builder.build_int_z_extend_or_bit_cast(len, llvm_sizeof_expr_t, "").unwrap();
|
||||
let len = ctx.builder.build_int_mul(len, src_elem_t.size_of().unwrap(), "").unwrap();
|
||||
|
||||
call_memcpy(ctx, dest, src, len, is_volatile);
|
||||
}
|
||||
|
||||
/// Macro to find and generate build call for llvm intrinsic (body of llvm intrinsic function)
|
||||
///
|
||||
/// Arguments:
|
||||
@ -357,25 +343,3 @@ pub fn call_float_powi<'ctx>(
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Invokes the [`llvm.ctpop`](https://llvm.org/docs/LangRef.html#llvm-ctpop-intrinsic) intrinsic.
|
||||
pub fn call_int_ctpop<'ctx>(
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
src: IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> IntValue<'ctx> {
|
||||
const FN_NAME: &str = "llvm.ctpop";
|
||||
|
||||
let llvm_src_t = src.get_type();
|
||||
|
||||
let intrinsic_fn = Intrinsic::find(FN_NAME)
|
||||
.and_then(|intrinsic| intrinsic.get_declaration(&ctx.module, &[llvm_src_t.into()]))
|
||||
.unwrap();
|
||||
|
||||
ctx.builder
|
||||
.build_call(intrinsic_fn, &[src.into()], name.unwrap_or_default())
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(|v| v.map_left(BasicValueEnum::into_int_value))
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
@ -30,11 +30,7 @@ use nac3parser::ast::{Location, Stmt, StrRef};
|
||||
|
||||
use crate::{
|
||||
symbol_resolver::{StaticValue, SymbolResolver},
|
||||
toplevel::{
|
||||
helper::{extract_ndims, PrimDef},
|
||||
numpy::unpack_ndarray_var_tys,
|
||||
TopLevelContext, TopLevelDef,
|
||||
},
|
||||
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
|
||||
typecheck::{
|
||||
type_inferencer::{CodeLocation, PrimitiveStore},
|
||||
typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
|
||||
@ -42,7 +38,8 @@ use crate::{
|
||||
};
|
||||
use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
|
||||
pub use generator::{CodeGenerator, DefaultCodeGenerator};
|
||||
use types::{ndarray::NDArrayType, ListType, ProxyType, RangeType, TupleType};
|
||||
use tracert::{TraceRuntimeConfig, TraceRuntimeState};
|
||||
use types::{ListType, NDArrayType, ProxyType, RangeType};
|
||||
|
||||
pub mod builtin_fns;
|
||||
pub mod concrete_type;
|
||||
@ -53,6 +50,7 @@ pub mod irrt;
|
||||
pub mod llvm_intrinsics;
|
||||
pub mod numpy;
|
||||
pub mod stmt;
|
||||
pub mod tracert;
|
||||
pub mod types;
|
||||
pub mod values;
|
||||
|
||||
@ -224,11 +222,13 @@ pub struct CodeGenContext<'ctx, 'a> {
|
||||
/// See [`need_sret`].
|
||||
pub need_sret: bool,
|
||||
|
||||
pub tracert_state: Option<TraceRuntimeState>,
|
||||
|
||||
/// The current source location.
|
||||
pub current_loc: Location,
|
||||
}
|
||||
|
||||
impl CodeGenContext<'_, '_> {
|
||||
impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
|
||||
/// Whether the [current basic block][Builder::get_insert_block] referenced by `builder`
|
||||
/// contains a [terminator statement][BasicBlock::get_terminator].
|
||||
pub fn is_terminated(&self) -> bool {
|
||||
@ -271,6 +271,8 @@ pub struct WorkerRegistry {
|
||||
|
||||
/// LLVM-related options for code generation.
|
||||
pub llvm_options: CodeGenLLVMOptions,
|
||||
|
||||
tracert_config: TraceRuntimeConfig,
|
||||
}
|
||||
|
||||
impl WorkerRegistry {
|
||||
@ -280,6 +282,7 @@ impl WorkerRegistry {
|
||||
generators: Vec<Box<G>>,
|
||||
top_level_ctx: Arc<TopLevelContext>,
|
||||
llvm_options: &CodeGenLLVMOptions,
|
||||
tracert_config: &TraceRuntimeConfig,
|
||||
f: &Arc<WithCall>,
|
||||
) -> (Arc<WorkerRegistry>, Vec<thread::JoinHandle<()>>) {
|
||||
let (sender, receiver) = unbounded();
|
||||
@ -301,6 +304,7 @@ impl WorkerRegistry {
|
||||
wait_condvar,
|
||||
top_level_ctx,
|
||||
llvm_options: llvm_options.clone(),
|
||||
tracert_config: tracert_config.clone(),
|
||||
});
|
||||
|
||||
let mut handles = Vec::new();
|
||||
@ -514,13 +518,12 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
||||
}
|
||||
|
||||
TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(unifier, ty);
|
||||
let ndims = extract_ndims(unifier, ndims);
|
||||
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, ndims).as_base_type().into()
|
||||
NDArrayType::new(generator, ctx, element_type).as_base_type().into()
|
||||
}
|
||||
|
||||
_ => unreachable!(
|
||||
@ -574,7 +577,7 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
||||
get_llvm_type(ctx, module, generator, unifier, top_level, type_cache, *ty)
|
||||
})
|
||||
.collect_vec();
|
||||
TupleType::new(generator, ctx, &fields).as_base_type().into()
|
||||
ctx.struct_type(&fields, false).into()
|
||||
}
|
||||
TVirtual { .. } => unimplemented!(),
|
||||
_ => unreachable!("{}", ty_enum.get_type_name()),
|
||||
@ -985,6 +988,13 @@ pub fn gen_func_impl<
|
||||
unifier,
|
||||
static_value_store,
|
||||
need_sret: has_sret,
|
||||
tracert_state: if cfg!(feature = "tracing")
|
||||
|| registry.tracert_config.enabled_tags.is_empty()
|
||||
{
|
||||
None
|
||||
} else {
|
||||
Some(TraceRuntimeState::create(registry.tracert_config.clone()))
|
||||
},
|
||||
current_loc: Location::default(),
|
||||
debug_info: (dibuilder, compile_unit, func_scope.as_debug_info_scope()),
|
||||
};
|
||||
@ -1124,106 +1134,3 @@ fn gen_in_range_check<'ctx>(
|
||||
fn get_va_count_arg_name(arg_name: StrRef) -> StrRef {
|
||||
format!("__{}_va_count", &arg_name).into()
|
||||
}
|
||||
|
||||
/// Returns the alignment of the type.
|
||||
///
|
||||
/// This is necessary as `get_alignment` is not implemented as part of [`BasicType`].
|
||||
pub fn get_type_alignment<'ctx>(ty: impl Into<BasicTypeEnum<'ctx>>) -> IntValue<'ctx> {
|
||||
match ty.into() {
|
||||
BasicTypeEnum::ArrayType(ty) => ty.get_alignment(),
|
||||
BasicTypeEnum::FloatType(ty) => ty.get_alignment(),
|
||||
BasicTypeEnum::IntType(ty) => ty.get_alignment(),
|
||||
BasicTypeEnum::PointerType(ty) => ty.get_alignment(),
|
||||
BasicTypeEnum::StructType(ty) => ty.get_alignment(),
|
||||
BasicTypeEnum::VectorType(ty) => ty.get_alignment(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Inserts an `alloca` instruction with allocation `size` given in bytes and the alignment of the
|
||||
/// given type.
|
||||
///
|
||||
/// The returned [`PointerValue`] will have a type of `i8*`, a size of at least `size`, and will be
|
||||
/// aligned with the alignment of `align_ty`.
|
||||
pub fn type_aligned_alloca<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
align_ty: impl Into<BasicTypeEnum<'ctx>>,
|
||||
size: IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
/// Round `val` up to its modulo `power_of_two`.
|
||||
fn round_up<'ctx>(
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
val: IntValue<'ctx>,
|
||||
power_of_two: IntValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
debug_assert_eq!(
|
||||
val.get_type().get_bit_width(),
|
||||
power_of_two.get_type().get_bit_width(),
|
||||
"`val` ({}) and `power_of_two` ({}) must be the same type",
|
||||
val.get_type(),
|
||||
power_of_two.get_type(),
|
||||
);
|
||||
|
||||
let llvm_val_t = val.get_type();
|
||||
|
||||
let max_rem =
|
||||
ctx.builder.build_int_sub(power_of_two, llvm_val_t.const_int(1, false), "").unwrap();
|
||||
ctx.builder
|
||||
.build_and(
|
||||
ctx.builder.build_int_add(val, max_rem, "").unwrap(),
|
||||
ctx.builder.build_not(max_rem, "").unwrap(),
|
||||
"",
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let align_ty = align_ty.into();
|
||||
|
||||
let size = ctx.builder.build_int_truncate_or_bit_cast(size, llvm_usize, "").unwrap();
|
||||
|
||||
debug_assert_eq!(
|
||||
size.get_type().get_bit_width(),
|
||||
llvm_usize.get_bit_width(),
|
||||
"Expected size_t ({}) for parameter `size` of `aligned_alloca`, got {}",
|
||||
llvm_usize,
|
||||
size.get_type(),
|
||||
);
|
||||
|
||||
let alignment = get_type_alignment(align_ty);
|
||||
let alignment = ctx.builder.build_int_truncate_or_bit_cast(alignment, llvm_usize, "").unwrap();
|
||||
|
||||
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
|
||||
let alignment_bitcount = llvm_intrinsics::call_int_ctpop(ctx, alignment, None);
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
ctx.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::EQ,
|
||||
alignment_bitcount,
|
||||
alignment_bitcount.get_type().const_int(1, false),
|
||||
"",
|
||||
)
|
||||
.unwrap(),
|
||||
"0:AssertionError",
|
||||
"Expected power-of-two alignment for aligned_alloca, got {0}",
|
||||
[Some(alignment), None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
let buffer_size = round_up(ctx, size, alignment);
|
||||
let aligned_slices = ctx.builder.build_int_unsigned_div(buffer_size, alignment, "").unwrap();
|
||||
|
||||
// Just to be absolutely sure, alloca in [i8 x alignment] slices
|
||||
let buffer = ctx.builder.build_array_alloca(align_ty, aligned_slices, "").unwrap();
|
||||
|
||||
ctx.builder
|
||||
.build_bit_cast(buffer, llvm_pi8, name.unwrap_or_default())
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -16,11 +16,7 @@ use super::{
|
||||
gen_in_range_check,
|
||||
irrt::{handle_slice_indices, list_slice_assignment},
|
||||
macros::codegen_unreachable,
|
||||
types::ndarray::NDArrayType,
|
||||
values::{
|
||||
ndarray::{RustNDIndex, ScalarOrNDArray},
|
||||
ArrayLikeIndexer, ArraySliceValue, ListValue, ProxyValue, RangeValue,
|
||||
},
|
||||
values::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
use crate::{
|
||||
@ -415,52 +411,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
// Handle NDArray item assignment
|
||||
// Process target
|
||||
let target = generator
|
||||
.gen_expr(ctx, target)?
|
||||
.unwrap()
|
||||
.to_basic_value_enum(ctx, generator, target_ty)?;
|
||||
|
||||
// Process key
|
||||
let key = RustNDIndex::from_subscript_expr(generator, ctx, key)?;
|
||||
|
||||
// Process value
|
||||
let value = value.to_basic_value_enum(ctx, generator, 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 = NDArrayType::from_unifier_type(generator, ctx, target_ty)
|
||||
.map_value(target.into_pointer_value(), None);
|
||||
let target = target.index(generator, ctx, &key);
|
||||
|
||||
let value = ScalarOrNDArray::from_value(generator, ctx, (value_ty, value))
|
||||
.to_ndarray(generator, ctx);
|
||||
|
||||
let broadcast_ndims =
|
||||
[target.get_type().ndims(), value.get_type().ndims()].into_iter().max().unwrap();
|
||||
let broadcast_result = NDArrayType::new(
|
||||
generator,
|
||||
ctx.ctx,
|
||||
value.get_type().element_type(),
|
||||
broadcast_ndims,
|
||||
)
|
||||
.broadcast(generator, ctx, &[target, value]);
|
||||
|
||||
let target = broadcast_result.ndarrays[0];
|
||||
let value = broadcast_result.ndarrays[1];
|
||||
|
||||
target.copy_data_from(generator, ctx, value);
|
||||
todo!("ndarray subscript assignment is not yet implemented");
|
||||
}
|
||||
_ => {
|
||||
panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
|
||||
|
||||
@ -17,7 +17,8 @@ use parking_lot::RwLock;
|
||||
|
||||
use super::{
|
||||
concrete_type::ConcreteTypeStore,
|
||||
types::{ndarray::NDArrayType, ListType, ProxyType, RangeType},
|
||||
tracert::TraceRuntimeConfig,
|
||||
types::{ListType, NDArrayType, ProxyType, RangeType},
|
||||
CodeGenContext, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask, CodeGenerator,
|
||||
DefaultCodeGenerator, WithCall, WorkerRegistry,
|
||||
};
|
||||
@ -36,6 +37,7 @@ use crate::{
|
||||
struct Resolver {
|
||||
id_to_type: HashMap<StrRef, Type>,
|
||||
id_to_def: RwLock<HashMap<StrRef, DefinitionId>>,
|
||||
class_names: HashMap<StrRef, Type>,
|
||||
}
|
||||
|
||||
impl Resolver {
|
||||
@ -103,9 +105,11 @@ fn test_primitives() {
|
||||
let top_level = Arc::new(composer.make_top_level_context());
|
||||
unifier.top_level = Some(top_level.clone());
|
||||
|
||||
let resolver =
|
||||
Arc::new(Resolver { id_to_type: HashMap::new(), id_to_def: RwLock::new(HashMap::new()) })
|
||||
as Arc<dyn SymbolResolver + Send + Sync>;
|
||||
let resolver = Arc::new(Resolver {
|
||||
id_to_type: HashMap::new(),
|
||||
id_to_def: RwLock::new(HashMap::new()),
|
||||
class_names: HashMap::default(),
|
||||
}) as Arc<dyn SymbolResolver + Send + Sync>;
|
||||
|
||||
let threads = vec![DefaultCodeGenerator::new("test".into(), 32).into()];
|
||||
let signature = FunSignature {
|
||||
@ -242,7 +246,13 @@ fn test_primitives() {
|
||||
opt_level: OptimizationLevel::Default,
|
||||
target: CodeGenTargetMachineOptions::from_host_triple(),
|
||||
};
|
||||
let (registry, handles) = WorkerRegistry::create_workers(threads, top_level, &llvm_options, &f);
|
||||
let (registry, handles) = WorkerRegistry::create_workers(
|
||||
threads,
|
||||
top_level,
|
||||
&llvm_options,
|
||||
&TraceRuntimeConfig::default(),
|
||||
&f,
|
||||
);
|
||||
registry.add_task(task);
|
||||
registry.wait_tasks_complete(handles);
|
||||
}
|
||||
@ -295,7 +305,11 @@ fn test_simple_call() {
|
||||
loc: None,
|
||||
})));
|
||||
|
||||
let resolver = Resolver { id_to_type: HashMap::new(), id_to_def: RwLock::new(HashMap::new()) };
|
||||
let resolver = Resolver {
|
||||
id_to_type: HashMap::new(),
|
||||
id_to_def: RwLock::new(HashMap::new()),
|
||||
class_names: HashMap::default(),
|
||||
};
|
||||
resolver.add_id_def("foo".into(), DefinitionId(foo_id));
|
||||
let resolver = Arc::new(resolver) as Arc<dyn SymbolResolver + Send + Sync>;
|
||||
|
||||
@ -431,7 +445,13 @@ fn test_simple_call() {
|
||||
opt_level: OptimizationLevel::Default,
|
||||
target: CodeGenTargetMachineOptions::from_host_triple(),
|
||||
};
|
||||
let (registry, handles) = WorkerRegistry::create_workers(threads, top_level, &llvm_options, &f);
|
||||
let (registry, handles) = WorkerRegistry::create_workers(
|
||||
threads,
|
||||
top_level,
|
||||
&llvm_options,
|
||||
&TraceRuntimeConfig::default(),
|
||||
&f,
|
||||
);
|
||||
registry.add_task(task);
|
||||
registry.wait_tasks_complete(handles);
|
||||
}
|
||||
@ -464,6 +484,6 @@ fn test_classes_ndarray_type_new() {
|
||||
let llvm_i32 = ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(&ctx);
|
||||
|
||||
let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into(), 2);
|
||||
let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into());
|
||||
assert!(NDArrayType::is_representable(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
|
||||
}
|
||||
|
||||
110
nac3core/src/codegen/tracert/mod.rs
Normal file
110
nac3core/src/codegen/tracert/mod.rs
Normal file
@ -0,0 +1,110 @@
|
||||
use std::panic::Location;
|
||||
|
||||
use inkwell::context::Context;
|
||||
use inkwell::memory_buffer::MemoryBuffer;
|
||||
use inkwell::module::Module;
|
||||
use inkwell::values::BasicValueEnum;
|
||||
use inkwell::AtomicOrdering;
|
||||
|
||||
use crate::codegen::{extern_fns, CodeGenContext};
|
||||
|
||||
#[derive(Clone, Default, Eq, PartialEq)]
|
||||
pub struct TraceRuntimeConfig {
|
||||
pub enabled_tags: Vec<String>,
|
||||
}
|
||||
|
||||
#[derive(Eq, Clone, PartialEq)]
|
||||
pub struct TraceRuntimeState {
|
||||
config: TraceRuntimeConfig,
|
||||
indent: usize,
|
||||
}
|
||||
|
||||
impl TraceRuntimeState {
|
||||
#[must_use]
|
||||
pub fn create(config: TraceRuntimeConfig) -> TraceRuntimeState {
|
||||
TraceRuntimeState { config, indent: 0 }
|
||||
}
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn load_tracert<'ctx>(ctx: &'ctx Context, config: &TraceRuntimeConfig) -> Option<Module<'ctx>> {
|
||||
if cfg!(feature = "tracing") && !config.enabled_tags.is_empty() {
|
||||
let bitcode_buf = MemoryBuffer::create_from_memory_range(
|
||||
include_bytes!(concat!(env!("OUT_DIR"), "/tracert.bc")),
|
||||
"tracert_bitcode_buffer",
|
||||
);
|
||||
let module = Module::parse_bitcode_from_buffer(&bitcode_buf, ctx).unwrap();
|
||||
|
||||
return Some(module);
|
||||
}
|
||||
|
||||
None
|
||||
}
|
||||
|
||||
// TODO: Might need to redesign how trace logging should be done
|
||||
|
||||
pub fn trace_log<'ctx>(
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
tag: &'static str,
|
||||
format: &'static str,
|
||||
args: &[BasicValueEnum<'ctx>],
|
||||
) {
|
||||
if ctx.tracert_state.is_none() {
|
||||
return;
|
||||
}
|
||||
|
||||
// TODO: Add indentation
|
||||
let str = format!("[TRACING] {tag} - {format}\n\0");
|
||||
extern_fns::call_printf(ctx, &str, args);
|
||||
}
|
||||
|
||||
#[track_caller]
|
||||
pub fn trace_log_with_location<'ctx>(
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
tag: &'static str,
|
||||
format: &str,
|
||||
args: &[BasicValueEnum<'ctx>],
|
||||
) {
|
||||
if ctx.tracert_state.is_none() {
|
||||
return;
|
||||
}
|
||||
|
||||
// TODO: Add indentation
|
||||
let caller_loc = Location::caller();
|
||||
let str = format!(
|
||||
"[TRACING] {}:{}:{}: {tag} - {format}\n\0",
|
||||
caller_loc.file(),
|
||||
caller_loc.line(),
|
||||
caller_loc.column()
|
||||
);
|
||||
extern_fns::call_printf(ctx, &str, args);
|
||||
}
|
||||
|
||||
pub fn trace_push_level(ctx: &mut CodeGenContext<'_, '_>) {
|
||||
let Some(tracert_state) = &mut ctx.tracert_state else {
|
||||
return;
|
||||
};
|
||||
|
||||
debug_assert!(tracert_state.indent < usize::MAX);
|
||||
if tracert_state.indent < usize::MAX {
|
||||
tracert_state.indent = tracert_state.indent.saturating_add(1);
|
||||
}
|
||||
}
|
||||
|
||||
pub fn trace_pop_level(ctx: &mut CodeGenContext<'_, '_>) {
|
||||
let Some(tracert_state) = &mut ctx.tracert_state else {
|
||||
return;
|
||||
};
|
||||
|
||||
debug_assert!(tracert_state.indent > 0);
|
||||
if tracert_state.indent > 0 {
|
||||
tracert_state.indent = tracert_state.indent.saturating_sub(1);
|
||||
}
|
||||
}
|
||||
|
||||
#[inline]
|
||||
pub fn mfence(ctx: &mut CodeGenContext<'_, '_>) {
|
||||
if ctx.tracert_state.is_some() {
|
||||
ctx.builder.build_fence(AtomicOrdering::SequentiallyConsistent, 0, "").unwrap();
|
||||
}
|
||||
}
|
||||
@ -1,113 +1,69 @@
|
||||
use inkwell::{
|
||||
context::{AsContextRef, Context},
|
||||
context::Context,
|
||||
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace, IntPredicate, OptimizationLevel,
|
||||
values::IntValue,
|
||||
AddressSpace,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use nac3core_derive::StructFields;
|
||||
|
||||
use super::ProxyType;
|
||||
use crate::{
|
||||
codegen::{
|
||||
types::structure::{
|
||||
check_struct_type_matches_fields, FieldIndexCounter, StructField, StructFields,
|
||||
},
|
||||
values::{ListValue, ProxyValue},
|
||||
use crate::codegen::{
|
||||
values::{ArraySliceValue, ListValue, ProxyValue},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::{iter_type_vars, Type, TypeEnum},
|
||||
};
|
||||
|
||||
/// Proxy type for a `list` type in LLVM.
|
||||
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
|
||||
pub struct ListType<'ctx> {
|
||||
ty: PointerType<'ctx>,
|
||||
item: Option<BasicTypeEnum<'ctx>>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
}
|
||||
|
||||
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
|
||||
pub struct ListStructFields<'ctx> {
|
||||
/// Array pointer to content.
|
||||
#[value_type(i8_type().ptr_type(AddressSpace::default()))]
|
||||
pub items: StructField<'ctx, PointerValue<'ctx>>,
|
||||
|
||||
/// Number of items in the array.
|
||||
#[value_type(usize)]
|
||||
pub len: StructField<'ctx, IntValue<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx> ListStructFields<'ctx> {
|
||||
#[must_use]
|
||||
pub fn new_typed(item: BasicTypeEnum<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
|
||||
let mut counter = FieldIndexCounter::default();
|
||||
|
||||
ListStructFields {
|
||||
items: StructField::create(
|
||||
&mut counter,
|
||||
"items",
|
||||
item.ptr_type(AddressSpace::default()),
|
||||
),
|
||||
len: StructField::create(&mut counter, "len", llvm_usize),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ListType<'ctx> {
|
||||
/// Checks whether `llvm_ty` represents a `list` type, returning [Err] if it does not.
|
||||
pub fn is_representable(
|
||||
llvm_ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
let ctx = llvm_ty.get_context();
|
||||
let llvm_list_ty = llvm_ty.get_element_type();
|
||||
let AnyTypeEnum::StructType(llvm_list_ty) = llvm_list_ty else {
|
||||
return Err(format!("Expected struct type for `list` type, got {llvm_list_ty}"));
|
||||
};
|
||||
if llvm_list_ty.count_fields() != 2 {
|
||||
return Err(format!(
|
||||
"Expected 2 fields in `list`, got {}",
|
||||
llvm_list_ty.count_fields()
|
||||
));
|
||||
}
|
||||
|
||||
let llvm_ty = llvm_ty.get_element_type();
|
||||
let AnyTypeEnum::StructType(llvm_ty) = llvm_ty else {
|
||||
return Err(format!("Expected struct type for `list` type, got {llvm_ty}"));
|
||||
let list_size_ty = llvm_list_ty.get_field_type_at_index(0).unwrap();
|
||||
let Ok(_) = PointerType::try_from(list_size_ty) else {
|
||||
return Err(format!("Expected pointer type for `list.0`, got {list_size_ty}"));
|
||||
};
|
||||
|
||||
let fields = ListStructFields::new(ctx, llvm_usize);
|
||||
let list_data_ty = llvm_list_ty.get_field_type_at_index(1).unwrap();
|
||||
let Ok(list_data_ty) = IntType::try_from(list_data_ty) else {
|
||||
return Err(format!("Expected int type for `list.1`, got {list_data_ty}"));
|
||||
};
|
||||
if list_data_ty.get_bit_width() != llvm_usize.get_bit_width() {
|
||||
return Err(format!(
|
||||
"Expected {}-bit int type for `list.1`, got {}-bit int",
|
||||
llvm_usize.get_bit_width(),
|
||||
list_data_ty.get_bit_width()
|
||||
));
|
||||
}
|
||||
|
||||
check_struct_type_matches_fields(
|
||||
fields,
|
||||
llvm_ty,
|
||||
"list",
|
||||
&[(fields.items.name(), &|ty| {
|
||||
if ty.is_pointer_type() {
|
||||
Ok(())
|
||||
} else {
|
||||
Err(format!("Expected T* for `list.items`, got {ty}"))
|
||||
}
|
||||
})],
|
||||
)
|
||||
}
|
||||
|
||||
/// Returns an instance of [`StructFields`] containing all field accessors for this type.
|
||||
#[must_use]
|
||||
fn fields(item: BasicTypeEnum<'ctx>, llvm_usize: IntType<'ctx>) -> ListStructFields<'ctx> {
|
||||
ListStructFields::new_typed(item, llvm_usize)
|
||||
}
|
||||
|
||||
/// See [`ListType::fields`].
|
||||
// TODO: Move this into e.g. StructProxyType
|
||||
#[must_use]
|
||||
pub fn get_fields(&self, _ctx: &impl AsContextRef<'ctx>) -> ListStructFields<'ctx> {
|
||||
Self::fields(self.item.unwrap_or(self.llvm_usize.into()), self.llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an LLVM type corresponding to the expected structure of a `List`.
|
||||
#[must_use]
|
||||
fn llvm_type(
|
||||
ctx: &'ctx Context,
|
||||
element_type: Option<BasicTypeEnum<'ctx>>,
|
||||
element_type: BasicTypeEnum<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> PointerType<'ctx> {
|
||||
let element_type = element_type.unwrap_or(llvm_usize.into());
|
||||
|
||||
let field_tys =
|
||||
Self::fields(element_type, llvm_usize).into_iter().map(|field| field.1).collect_vec();
|
||||
// struct List { data: T*, size: size_t }
|
||||
let field_tys = [element_type.ptr_type(AddressSpace::default()).into(), llvm_usize.into()];
|
||||
|
||||
ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
|
||||
}
|
||||
@ -120,50 +76,9 @@ impl<'ctx> ListType<'ctx> {
|
||||
element_type: BasicTypeEnum<'ctx>,
|
||||
) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
let llvm_list = Self::llvm_type(ctx, Some(element_type), llvm_usize);
|
||||
let llvm_list = Self::llvm_type(ctx, element_type, llvm_usize);
|
||||
|
||||
Self { ty: llvm_list, item: Some(element_type), llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an instance of [`ListType`] with an unknown element type.
|
||||
#[must_use]
|
||||
pub fn new_untyped<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
let llvm_list = Self::llvm_type(ctx, None, llvm_usize);
|
||||
|
||||
Self { ty: llvm_list, item: None, llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an [`ListType`] from a [unifier type][Type].
|
||||
#[must_use]
|
||||
pub fn from_unifier_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ty: Type,
|
||||
) -> Self {
|
||||
// Check unifier type and extract `item_type`
|
||||
let elem_type = match &*ctx.unifier.get_ty_immutable(ty) {
|
||||
TypeEnum::TObj { obj_id, params, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
iter_type_vars(params).next().unwrap().ty
|
||||
}
|
||||
|
||||
_ => panic!("Expected `list` type, but got {}", ctx.unifier.stringify(ty)),
|
||||
};
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_elem_type = if let TypeEnum::TVar { .. } = &*ctx.unifier.get_ty_immutable(ty) {
|
||||
None
|
||||
} else {
|
||||
Some(ctx.get_llvm_type(generator, elem_type))
|
||||
};
|
||||
|
||||
Self {
|
||||
ty: Self::llvm_type(ctx.ctx, llvm_elem_type, llvm_usize),
|
||||
item: llvm_elem_type,
|
||||
llvm_usize,
|
||||
}
|
||||
ListType::from_type(llvm_list, llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an [`ListType`] from a [`PointerType`].
|
||||
@ -171,141 +86,47 @@ impl<'ctx> ListType<'ctx> {
|
||||
pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
|
||||
|
||||
let ctx = ptr_ty.get_context();
|
||||
|
||||
// We are just searching for the index off a field - Slot an arbitrary element type in.
|
||||
let item_field_idx =
|
||||
Self::fields(ctx.i8_type().into(), llvm_usize).index_of_field(|f| f.items);
|
||||
let item = unsafe {
|
||||
ptr_ty
|
||||
.get_element_type()
|
||||
.into_struct_type()
|
||||
.get_field_type_at_index_unchecked(item_field_idx)
|
||||
.into_pointer_type()
|
||||
.get_element_type()
|
||||
};
|
||||
let item = BasicTypeEnum::try_from(item).unwrap_or_else(|()| {
|
||||
panic!(
|
||||
"Expected BasicTypeEnum for list element type, got {}",
|
||||
ptr_ty.get_element_type().print_to_string()
|
||||
)
|
||||
});
|
||||
|
||||
ListType { ty: ptr_ty, item: Some(item), llvm_usize }
|
||||
ListType { ty: ptr_ty, llvm_usize }
|
||||
}
|
||||
|
||||
/// Returns the type of the `size` field of this `list` type.
|
||||
#[must_use]
|
||||
pub fn size_type(&self) -> IntType<'ctx> {
|
||||
self.llvm_usize
|
||||
self.as_base_type()
|
||||
.get_element_type()
|
||||
.into_struct_type()
|
||||
.get_field_type_at_index(1)
|
||||
.map(BasicTypeEnum::into_int_type)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Returns the element type of this `list` type.
|
||||
#[must_use]
|
||||
pub fn element_type(&self) -> Option<BasicTypeEnum<'ctx>> {
|
||||
self.item
|
||||
pub fn element_type(&self) -> AnyTypeEnum<'ctx> {
|
||||
self.as_base_type()
|
||||
.get_element_type()
|
||||
.into_struct_type()
|
||||
.get_field_type_at_index(0)
|
||||
.map(BasicTypeEnum::into_pointer_type)
|
||||
.map(PointerType::get_element_type)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`ListValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca`].
|
||||
#[must_use]
|
||||
pub fn alloca(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca(ctx, name),
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`ListValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca_var`].
|
||||
#[must_use]
|
||||
pub fn alloca_var<G: CodeGenerator + ?Sized>(
|
||||
pub fn alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca_var(generator, ctx, name),
|
||||
self.raw_alloca(generator, ctx, name),
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Allocates a [`ListValue`] on the stack using `item` of this [`ListType`] instance.
|
||||
///
|
||||
/// The returned list will contain:
|
||||
///
|
||||
/// - `data`: Allocated with `len` number of elements.
|
||||
/// - `len`: Initialized to the value of `len` passed to this function.
|
||||
#[must_use]
|
||||
pub fn construct<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
len: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
let len = ctx.builder.build_int_z_extend(len, self.llvm_usize, "").unwrap();
|
||||
|
||||
// Generate a runtime assertion if allocating a non-empty list with unknown element type
|
||||
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None && self.item.is_none() {
|
||||
let len_eqz = ctx
|
||||
.builder
|
||||
.build_int_compare(IntPredicate::EQ, len, self.llvm_usize.const_zero(), "")
|
||||
.unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
len_eqz,
|
||||
"0:AssertionError",
|
||||
"Cannot allocate a non-empty list with unknown element type",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
let plist = self.alloca_var(generator, ctx, name);
|
||||
plist.store_size(ctx, generator, len);
|
||||
|
||||
let item = self.item.unwrap_or(self.llvm_usize.into());
|
||||
plist.create_data(ctx, item, None);
|
||||
|
||||
plist
|
||||
}
|
||||
|
||||
/// Convenience function for creating a list with zero elements.
|
||||
///
|
||||
/// This function is preferred over [`ListType::construct`] if the length is known to always be
|
||||
/// 0, as this function avoids injecting an IR assertion for checking if a non-empty untyped
|
||||
/// list is being allocated.
|
||||
///
|
||||
/// The returned list will contain:
|
||||
///
|
||||
/// - `data`: Initialized to `(T*) 0`.
|
||||
/// - `len`: Initialized to `0`.
|
||||
#[must_use]
|
||||
pub fn construct_empty<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
let plist = self.alloca_var(generator, ctx, name);
|
||||
|
||||
plist.store_size(ctx, generator, self.llvm_usize.const_zero());
|
||||
plist.create_data(ctx, self.item.unwrap_or(self.llvm_usize.into()), None);
|
||||
|
||||
plist
|
||||
}
|
||||
|
||||
/// Converts an existing value into a [`ListValue`].
|
||||
#[must_use]
|
||||
pub fn map_value(
|
||||
@ -341,8 +162,36 @@ impl<'ctx> ProxyType<'ctx> for ListType<'ctx> {
|
||||
Self::is_representable(llvm_ty, generator.get_size_type(ctx))
|
||||
}
|
||||
|
||||
fn alloca_type(&self) -> impl BasicType<'ctx> {
|
||||
self.as_base_type().get_element_type().into_struct_type()
|
||||
fn raw_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self::Value as ProxyValue<'ctx>>::Base {
|
||||
generator
|
||||
.gen_var_alloc(
|
||||
ctx,
|
||||
self.as_base_type().get_element_type().into_struct_type().into(),
|
||||
name,
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
fn array_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
size: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> ArraySliceValue<'ctx> {
|
||||
generator
|
||||
.gen_array_var_alloc(
|
||||
ctx,
|
||||
self.as_base_type().get_element_type().into_struct_type().into(),
|
||||
size,
|
||||
name,
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
fn as_base_type(&self) -> Self::Base {
|
||||
|
||||
@ -16,26 +16,20 @@
|
||||
//! the returned object. This is similar to a `new` expression in C++ but the object is allocated
|
||||
//! on the stack.
|
||||
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::BasicType,
|
||||
values::{IntValue, PointerValue},
|
||||
};
|
||||
use inkwell::{context::Context, types::BasicType, values::IntValue};
|
||||
|
||||
use super::{
|
||||
values::{ArraySliceValue, ProxyValue},
|
||||
{CodeGenContext, CodeGenerator},
|
||||
};
|
||||
pub use list::*;
|
||||
pub use ndarray::*;
|
||||
pub use range::*;
|
||||
pub use tuple::*;
|
||||
|
||||
mod list;
|
||||
pub mod ndarray;
|
||||
mod ndarray;
|
||||
mod range;
|
||||
pub mod structure;
|
||||
mod tuple;
|
||||
pub mod utils;
|
||||
|
||||
/// A LLVM type that is used to represent a corresponding type in NAC3.
|
||||
pub trait ProxyType<'ctx>: Into<Self::Base> {
|
||||
@ -59,66 +53,23 @@ pub trait ProxyType<'ctx>: Into<Self::Base> {
|
||||
llvm_ty: Self::Base,
|
||||
) -> Result<(), String>;
|
||||
|
||||
/// Returns the type that should be used in `alloca` IR statements.
|
||||
fn alloca_type(&self) -> impl BasicType<'ctx>;
|
||||
|
||||
/// Creates a new value of this type by invoking `alloca` at the current builder location,
|
||||
/// returning a [`PointerValue`] instance representing the allocated value.
|
||||
fn raw_alloca(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
ctx.builder
|
||||
.build_alloca(self.alloca_type().as_basic_type_enum(), name.unwrap_or_default())
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Creates a new value of this type by invoking `alloca` at the beginning of the function,
|
||||
/// returning a [`PointerValue`] instance representing the allocated value.
|
||||
fn raw_alloca_var<G: CodeGenerator + ?Sized>(
|
||||
/// Creates a new value of this type, returning the LLVM instance of this value.
|
||||
fn raw_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
generator.gen_var_alloc(ctx, self.alloca_type().as_basic_type_enum(), name).unwrap()
|
||||
}
|
||||
) -> <Self::Value as ProxyValue<'ctx>>::Base;
|
||||
|
||||
/// Creates a new array value of this type by invoking `alloca` at the current builder location,
|
||||
/// returning an [`ArraySliceValue`] encapsulating the resulting array.
|
||||
fn array_alloca(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
size: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> ArraySliceValue<'ctx> {
|
||||
ArraySliceValue::from_ptr_val(
|
||||
ctx.builder
|
||||
.build_array_alloca(
|
||||
self.alloca_type().as_basic_type_enum(),
|
||||
size,
|
||||
name.unwrap_or_default(),
|
||||
)
|
||||
.unwrap(),
|
||||
size,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Creates a new array value of this type by invoking `alloca` at the beginning of the
|
||||
/// function, returning an [`ArraySliceValue`] encapsulating the resulting array.
|
||||
fn array_alloca_var<G: CodeGenerator + ?Sized>(
|
||||
/// Creates a new array value of this type, returning an [`ArraySliceValue`] encapsulating the
|
||||
/// resulting array.
|
||||
fn array_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
size: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> ArraySliceValue<'ctx> {
|
||||
generator
|
||||
.gen_array_var_alloc(ctx, self.alloca_type().as_basic_type_enum(), size, name)
|
||||
.unwrap()
|
||||
}
|
||||
) -> ArraySliceValue<'ctx>;
|
||||
|
||||
/// Returns the [base type][Self::Base] of this proxy.
|
||||
fn as_base_type(&self) -> Self::Base;
|
||||
|
||||
@ -1,243 +0,0 @@
|
||||
use inkwell::{
|
||||
types::BasicTypeEnum,
|
||||
values::{BasicValueEnum, IntValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt,
|
||||
stmt::gen_if_else_expr_callback,
|
||||
types::{ndarray::NDArrayType, ListType, ProxyType},
|
||||
values::{
|
||||
ndarray::NDArrayValue, ArrayLikeValue, ArraySliceValue, ListValue, ProxyValue,
|
||||
TypedArrayLikeAdapter, TypedArrayLikeMutator,
|
||||
},
|
||||
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, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
list_ty: Type,
|
||||
) -> (BasicTypeEnum<'ctx>, u64) {
|
||||
let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list_ty);
|
||||
let ndims = arraylike_get_ndims(&mut ctx.unifier, list_ty);
|
||||
|
||||
(ctx.get_llvm_type(generator, dtype), ndims)
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayType<'ctx> {
|
||||
/// Implementation of `np_array(<list>, copy=True)`
|
||||
fn construct_numpy_array_from_list_copy_true_impl<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
(list_ty, list): (Type, ListValue<'ctx>),
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
let (dtype, ndims_int) = get_list_object_dtype_and_ndims(generator, ctx, list_ty);
|
||||
assert!(self.ndims >= ndims_int);
|
||||
assert_eq!(dtype, self.dtype);
|
||||
|
||||
let list_value = list.as_i8_list(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 = self.llvm_usize.const_int(ndims_int, false);
|
||||
let shape = ctx.builder.build_array_alloca(self.llvm_usize, ndims, "").unwrap();
|
||||
let shape = ArraySliceValue::from_ptr_val(shape, ndims, None);
|
||||
let shape = TypedArrayLikeAdapter::from(
|
||||
shape,
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
);
|
||||
irrt::ndarray::call_nac3_ndarray_array_set_and_validate_list_shape(
|
||||
generator, ctx, list_value, ndims, &shape,
|
||||
);
|
||||
|
||||
let ndarray = Self::new(generator, ctx.ctx, dtype, ndims_int)
|
||||
.construct_uninitialized(generator, ctx, name);
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape.base_ptr(ctx, generator));
|
||||
unsafe { ndarray.create_data(generator, ctx) };
|
||||
|
||||
// Copy all contents from the list.
|
||||
irrt::ndarray::call_nac3_ndarray_array_write_list_to_array(
|
||||
generator, ctx, list_value, ndarray,
|
||||
);
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Implementation of `np_array(<list>, copy=None)`
|
||||
fn construct_numpy_array_from_list_copy_none_impl<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
(list_ty, list): (Type, ListValue<'ctx>),
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
// 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(generator, ctx, list_ty);
|
||||
if ndims == 1 {
|
||||
// `list` is not nested
|
||||
assert_eq!(ndims, 1);
|
||||
assert!(self.ndims >= ndims);
|
||||
assert_eq!(dtype, self.dtype);
|
||||
|
||||
let llvm_pi8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
|
||||
|
||||
let ndarray = Self::new(generator, ctx.ctx, dtype, 1)
|
||||
.construct_uninitialized(generator, ctx, name);
|
||||
|
||||
// Set data
|
||||
let data = ctx
|
||||
.builder
|
||||
.build_pointer_cast(list.data().base_ptr(ctx, generator), llvm_pi8, "")
|
||||
.unwrap();
|
||||
ndarray.store_data(ctx, data);
|
||||
|
||||
// ndarray->shape[0] = list->len;
|
||||
let shape = ndarray.shape();
|
||||
let list_len = list.load_size(ctx, None);
|
||||
unsafe {
|
||||
shape.set_typed_unchecked(ctx, generator, &self.llvm_usize.const_zero(), list_len);
|
||||
}
|
||||
|
||||
// Set strides, the `data` is contiguous
|
||||
ndarray.set_strides_contiguous(generator, ctx);
|
||||
|
||||
ndarray
|
||||
} else {
|
||||
// `list` is nested, copy
|
||||
self.construct_numpy_array_from_list_copy_true_impl(
|
||||
generator,
|
||||
ctx,
|
||||
(list_ty, list),
|
||||
name,
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
/// Implementation of `np_array(<list>, copy=copy)`
|
||||
fn construct_numpy_array_list_impl<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
(list_ty, list): (Type, ListValue<'ctx>),
|
||||
copy: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
assert_eq!(copy.get_type(), ctx.ctx.bool_type());
|
||||
|
||||
let (dtype, ndims) = get_list_object_dtype_and_ndims(generator, ctx, list_ty);
|
||||
|
||||
let ndarray = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_generator, _ctx| Ok(copy),
|
||||
|generator, ctx| {
|
||||
let ndarray = self.construct_numpy_array_from_list_copy_true_impl(
|
||||
generator,
|
||||
ctx,
|
||||
(list_ty, list),
|
||||
name,
|
||||
);
|
||||
Ok(Some(ndarray.as_base_value()))
|
||||
},
|
||||
|generator, ctx| {
|
||||
let ndarray = self.construct_numpy_array_from_list_copy_none_impl(
|
||||
generator,
|
||||
ctx,
|
||||
(list_ty, list),
|
||||
name,
|
||||
);
|
||||
Ok(Some(ndarray.as_base_value()))
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
|
||||
NDArrayType::new(generator, ctx.ctx, dtype, ndims).map_value(ndarray, None)
|
||||
}
|
||||
|
||||
/// Implementation of `np_array(<ndarray>, copy=copy)`.
|
||||
pub fn construct_numpy_array_ndarray_impl<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
copy: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
assert_eq!(ndarray.get_type().dtype, self.dtype);
|
||||
assert!(self.ndims >= ndarray.get_type().ndims);
|
||||
assert_eq!(copy.get_type(), ctx.ctx.bool_type());
|
||||
|
||||
let ndarray_val = gen_if_else_expr_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|_generator, _ctx| Ok(copy),
|
||||
|generator, ctx| {
|
||||
let ndarray = ndarray.make_copy(generator, ctx); // Force copy
|
||||
Ok(Some(ndarray.as_base_value()))
|
||||
},
|
||||
|_generator, _ctx| {
|
||||
// No need to copy. Return `ndarray` itself.
|
||||
Ok(Some(ndarray.as_base_value()))
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
.map(BasicValueEnum::into_pointer_value)
|
||||
.unwrap();
|
||||
|
||||
ndarray.get_type().map_value(ndarray_val, name)
|
||||
}
|
||||
|
||||
/// Create a new ndarray like
|
||||
/// [`np.array()`](https://numpy.org/doc/stable/reference/generated/numpy.array.html).
|
||||
///
|
||||
/// Note that the returned [`NDArrayValue`] may have fewer dimensions than is specified by this
|
||||
/// instance. Use [`NDArrayValue::atleast_nd`] on the returned value if an `ndarray` instance
|
||||
/// with the exact number of dimensions is needed.
|
||||
pub fn construct_numpy_array<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
(object_ty, object): (Type, BasicValueEnum<'ctx>),
|
||||
copy: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
match &*ctx.unifier.get_ty_immutable(object_ty) {
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let list = ListType::from_unifier_type(generator, ctx, object_ty)
|
||||
.map_value(object.into_pointer_value(), None);
|
||||
self.construct_numpy_array_list_impl(generator, ctx, (object_ty, list), copy, name)
|
||||
}
|
||||
|
||||
TypeEnum::TObj { obj_id, .. }
|
||||
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
|
||||
{
|
||||
let ndarray = NDArrayType::from_unifier_type(generator, ctx, object_ty)
|
||||
.map_value(object.into_pointer_value(), None);
|
||||
self.construct_numpy_array_ndarray_impl(generator, ctx, ndarray, copy, name)
|
||||
}
|
||||
|
||||
_ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object_ty)), // Typechecker ensures this
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,176 +0,0 @@
|
||||
use inkwell::{
|
||||
context::{AsContextRef, Context},
|
||||
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use nac3core_derive::StructFields;
|
||||
|
||||
use crate::codegen::{
|
||||
types::{
|
||||
structure::{check_struct_type_matches_fields, StructField, StructFields},
|
||||
ProxyType,
|
||||
},
|
||||
values::{ndarray::ShapeEntryValue, ProxyValue},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
|
||||
pub struct ShapeEntryType<'ctx> {
|
||||
ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
}
|
||||
|
||||
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
|
||||
pub struct ShapeEntryStructFields<'ctx> {
|
||||
#[value_type(usize)]
|
||||
pub ndims: StructField<'ctx, IntValue<'ctx>>,
|
||||
#[value_type(usize.ptr_type(AddressSpace::default()))]
|
||||
pub shape: StructField<'ctx, PointerValue<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx> ShapeEntryType<'ctx> {
|
||||
/// Checks whether `llvm_ty` represents a [`ShapeEntryType`], returning [Err] if it does not.
|
||||
pub fn is_representable(
|
||||
llvm_ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
let ctx = llvm_ty.get_context();
|
||||
|
||||
let llvm_ndarray_ty = llvm_ty.get_element_type();
|
||||
let AnyTypeEnum::StructType(llvm_ndarray_ty) = llvm_ndarray_ty else {
|
||||
return Err(format!(
|
||||
"Expected struct type for `ShapeEntry` type, got {llvm_ndarray_ty}"
|
||||
));
|
||||
};
|
||||
|
||||
check_struct_type_matches_fields(
|
||||
Self::fields(ctx, llvm_usize),
|
||||
llvm_ndarray_ty,
|
||||
"NDArray",
|
||||
&[],
|
||||
)
|
||||
}
|
||||
|
||||
/// Returns an instance of [`StructFields`] containing all field accessors for this type.
|
||||
#[must_use]
|
||||
fn fields(
|
||||
ctx: impl AsContextRef<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> ShapeEntryStructFields<'ctx> {
|
||||
ShapeEntryStructFields::new(ctx, llvm_usize)
|
||||
}
|
||||
|
||||
/// See [`ShapeEntryStructFields::fields`].
|
||||
// TODO: Move this into e.g. StructProxyType
|
||||
#[must_use]
|
||||
pub fn get_fields(&self, ctx: impl AsContextRef<'ctx>) -> ShapeEntryStructFields<'ctx> {
|
||||
Self::fields(ctx, self.llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an LLVM type corresponding to the expected structure of a `ShapeEntry`.
|
||||
#[must_use]
|
||||
fn llvm_type(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> PointerType<'ctx> {
|
||||
let field_tys =
|
||||
Self::fields(ctx, llvm_usize).into_iter().map(|field| field.1).collect_vec();
|
||||
|
||||
ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
|
||||
}
|
||||
|
||||
/// Creates an instance of [`ShapeEntryType`].
|
||||
#[must_use]
|
||||
pub fn new<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
let llvm_ty = Self::llvm_type(ctx, llvm_usize);
|
||||
|
||||
Self { ty: llvm_ty, llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates a [`ShapeEntryType`] from a [`PointerType`] representing an `ShapeEntry`.
|
||||
#[must_use]
|
||||
pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
|
||||
|
||||
Self { ty: ptr_ty, llvm_usize }
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`ShapeEntryValue`] as if by calling `alloca` on the base type.
|
||||
#[must_use]
|
||||
pub fn alloca(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca(ctx, name),
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`ShapeEntryValue`] as if by calling `alloca` on the base type.
|
||||
#[must_use]
|
||||
pub fn alloca_var<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca_var(generator, ctx, name),
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Converts an existing value into a [`ShapeEntryValue`].
|
||||
#[must_use]
|
||||
pub fn map_value(
|
||||
&self,
|
||||
value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(value, self.llvm_usize, name)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyType<'ctx> for ShapeEntryType<'ctx> {
|
||||
type Base = PointerType<'ctx>;
|
||||
type Value = ShapeEntryValue<'ctx>;
|
||||
|
||||
fn is_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: impl BasicType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
|
||||
<Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
|
||||
} else {
|
||||
Err(format!("Expected pointer type, got {llvm_ty:?}"))
|
||||
}
|
||||
}
|
||||
|
||||
fn is_representable<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: Self::Base,
|
||||
) -> Result<(), String> {
|
||||
Self::is_representable(llvm_ty, generator.get_size_type(ctx))
|
||||
}
|
||||
|
||||
fn alloca_type(&self) -> impl BasicType<'ctx> {
|
||||
self.as_base_type().get_element_type().into_struct_type()
|
||||
}
|
||||
|
||||
fn as_base_type(&self) -> Self::Base {
|
||||
self.ty
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<ShapeEntryType<'ctx>> for PointerType<'ctx> {
|
||||
fn from(value: ShapeEntryType<'ctx>) -> Self {
|
||||
value.as_base_type()
|
||||
}
|
||||
}
|
||||
@ -1,250 +0,0 @@
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use nac3core_derive::StructFields;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
types::{
|
||||
structure::{
|
||||
check_struct_type_matches_fields, FieldIndexCounter, StructField, StructFields,
|
||||
},
|
||||
ProxyType,
|
||||
},
|
||||
values::{ndarray::ContiguousNDArrayValue, ProxyValue},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
toplevel::numpy::unpack_ndarray_var_tys,
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
|
||||
pub struct ContiguousNDArrayType<'ctx> {
|
||||
ty: PointerType<'ctx>,
|
||||
item: BasicTypeEnum<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
}
|
||||
|
||||
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
|
||||
pub struct ContiguousNDArrayStructFields<'ctx> {
|
||||
#[value_type(usize)]
|
||||
pub ndims: StructField<'ctx, IntValue<'ctx>>,
|
||||
#[value_type(usize.ptr_type(AddressSpace::default()))]
|
||||
pub shape: StructField<'ctx, PointerValue<'ctx>>,
|
||||
#[value_type(i8_type().ptr_type(AddressSpace::default()))]
|
||||
pub data: StructField<'ctx, PointerValue<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx> ContiguousNDArrayStructFields<'ctx> {
|
||||
#[must_use]
|
||||
pub fn new_typed(item: BasicTypeEnum<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
|
||||
let mut counter = FieldIndexCounter::default();
|
||||
|
||||
ContiguousNDArrayStructFields {
|
||||
ndims: StructField::create(&mut counter, "ndims", llvm_usize),
|
||||
shape: StructField::create(
|
||||
&mut counter,
|
||||
"shape",
|
||||
llvm_usize.ptr_type(AddressSpace::default()),
|
||||
),
|
||||
data: StructField::create(&mut counter, "data", item.ptr_type(AddressSpace::default())),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ContiguousNDArrayType<'ctx> {
|
||||
/// Checks whether `llvm_ty` represents a `ndarray` type, returning [Err] if it does not.
|
||||
pub fn is_representable(
|
||||
llvm_ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
let ctx = llvm_ty.get_context();
|
||||
|
||||
let llvm_ty = llvm_ty.get_element_type();
|
||||
let AnyTypeEnum::StructType(llvm_ty) = llvm_ty else {
|
||||
return Err(format!(
|
||||
"Expected struct type for `ContiguousNDArray` type, got {llvm_ty}"
|
||||
));
|
||||
};
|
||||
|
||||
let fields = ContiguousNDArrayStructFields::new(ctx, llvm_usize);
|
||||
|
||||
check_struct_type_matches_fields(
|
||||
fields,
|
||||
llvm_ty,
|
||||
"ContiguousNDArray",
|
||||
&[(fields.data.name(), &|ty| {
|
||||
if ty.is_pointer_type() {
|
||||
Ok(())
|
||||
} else {
|
||||
Err(format!("Expected T* for `ContiguousNDArray.data`, got {ty}"))
|
||||
}
|
||||
})],
|
||||
)
|
||||
}
|
||||
|
||||
/// Returns an instance of [`StructFields`] containing all field accessors for this type.
|
||||
#[must_use]
|
||||
fn fields(
|
||||
item: BasicTypeEnum<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> ContiguousNDArrayStructFields<'ctx> {
|
||||
ContiguousNDArrayStructFields::new_typed(item, llvm_usize)
|
||||
}
|
||||
|
||||
/// See [`NDArrayType::fields`].
|
||||
// TODO: Move this into e.g. StructProxyType
|
||||
#[must_use]
|
||||
pub fn get_fields(&self) -> ContiguousNDArrayStructFields<'ctx> {
|
||||
Self::fields(self.item, self.llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an LLVM type corresponding to the expected structure of an `NDArray`.
|
||||
#[must_use]
|
||||
fn llvm_type(
|
||||
ctx: &'ctx Context,
|
||||
item: BasicTypeEnum<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> PointerType<'ctx> {
|
||||
let field_tys =
|
||||
Self::fields(item, llvm_usize).into_iter().map(|field| field.1).collect_vec();
|
||||
|
||||
ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
|
||||
}
|
||||
|
||||
/// Creates an instance of [`ContiguousNDArrayType`].
|
||||
#[must_use]
|
||||
pub fn new<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
item: BasicTypeEnum<'ctx>,
|
||||
) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
let llvm_cndarray = Self::llvm_type(ctx, item, llvm_usize);
|
||||
|
||||
Self { ty: llvm_cndarray, item, llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an [`ContiguousNDArrayType`] from a [unifier type][Type].
|
||||
#[must_use]
|
||||
pub fn from_unifier_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ty: Type,
|
||||
) -> Self {
|
||||
let (dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
|
||||
|
||||
let llvm_dtype = ctx.get_llvm_type(generator, dtype);
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
Self { ty: Self::llvm_type(ctx.ctx, llvm_dtype, llvm_usize), item: llvm_dtype, llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an [`ContiguousNDArrayType`] from a [`PointerType`] representing an `NDArray`.
|
||||
#[must_use]
|
||||
pub fn from_type(
|
||||
ptr_ty: PointerType<'ctx>,
|
||||
item: BasicTypeEnum<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
|
||||
|
||||
Self { ty: ptr_ty, item, llvm_usize }
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`ContiguousNDArrayValue`] as if by calling `alloca` on the base
|
||||
/// type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca`].
|
||||
#[must_use]
|
||||
pub fn alloca(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca(ctx, name),
|
||||
self.item,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`ContiguousNDArrayValue`] as if by calling `alloca` on the base
|
||||
/// type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca_var`].
|
||||
#[must_use]
|
||||
pub fn alloca_var<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca_var(generator, ctx, name),
|
||||
self.item,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Converts an existing value into a [`ContiguousNDArrayValue`].
|
||||
#[must_use]
|
||||
pub fn map_value(
|
||||
&self,
|
||||
value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
value,
|
||||
self.item,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyType<'ctx> for ContiguousNDArrayType<'ctx> {
|
||||
type Base = PointerType<'ctx>;
|
||||
type Value = ContiguousNDArrayValue<'ctx>;
|
||||
|
||||
fn is_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: impl BasicType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
|
||||
<Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
|
||||
} else {
|
||||
Err(format!("Expected pointer type, got {llvm_ty:?}"))
|
||||
}
|
||||
}
|
||||
|
||||
fn is_representable<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: Self::Base,
|
||||
) -> Result<(), String> {
|
||||
Self::is_representable(llvm_ty, generator.get_size_type(ctx))
|
||||
}
|
||||
|
||||
fn alloca_type(&self) -> impl BasicType<'ctx> {
|
||||
self.as_base_type().get_element_type().into_struct_type()
|
||||
}
|
||||
|
||||
fn as_base_type(&self) -> Self::Base {
|
||||
self.ty
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<ContiguousNDArrayType<'ctx>> for PointerType<'ctx> {
|
||||
fn from(value: ContiguousNDArrayType<'ctx>) -> Self {
|
||||
value.as_base_type()
|
||||
}
|
||||
}
|
||||
@ -1,236 +0,0 @@
|
||||
use inkwell::{
|
||||
values::{BasicValueEnum, IntValue},
|
||||
IntPredicate,
|
||||
};
|
||||
|
||||
use super::NDArrayType;
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt, types::ProxyType, values::TypedArrayLikeAccessor, CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
/// Get the zero value in `np.zeros()` of a `dtype`.
|
||||
fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
ctx.ctx.i32_type().const_zero().into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64]
|
||||
.iter()
|
||||
.any(|ty| ctx.unifier.unioned(dtype, *ty))
|
||||
{
|
||||
ctx.ctx.i64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "").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> NDArrayType<'ctx> {
|
||||
/// Create an ndarray like
|
||||
/// [`np.empty`](https://numpy.org/doc/stable/reference/generated/numpy.empty.html).
|
||||
pub fn construct_numpy_empty<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
let ndarray = self.construct_uninitialized(generator, ctx, name);
|
||||
|
||||
// Validate `shape`
|
||||
irrt::ndarray::call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, shape);
|
||||
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape.base_ptr(ctx, generator));
|
||||
unsafe { ndarray.create_data(generator, ctx) };
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like
|
||||
/// [`np.full`](https://numpy.org/doc/stable/reference/generated/numpy.full.html).
|
||||
pub fn construct_numpy_full<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
fill_value: BasicValueEnum<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
let ndarray = self.construct_numpy_empty(generator, ctx, shape, name);
|
||||
ndarray.fill(generator, ctx, fill_value);
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like
|
||||
/// [`np.zero`](https://numpy.org/doc/stable/reference/generated/numpy.zeros.html).
|
||||
pub fn construct_numpy_zeros<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
assert_eq!(
|
||||
ctx.get_llvm_type(generator, dtype),
|
||||
self.dtype,
|
||||
"Expected LLVM dtype={} but got {}",
|
||||
self.dtype.print_to_string(),
|
||||
ctx.get_llvm_type(generator, dtype).print_to_string(),
|
||||
);
|
||||
|
||||
let fill_value = ndarray_zero_value(generator, ctx, dtype);
|
||||
self.construct_numpy_full(generator, ctx, shape, fill_value, name)
|
||||
}
|
||||
|
||||
/// Create an ndarray like
|
||||
/// [`np.ones`](https://numpy.org/doc/stable/reference/generated/numpy.ones.html).
|
||||
pub fn construct_numpy_ones<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
assert_eq!(
|
||||
ctx.get_llvm_type(generator, dtype),
|
||||
self.dtype,
|
||||
"Expected LLVM dtype={} but got {}",
|
||||
self.dtype.print_to_string(),
|
||||
ctx.get_llvm_type(generator, dtype).print_to_string(),
|
||||
);
|
||||
|
||||
let fill_value = ndarray_one_value(generator, ctx, dtype);
|
||||
self.construct_numpy_full(generator, ctx, shape, fill_value, name)
|
||||
}
|
||||
|
||||
/// Create an ndarray like
|
||||
/// [`np.eye`](https://numpy.org/doc/stable/reference/generated/numpy.eye.html).
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
pub fn construct_numpy_eye<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
nrows: IntValue<'ctx>,
|
||||
ncols: IntValue<'ctx>,
|
||||
offset: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
assert_eq!(
|
||||
ctx.get_llvm_type(generator, dtype),
|
||||
self.dtype,
|
||||
"Expected LLVM dtype={} but got {}",
|
||||
self.dtype.print_to_string(),
|
||||
ctx.get_llvm_type(generator, dtype).print_to_string(),
|
||||
);
|
||||
assert_eq!(nrows.get_type(), self.llvm_usize);
|
||||
assert_eq!(ncols.get_type(), self.llvm_usize);
|
||||
assert_eq!(offset.get_type(), self.llvm_usize);
|
||||
|
||||
let ndzero = ndarray_zero_value(generator, ctx, dtype);
|
||||
let ndone = ndarray_one_value(generator, ctx, dtype);
|
||||
|
||||
let ndarray = self.construct_dyn_shape(generator, ctx, &[nrows, ncols], name);
|
||||
|
||||
// Create data and make the matrix like look np.eye()
|
||||
unsafe {
|
||||
ndarray.create_data(generator, ctx);
|
||||
}
|
||||
ndarray
|
||||
.foreach(generator, ctx, |generator, ctx, _, 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.
|
||||
|
||||
let indices = nditer.get_indices();
|
||||
|
||||
let row_i = unsafe {
|
||||
indices.get_typed_unchecked(ctx, generator, &self.llvm_usize.const_zero(), None)
|
||||
};
|
||||
let col_i = unsafe {
|
||||
indices.get_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&self.llvm_usize.const_int(1, false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
|
||||
let be_one = ctx
|
||||
.builder
|
||||
.build_int_compare(
|
||||
IntPredicate::EQ,
|
||||
ctx.builder.build_int_add(row_i, offset, "").unwrap(),
|
||||
col_i,
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let value = ctx.builder.build_select(be_one, ndone, ndzero, "value").unwrap();
|
||||
|
||||
let p = nditer.get_pointer(ctx);
|
||||
ctx.builder.build_store(p, value).unwrap();
|
||||
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an ndarray like
|
||||
/// [`np.identity`](https://numpy.org/doc/stable/reference/generated/numpy.identity.html).
|
||||
pub fn construct_numpy_identity<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dtype: Type,
|
||||
size: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
let offset = self.llvm_usize.const_zero();
|
||||
self.construct_numpy_eye(generator, ctx, dtype, size, size, offset, name)
|
||||
}
|
||||
}
|
||||
@ -1,205 +0,0 @@
|
||||
use inkwell::{
|
||||
context::{AsContextRef, Context},
|
||||
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use nac3core_derive::StructFields;
|
||||
|
||||
use crate::codegen::{
|
||||
types::{
|
||||
structure::{check_struct_type_matches_fields, StructField, StructFields},
|
||||
ProxyType,
|
||||
},
|
||||
values::{
|
||||
ndarray::{NDIndexValue, RustNDIndex},
|
||||
ArrayLikeIndexer, ArraySliceValue, ProxyValue,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
|
||||
pub struct NDIndexType<'ctx> {
|
||||
ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
}
|
||||
|
||||
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
|
||||
pub struct NDIndexStructFields<'ctx> {
|
||||
#[value_type(i8_type())]
|
||||
pub type_: StructField<'ctx, IntValue<'ctx>>,
|
||||
#[value_type(i8_type().ptr_type(AddressSpace::default()))]
|
||||
pub data: StructField<'ctx, PointerValue<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDIndexType<'ctx> {
|
||||
/// Checks whether `llvm_ty` represents a `ndindex` type, returning [Err] if it does not.
|
||||
pub fn is_representable(
|
||||
llvm_ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
let ctx = llvm_ty.get_context();
|
||||
|
||||
let llvm_ty = llvm_ty.get_element_type();
|
||||
let AnyTypeEnum::StructType(llvm_ty) = llvm_ty else {
|
||||
return Err(format!(
|
||||
"Expected struct type for `ContiguousNDArray` type, got {llvm_ty}"
|
||||
));
|
||||
};
|
||||
|
||||
let fields = NDIndexStructFields::new(ctx, llvm_usize);
|
||||
|
||||
check_struct_type_matches_fields(fields, llvm_ty, "NDIndex", &[])
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
fn fields(
|
||||
ctx: impl AsContextRef<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> NDIndexStructFields<'ctx> {
|
||||
NDIndexStructFields::new(ctx, llvm_usize)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn get_fields(&self) -> NDIndexStructFields<'ctx> {
|
||||
Self::fields(self.ty.get_context(), self.llvm_usize)
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
fn llvm_type(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> PointerType<'ctx> {
|
||||
let field_tys =
|
||||
Self::fields(ctx, llvm_usize).into_iter().map(|field| field.1).collect_vec();
|
||||
|
||||
ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn new<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
let llvm_ndindex = Self::llvm_type(ctx, llvm_usize);
|
||||
|
||||
Self { ty: llvm_ndindex, llvm_usize }
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
|
||||
|
||||
Self { ty: ptr_ty, llvm_usize }
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`NDIndexValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca`].
|
||||
#[must_use]
|
||||
pub fn alloca(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca(ctx, name),
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
/// Allocates an instance of [`NDIndexValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca_var`].
|
||||
#[must_use]
|
||||
pub fn alloca_var<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca_var(generator, ctx, name),
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Serialize a list of [`RustNDIndex`] as a newly allocated LLVM array of [`NDIndexValue`].
|
||||
#[must_use]
|
||||
pub fn construct_ndindices<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
in_ndindices: &[RustNDIndex<'ctx>],
|
||||
) -> ArraySliceValue<'ctx> {
|
||||
// Allocate the LLVM ndindices.
|
||||
let num_ndindices = self.llvm_usize.const_int(in_ndindices.len() as u64, false);
|
||||
let ndindices = self.array_alloca_var(generator, ctx, num_ndindices, None);
|
||||
|
||||
// Initialize all of them.
|
||||
for (i, in_ndindex) in in_ndindices.iter().enumerate() {
|
||||
let pndindex = unsafe {
|
||||
ndindices.ptr_offset_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&ctx.ctx.i64_type().const_int(u64::try_from(i).unwrap(), false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
|
||||
in_ndindex.write_to_ndindex(
|
||||
generator,
|
||||
ctx,
|
||||
NDIndexValue::from_pointer_value(pndindex, self.llvm_usize, None),
|
||||
);
|
||||
}
|
||||
|
||||
ndindices
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn map_value(
|
||||
&self,
|
||||
value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(value, self.llvm_usize, name)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyType<'ctx> for NDIndexType<'ctx> {
|
||||
type Base = PointerType<'ctx>;
|
||||
type Value = NDIndexValue<'ctx>;
|
||||
|
||||
fn is_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: impl BasicType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
|
||||
<Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
|
||||
} else {
|
||||
Err(format!("Expected pointer type, got {llvm_ty:?}"))
|
||||
}
|
||||
}
|
||||
|
||||
fn is_representable<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: Self::Base,
|
||||
) -> Result<(), String> {
|
||||
Self::is_representable(llvm_ty, generator.get_size_type(ctx))
|
||||
}
|
||||
|
||||
fn alloca_type(&self) -> impl BasicType<'ctx> {
|
||||
self.as_base_type().get_element_type().into_struct_type()
|
||||
}
|
||||
|
||||
fn as_base_type(&self) -> Self::Base {
|
||||
self.ty
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<NDIndexType<'ctx>> for PointerType<'ctx> {
|
||||
fn from(value: NDIndexType<'ctx>) -> Self {
|
||||
value.as_base_type()
|
||||
}
|
||||
}
|
||||
@ -1,187 +0,0 @@
|
||||
use inkwell::{types::BasicTypeEnum, values::BasicValueEnum};
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::codegen::{
|
||||
stmt::gen_for_callback,
|
||||
types::{
|
||||
ndarray::{NDArrayType, NDIterType},
|
||||
ProxyType,
|
||||
},
|
||||
values::{
|
||||
ndarray::{NDArrayOut, NDArrayValue, ScalarOrNDArray},
|
||||
ArrayLikeValue, ProxyValue,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
impl<'ctx> NDArrayType<'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>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ndarrays: &[NDArrayValue<'ctx>],
|
||||
out: NDArrayOut<'ctx>,
|
||||
mapping: MappingFn,
|
||||
) -> Result<<Self as ProxyType<'ctx>>::Value, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
MappingFn: FnOnce(
|
||||
&mut G,
|
||||
&mut CodeGenContext<'ctx, 'a>,
|
||||
&[BasicValueEnum<'ctx>],
|
||||
) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
// Broadcast inputs
|
||||
let broadcast_result = self.broadcast(generator, ctx, ndarrays);
|
||||
|
||||
let out_ndarray = match out {
|
||||
NDArrayOut::NewNDArray { dtype } => {
|
||||
// Create a new ndarray based on the broadcast shape.
|
||||
let result_ndarray =
|
||||
NDArrayType::new(generator, ctx.ctx, dtype, broadcast_result.ndims)
|
||||
.construct_uninitialized(generator, ctx, None);
|
||||
result_ndarray.copy_shape_from_array(
|
||||
generator,
|
||||
ctx,
|
||||
broadcast_result.shape.base_ptr(ctx, generator),
|
||||
);
|
||||
unsafe {
|
||||
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.shape);
|
||||
result_ndarray
|
||||
}
|
||||
};
|
||||
|
||||
// Map element-wise and store results into `mapped_ndarray`.
|
||||
let nditer = NDIterType::new(generator, ctx.ctx).construct(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| {
|
||||
NDIterType::new(generator, ctx.ctx).construct(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))
|
||||
},
|
||||
|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(ctx)).collect_vec();
|
||||
|
||||
let result = mapping(generator, ctx, &in_scalars)?;
|
||||
|
||||
let p = out_nditer.get_pointer(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)
|
||||
}
|
||||
}
|
||||
|
||||
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
|
||||
/// [`NDArrayValue::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: BasicTypeEnum<'ctx>,
|
||||
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(BasicValueEnum::<'ctx>::try_from).try_collect().ok();
|
||||
|
||||
if let Some(scalars) = all_scalars {
|
||||
let scalars = scalars.iter().copied().collect_vec();
|
||||
let value = mapping(generator, ctx, &scalars)?;
|
||||
|
||||
Ok(ScalarOrNDArray::Scalar(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 = NDArrayType::new_broadcast(
|
||||
generator,
|
||||
ctx.ctx,
|
||||
ret_dtype,
|
||||
&inputs.iter().map(NDArrayValue::get_type).collect_vec(),
|
||||
)
|
||||
.broadcast_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&inputs,
|
||||
NDArrayOut::NewNDArray { dtype: ret_dtype },
|
||||
mapping,
|
||||
)?;
|
||||
Ok(ScalarOrNDArray::NDArray(ndarray))
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,7 +1,7 @@
|
||||
use inkwell::{
|
||||
context::{AsContextRef, Context},
|
||||
context::Context,
|
||||
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
|
||||
values::{BasicValue, IntValue, PointerValue},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
@ -9,54 +9,28 @@ use itertools::Itertools;
|
||||
use nac3core_derive::StructFields;
|
||||
|
||||
use super::{
|
||||
structure::{check_struct_type_matches_fields, StructField, StructFields},
|
||||
structure::{StructField, StructFields},
|
||||
ProxyType,
|
||||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
values::{ndarray::NDArrayValue, ProxyValue, TypedArrayLikeMutator},
|
||||
use crate::codegen::{
|
||||
values::{ArraySliceValue, NDArrayValue, ProxyValue},
|
||||
{CodeGenContext, CodeGenerator},
|
||||
},
|
||||
toplevel::{helper::extract_ndims, numpy::unpack_ndarray_var_tys},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
pub use broadcast::*;
|
||||
pub use contiguous::*;
|
||||
pub use indexing::*;
|
||||
pub use nditer::*;
|
||||
|
||||
mod array;
|
||||
mod broadcast;
|
||||
mod contiguous;
|
||||
pub mod factory;
|
||||
mod indexing;
|
||||
mod map;
|
||||
mod nditer;
|
||||
|
||||
/// Proxy type for a `ndarray` type in LLVM.
|
||||
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
|
||||
pub struct NDArrayType<'ctx> {
|
||||
ty: PointerType<'ctx>,
|
||||
dtype: BasicTypeEnum<'ctx>,
|
||||
ndims: u64,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
}
|
||||
|
||||
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
|
||||
pub struct NDArrayStructFields<'ctx> {
|
||||
/// The size of each `NDArray` element in bytes.
|
||||
#[value_type(usize)]
|
||||
pub itemsize: StructField<'ctx, IntValue<'ctx>>,
|
||||
/// Number of dimensions in the array.
|
||||
#[value_type(usize)]
|
||||
pub ndims: StructField<'ctx, IntValue<'ctx>>,
|
||||
/// Pointer to an array containing the shape of the `NDArray`.
|
||||
#[value_type(usize.ptr_type(AddressSpace::default()))]
|
||||
pub shape: StructField<'ctx, PointerValue<'ctx>>,
|
||||
/// Pointer to an array indicating the number of bytes between each element at a dimension
|
||||
#[value_type(usize.ptr_type(AddressSpace::default()))]
|
||||
pub strides: StructField<'ctx, PointerValue<'ctx>>,
|
||||
/// Pointer to an array containing the array data
|
||||
#[value_type(i8_type().ptr_type(AddressSpace::default()))]
|
||||
pub data: StructField<'ctx, PointerValue<'ctx>>,
|
||||
}
|
||||
@ -67,40 +41,90 @@ impl<'ctx> NDArrayType<'ctx> {
|
||||
llvm_ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
let ctx = llvm_ty.get_context();
|
||||
|
||||
let llvm_ndarray_ty = llvm_ty.get_element_type();
|
||||
let AnyTypeEnum::StructType(llvm_ndarray_ty) = llvm_ndarray_ty else {
|
||||
return Err(format!("Expected struct type for `NDArray` type, got {llvm_ndarray_ty}"));
|
||||
};
|
||||
if llvm_ndarray_ty.count_fields() != 3 {
|
||||
return Err(format!(
|
||||
"Expected 3 fields in `NDArray`, got {}",
|
||||
llvm_ndarray_ty.count_fields()
|
||||
));
|
||||
}
|
||||
|
||||
check_struct_type_matches_fields(
|
||||
Self::fields(ctx, llvm_usize),
|
||||
llvm_ndarray_ty,
|
||||
"NDArray",
|
||||
&[],
|
||||
)
|
||||
let ndarray_ndims_ty = llvm_ndarray_ty.get_field_type_at_index(0).unwrap();
|
||||
let Ok(ndarray_ndims_ty) = IntType::try_from(ndarray_ndims_ty) else {
|
||||
return Err(format!("Expected int type for `ndarray.0`, got {ndarray_ndims_ty}"));
|
||||
};
|
||||
if ndarray_ndims_ty.get_bit_width() != llvm_usize.get_bit_width() {
|
||||
return Err(format!(
|
||||
"Expected {}-bit int type for `ndarray.0`, got {}-bit int",
|
||||
llvm_usize.get_bit_width(),
|
||||
ndarray_ndims_ty.get_bit_width()
|
||||
));
|
||||
}
|
||||
|
||||
let ndarray_dims_ty = llvm_ndarray_ty.get_field_type_at_index(1).unwrap();
|
||||
let Ok(ndarray_pdims) = PointerType::try_from(ndarray_dims_ty) else {
|
||||
return Err(format!("Expected pointer type for `ndarray.1`, got {ndarray_dims_ty}"));
|
||||
};
|
||||
let ndarray_dims = ndarray_pdims.get_element_type();
|
||||
let Ok(ndarray_dims) = IntType::try_from(ndarray_dims) else {
|
||||
return Err(format!(
|
||||
"Expected pointer-to-int type for `ndarray.1`, got pointer-to-{ndarray_dims}"
|
||||
));
|
||||
};
|
||||
if ndarray_dims.get_bit_width() != llvm_usize.get_bit_width() {
|
||||
return Err(format!(
|
||||
"Expected pointer-to-{}-bit int type for `ndarray.1`, got pointer-to-{}-bit int",
|
||||
llvm_usize.get_bit_width(),
|
||||
ndarray_dims.get_bit_width()
|
||||
));
|
||||
}
|
||||
|
||||
let ndarray_data_ty = llvm_ndarray_ty.get_field_type_at_index(2).unwrap();
|
||||
let Ok(ndarray_pdata) = PointerType::try_from(ndarray_data_ty) else {
|
||||
return Err(format!("Expected pointer type for `ndarray.2`, got {ndarray_data_ty}"));
|
||||
};
|
||||
let ndarray_data = ndarray_pdata.get_element_type();
|
||||
let Ok(ndarray_data) = IntType::try_from(ndarray_data) else {
|
||||
return Err(format!(
|
||||
"Expected pointer-to-int type for `ndarray.2`, got pointer-to-{ndarray_data}"
|
||||
));
|
||||
};
|
||||
if ndarray_data.get_bit_width() != 8 {
|
||||
return Err(format!(
|
||||
"Expected pointer-to-8-bit int type for `ndarray.1`, got pointer-to-{}-bit int",
|
||||
ndarray_data.get_bit_width()
|
||||
));
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Returns an instance of [`StructFields`] containing all field accessors for this type.
|
||||
#[must_use]
|
||||
fn fields(
|
||||
ctx: impl AsContextRef<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> NDArrayStructFields<'ctx> {
|
||||
fn fields(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> NDArrayStructFields<'ctx> {
|
||||
NDArrayStructFields::new(ctx, llvm_usize)
|
||||
}
|
||||
|
||||
/// See [`NDArrayType::fields`].
|
||||
// TODO: Move this into e.g. StructProxyType
|
||||
#[must_use]
|
||||
pub fn get_fields(&self, ctx: impl AsContextRef<'ctx>) -> NDArrayStructFields<'ctx> {
|
||||
pub fn get_fields(&self, ctx: &'ctx Context) -> NDArrayStructFields<'ctx> {
|
||||
Self::fields(ctx, self.llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an LLVM type corresponding to the expected structure of an `NDArray`.
|
||||
#[must_use]
|
||||
fn llvm_type(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> PointerType<'ctx> {
|
||||
// struct NDArray { num_dims: size_t, dims: size_t*, data: i8* }
|
||||
//
|
||||
// * data : Pointer to an array containing the array data
|
||||
// * itemsize: The size of each NDArray elements in bytes
|
||||
// * ndims : Number of dimensions in the array
|
||||
// * shape : Pointer to an array containing the shape of the NDArray
|
||||
// * strides : Pointer to an array indicating the number of bytes between each element at a dimension
|
||||
let field_tys =
|
||||
Self::fields(ctx, llvm_usize).into_iter().map(|field| field.1).collect_vec();
|
||||
|
||||
@ -113,60 +137,11 @@ impl<'ctx> NDArrayType<'ctx> {
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
dtype: BasicTypeEnum<'ctx>,
|
||||
ndims: u64,
|
||||
) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
let llvm_ndarray = Self::llvm_type(ctx, llvm_usize);
|
||||
|
||||
NDArrayType { ty: llvm_ndarray, dtype, ndims, llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an instance of [`NDArrayType`] as a result of a broadcast operation over one or more
|
||||
/// `ndarray` operands.
|
||||
#[must_use]
|
||||
pub fn new_broadcast<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
dtype: BasicTypeEnum<'ctx>,
|
||||
inputs: &[NDArrayType<'ctx>],
|
||||
) -> Self {
|
||||
assert!(!inputs.is_empty());
|
||||
|
||||
Self::new(generator, ctx, dtype, inputs.iter().map(NDArrayType::ndims).max().unwrap())
|
||||
}
|
||||
|
||||
/// Creates an instance of [`NDArrayType`] with `ndims` of 0.
|
||||
#[must_use]
|
||||
pub fn new_unsized<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
dtype: BasicTypeEnum<'ctx>,
|
||||
) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
let llvm_ndarray = Self::llvm_type(ctx, llvm_usize);
|
||||
|
||||
NDArrayType { ty: llvm_ndarray, dtype, ndims: 0, llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an [`NDArrayType`] from a [unifier type][Type].
|
||||
#[must_use]
|
||||
pub fn from_unifier_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ty: Type,
|
||||
) -> Self {
|
||||
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
|
||||
|
||||
let llvm_dtype = ctx.get_llvm_type(generator, dtype);
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let ndims = extract_ndims(&ctx.unifier, ndims);
|
||||
|
||||
NDArrayType {
|
||||
ty: Self::llvm_type(ctx.ctx, llvm_usize),
|
||||
dtype: llvm_dtype,
|
||||
ndims,
|
||||
llvm_usize,
|
||||
}
|
||||
NDArrayType { ty: llvm_ndarray, dtype, llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an [`NDArrayType`] from a [`PointerType`] representing an `NDArray`.
|
||||
@ -174,18 +149,22 @@ impl<'ctx> NDArrayType<'ctx> {
|
||||
pub fn from_type(
|
||||
ptr_ty: PointerType<'ctx>,
|
||||
dtype: BasicTypeEnum<'ctx>,
|
||||
ndims: u64,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
|
||||
|
||||
NDArrayType { ty: ptr_ty, dtype, ndims, llvm_usize }
|
||||
NDArrayType { ty: ptr_ty, dtype, llvm_usize }
|
||||
}
|
||||
|
||||
/// Returns the type of the `size` field of this `ndarray` type.
|
||||
#[must_use]
|
||||
pub fn size_type(&self) -> IntType<'ctx> {
|
||||
self.llvm_usize
|
||||
self.as_base_type()
|
||||
.get_element_type()
|
||||
.into_struct_type()
|
||||
.get_field_type_at_index(0)
|
||||
.map(BasicTypeEnum::into_int_type)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Returns the element type of this `ndarray` type.
|
||||
@ -194,207 +173,22 @@ impl<'ctx> NDArrayType<'ctx> {
|
||||
self.dtype
|
||||
}
|
||||
|
||||
/// Returns the number of dimensions of this `ndarray` type.
|
||||
#[must_use]
|
||||
pub fn ndims(&self) -> u64 {
|
||||
self.ndims
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`NDArrayValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca`].
|
||||
#[must_use]
|
||||
pub fn alloca(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca(ctx, name),
|
||||
self.dtype,
|
||||
self.ndims,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`NDArrayValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca_var`].
|
||||
#[must_use]
|
||||
pub fn alloca_var<G: CodeGenerator + ?Sized>(
|
||||
pub fn alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca_var(generator, ctx, name),
|
||||
self.raw_alloca(generator, ctx, name),
|
||||
self.dtype,
|
||||
self.ndims,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Allocates an [`NDArrayValue`] on the stack and initializes all fields as follows:
|
||||
///
|
||||
/// - `data`: uninitialized.
|
||||
/// - `itemsize`: set to the size of `self.dtype`.
|
||||
/// - `ndims`: set to the value of `ndims`.
|
||||
/// - `shape`: allocated on the stack with an array of length `ndims` with uninitialized values.
|
||||
/// - `strides`: allocated on the stack with an array of length `ndims` with uninitialized
|
||||
/// values.
|
||||
#[must_use]
|
||||
fn construct_impl<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndims: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
let ndarray = self.alloca_var(generator, ctx, name);
|
||||
|
||||
let itemsize = ctx
|
||||
.builder
|
||||
.build_int_truncate_or_bit_cast(self.dtype.size_of().unwrap(), self.llvm_usize, "")
|
||||
.unwrap();
|
||||
ndarray.store_itemsize(ctx, generator, itemsize);
|
||||
|
||||
ndarray.store_ndims(ctx, generator, ndims);
|
||||
|
||||
ndarray.create_shape(ctx, self.llvm_usize, ndims);
|
||||
ndarray.create_strides(ctx, self.llvm_usize, ndims);
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Allocate an [`NDArrayValue`] on the stack using `dtype` and `ndims` of this [`NDArrayType`]
|
||||
/// instance.
|
||||
///
|
||||
/// The returned ndarray's content will be:
|
||||
/// - `data`: uninitialized.
|
||||
/// - `itemsize`: set to the size of `dtype`.
|
||||
/// - `ndims`: set to the value of `self.ndims`.
|
||||
/// - `shape`: allocated on the stack with an array of length `ndims` with uninitialized values.
|
||||
/// - `strides`: allocated on the stack with an array of length `ndims` with uninitialized
|
||||
/// values.
|
||||
#[must_use]
|
||||
pub fn construct_uninitialized<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
let ndims = self.llvm_usize.const_int(self.ndims, false);
|
||||
|
||||
self.construct_impl(generator, ctx, ndims, name)
|
||||
}
|
||||
|
||||
/// Convenience function. Allocate an [`NDArrayValue`] with a statically known shape.
|
||||
///
|
||||
/// The returned [`NDArrayValue`]'s `data` and `strides` are uninitialized.
|
||||
#[must_use]
|
||||
pub fn construct_const_shape<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
shape: &[u64],
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
assert_eq!(shape.len() as u64, self.ndims);
|
||||
|
||||
let ndarray = Self::new(generator, ctx.ctx, self.dtype, shape.len() as u64)
|
||||
.construct_uninitialized(generator, ctx, name);
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
// Write shape
|
||||
let ndarray_shape = ndarray.shape();
|
||||
for (i, dim) in shape.iter().enumerate() {
|
||||
let dim = llvm_usize.const_int(*dim, false);
|
||||
unsafe {
|
||||
ndarray_shape.set_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(i as u64, false),
|
||||
dim,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Convenience function. Allocate an [`NDArrayValue`] with a dynamically known shape.
|
||||
///
|
||||
/// The returned [`NDArrayValue`]'s `data` and `strides` are uninitialized.
|
||||
#[must_use]
|
||||
pub fn construct_dyn_shape<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
shape: &[IntValue<'ctx>],
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
assert_eq!(shape.len() as u64, self.ndims);
|
||||
|
||||
let ndarray = Self::new(generator, ctx.ctx, self.dtype, shape.len() as u64)
|
||||
.construct_uninitialized(generator, ctx, name);
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
// Write shape
|
||||
let ndarray_shape = ndarray.shape();
|
||||
for (i, dim) in shape.iter().enumerate() {
|
||||
assert_eq!(
|
||||
dim.get_type(),
|
||||
llvm_usize,
|
||||
"Expected {} but got {}",
|
||||
llvm_usize.print_to_string(),
|
||||
dim.get_type().print_to_string()
|
||||
);
|
||||
unsafe {
|
||||
ndarray_shape.set_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(i as u64, false),
|
||||
*dim,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Create an unsized ndarray to contain `value`.
|
||||
#[must_use]
|
||||
pub fn construct_unsized<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: &impl BasicValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> NDArrayValue<'ctx> {
|
||||
let value = value.as_basic_value_enum();
|
||||
|
||||
assert_eq!(value.get_type(), self.dtype);
|
||||
assert_eq!(self.ndims, 0);
|
||||
|
||||
// We have to put the value on the stack to get a data pointer.
|
||||
let data = ctx.builder.build_alloca(value.get_type(), "construct_unsized").unwrap();
|
||||
ctx.builder.build_store(data, value).unwrap();
|
||||
let data = ctx
|
||||
.builder
|
||||
.build_pointer_cast(data, ctx.ctx.i8_type().ptr_type(AddressSpace::default()), "")
|
||||
.unwrap();
|
||||
|
||||
let ndarray = Self::new_unsized(generator, ctx.ctx, value.get_type())
|
||||
.construct_uninitialized(generator, ctx, name);
|
||||
ctx.builder.build_store(ndarray.ptr_to_data(ctx), data).unwrap();
|
||||
ndarray
|
||||
}
|
||||
|
||||
/// Converts an existing value into a [`NDArrayValue`].
|
||||
#[must_use]
|
||||
pub fn map_value(
|
||||
@ -405,7 +199,6 @@ impl<'ctx> NDArrayType<'ctx> {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
value,
|
||||
self.dtype,
|
||||
self.ndims,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
@ -436,8 +229,36 @@ impl<'ctx> ProxyType<'ctx> for NDArrayType<'ctx> {
|
||||
Self::is_representable(llvm_ty, generator.get_size_type(ctx))
|
||||
}
|
||||
|
||||
fn alloca_type(&self) -> impl BasicType<'ctx> {
|
||||
self.as_base_type().get_element_type().into_struct_type()
|
||||
fn raw_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self::Value as ProxyValue<'ctx>>::Base {
|
||||
generator
|
||||
.gen_var_alloc(
|
||||
ctx,
|
||||
self.as_base_type().get_element_type().into_struct_type().into(),
|
||||
name,
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
fn array_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
size: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> ArraySliceValue<'ctx> {
|
||||
generator
|
||||
.gen_array_var_alloc(
|
||||
ctx,
|
||||
self.as_base_type().get_element_type().into_struct_type().into(),
|
||||
size,
|
||||
name,
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
fn as_base_type(&self) -> Self::Base {
|
||||
|
||||
@ -1,237 +0,0 @@
|
||||
use inkwell::{
|
||||
context::{AsContextRef, Context},
|
||||
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use nac3core_derive::StructFields;
|
||||
|
||||
use super::ProxyType;
|
||||
use crate::codegen::{
|
||||
irrt,
|
||||
types::structure::{check_struct_type_matches_fields, StructField, StructFields},
|
||||
values::{
|
||||
ndarray::{NDArrayValue, NDIterValue},
|
||||
ArrayLikeValue, ArraySliceValue, ProxyValue, TypedArrayLikeAdapter,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
|
||||
pub struct NDIterType<'ctx> {
|
||||
ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
}
|
||||
|
||||
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
|
||||
pub struct NDIterStructFields<'ctx> {
|
||||
#[value_type(usize)]
|
||||
pub ndims: StructField<'ctx, IntValue<'ctx>>,
|
||||
#[value_type(usize.ptr_type(AddressSpace::default()))]
|
||||
pub shape: StructField<'ctx, PointerValue<'ctx>>,
|
||||
#[value_type(usize.ptr_type(AddressSpace::default()))]
|
||||
pub strides: StructField<'ctx, PointerValue<'ctx>>,
|
||||
#[value_type(usize.ptr_type(AddressSpace::default()))]
|
||||
pub indices: StructField<'ctx, PointerValue<'ctx>>,
|
||||
#[value_type(usize)]
|
||||
pub nth: StructField<'ctx, IntValue<'ctx>>,
|
||||
#[value_type(i8_type().ptr_type(AddressSpace::default()))]
|
||||
pub element: StructField<'ctx, PointerValue<'ctx>>,
|
||||
#[value_type(usize)]
|
||||
pub size: StructField<'ctx, IntValue<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDIterType<'ctx> {
|
||||
/// Checks whether `llvm_ty` represents a `nditer` type, returning [Err] if it does not.
|
||||
pub fn is_representable(
|
||||
llvm_ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
let ctx = llvm_ty.get_context();
|
||||
|
||||
let llvm_ty = llvm_ty.get_element_type();
|
||||
let AnyTypeEnum::StructType(llvm_ndarray_ty) = llvm_ty else {
|
||||
return Err(format!("Expected struct type for `NDIter` type, got {llvm_ty}"));
|
||||
};
|
||||
|
||||
check_struct_type_matches_fields(
|
||||
Self::fields(ctx, llvm_usize),
|
||||
llvm_ndarray_ty,
|
||||
"NDIter",
|
||||
&[],
|
||||
)
|
||||
}
|
||||
|
||||
/// Returns an instance of [`StructFields`] containing all field accessors for this type.
|
||||
#[must_use]
|
||||
fn fields(ctx: impl AsContextRef<'ctx>, llvm_usize: IntType<'ctx>) -> NDIterStructFields<'ctx> {
|
||||
NDIterStructFields::new(ctx, llvm_usize)
|
||||
}
|
||||
|
||||
/// See [`NDIterType::fields`].
|
||||
// TODO: Move this into e.g. StructProxyType
|
||||
#[must_use]
|
||||
pub fn get_fields(&self, ctx: impl AsContextRef<'ctx>) -> NDIterStructFields<'ctx> {
|
||||
Self::fields(ctx, self.llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an LLVM type corresponding to the expected structure of an `NDIter`.
|
||||
#[must_use]
|
||||
fn llvm_type(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> PointerType<'ctx> {
|
||||
let field_tys =
|
||||
Self::fields(ctx, llvm_usize).into_iter().map(|field| field.1).collect_vec();
|
||||
|
||||
ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
|
||||
}
|
||||
|
||||
/// Creates an instance of [`NDIter`].
|
||||
#[must_use]
|
||||
pub fn new<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
let llvm_nditer = Self::llvm_type(ctx, llvm_usize);
|
||||
|
||||
Self { ty: llvm_nditer, llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an [`NDIterType`] from a [`PointerType`] representing an `NDIter`.
|
||||
#[must_use]
|
||||
pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
|
||||
|
||||
Self { ty: ptr_ty, llvm_usize }
|
||||
}
|
||||
|
||||
/// Returns the type of the `size` field of this `nditer` type.
|
||||
#[must_use]
|
||||
pub fn size_type(&self) -> IntType<'ctx> {
|
||||
self.llvm_usize
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`NDIterValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca`].
|
||||
#[must_use]
|
||||
pub fn alloca(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
parent: NDArrayValue<'ctx>,
|
||||
indices: ArraySliceValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca(ctx, name),
|
||||
parent,
|
||||
indices,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`NDIterValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca_var`].
|
||||
#[must_use]
|
||||
pub fn alloca_var<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
parent: NDArrayValue<'ctx>,
|
||||
indices: ArraySliceValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca_var(generator, ctx, name),
|
||||
parent,
|
||||
indices,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Allocate an [`NDIter`] that iterates through the given `ndarray`.
|
||||
///
|
||||
/// Note: This function allocates an array on the stack at the current builder location, which
|
||||
/// may lead to stack explosion if called in a hot loop. Therefore, callers are recommended to
|
||||
/// call `llvm.stacksave` before calling this function and call `llvm.stackrestore` after the
|
||||
/// [`NDIter`] is no longer needed.
|
||||
#[must_use]
|
||||
pub fn construct<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
let nditer = self.raw_alloca_var(generator, ctx, None);
|
||||
let ndims = self.llvm_usize.const_int(ndarray.get_type().ndims(), false);
|
||||
|
||||
// The caller has the responsibility to allocate 'indices' for `NDIter`.
|
||||
let indices =
|
||||
generator.gen_array_var_alloc(ctx, self.llvm_usize.into(), ndims, None).unwrap();
|
||||
let indices =
|
||||
TypedArrayLikeAdapter::from(indices, |_, _, v| v.into_int_value(), |_, _, v| v.into());
|
||||
|
||||
let nditer = self.map_value(nditer, ndarray, indices.as_slice_value(ctx, generator), None);
|
||||
|
||||
irrt::ndarray::call_nac3_nditer_initialize(generator, ctx, nditer, ndarray, &indices);
|
||||
|
||||
nditer
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn map_value(
|
||||
&self,
|
||||
value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
|
||||
parent: NDArrayValue<'ctx>,
|
||||
indices: ArraySliceValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
value,
|
||||
parent,
|
||||
indices,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyType<'ctx> for NDIterType<'ctx> {
|
||||
type Base = PointerType<'ctx>;
|
||||
type Value = NDIterValue<'ctx>;
|
||||
|
||||
fn is_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: impl BasicType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
|
||||
<Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
|
||||
} else {
|
||||
Err(format!("Expected pointer type, got {llvm_ty:?}"))
|
||||
}
|
||||
}
|
||||
|
||||
fn is_representable<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: Self::Base,
|
||||
) -> Result<(), String> {
|
||||
Self::is_representable(llvm_ty, generator.get_size_type(ctx))
|
||||
}
|
||||
|
||||
fn alloca_type(&self) -> impl BasicType<'ctx> {
|
||||
self.as_base_type().get_element_type().into_struct_type()
|
||||
}
|
||||
|
||||
fn as_base_type(&self) -> Self::Base {
|
||||
self.ty
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<NDIterType<'ctx>> for PointerType<'ctx> {
|
||||
fn from(value: NDIterType<'ctx>) -> Self {
|
||||
value.as_base_type()
|
||||
}
|
||||
}
|
||||
@ -1,12 +1,13 @@
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
|
||||
values::IntValue,
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use super::ProxyType;
|
||||
use crate::codegen::{
|
||||
values::{ProxyValue, RangeValue},
|
||||
values::{ArraySliceValue, ProxyValue, RangeValue},
|
||||
{CodeGenContext, CodeGenerator},
|
||||
};
|
||||
|
||||
@ -77,29 +78,15 @@ impl<'ctx> RangeType<'ctx> {
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`RangeValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca`].
|
||||
#[must_use]
|
||||
pub fn alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(self.raw_alloca(ctx, name), name)
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`RangeValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca_var`].
|
||||
#[must_use]
|
||||
pub fn alloca_var<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca_var(generator, ctx, name),
|
||||
self.raw_alloca(generator, ctx, name),
|
||||
name,
|
||||
)
|
||||
}
|
||||
@ -139,8 +126,36 @@ impl<'ctx> ProxyType<'ctx> for RangeType<'ctx> {
|
||||
Self::is_representable(llvm_ty)
|
||||
}
|
||||
|
||||
fn alloca_type(&self) -> impl BasicType<'ctx> {
|
||||
self.as_base_type().get_element_type().into_struct_type()
|
||||
fn raw_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self::Value as ProxyValue<'ctx>>::Base {
|
||||
generator
|
||||
.gen_var_alloc(
|
||||
ctx,
|
||||
self.as_base_type().get_element_type().into_struct_type().into(),
|
||||
name,
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
fn array_alloca<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
size: IntValue<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> ArraySliceValue<'ctx> {
|
||||
generator
|
||||
.gen_array_var_alloc(
|
||||
ctx,
|
||||
self.as_base_type().get_element_type().into_struct_type().into(),
|
||||
size,
|
||||
name,
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
fn as_base_type(&self) -> Self::Base {
|
||||
|
||||
@ -2,10 +2,9 @@ use std::marker::PhantomData;
|
||||
|
||||
use inkwell::{
|
||||
context::AsContextRef,
|
||||
types::{BasicTypeEnum, IntType, StructType},
|
||||
types::{BasicTypeEnum, IntType},
|
||||
values::{BasicValue, BasicValueEnum, IntValue, PointerValue, StructValue},
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::codegen::CodeGenContext;
|
||||
|
||||
@ -56,20 +55,6 @@ pub trait StructFields<'ctx>: Eq + Copy {
|
||||
{
|
||||
self.into_vec().into_iter()
|
||||
}
|
||||
|
||||
/// Returns the field index of a field in this structure.
|
||||
fn index_of_field<V>(&self, name: impl FnOnce(&Self) -> StructField<'ctx, V>) -> u32
|
||||
where
|
||||
V: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>, Error = ()>,
|
||||
{
|
||||
let field_name = name(self).name;
|
||||
self.index_of_field_name(field_name).unwrap()
|
||||
}
|
||||
|
||||
/// Returns the field index of a field with the given name in this structure.
|
||||
fn index_of_field_name(&self, field_name: &str) -> Option<u32> {
|
||||
self.iter().find_position(|(name, _)| *name == field_name).map(|(idx, _)| idx as u32)
|
||||
}
|
||||
}
|
||||
|
||||
/// A single field of an LLVM structure.
|
||||
@ -118,12 +103,6 @@ where
|
||||
StructField { index, name, ty: ty.into(), _value_ty: PhantomData }
|
||||
}
|
||||
|
||||
/// Returns the name of this field.
|
||||
#[must_use]
|
||||
pub fn name(&self) -> &'static str {
|
||||
self.name
|
||||
}
|
||||
|
||||
/// Creates a pointer to this field in an arbitrary structure by performing a `getelementptr i32
|
||||
/// {idx...}, i32 {self.index}`.
|
||||
pub fn ptr_by_array_gep(
|
||||
@ -222,49 +201,3 @@ impl FieldIndexCounter {
|
||||
v
|
||||
}
|
||||
}
|
||||
|
||||
type FieldTypeVerifier<'ctx> = dyn Fn(BasicTypeEnum<'ctx>) -> Result<(), String>;
|
||||
|
||||
/// Checks whether [`llvm_ty`][StructType] contains the fields described by the given
|
||||
/// [`StructFields`] instance.
|
||||
///
|
||||
/// By default, this function will compare the type of each field in `expected_fields` against
|
||||
/// `llvm_ty`. To override this behavior for individual fields, pass in overrides to
|
||||
/// `custom_verifiers`, which will use the specified verifier when a field with the matching field
|
||||
/// name is being checked.
|
||||
pub(super) fn check_struct_type_matches_fields<'ctx>(
|
||||
expected_fields: impl StructFields<'ctx>,
|
||||
llvm_ty: StructType<'ctx>,
|
||||
ty_name: &'static str,
|
||||
custom_verifiers: &[(&str, &FieldTypeVerifier<'ctx>)],
|
||||
) -> Result<(), String> {
|
||||
let expected_fields = expected_fields.to_vec();
|
||||
|
||||
if llvm_ty.count_fields() != u32::try_from(expected_fields.len()).unwrap() {
|
||||
return Err(format!(
|
||||
"Expected {} fields in `{ty_name}`, got {}",
|
||||
expected_fields.len(),
|
||||
llvm_ty.count_fields(),
|
||||
));
|
||||
}
|
||||
|
||||
expected_fields
|
||||
.into_iter()
|
||||
.enumerate()
|
||||
.map(|(i, (field_name, expected_ty))| {
|
||||
(field_name, expected_ty, llvm_ty.get_field_type_at_index(i as u32).unwrap())
|
||||
})
|
||||
.try_for_each(|(field_name, expected_ty, actual_ty)| {
|
||||
if let Some((_, verifier)) =
|
||||
custom_verifiers.iter().find(|verifier| verifier.0 == field_name)
|
||||
{
|
||||
verifier(actual_ty)
|
||||
} else if expected_ty == actual_ty {
|
||||
Ok(())
|
||||
} else {
|
||||
Err(format!("Expected {expected_ty} for `{ty_name}.{field_name}`, got {actual_ty}"))
|
||||
}
|
||||
})?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
@ -1,184 +0,0 @@
|
||||
use inkwell::{
|
||||
context::Context,
|
||||
types::{BasicType, BasicTypeEnum, IntType, StructType},
|
||||
values::BasicValueEnum,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use super::ProxyType;
|
||||
use crate::{
|
||||
codegen::{
|
||||
values::{ProxyValue, TupleValue},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
|
||||
#[derive(Debug, PartialEq, Eq, Clone)]
|
||||
pub struct TupleType<'ctx> {
|
||||
ty: StructType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
}
|
||||
|
||||
impl<'ctx> TupleType<'ctx> {
|
||||
/// Checks whether `llvm_ty` represents any tuple type, returning [Err] if it does not.
|
||||
pub fn is_representable(_value: StructType<'ctx>) -> Result<(), String> {
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Creates an LLVM type corresponding to the expected structure of a tuple.
|
||||
#[must_use]
|
||||
fn llvm_type(ctx: &'ctx Context, tys: &[BasicTypeEnum<'ctx>]) -> StructType<'ctx> {
|
||||
ctx.struct_type(tys, false)
|
||||
}
|
||||
|
||||
/// Creates an instance of [`TupleType`].
|
||||
#[must_use]
|
||||
pub fn new<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
tys: &[BasicTypeEnum<'ctx>],
|
||||
) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
let llvm_tuple = Self::llvm_type(ctx, tys);
|
||||
|
||||
Self { ty: llvm_tuple, llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an [`TupleType`] from a [unifier type][Type].
|
||||
#[must_use]
|
||||
pub fn from_unifier_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ty: Type,
|
||||
) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
// Sanity check on object type.
|
||||
let TypeEnum::TTuple { ty: tys, .. } = &*ctx.unifier.get_ty_immutable(ty) else {
|
||||
panic!("Expected type to be a TypeEnum::TTuple, got {}", ctx.unifier.stringify(ty));
|
||||
};
|
||||
|
||||
let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
|
||||
Self { ty: Self::llvm_type(ctx.ctx, &llvm_tys), llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an [`TupleType`] from a [`StructType`].
|
||||
#[must_use]
|
||||
pub fn from_type(struct_ty: StructType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
|
||||
debug_assert!(Self::is_representable(struct_ty).is_ok());
|
||||
|
||||
TupleType { ty: struct_ty, llvm_usize }
|
||||
}
|
||||
|
||||
/// Returns the number of elements present in this [`TupleType`].
|
||||
#[must_use]
|
||||
pub fn num_elements(&self) -> u32 {
|
||||
self.ty.count_fields()
|
||||
}
|
||||
|
||||
/// Returns the type of the tuple element at the given `index`, or [`None`] if `index` is out of
|
||||
/// range.
|
||||
#[must_use]
|
||||
pub fn type_at_index(&self, index: u32) -> Option<BasicTypeEnum<'ctx>> {
|
||||
if index < self.num_elements() {
|
||||
Some(unsafe { self.type_at_index_unchecked(index) })
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the type of the tuple element at the given `index`.
|
||||
///
|
||||
/// # Safety
|
||||
///
|
||||
/// The caller must ensure that the index is valid.
|
||||
#[must_use]
|
||||
pub unsafe fn type_at_index_unchecked(&self, index: u32) -> BasicTypeEnum<'ctx> {
|
||||
self.ty.get_field_type_at_index_unchecked(index)
|
||||
}
|
||||
|
||||
/// Constructs a [`TupleValue`] from this type by zero-initializing the tuple value.
|
||||
#[must_use]
|
||||
pub fn construct(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
self.map_value(Self::llvm_type(ctx.ctx, &self.ty.get_field_types()).const_zero(), name)
|
||||
}
|
||||
|
||||
/// Constructs a [`TupleValue`] from `objects`. The resulting tuple preserves the order of
|
||||
/// objects.
|
||||
#[must_use]
|
||||
pub fn construct_from_objects<I: IntoIterator<Item = BasicValueEnum<'ctx>>>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
objects: I,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
let values = objects.into_iter().collect_vec();
|
||||
|
||||
assert_eq!(values.len(), self.num_elements() as usize);
|
||||
assert!(values
|
||||
.iter()
|
||||
.enumerate()
|
||||
.all(|(i, v)| { v.get_type() == unsafe { self.type_at_index_unchecked(i as u32) } }));
|
||||
|
||||
let mut value = self.construct(ctx, name);
|
||||
for (i, val) in values.into_iter().enumerate() {
|
||||
value.store_element(ctx, i as u32, val);
|
||||
}
|
||||
|
||||
value
|
||||
}
|
||||
|
||||
/// Converts an existing value into a [`ListValue`].
|
||||
#[must_use]
|
||||
pub fn map_value(
|
||||
&self,
|
||||
value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_struct_value(value, self.llvm_usize, name)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyType<'ctx> for TupleType<'ctx> {
|
||||
type Base = StructType<'ctx>;
|
||||
type Value = TupleValue<'ctx>;
|
||||
|
||||
fn is_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: impl BasicType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
if let BasicTypeEnum::StructType(ty) = llvm_ty.as_basic_type_enum() {
|
||||
<Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
|
||||
} else {
|
||||
Err(format!("Expected struct type, got {llvm_ty:?}"))
|
||||
}
|
||||
}
|
||||
|
||||
fn is_representable<G: CodeGenerator + ?Sized>(
|
||||
_generator: &G,
|
||||
_ctx: &'ctx Context,
|
||||
llvm_ty: Self::Base,
|
||||
) -> Result<(), String> {
|
||||
Self::is_representable(llvm_ty)
|
||||
}
|
||||
|
||||
fn alloca_type(&self) -> impl BasicType<'ctx> {
|
||||
self.as_base_type()
|
||||
}
|
||||
|
||||
fn as_base_type(&self) -> Self::Base {
|
||||
self.ty
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<TupleType<'ctx>> for StructType<'ctx> {
|
||||
fn from(value: TupleType<'ctx>) -> Self {
|
||||
value.as_base_type()
|
||||
}
|
||||
}
|
||||
@ -1,3 +0,0 @@
|
||||
pub use slice::*;
|
||||
|
||||
mod slice;
|
||||
@ -1,245 +0,0 @@
|
||||
use inkwell::{
|
||||
context::{AsContextRef, Context, ContextRef},
|
||||
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
|
||||
values::IntValue,
|
||||
AddressSpace,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use nac3core_derive::StructFields;
|
||||
|
||||
use crate::codegen::{
|
||||
types::{
|
||||
structure::{
|
||||
check_struct_type_matches_fields, FieldIndexCounter, StructField, StructFields,
|
||||
},
|
||||
ProxyType,
|
||||
},
|
||||
values::{utils::SliceValue, ProxyValue},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
|
||||
pub struct SliceType<'ctx> {
|
||||
ty: PointerType<'ctx>,
|
||||
int_ty: IntType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
}
|
||||
|
||||
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
|
||||
pub struct SliceFields<'ctx> {
|
||||
#[value_type(bool_type())]
|
||||
pub start_defined: StructField<'ctx, IntValue<'ctx>>,
|
||||
#[value_type(usize)]
|
||||
pub start: StructField<'ctx, IntValue<'ctx>>,
|
||||
#[value_type(bool_type())]
|
||||
pub stop_defined: StructField<'ctx, IntValue<'ctx>>,
|
||||
#[value_type(usize)]
|
||||
pub stop: StructField<'ctx, IntValue<'ctx>>,
|
||||
#[value_type(bool_type())]
|
||||
pub step_defined: StructField<'ctx, IntValue<'ctx>>,
|
||||
#[value_type(usize)]
|
||||
pub step: StructField<'ctx, IntValue<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx> SliceFields<'ctx> {
|
||||
/// Creates a new instance of [`SliceFields`] with a custom integer type for its range values.
|
||||
#[must_use]
|
||||
pub fn new_sized(ctx: &impl AsContextRef<'ctx>, int_ty: IntType<'ctx>) -> Self {
|
||||
let ctx = unsafe { ContextRef::new(ctx.as_ctx_ref()) };
|
||||
let mut counter = FieldIndexCounter::default();
|
||||
|
||||
SliceFields {
|
||||
start_defined: StructField::create(&mut counter, "start_defined", ctx.bool_type()),
|
||||
start: StructField::create(&mut counter, "start", int_ty),
|
||||
stop_defined: StructField::create(&mut counter, "stop_defined", ctx.bool_type()),
|
||||
stop: StructField::create(&mut counter, "stop", int_ty),
|
||||
step_defined: StructField::create(&mut counter, "step_defined", ctx.bool_type()),
|
||||
step: StructField::create(&mut counter, "step", int_ty),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> SliceType<'ctx> {
|
||||
/// Checks whether `llvm_ty` represents a `slice` type, returning [Err] if it does not.
|
||||
pub fn is_representable(
|
||||
llvm_ty: PointerType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
let ctx = llvm_ty.get_context();
|
||||
|
||||
let fields = SliceFields::new(ctx, llvm_usize);
|
||||
|
||||
let llvm_ty = llvm_ty.get_element_type();
|
||||
let AnyTypeEnum::StructType(llvm_ty) = llvm_ty else {
|
||||
return Err(format!("Expected struct type for `Slice` type, got {llvm_ty}"));
|
||||
};
|
||||
|
||||
check_struct_type_matches_fields(
|
||||
fields,
|
||||
llvm_ty,
|
||||
"Slice",
|
||||
&[
|
||||
(fields.start.name(), &|ty| {
|
||||
if ty.is_int_type() {
|
||||
Ok(())
|
||||
} else {
|
||||
Err(format!("Expected int type for `Slice.start`, got {ty}"))
|
||||
}
|
||||
}),
|
||||
(fields.stop.name(), &|ty| {
|
||||
if ty.is_int_type() {
|
||||
Ok(())
|
||||
} else {
|
||||
Err(format!("Expected int type for `Slice.stop`, got {ty}"))
|
||||
}
|
||||
}),
|
||||
(fields.step.name(), &|ty| {
|
||||
if ty.is_int_type() {
|
||||
Ok(())
|
||||
} else {
|
||||
Err(format!("Expected int type for `Slice.step`, got {ty}"))
|
||||
}
|
||||
}),
|
||||
],
|
||||
)
|
||||
}
|
||||
|
||||
// TODO: Move this into e.g. StructProxyType
|
||||
#[must_use]
|
||||
pub fn get_fields(&self) -> SliceFields<'ctx> {
|
||||
SliceFields::new_sized(&self.int_ty.get_context(), self.int_ty)
|
||||
}
|
||||
|
||||
/// Creates an LLVM type corresponding to the expected structure of a `Slice`.
|
||||
#[must_use]
|
||||
fn llvm_type(ctx: &'ctx Context, int_ty: IntType<'ctx>) -> PointerType<'ctx> {
|
||||
let field_tys = SliceFields::new_sized(&int_ty.get_context(), int_ty)
|
||||
.into_iter()
|
||||
.map(|field| field.1)
|
||||
.collect_vec();
|
||||
|
||||
ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
|
||||
}
|
||||
|
||||
/// Creates an instance of [`SliceType`] with `int_ty` as its backing integer type.
|
||||
#[must_use]
|
||||
pub fn new(ctx: &'ctx Context, int_ty: IntType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
|
||||
let llvm_ty = Self::llvm_type(ctx, int_ty);
|
||||
|
||||
Self { ty: llvm_ty, int_ty, llvm_usize }
|
||||
}
|
||||
|
||||
/// Creates an instance of [`SliceType`] with `usize` as its backing integer type.
|
||||
#[must_use]
|
||||
pub fn new_usize<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
Self::new(ctx, llvm_usize, llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an [`SliceType`] from a [`PointerType`] representing a `slice`.
|
||||
#[must_use]
|
||||
pub fn from_type(
|
||||
ptr_ty: PointerType<'ctx>,
|
||||
int_ty: IntType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr_ty, int_ty).is_ok());
|
||||
|
||||
Self { ty: ptr_ty, int_ty, llvm_usize }
|
||||
}
|
||||
|
||||
#[must_use]
|
||||
pub fn element_type(&self) -> IntType<'ctx> {
|
||||
self.int_ty
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`SliceValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca`].
|
||||
#[must_use]
|
||||
pub fn alloca(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca(ctx, name),
|
||||
self.int_ty,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Allocates an instance of [`SliceValue`] as if by calling `alloca` on the base type.
|
||||
///
|
||||
/// See [`ProxyType::raw_alloca_var`].
|
||||
#[must_use]
|
||||
pub fn alloca_var<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
self.raw_alloca_var(generator, ctx, name),
|
||||
self.int_ty,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
|
||||
/// Converts an existing value into a [`ContiguousNDArrayValue`].
|
||||
#[must_use]
|
||||
pub fn map_value(
|
||||
&self,
|
||||
value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
|
||||
name: Option<&'ctx str>,
|
||||
) -> <Self as ProxyType<'ctx>>::Value {
|
||||
<Self as ProxyType<'ctx>>::Value::from_pointer_value(
|
||||
value,
|
||||
self.int_ty,
|
||||
self.llvm_usize,
|
||||
name,
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyType<'ctx> for SliceType<'ctx> {
|
||||
type Base = PointerType<'ctx>;
|
||||
type Value = SliceValue<'ctx>;
|
||||
|
||||
fn is_type<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: impl BasicType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
|
||||
<Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
|
||||
} else {
|
||||
Err(format!("Expected pointer type, got {llvm_ty:?}"))
|
||||
}
|
||||
}
|
||||
|
||||
fn is_representable<G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &'ctx Context,
|
||||
llvm_ty: Self::Base,
|
||||
) -> Result<(), String> {
|
||||
Self::is_representable(llvm_ty, generator.get_size_type(ctx))
|
||||
}
|
||||
|
||||
fn alloca_type(&self) -> impl BasicType<'ctx> {
|
||||
self.as_base_type().get_element_type().into_struct_type()
|
||||
}
|
||||
|
||||
fn as_base_type(&self) -> Self::Base {
|
||||
self.ty
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<SliceType<'ctx>> for PointerType<'ctx> {
|
||||
fn from(value: SliceType<'ctx>) -> Self {
|
||||
value.as_base_type()
|
||||
}
|
||||
}
|
||||
@ -51,8 +51,8 @@ pub trait ArrayLikeIndexer<'ctx, Index = IntValue<'ctx>>: ArrayLikeValue<'ctx> {
|
||||
/// This function should be called with a valid index.
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &Index,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx>;
|
||||
@ -76,8 +76,8 @@ pub trait UntypedArrayLikeAccessor<'ctx, Index = IntValue<'ctx>>:
|
||||
/// This function should be called with a valid index.
|
||||
unsafe fn get_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &Index,
|
||||
name: Option<&str>,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
@ -107,8 +107,8 @@ pub trait UntypedArrayLikeMutator<'ctx, Index = IntValue<'ctx>>:
|
||||
/// This function should be called with a valid index.
|
||||
unsafe fn set_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &Index,
|
||||
value: BasicValueEnum<'ctx>,
|
||||
) {
|
||||
@ -130,33 +130,32 @@ pub trait UntypedArrayLikeMutator<'ctx, Index = IntValue<'ctx>>:
|
||||
}
|
||||
|
||||
/// An array-like value that can have its array elements accessed as an arbitrary type `T`.
|
||||
pub trait TypedArrayLikeAccessor<'ctx, G: CodeGenerator + ?Sized, T, Index = IntValue<'ctx>>:
|
||||
pub trait TypedArrayLikeAccessor<'ctx, T, Index = IntValue<'ctx>>:
|
||||
UntypedArrayLikeAccessor<'ctx, Index>
|
||||
{
|
||||
/// Casts an element from [`BasicValueEnum`] into `T`.
|
||||
fn downcast_to_type(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: BasicValueEnum<'ctx>,
|
||||
) -> T;
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// This function should be called with a valid index.
|
||||
unsafe fn get_typed_unchecked(
|
||||
unsafe fn get_typed_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &Index,
|
||||
name: Option<&str>,
|
||||
) -> T {
|
||||
let value = unsafe { self.get_unchecked(ctx, generator, idx, name) };
|
||||
self.downcast_to_type(ctx, generator, value)
|
||||
self.downcast_to_type(ctx, value)
|
||||
}
|
||||
|
||||
/// Returns the data at the `idx`-th index.
|
||||
fn get_typed(
|
||||
fn get_typed<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
@ -164,63 +163,62 @@ pub trait TypedArrayLikeAccessor<'ctx, G: CodeGenerator + ?Sized, T, Index = Int
|
||||
name: Option<&str>,
|
||||
) -> T {
|
||||
let value = self.get(ctx, generator, idx, name);
|
||||
self.downcast_to_type(ctx, generator, value)
|
||||
self.downcast_to_type(ctx, value)
|
||||
}
|
||||
}
|
||||
|
||||
/// An array-like value that can have its array elements mutated as an arbitrary type `T`.
|
||||
pub trait TypedArrayLikeMutator<'ctx, G: CodeGenerator + ?Sized, T, Index = IntValue<'ctx>>:
|
||||
pub trait TypedArrayLikeMutator<'ctx, T, Index = IntValue<'ctx>>:
|
||||
UntypedArrayLikeMutator<'ctx, Index>
|
||||
{
|
||||
/// Casts an element from T into [`BasicValueEnum`].
|
||||
fn upcast_from_type(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: T,
|
||||
) -> BasicValueEnum<'ctx>;
|
||||
|
||||
/// # Safety
|
||||
///
|
||||
/// This function should be called with a valid index.
|
||||
unsafe fn set_typed_unchecked(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
idx: &Index,
|
||||
value: T,
|
||||
) {
|
||||
let value = self.upcast_from_type(ctx, generator, value);
|
||||
unsafe { self.set_unchecked(ctx, generator, idx, value) }
|
||||
}
|
||||
|
||||
/// Sets the data at the `idx`-th index.
|
||||
fn set_typed(
|
||||
unsafe fn set_typed_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &Index,
|
||||
value: T,
|
||||
) {
|
||||
let value = self.upcast_from_type(ctx, generator, value);
|
||||
let value = self.upcast_from_type(ctx, value);
|
||||
unsafe { self.set_unchecked(ctx, generator, idx, value) }
|
||||
}
|
||||
|
||||
/// Sets the data at the `idx`-th index.
|
||||
fn set_typed<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &Index,
|
||||
value: T,
|
||||
) {
|
||||
let value = self.upcast_from_type(ctx, value);
|
||||
self.set(ctx, generator, idx, value);
|
||||
}
|
||||
}
|
||||
|
||||
/// Type alias for a function that casts a [`BasicValueEnum`] into a `T`.
|
||||
type ValueDowncastFn<'ctx, T> =
|
||||
Box<dyn Fn(&mut CodeGenContext<'ctx, '_>, BasicValueEnum<'ctx>) -> T>;
|
||||
/// Type alias for a function that casts a `T` into a [`BasicValueEnum`].
|
||||
type ValueUpcastFn<'ctx, T> = Box<dyn Fn(&mut CodeGenContext<'ctx, '_>, T) -> BasicValueEnum<'ctx>>;
|
||||
|
||||
/// An adapter for constraining untyped array values as typed values.
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct TypedArrayLikeAdapter<
|
||||
'ctx,
|
||||
G: CodeGenerator + ?Sized,
|
||||
T,
|
||||
Adapted: ArrayLikeValue<'ctx> = ArraySliceValue<'ctx>,
|
||||
> {
|
||||
pub struct TypedArrayLikeAdapter<'ctx, T, Adapted: ArrayLikeValue<'ctx> = ArraySliceValue<'ctx>> {
|
||||
adapted: Adapted,
|
||||
downcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, BasicValueEnum<'ctx>) -> T,
|
||||
upcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, T) -> BasicValueEnum<'ctx>,
|
||||
downcast_fn: ValueDowncastFn<'ctx, T>,
|
||||
upcast_fn: ValueUpcastFn<'ctx, T>,
|
||||
}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized, T, Adapted> TypedArrayLikeAdapter<'ctx, G, T, Adapted>
|
||||
impl<'ctx, T, Adapted> TypedArrayLikeAdapter<'ctx, T, Adapted>
|
||||
where
|
||||
Adapted: ArrayLikeValue<'ctx>,
|
||||
{
|
||||
@ -231,70 +229,61 @@ where
|
||||
/// * `upcast_fn` - The function converting a T into a [`BasicValueEnum`].
|
||||
pub fn from(
|
||||
adapted: Adapted,
|
||||
downcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, BasicValueEnum<'ctx>) -> T,
|
||||
upcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, T) -> BasicValueEnum<'ctx>,
|
||||
downcast_fn: ValueDowncastFn<'ctx, T>,
|
||||
upcast_fn: ValueUpcastFn<'ctx, T>,
|
||||
) -> Self {
|
||||
TypedArrayLikeAdapter { adapted, downcast_fn, upcast_fn }
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized, T, Adapted> ArrayLikeValue<'ctx>
|
||||
for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
|
||||
impl<'ctx, T, Adapted> ArrayLikeValue<'ctx> for TypedArrayLikeAdapter<'ctx, T, Adapted>
|
||||
where
|
||||
Adapted: ArrayLikeValue<'ctx>,
|
||||
{
|
||||
fn element_type<CG: CodeGenerator + ?Sized>(
|
||||
fn element_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &CG,
|
||||
generator: &G,
|
||||
) -> AnyTypeEnum<'ctx> {
|
||||
self.adapted.element_type(ctx, generator)
|
||||
}
|
||||
|
||||
fn base_ptr<CG: CodeGenerator + ?Sized>(
|
||||
fn base_ptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &CG,
|
||||
generator: &G,
|
||||
) -> PointerValue<'ctx> {
|
||||
self.adapted.base_ptr(ctx, generator)
|
||||
}
|
||||
|
||||
fn size<CG: CodeGenerator + ?Sized>(
|
||||
fn size<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &CG,
|
||||
generator: &G,
|
||||
) -> IntValue<'ctx> {
|
||||
self.adapted.size(ctx, generator)
|
||||
}
|
||||
|
||||
fn as_slice_value<CG: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &CG,
|
||||
) -> ArraySliceValue<'ctx> {
|
||||
self.adapted.as_slice_value(ctx, generator)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> ArrayLikeIndexer<'ctx, Index>
|
||||
for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
|
||||
impl<'ctx, T, Index, Adapted> ArrayLikeIndexer<'ctx, Index>
|
||||
for TypedArrayLikeAdapter<'ctx, T, Adapted>
|
||||
where
|
||||
Adapted: ArrayLikeIndexer<'ctx, Index>,
|
||||
{
|
||||
unsafe fn ptr_offset_unchecked<CG: CodeGenerator + ?Sized>(
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &CG,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &Index,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
unsafe { self.adapted.ptr_offset_unchecked(ctx, generator, idx, name) }
|
||||
}
|
||||
|
||||
fn ptr_offset<CG: CodeGenerator + ?Sized>(
|
||||
fn ptr_offset<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut CG,
|
||||
generator: &mut G,
|
||||
idx: &Index,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
@ -302,46 +291,44 @@ where
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> UntypedArrayLikeAccessor<'ctx, Index>
|
||||
for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
|
||||
impl<'ctx, T, Index, Adapted> UntypedArrayLikeAccessor<'ctx, Index>
|
||||
for TypedArrayLikeAdapter<'ctx, T, Adapted>
|
||||
where
|
||||
Adapted: UntypedArrayLikeAccessor<'ctx, Index>,
|
||||
{
|
||||
}
|
||||
impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> UntypedArrayLikeMutator<'ctx, Index>
|
||||
for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
|
||||
impl<'ctx, T, Index, Adapted> UntypedArrayLikeMutator<'ctx, Index>
|
||||
for TypedArrayLikeAdapter<'ctx, T, Adapted>
|
||||
where
|
||||
Adapted: UntypedArrayLikeMutator<'ctx, Index>,
|
||||
{
|
||||
}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> TypedArrayLikeAccessor<'ctx, G, T, Index>
|
||||
for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
|
||||
impl<'ctx, T, Index, Adapted> TypedArrayLikeAccessor<'ctx, T, Index>
|
||||
for TypedArrayLikeAdapter<'ctx, T, Adapted>
|
||||
where
|
||||
Adapted: UntypedArrayLikeAccessor<'ctx, Index>,
|
||||
{
|
||||
fn downcast_to_type(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: BasicValueEnum<'ctx>,
|
||||
) -> T {
|
||||
(self.downcast_fn)(ctx, generator, value)
|
||||
(self.downcast_fn)(ctx, value)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> TypedArrayLikeMutator<'ctx, G, T, Index>
|
||||
for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
|
||||
impl<'ctx, T, Index, Adapted> TypedArrayLikeMutator<'ctx, T, Index>
|
||||
for TypedArrayLikeAdapter<'ctx, T, Adapted>
|
||||
where
|
||||
Adapted: UntypedArrayLikeMutator<'ctx, Index>,
|
||||
{
|
||||
fn upcast_from_type(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
value: T,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
(self.upcast_fn)(ctx, generator, value)
|
||||
(self.upcast_fn)(ctx, value)
|
||||
}
|
||||
}
|
||||
|
||||
@ -397,12 +384,12 @@ impl<'ctx> ArrayLikeValue<'ctx> for ArraySliceValue<'ctx> {
|
||||
impl<'ctx> ArrayLikeIndexer<'ctx> for ArraySliceValue<'ctx> {
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let var_name = name.or(self.2).map(|v| format!("{v}.addr")).unwrap_or_default();
|
||||
let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
|
||||
|
||||
unsafe {
|
||||
ctx.builder
|
||||
|
||||
@ -8,7 +8,7 @@ use super::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ProxyValue, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
|
||||
};
|
||||
use crate::codegen::{
|
||||
types::{structure::StructField, ListType, ProxyType},
|
||||
types::ListType,
|
||||
{CodeGenContext, CodeGenerator},
|
||||
};
|
||||
|
||||
@ -42,26 +42,48 @@ impl<'ctx> ListValue<'ctx> {
|
||||
ListValue { value: ptr, llvm_usize, name }
|
||||
}
|
||||
|
||||
fn items_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, PointerValue<'ctx>> {
|
||||
self.get_type().get_fields(&ctx.ctx).items
|
||||
}
|
||||
|
||||
/// Returns the double-indirection pointer to the `data` array, as if by calling `getelementptr`
|
||||
/// on the field.
|
||||
fn pptr_to_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
self.items_field(ctx).ptr_by_gep(ctx, self.value, self.name)
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let var_name = self.name.map(|v| format!("{v}.data.addr")).unwrap_or_default();
|
||||
|
||||
unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.as_base_value(),
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_zero()],
|
||||
var_name.as_str(),
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the pointer to the field storing the size of this `list`.
|
||||
fn ptr_to_size(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let var_name = self.name.map(|v| format!("{v}.size.addr")).unwrap_or_default();
|
||||
|
||||
unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.as_base_value(),
|
||||
&[llvm_i32.const_zero(), llvm_i32.const_int(1, true)],
|
||||
var_name.as_str(),
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
/// Stores the array of data elements `data` into this instance.
|
||||
fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
|
||||
self.items_field(ctx).set(ctx, self.value, data, self.name);
|
||||
ctx.builder.build_store(self.pptr_to_data(ctx), data).unwrap();
|
||||
}
|
||||
|
||||
/// Convenience method for creating a new array storing data elements with the given element
|
||||
/// type `elem_ty` and `size`.
|
||||
///
|
||||
/// If `size` is [None], the size stored in the field of this instance is used instead. If
|
||||
/// `size` is resolved to `0` at runtime, `(T*) 0` will be assigned to `data`.
|
||||
/// If `size` is [None], the size stored in the field of this instance is used instead.
|
||||
pub fn create_data(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
@ -92,10 +114,6 @@ impl<'ctx> ListValue<'ctx> {
|
||||
ListDataProxy(self)
|
||||
}
|
||||
|
||||
fn len_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields(&ctx.ctx).len
|
||||
}
|
||||
|
||||
/// Stores the `size` of this `list` into this instance.
|
||||
pub fn store_size<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
@ -105,33 +123,22 @@ impl<'ctx> ListValue<'ctx> {
|
||||
) {
|
||||
debug_assert_eq!(size.get_type(), generator.get_size_type(ctx.ctx));
|
||||
|
||||
self.len_field(ctx).set(ctx, self.value, size, self.name);
|
||||
let psize = self.ptr_to_size(ctx);
|
||||
ctx.builder.build_store(psize, size).unwrap();
|
||||
}
|
||||
|
||||
/// Returns the size of this `list` as a value.
|
||||
pub fn load_size(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> IntValue<'ctx> {
|
||||
self.len_field(ctx).get(ctx, self.value, name)
|
||||
}
|
||||
pub fn load_size(&self, ctx: &CodeGenContext<'ctx, '_>, name: Option<&str>) -> IntValue<'ctx> {
|
||||
let psize = self.ptr_to_size(ctx);
|
||||
let var_name = name
|
||||
.map(ToString::to_string)
|
||||
.or_else(|| self.name.map(|v| format!("{v}.size")))
|
||||
.unwrap_or_default();
|
||||
|
||||
/// Returns an instance of [`ListValue`] with the `items` pointer cast to `i8*`.
|
||||
#[must_use]
|
||||
pub fn as_i8_list<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> ListValue<'ctx> {
|
||||
let llvm_i8 = ctx.ctx.i8_type();
|
||||
let llvm_list_i8 = <Self as ProxyValue>::Type::new(generator, ctx.ctx, llvm_i8.into());
|
||||
|
||||
Self::from_pointer_value(
|
||||
ctx.builder.build_pointer_cast(self.value, llvm_list_i8.as_base_type(), "").unwrap(),
|
||||
self.llvm_usize,
|
||||
self.name,
|
||||
)
|
||||
ctx.builder
|
||||
.build_load(psize, var_name.as_str())
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
@ -192,8 +199,8 @@ impl<'ctx> ArrayLikeValue<'ctx> for ListDataProxy<'ctx, '_> {
|
||||
impl<'ctx> ArrayLikeIndexer<'ctx> for ListDataProxy<'ctx, '_> {
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
|
||||
@ -4,15 +4,13 @@ use super::types::ProxyType;
|
||||
use crate::codegen::CodeGenerator;
|
||||
pub use array::*;
|
||||
pub use list::*;
|
||||
pub use ndarray::*;
|
||||
pub use range::*;
|
||||
pub use tuple::*;
|
||||
|
||||
mod array;
|
||||
mod list;
|
||||
pub mod ndarray;
|
||||
mod ndarray;
|
||||
mod range;
|
||||
mod tuple;
|
||||
pub mod utils;
|
||||
|
||||
/// A LLVM type that is used to represent a non-primitive value in NAC3.
|
||||
pub trait ProxyValue<'ctx>: Into<Self::Base> {
|
||||
|
||||
@ -1,243 +0,0 @@
|
||||
use inkwell::{
|
||||
types::IntType,
|
||||
values::{IntValue, PointerValue},
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::codegen::{
|
||||
irrt,
|
||||
types::{
|
||||
ndarray::{NDArrayType, ShapeEntryType},
|
||||
structure::StructField,
|
||||
ProxyType,
|
||||
},
|
||||
values::{
|
||||
ndarray::NDArrayValue, ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ProxyValue,
|
||||
TypedArrayLikeAccessor, TypedArrayLikeAdapter, TypedArrayLikeMutator,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct ShapeEntryValue<'ctx> {
|
||||
value: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
}
|
||||
|
||||
impl<'ctx> ShapeEntryValue<'ctx> {
|
||||
/// Checks whether `value` is an instance of `ShapeEntry`, returning [Err] if `value` is
|
||||
/// not an instance.
|
||||
pub fn is_representable(
|
||||
value: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
<Self as ProxyValue<'ctx>>::Type::is_representable(value.get_type(), llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an [`ShapeEntryValue`] from a [`PointerValue`].
|
||||
#[must_use]
|
||||
pub fn from_pointer_value(
|
||||
ptr: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
|
||||
|
||||
Self { value: ptr, llvm_usize, name }
|
||||
}
|
||||
|
||||
fn ndims_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields(self.value.get_type().get_context()).ndims
|
||||
}
|
||||
|
||||
/// Stores the number of dimensions into this value.
|
||||
pub fn store_ndims(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
|
||||
self.ndims_field().set(ctx, self.value, value, self.name);
|
||||
}
|
||||
|
||||
fn shape_field(&self) -> StructField<'ctx, PointerValue<'ctx>> {
|
||||
self.get_type().get_fields(self.value.get_type().get_context()).shape
|
||||
}
|
||||
|
||||
/// Stores the shape into this value.
|
||||
pub fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
|
||||
self.shape_field().set(ctx, self.value, value, self.name);
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyValue<'ctx> for ShapeEntryValue<'ctx> {
|
||||
type Base = PointerValue<'ctx>;
|
||||
type Type = ShapeEntryType<'ctx>;
|
||||
|
||||
fn get_type(&self) -> Self::Type {
|
||||
Self::Type::from_type(self.value.get_type(), self.llvm_usize)
|
||||
}
|
||||
|
||||
fn as_base_value(&self) -> Self::Base {
|
||||
self.value
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<ShapeEntryValue<'ctx>> for PointerValue<'ctx> {
|
||||
fn from(value: ShapeEntryValue<'ctx>) -> Self {
|
||||
value.as_base_value()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayValue<'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: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
) -> Self {
|
||||
assert!(self.ndims <= target_ndims);
|
||||
assert_eq!(target_shape.element_type(ctx, generator), self.llvm_usize.into());
|
||||
|
||||
let broadcast_ndarray = NDArrayType::new(generator, ctx.ctx, self.dtype, target_ndims)
|
||||
.construct_uninitialized(generator, ctx, None);
|
||||
broadcast_ndarray.copy_shape_from_array(
|
||||
generator,
|
||||
ctx,
|
||||
target_shape.base_ptr(ctx, generator),
|
||||
);
|
||||
|
||||
irrt::ndarray::call_nac3_ndarray_broadcast_to(generator, ctx, *self, broadcast_ndarray);
|
||||
broadcast_ndarray
|
||||
}
|
||||
}
|
||||
|
||||
/// A result produced by [`broadcast_all_ndarrays`]
|
||||
#[derive(Clone)]
|
||||
pub struct BroadcastAllResult<'ctx, G: CodeGenerator + ?Sized> {
|
||||
/// The statically known `ndims` of the broadcast result.
|
||||
pub ndims: u64,
|
||||
|
||||
/// The broadcasting shape.
|
||||
pub shape: TypedArrayLikeAdapter<'ctx, G, IntValue<'ctx>>,
|
||||
|
||||
/// 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<NDArrayValue<'ctx>>,
|
||||
}
|
||||
|
||||
/// Helper function to call [`irrt::ndarray::call_nac3_ndarray_broadcast_shapes`].
|
||||
fn broadcast_shapes<'ctx, G, Shape>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
in_shape_entries: &[(ArraySliceValue<'ctx>, u64)], // (shape, shape's length/ndims)
|
||||
broadcast_ndims: u64,
|
||||
broadcast_shape: &Shape,
|
||||
) where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Shape: TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
|
||||
+ TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>,
|
||||
{
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_shape_ty = ShapeEntryType::new(generator, ctx.ctx);
|
||||
|
||||
assert!(in_shape_entries
|
||||
.iter()
|
||||
.all(|entry| entry.0.element_type(ctx, generator) == llvm_usize.into()));
|
||||
assert_eq!(broadcast_shape.element_type(ctx, generator), llvm_usize.into());
|
||||
|
||||
// Prepare input shape entries to be passed to `call_nac3_ndarray_broadcast_shapes`.
|
||||
let num_shape_entries =
|
||||
llvm_usize.const_int(u64::try_from(in_shape_entries.len()).unwrap(), false);
|
||||
let shape_entries = llvm_shape_ty.array_alloca(ctx, num_shape_entries, None);
|
||||
for (i, (in_shape, in_ndims)) in in_shape_entries.iter().enumerate() {
|
||||
let pshape_entry = unsafe {
|
||||
shape_entries.ptr_offset_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(i as u64, false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
let shape_entry = llvm_shape_ty.map_value(pshape_entry, None);
|
||||
|
||||
let in_ndims = llvm_usize.const_int(*in_ndims, false);
|
||||
shape_entry.store_ndims(ctx, in_ndims);
|
||||
|
||||
shape_entry.store_shape(ctx, in_shape.base_ptr(ctx, generator));
|
||||
}
|
||||
|
||||
let broadcast_ndims = llvm_usize.const_int(broadcast_ndims, false);
|
||||
irrt::ndarray::call_nac3_ndarray_broadcast_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
num_shape_entries,
|
||||
shape_entries,
|
||||
broadcast_ndims,
|
||||
broadcast_shape,
|
||||
);
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayType<'ctx> {
|
||||
/// Broadcast all ndarrays according to
|
||||
/// [`np.broadcast()`](https://numpy.org/doc/stable/reference/generated/numpy.broadcast.html)
|
||||
/// and return a [`BroadcastAllResult`] containing all the information of the result of the
|
||||
/// broadcast operation.
|
||||
pub fn broadcast<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarrays: &[NDArrayValue<'ctx>],
|
||||
) -> BroadcastAllResult<'ctx, G> {
|
||||
assert!(!ndarrays.is_empty());
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
// Infer the broadcast output ndims.
|
||||
let broadcast_ndims_int =
|
||||
ndarrays.iter().map(|ndarray| ndarray.get_type().ndims()).max().unwrap();
|
||||
assert!(self.ndims() >= broadcast_ndims_int);
|
||||
|
||||
let broadcast_ndims = llvm_usize.const_int(broadcast_ndims_int, false);
|
||||
let broadcast_shape = ArraySliceValue::from_ptr_val(
|
||||
ctx.builder.build_array_alloca(llvm_usize, broadcast_ndims, "").unwrap(),
|
||||
broadcast_ndims,
|
||||
None,
|
||||
);
|
||||
let broadcast_shape = TypedArrayLikeAdapter::from(
|
||||
broadcast_shape,
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
);
|
||||
|
||||
let shape_entries = ndarrays
|
||||
.iter()
|
||||
.map(|ndarray| {
|
||||
(ndarray.shape().as_slice_value(ctx, generator), ndarray.get_type().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 = 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,
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,200 +0,0 @@
|
||||
use inkwell::{
|
||||
types::{BasicType, BasicTypeEnum, IntType},
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use super::{ArrayLikeValue, NDArrayValue, ProxyValue};
|
||||
use crate::codegen::{
|
||||
stmt::gen_if_callback,
|
||||
types::{
|
||||
ndarray::{ContiguousNDArrayType, NDArrayType},
|
||||
structure::StructField,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct ContiguousNDArrayValue<'ctx> {
|
||||
value: PointerValue<'ctx>,
|
||||
item: BasicTypeEnum<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
}
|
||||
|
||||
impl<'ctx> ContiguousNDArrayValue<'ctx> {
|
||||
/// Checks whether `value` is an instance of `ContiguousNDArray`, returning [Err] if `value` is
|
||||
/// not an instance.
|
||||
pub fn is_representable(
|
||||
value: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
<Self as ProxyValue<'ctx>>::Type::is_representable(value.get_type(), llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an [`ContiguousNDArrayValue`] from a [`PointerValue`].
|
||||
#[must_use]
|
||||
pub fn from_pointer_value(
|
||||
ptr: PointerValue<'ctx>,
|
||||
dtype: BasicTypeEnum<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
|
||||
|
||||
Self { value: ptr, item: dtype, llvm_usize, name }
|
||||
}
|
||||
|
||||
fn ndims_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields().ndims
|
||||
}
|
||||
|
||||
pub fn store_ndims(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
|
||||
self.ndims_field().set(ctx, self.as_base_value(), value, self.name);
|
||||
}
|
||||
|
||||
fn shape_field(&self) -> StructField<'ctx, PointerValue<'ctx>> {
|
||||
self.get_type().get_fields().shape
|
||||
}
|
||||
|
||||
pub fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
|
||||
self.shape_field().set(ctx, self.as_base_value(), value, self.name);
|
||||
}
|
||||
|
||||
pub fn load_shape(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
self.shape_field().get(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
fn data_field(&self) -> StructField<'ctx, PointerValue<'ctx>> {
|
||||
self.get_type().get_fields().data
|
||||
}
|
||||
|
||||
pub fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
|
||||
self.data_field().set(ctx, self.as_base_value(), value, self.name);
|
||||
}
|
||||
|
||||
pub fn load_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
self.data_field().get(ctx, self.value, self.name)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyValue<'ctx> for ContiguousNDArrayValue<'ctx> {
|
||||
type Base = PointerValue<'ctx>;
|
||||
type Type = ContiguousNDArrayType<'ctx>;
|
||||
|
||||
fn get_type(&self) -> Self::Type {
|
||||
<Self as ProxyValue<'ctx>>::Type::from_type(
|
||||
self.as_base_value().get_type(),
|
||||
self.item,
|
||||
self.llvm_usize,
|
||||
)
|
||||
}
|
||||
|
||||
fn as_base_value(&self) -> Self::Base {
|
||||
self.value
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<ContiguousNDArrayValue<'ctx>> for PointerValue<'ctx> {
|
||||
fn from(value: ContiguousNDArrayValue<'ctx>) -> Self {
|
||||
value.as_base_value()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayValue<'ctx> {
|
||||
/// Create a [`ContiguousNDArrayValue`] 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 [`ContiguousNDArrayValue`] and copy contents of this ndarray to
|
||||
/// there.
|
||||
///
|
||||
/// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created
|
||||
/// [`ContiguousNDArrayValue`] will share memory with this ndarray.
|
||||
pub fn make_contiguous_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> ContiguousNDArrayValue<'ctx> {
|
||||
let result = ContiguousNDArrayType::new(generator, ctx.ctx, self.dtype)
|
||||
.alloca_var(generator, ctx, self.name);
|
||||
|
||||
// Set ndims and shape.
|
||||
let ndims = self.llvm_usize.const_int(self.ndims, false);
|
||||
result.store_ndims(ctx, ndims);
|
||||
|
||||
let shape = self.shape();
|
||||
result.store_shape(ctx, shape.base_ptr(ctx, generator));
|
||||
|
||||
gen_if_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|generator, ctx| Ok(self.is_c_contiguous(generator, ctx)),
|
||||
|_, ctx| {
|
||||
// This ndarray is contiguous.
|
||||
let data = self.data_field(ctx).get(ctx, self.as_base_value(), self.name);
|
||||
let data = ctx
|
||||
.builder
|
||||
.build_pointer_cast(data, result.item.ptr_type(AddressSpace::default()), "")
|
||||
.unwrap();
|
||||
result.store_data(ctx, data);
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|generator, ctx| {
|
||||
// This ndarray is not contiguous. Do a full-copy on `data`. `make_copy` produces an
|
||||
// ndarray with contiguous `data`.
|
||||
let copied_ndarray = self.make_copy(generator, ctx);
|
||||
let data = copied_ndarray.data().base_ptr(ctx, generator);
|
||||
let data = ctx
|
||||
.builder
|
||||
.build_pointer_cast(data, result.item.ptr_type(AddressSpace::default()), "")
|
||||
.unwrap();
|
||||
result.store_data(ctx, data);
|
||||
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
/// Create an [`NDArrayValue`] from a [`ContiguousNDArrayValue`].
|
||||
///
|
||||
/// The operation is cheap. The newly created [`NDArrayValue`] will share the same memory as the
|
||||
/// [`ContiguousNDArrayValue`].
|
||||
///
|
||||
/// `ndims` has to be provided as [`NDArrayValue`] requires a statically known `ndims` value,
|
||||
/// despite the fact that the information should be contained within the
|
||||
/// [`ContiguousNDArrayValue`].
|
||||
pub fn from_contiguous_ndarray<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
carray: ContiguousNDArrayValue<'ctx>,
|
||||
ndims: u64,
|
||||
) -> Self {
|
||||
// TODO: Debug assert `ndims == carray.ndims` to catch bugs.
|
||||
|
||||
// Allocate the resulting ndarray.
|
||||
let ndarray = NDArrayType::new(generator, ctx.ctx, carray.item, ndims)
|
||||
.construct_uninitialized(generator, ctx, carray.name);
|
||||
|
||||
// Copy shape and update strides
|
||||
let shape = carray.load_shape(ctx);
|
||||
ndarray.copy_shape_from_array(generator, ctx, shape);
|
||||
ndarray.set_strides_contiguous(generator, ctx);
|
||||
|
||||
// Share data
|
||||
let data = carray.load_data(ctx);
|
||||
ndarray.store_data(
|
||||
ctx,
|
||||
ctx.builder
|
||||
.build_pointer_cast(data, ctx.ctx.i8_type().ptr_type(AddressSpace::default()), "")
|
||||
.unwrap(),
|
||||
);
|
||||
|
||||
ndarray
|
||||
}
|
||||
}
|
||||
@ -1,260 +0,0 @@
|
||||
use inkwell::{
|
||||
types::IntType,
|
||||
values::{IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use nac3parser::ast::{Expr, ExprKind};
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt,
|
||||
types::{
|
||||
ndarray::{NDArrayType, NDIndexType},
|
||||
structure::StructField,
|
||||
utils::SliceType,
|
||||
},
|
||||
values::{ndarray::NDArrayValue, utils::RustSlice, ProxyValue},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
/// An IRRT representation of an ndarray subscript index.
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct NDIndexValue<'ctx> {
|
||||
value: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDIndexValue<'ctx> {
|
||||
/// Checks whether `value` is an instance of `ndindex`, returning [Err] if `value` is not an
|
||||
/// instance.
|
||||
pub fn is_representable(
|
||||
value: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
<Self as ProxyValue<'ctx>>::Type::is_representable(value.get_type(), llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an [`NDIndexValue`] from a [`PointerValue`].
|
||||
#[must_use]
|
||||
pub fn from_pointer_value(
|
||||
ptr: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
|
||||
|
||||
Self { value: ptr, llvm_usize, name }
|
||||
}
|
||||
|
||||
fn type_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields().type_
|
||||
}
|
||||
|
||||
pub fn load_type(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
self.type_field().get(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
pub fn store_type(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
|
||||
self.type_field().set(ctx, self.value, value, self.name);
|
||||
}
|
||||
|
||||
fn data_field(&self) -> StructField<'ctx, PointerValue<'ctx>> {
|
||||
self.get_type().get_fields().data
|
||||
}
|
||||
|
||||
pub fn load_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
self.data_field().get(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
pub fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
|
||||
self.data_field().set(ctx, self.value, value, self.name);
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyValue<'ctx> for NDIndexValue<'ctx> {
|
||||
type Base = PointerValue<'ctx>;
|
||||
type Type = NDIndexType<'ctx>;
|
||||
|
||||
fn get_type(&self) -> Self::Type {
|
||||
Self::Type::from_type(self.value.get_type(), self.llvm_usize)
|
||||
}
|
||||
|
||||
fn as_base_value(&self) -> Self::Base {
|
||||
self.value
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<NDIndexValue<'ctx>> for PointerValue<'ctx> {
|
||||
fn from(value: NDIndexValue<'ctx>) -> Self {
|
||||
value.as_base_value()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayValue<'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 = NDArrayType::new(generator, ctx.ctx, self.dtype, dst_ndims)
|
||||
.construct_uninitialized(generator, ctx, None);
|
||||
|
||||
let indices =
|
||||
NDIndexType::new(generator, ctx.ctx).construct_ndindices(generator, ctx, indices);
|
||||
irrt::ndarray::call_nac3_ndarray_index(generator, ctx, indices, *self, dst_ndarray);
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
}
|
||||
|
||||
/// A convenience enum representing a [`NDIndexValue`].
|
||||
// TODO: Rename to CTConstNDIndex
|
||||
#[derive(Debug, Clone)]
|
||||
pub enum RustNDIndex<'ctx> {
|
||||
SingleElement(IntValue<'ctx>),
|
||||
Slice(RustSlice<'ctx>),
|
||||
NewAxis,
|
||||
Ellipsis,
|
||||
}
|
||||
|
||||
impl<'ctx> RustNDIndex<'ctx> {
|
||||
/// 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 from_subscript_expr<G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
subscript: &Expr<Option<Type>>,
|
||||
) -> Result<Vec<RustNDIndex<'ctx>>, String> {
|
||||
// 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 = RustSlice::from_slice_expr(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 = index.into_int_value();
|
||||
|
||||
RustNDIndex::SingleElement(index)
|
||||
};
|
||||
rust_ndindices.push(ndindex);
|
||||
}
|
||||
Ok(rust_ndindices)
|
||||
}
|
||||
|
||||
/// Get the value to set `NDIndex::type` for this variant.
|
||||
#[must_use]
|
||||
pub 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 [`NDIndexValue`].
|
||||
pub fn write_to_ndindex<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
dst_ndindex: NDIndexValue<'ctx>,
|
||||
) {
|
||||
let llvm_pi8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
|
||||
|
||||
// Set `dst_ndindex.type`
|
||||
dst_ndindex.store_type(ctx, ctx.ctx.i8_type().const_int(self.get_type_id(), false));
|
||||
|
||||
// Set `dst_ndindex_ptr->data`
|
||||
match self {
|
||||
RustNDIndex::SingleElement(in_index) => {
|
||||
let index_ptr = ctx.builder.build_alloca(ctx.ctx.i32_type(), "").unwrap();
|
||||
ctx.builder.build_store(index_ptr, *in_index).unwrap();
|
||||
|
||||
dst_ndindex.store_data(
|
||||
ctx,
|
||||
ctx.builder.build_pointer_cast(index_ptr, llvm_pi8, "").unwrap(),
|
||||
);
|
||||
}
|
||||
RustNDIndex::Slice(in_rust_slice) => {
|
||||
let user_slice_ptr =
|
||||
SliceType::new(ctx.ctx, ctx.ctx.i32_type(), generator.get_size_type(ctx.ctx))
|
||||
.alloca_var(generator, ctx, None);
|
||||
in_rust_slice.write_to_slice(ctx, user_slice_ptr);
|
||||
|
||||
dst_ndindex.store_data(
|
||||
ctx,
|
||||
ctx.builder.build_pointer_cast(user_slice_ptr.into(), llvm_pi8, "").unwrap(),
|
||||
);
|
||||
}
|
||||
RustNDIndex::NewAxis | RustNDIndex::Ellipsis => {}
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,69 +0,0 @@
|
||||
use inkwell::{types::BasicTypeEnum, values::BasicValueEnum};
|
||||
|
||||
use crate::codegen::{
|
||||
values::{
|
||||
ndarray::{NDArrayOut, NDArrayValue, ScalarOrNDArray},
|
||||
ProxyValue,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
impl<'ctx> NDArrayValue<'ctx> {
|
||||
/// 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>,
|
||||
{
|
||||
self.get_type().broadcast_starmap(
|
||||
generator,
|
||||
ctx,
|
||||
&[*self],
|
||||
out,
|
||||
|generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ScalarOrNDArray<'ctx> {
|
||||
/// 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: BasicTypeEnum<'ctx>,
|
||||
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]),
|
||||
)
|
||||
}
|
||||
}
|
||||
@ -1,323 +0,0 @@
|
||||
use std::cmp::max;
|
||||
|
||||
use nac3parser::ast::Operator;
|
||||
|
||||
use super::{NDArrayOut, NDArrayValue, RustNDIndex};
|
||||
use crate::{
|
||||
codegen::{
|
||||
expr::gen_binop_expr_with_values,
|
||||
irrt,
|
||||
stmt::gen_for_callback_incrementing,
|
||||
types::ndarray::NDArrayType,
|
||||
values::{
|
||||
ArrayLikeValue, ArraySliceValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
|
||||
UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
toplevel::helper::arraylike_flatten_element_type,
|
||||
typecheck::{magic_methods::Binop, typedef::Type},
|
||||
};
|
||||
|
||||
/// 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_ty, in_a): (Type, NDArrayValue<'ctx>),
|
||||
(in_b_ty, in_b): (Type, NDArrayValue<'ctx>),
|
||||
) -> NDArrayValue<'ctx> {
|
||||
assert!(in_a.ndims >= 2, "in_a (which is {}) must be >= 2", in_a.ndims);
|
||||
assert!(in_b.ndims >= 2, "in_b (which is {}) must be >= 2", in_b.ndims);
|
||||
|
||||
let lhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, in_a_ty);
|
||||
let rhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, in_b_ty);
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_dst_dtype = ctx.get_llvm_type(generator, dst_dtype);
|
||||
|
||||
// Deduce ndims of the result of matmul.
|
||||
let ndims_int = max(in_a.ndims, in_b.ndims);
|
||||
let ndims = llvm_usize.const_int(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 = llvm_usize.const_int(in_a.ndims, false);
|
||||
let in_lhs_shape = TypedArrayLikeAdapter::from(
|
||||
ArraySliceValue::from_ptr_val(
|
||||
in_a.shape().base_ptr(ctx, generator),
|
||||
in_lhs_ndims,
|
||||
None,
|
||||
),
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
);
|
||||
let in_rhs_ndims = llvm_usize.const_int(in_b.ndims, false);
|
||||
let in_rhs_shape = TypedArrayLikeAdapter::from(
|
||||
ArraySliceValue::from_ptr_val(
|
||||
in_b.shape().base_ptr(ctx, generator),
|
||||
in_rhs_ndims,
|
||||
None,
|
||||
),
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
);
|
||||
let lhs_shape = TypedArrayLikeAdapter::from(
|
||||
ArraySliceValue::from_ptr_val(
|
||||
ctx.builder.build_array_alloca(llvm_usize, ndims, "").unwrap(),
|
||||
ndims,
|
||||
None,
|
||||
),
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
);
|
||||
let rhs_shape = TypedArrayLikeAdapter::from(
|
||||
ArraySliceValue::from_ptr_val(
|
||||
ctx.builder.build_array_alloca(llvm_usize, ndims, "").unwrap(),
|
||||
ndims,
|
||||
None,
|
||||
),
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
);
|
||||
let dst_shape = TypedArrayLikeAdapter::from(
|
||||
ArraySliceValue::from_ptr_val(
|
||||
ctx.builder.build_array_alloca(llvm_usize, ndims, "").unwrap(),
|
||||
ndims,
|
||||
None,
|
||||
),
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
);
|
||||
|
||||
// Matmul dimension compatibility is checked here.
|
||||
irrt::ndarray::call_nac3_ndarray_matmul_calculate_shapes(
|
||||
generator,
|
||||
ctx,
|
||||
&in_lhs_shape,
|
||||
&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 = NDArrayType::new(generator, ctx.ctx, llvm_dst_dtype, ndims_int)
|
||||
.construct_uninitialized(generator, ctx, None);
|
||||
dst.copy_shape_from_array(generator, ctx, dst_shape.base_ptr(ctx, generator));
|
||||
unsafe {
|
||||
dst.create_data(generator, ctx);
|
||||
}
|
||||
|
||||
(lhs, rhs, dst)
|
||||
};
|
||||
|
||||
let len = unsafe {
|
||||
lhs.shape().get_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(ndims_int - 1, false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
|
||||
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(ctx);
|
||||
|
||||
ctx.builder.build_store(pdst_ij, dst_zero).unwrap();
|
||||
|
||||
let indices = hdl.get_indices::<G>();
|
||||
let i = unsafe {
|
||||
indices.get_unchecked(ctx, generator, &llvm_usize.const_int(at_row as u64, true), None)
|
||||
};
|
||||
let j = unsafe {
|
||||
indices.get_unchecked(ctx, generator, &llvm_usize.const_int(at_col as u64, true), None)
|
||||
};
|
||||
|
||||
let num_0 = llvm_usize.const_int(0, false);
|
||||
let num_1 = llvm_usize.const_int(1, false);
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
num_0,
|
||||
(len, false),
|
||||
|generator, ctx, _, k| {
|
||||
// `indices` is modified to index into `a` and `b`, and restored.
|
||||
unsafe {
|
||||
indices.set_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(at_row as u64, true),
|
||||
i,
|
||||
);
|
||||
indices.set_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(at_col as u64, true),
|
||||
k.into(),
|
||||
);
|
||||
}
|
||||
let a_ik = unsafe { lhs.data().get_unchecked(ctx, generator, &indices, None) };
|
||||
|
||||
unsafe {
|
||||
indices.set_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(at_row as u64, true),
|
||||
k.into(),
|
||||
);
|
||||
indices.set_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(at_col as u64, true),
|
||||
j,
|
||||
);
|
||||
}
|
||||
let b_kj = unsafe { rhs.data().get_unchecked(ctx, generator, &indices, None) };
|
||||
|
||||
// Restore `indices`.
|
||||
unsafe {
|
||||
indices.set_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(at_row as u64, true),
|
||||
i,
|
||||
);
|
||||
indices.set_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(at_col as u64, true),
|
||||
j,
|
||||
);
|
||||
}
|
||||
|
||||
// x = a_[...]ik * b_[...]kj
|
||||
let x = gen_binop_expr_with_values(
|
||||
generator,
|
||||
ctx,
|
||||
(&Some(lhs_dtype), a_ik),
|
||||
Binop::normal(Operator::Mult),
|
||||
(&Some(rhs_dtype), b_kj),
|
||||
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(())
|
||||
},
|
||||
num_1,
|
||||
)
|
||||
})
|
||||
.unwrap();
|
||||
|
||||
dst
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayValue<'ctx> {
|
||||
/// Perform [`np.matmul`](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).
|
||||
///
|
||||
/// This function always return an [`NDArrayValue`]. You may want to use
|
||||
/// [`NDArrayValue::split_unsized`] to handle when the output could be a scalar.
|
||||
///
|
||||
/// `dst_dtype` defines the dtype of the returned ndarray.
|
||||
#[must_use]
|
||||
pub fn matmul<G: CodeGenerator>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
self_ty: Type,
|
||||
(other_ty, other): (Type, Self),
|
||||
(out_dtype, out): (Type, NDArrayOut<'ctx>),
|
||||
) -> Self {
|
||||
// Sanity check, but type inference should prevent this.
|
||||
assert!(self.ndims > 0 && other.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 self.ndims == 1 {
|
||||
// Prepend 1 to its dimensions
|
||||
self.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis])
|
||||
} else {
|
||||
*self
|
||||
};
|
||||
|
||||
let new_b = if other.ndims == 1 {
|
||||
// Append 1 to its dimensions
|
||||
other.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis])
|
||||
} else {
|
||||
other
|
||||
};
|
||||
|
||||
// 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_dtype, (self_ty, new_a), (other_ty, new_b));
|
||||
|
||||
// Postprocessing on the result to remove prepended/appended axes.
|
||||
let mut postindices = vec![];
|
||||
let zero = ctx.ctx.i32_type().const_zero();
|
||||
|
||||
if self.ndims == 1 {
|
||||
// Remove the prepended 1
|
||||
postindices.push(RustNDIndex::SingleElement(zero));
|
||||
}
|
||||
|
||||
if other.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.shape();
|
||||
out_ndarray.assert_can_be_written_by_out(generator, ctx, result_shape);
|
||||
|
||||
out_ndarray.copy_data_from(generator, ctx, result);
|
||||
out_ndarray
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,48 +1,26 @@
|
||||
use std::iter::repeat_n;
|
||||
|
||||
use inkwell::{
|
||||
types::{AnyType, AnyTypeEnum, BasicType, BasicTypeEnum, IntType},
|
||||
values::{BasicValue, BasicValueEnum, IntValue, PointerValue},
|
||||
values::{BasicValueEnum, IntValue, PointerValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
use itertools::Itertools;
|
||||
|
||||
use super::{
|
||||
ArrayLikeIndexer, ArrayLikeValue, ProxyValue, TupleValue, TypedArrayLikeAccessor,
|
||||
TypedArrayLikeAdapter, TypedArrayLikeMutator, UntypedArrayLikeAccessor,
|
||||
UntypedArrayLikeMutator,
|
||||
ArrayLikeIndexer, ArrayLikeValue, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeMutator,
|
||||
UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
|
||||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
irrt,
|
||||
llvm_intrinsics::{call_int_umin, call_memcpy_generic_array},
|
||||
use crate::codegen::{
|
||||
irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
|
||||
llvm_intrinsics::call_int_umin,
|
||||
stmt::gen_for_callback_incrementing,
|
||||
type_aligned_alloca,
|
||||
types::{ndarray::NDArrayType, structure::StructField, TupleType},
|
||||
types::{structure::StructField, NDArrayType},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::{Type, TypeEnum},
|
||||
};
|
||||
pub use broadcast::*;
|
||||
pub use contiguous::*;
|
||||
pub use indexing::*;
|
||||
pub use nditer::*;
|
||||
|
||||
mod broadcast;
|
||||
mod contiguous;
|
||||
mod indexing;
|
||||
mod map;
|
||||
mod matmul;
|
||||
mod nditer;
|
||||
pub mod shape;
|
||||
mod view;
|
||||
|
||||
/// Proxy type for accessing an `NDArray` value in LLVM.
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct NDArrayValue<'ctx> {
|
||||
value: PointerValue<'ctx>,
|
||||
dtype: BasicTypeEnum<'ctx>,
|
||||
ndims: u64,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
}
|
||||
@ -62,13 +40,12 @@ impl<'ctx> NDArrayValue<'ctx> {
|
||||
pub fn from_pointer_value(
|
||||
ptr: PointerValue<'ctx>,
|
||||
dtype: BasicTypeEnum<'ctx>,
|
||||
ndims: u64,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
|
||||
|
||||
NDArrayValue { value: ptr, dtype, ndims, llvm_usize, name }
|
||||
NDArrayValue { value: ptr, dtype, llvm_usize, name }
|
||||
}
|
||||
|
||||
fn ndims_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
@ -99,31 +76,16 @@ impl<'ctx> NDArrayValue<'ctx> {
|
||||
ctx.builder.build_load(pndims, "").map(BasicValueEnum::into_int_value).unwrap()
|
||||
}
|
||||
|
||||
fn itemsize_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields(ctx.ctx).itemsize
|
||||
}
|
||||
|
||||
/// Stores the size of each element `itemsize` into this instance.
|
||||
pub fn store_itemsize<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
itemsize: IntValue<'ctx>,
|
||||
) {
|
||||
debug_assert_eq!(itemsize.get_type(), generator.get_size_type(ctx.ctx));
|
||||
|
||||
self.itemsize_field(ctx).set(ctx, self.value, itemsize, self.name);
|
||||
}
|
||||
|
||||
/// Returns the size of each element of this `NDArray` as a value.
|
||||
pub fn load_itemsize(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
self.itemsize_field(ctx).get(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
fn shape_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, PointerValue<'ctx>> {
|
||||
self.get_type().get_fields(ctx.ctx).shape
|
||||
}
|
||||
|
||||
/// Returns the double-indirection pointer to the `shape` array, as if by calling
|
||||
/// `getelementptr` on the field.
|
||||
fn ptr_to_shape(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
self.shape_field(ctx).ptr_by_gep(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
/// Stores the array of dimension sizes `dims` into this instance.
|
||||
fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, dims: PointerValue<'ctx>) {
|
||||
self.shape_field(ctx).set(ctx, self.as_base_value(), dims, self.name);
|
||||
@ -145,34 +107,6 @@ impl<'ctx> NDArrayValue<'ctx> {
|
||||
NDArrayShapeProxy(self)
|
||||
}
|
||||
|
||||
fn strides_field(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> StructField<'ctx, PointerValue<'ctx>> {
|
||||
self.get_type().get_fields(ctx.ctx).strides
|
||||
}
|
||||
|
||||
/// Stores the array of stride sizes `strides` into this instance.
|
||||
fn store_strides(&self, ctx: &CodeGenContext<'ctx, '_>, strides: PointerValue<'ctx>) {
|
||||
self.strides_field(ctx).set(ctx, self.as_base_value(), strides, self.name);
|
||||
}
|
||||
|
||||
/// Convenience method for creating a new array storing the stride with the given `size`.
|
||||
pub fn create_strides(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
size: IntValue<'ctx>,
|
||||
) {
|
||||
self.store_strides(ctx, ctx.builder.build_array_alloca(llvm_usize, size, "").unwrap());
|
||||
}
|
||||
|
||||
/// Returns a proxy object to the field storing the stride of each dimension of this `NDArray`.
|
||||
#[must_use]
|
||||
pub fn strides(&self) -> NDArrayStridesProxy<'ctx, '_> {
|
||||
NDArrayStridesProxy(self)
|
||||
}
|
||||
|
||||
fn data_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, PointerValue<'ctx>> {
|
||||
self.get_type().get_fields(ctx.ctx).data
|
||||
}
|
||||
@ -184,7 +118,7 @@ impl<'ctx> NDArrayValue<'ctx> {
|
||||
}
|
||||
|
||||
/// Stores the array of data elements `data` into this instance.
|
||||
pub fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
|
||||
fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
|
||||
let data = ctx
|
||||
.builder
|
||||
.build_bit_cast(data, ctx.ctx.i8_type().ptr_type(AddressSpace::default()), "")
|
||||
@ -194,23 +128,23 @@ impl<'ctx> NDArrayValue<'ctx> {
|
||||
|
||||
/// Convenience method for creating a new array storing data elements with the given element
|
||||
/// type `elem_ty` and `size`.
|
||||
///
|
||||
/// The data buffer will be allocated on the stack, and is considered to be owned by this ndarray instance.
|
||||
///
|
||||
/// # Safety
|
||||
///
|
||||
/// The caller must ensure that `shape` and `itemsize` of this ndarray instance is initialized.
|
||||
pub unsafe fn create_data<G: CodeGenerator + ?Sized>(
|
||||
pub fn create_data(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
elem_ty: BasicTypeEnum<'ctx>,
|
||||
size: IntValue<'ctx>,
|
||||
) {
|
||||
let nbytes = self.nbytes(generator, ctx);
|
||||
let itemsize = ctx
|
||||
.builder
|
||||
.build_int_z_extend_or_bit_cast(elem_ty.size_of().unwrap(), size.get_type(), "")
|
||||
.unwrap();
|
||||
let nbytes = ctx.builder.build_int_mul(size, itemsize, "").unwrap();
|
||||
|
||||
let data = type_aligned_alloca(generator, ctx, self.dtype, nbytes, None);
|
||||
self.store_data(ctx, data);
|
||||
|
||||
self.set_strides_contiguous(generator, ctx);
|
||||
// TODO: What about alignment?
|
||||
self.store_data(
|
||||
ctx,
|
||||
ctx.builder.build_array_alloca(ctx.ctx.i8_type(), nbytes, "").unwrap(),
|
||||
);
|
||||
}
|
||||
|
||||
/// Returns a proxy object to the field storing the data of this `NDArray`.
|
||||
@ -218,298 +152,6 @@ impl<'ctx> NDArrayValue<'ctx> {
|
||||
pub fn data(&self) -> NDArrayDataProxy<'ctx, '_> {
|
||||
NDArrayDataProxy(self)
|
||||
}
|
||||
|
||||
/// Copy shape dimensions from an array.
|
||||
pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
shape: PointerValue<'ctx>,
|
||||
) {
|
||||
let num_items = self.load_ndims(ctx);
|
||||
|
||||
call_memcpy_generic_array(
|
||||
ctx,
|
||||
self.shape().base_ptr(ctx, generator),
|
||||
shape,
|
||||
num_items,
|
||||
ctx.ctx.bool_type().const_zero(),
|
||||
);
|
||||
}
|
||||
|
||||
/// 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: NDArrayValue<'ctx>,
|
||||
) {
|
||||
assert_eq!(self.ndims, src_ndarray.ndims);
|
||||
|
||||
let src_shape = src_ndarray.shape().base_ptr(ctx, generator);
|
||||
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: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
strides: PointerValue<'ctx>,
|
||||
) {
|
||||
let num_items = self.load_ndims(ctx);
|
||||
|
||||
call_memcpy_generic_array(
|
||||
ctx,
|
||||
self.strides().base_ptr(ctx, generator),
|
||||
strides,
|
||||
num_items,
|
||||
ctx.ctx.bool_type().const_zero(),
|
||||
);
|
||||
}
|
||||
|
||||
/// 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: NDArrayValue<'ctx>,
|
||||
) {
|
||||
assert_eq!(self.ndims, src_ndarray.ndims);
|
||||
|
||||
let src_strides = src_ndarray.strides().base_ptr(ctx, generator);
|
||||
self.copy_strides_from_array(generator, ctx, src_strides);
|
||||
}
|
||||
|
||||
/// Get the `np.size()` of this ndarray.
|
||||
pub fn size<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> IntValue<'ctx> {
|
||||
irrt::ndarray::call_nac3_ndarray_size(generator, ctx, *self)
|
||||
}
|
||||
|
||||
/// Get the `ndarray.nbytes` of this ndarray.
|
||||
pub fn nbytes<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> IntValue<'ctx> {
|
||||
irrt::ndarray::call_nac3_ndarray_nbytes(generator, ctx, *self)
|
||||
}
|
||||
|
||||
/// Get the `len()` of this ndarray.
|
||||
pub fn len<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> IntValue<'ctx> {
|
||||
irrt::ndarray::call_nac3_ndarray_len(generator, ctx, *self)
|
||||
}
|
||||
|
||||
/// 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: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> IntValue<'ctx> {
|
||||
irrt::ndarray::call_nac3_ndarray_is_c_contiguous(generator, ctx, *self)
|
||||
}
|
||||
|
||||
/// 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: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) {
|
||||
irrt::ndarray::call_nac3_ndarray_set_strides_by_shape(generator, ctx, *self);
|
||||
}
|
||||
|
||||
/// 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 = self.get_type().construct_uninitialized(generator, ctx, None);
|
||||
|
||||
let shape = self.shape();
|
||||
clone.copy_shape_from_array(generator, ctx, shape.base_ptr(ctx, generator));
|
||||
unsafe { 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: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
src: NDArrayValue<'ctx>,
|
||||
) {
|
||||
assert_eq!(self.dtype, src.dtype, "self and src dtype should match");
|
||||
irrt::ndarray::call_nac3_ndarray_copy_data(generator, ctx, src, *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, |_, ctx, _, nditer| {
|
||||
let p = nditer.get_pointer(ctx);
|
||||
ctx.builder.build_store(p, value).unwrap();
|
||||
Ok(())
|
||||
})
|
||||
.unwrap();
|
||||
}
|
||||
|
||||
/// Create the shape tuple of this ndarray like
|
||||
/// [`np.shape(<ndarray>)`](https://numpy.org/doc/stable/reference/generated/numpy.shape.html).
|
||||
///
|
||||
/// All elements in the tuple are `i32`.
|
||||
pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> TupleValue<'ctx> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
|
||||
let objects = (0..self.ndims)
|
||||
.map(|i| {
|
||||
let dim = unsafe {
|
||||
self.shape().get_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&self.llvm_usize.const_int(i, false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
ctx.builder.build_int_truncate_or_bit_cast(dim, llvm_i32, "").unwrap()
|
||||
})
|
||||
.map(|obj| obj.as_basic_value_enum())
|
||||
.collect_vec();
|
||||
|
||||
TupleType::new(
|
||||
generator,
|
||||
ctx.ctx,
|
||||
&repeat_n(llvm_i32.into(), self.ndims as usize).collect_vec(),
|
||||
)
|
||||
.construct_from_objects(ctx, objects, None)
|
||||
}
|
||||
|
||||
/// Create the strides tuple of this ndarray like
|
||||
/// [`<ndarray>.strides`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html).
|
||||
///
|
||||
/// All elements in the tuple are `i32`.
|
||||
pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> TupleValue<'ctx> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
|
||||
let objects = (0..self.ndims)
|
||||
.map(|i| {
|
||||
let dim = unsafe {
|
||||
self.strides().get_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&self.llvm_usize.const_int(i, false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
ctx.builder.build_int_truncate_or_bit_cast(dim, llvm_i32, "").unwrap()
|
||||
})
|
||||
.map(|obj| obj.as_basic_value_enum())
|
||||
.collect_vec();
|
||||
|
||||
TupleType::new(
|
||||
generator,
|
||||
ctx.ctx,
|
||||
&repeat_n(llvm_i32.into(), self.ndims as usize).collect_vec(),
|
||||
)
|
||||
.construct_from_objects(ctx, objects, None)
|
||||
}
|
||||
|
||||
/// 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
|
||||
}
|
||||
|
||||
/// Returns the element present in this `ndarray` if this is unsized.
|
||||
pub fn get_unsized_element<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> Option<BasicValueEnum<'ctx>> {
|
||||
if self.is_unsized() {
|
||||
// NOTE: `np.size(self) == 0` here is never possible.
|
||||
let zero = generator.get_size_type(ctx.ctx).const_zero();
|
||||
let value = unsafe { self.data().get_unchecked(ctx, generator, &zero, None) };
|
||||
|
||||
Some(value)
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
|
||||
/// If this ndarray is unsized, return its sole value as an [`BasicValueEnum`].
|
||||
/// 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 let Some(unsized_elem) = self.get_unsized_element(generator, ctx) {
|
||||
ScalarOrNDArray::Scalar(unsized_elem)
|
||||
} else {
|
||||
ScalarOrNDArray::NDArray(*self)
|
||||
}
|
||||
}
|
||||
|
||||
/// 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_shape: impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
) {
|
||||
let ndarray_shape = self.shape();
|
||||
let output_shape = out_shape;
|
||||
|
||||
irrt::ndarray::call_nac3_ndarray_util_assert_output_shape_same(
|
||||
generator,
|
||||
ctx,
|
||||
&ndarray_shape,
|
||||
&output_shape,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyValue<'ctx> for NDArrayValue<'ctx> {
|
||||
@ -517,12 +159,7 @@ impl<'ctx> ProxyValue<'ctx> for NDArrayValue<'ctx> {
|
||||
type Type = NDArrayType<'ctx>;
|
||||
|
||||
fn get_type(&self) -> Self::Type {
|
||||
NDArrayType::from_type(
|
||||
self.as_base_value().get_type(),
|
||||
self.dtype,
|
||||
self.ndims,
|
||||
self.llvm_usize,
|
||||
)
|
||||
NDArrayType::from_type(self.as_base_value().get_type(), self.dtype, self.llvm_usize)
|
||||
}
|
||||
|
||||
fn as_base_value(&self) -> Self::Base {
|
||||
@ -536,7 +173,7 @@ impl<'ctx> From<NDArrayValue<'ctx>> for PointerValue<'ctx> {
|
||||
}
|
||||
}
|
||||
|
||||
/// Proxy type for accessing the `shape` array of an `NDArray` instance in LLVM.
|
||||
/// Proxy type for accessing the `dims` array of an `NDArray` instance in LLVM.
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct NDArrayShapeProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
|
||||
|
||||
@ -569,8 +206,8 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayShapeProxy<'ctx, '_> {
|
||||
impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
@ -608,124 +245,20 @@ impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_
|
||||
impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {}
|
||||
impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
|
||||
for NDArrayShapeProxy<'ctx, '_>
|
||||
{
|
||||
impl<'ctx> TypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
|
||||
fn downcast_to_type(
|
||||
&self,
|
||||
_: &CodeGenContext<'ctx, '_>,
|
||||
_: &G,
|
||||
_: &mut CodeGenContext<'ctx, '_>,
|
||||
value: BasicValueEnum<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
value.into_int_value()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>
|
||||
for NDArrayShapeProxy<'ctx, '_>
|
||||
{
|
||||
impl<'ctx> TypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
|
||||
fn upcast_from_type(
|
||||
&self,
|
||||
_: &CodeGenContext<'ctx, '_>,
|
||||
_: &G,
|
||||
value: IntValue<'ctx>,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
value.into()
|
||||
}
|
||||
}
|
||||
|
||||
/// Proxy type for accessing the `strides` array of an `NDArray` instance in LLVM.
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct NDArrayStridesProxy<'ctx, 'a>(&'a NDArrayValue<'ctx>);
|
||||
|
||||
impl<'ctx> ArrayLikeValue<'ctx> for NDArrayStridesProxy<'ctx, '_> {
|
||||
fn element_type<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
) -> AnyTypeEnum<'ctx> {
|
||||
self.0.strides().base_ptr(ctx, generator).get_type().get_element_type()
|
||||
}
|
||||
|
||||
fn base_ptr<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
_: &G,
|
||||
) -> PointerValue<'ctx> {
|
||||
self.0.strides_field(ctx).get(ctx, self.0.as_base_value(), self.0.name)
|
||||
}
|
||||
|
||||
fn size<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
_: &G,
|
||||
) -> IntValue<'ctx> {
|
||||
self.0.load_ndims(ctx)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
idx: &IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
|
||||
|
||||
unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(self.base_ptr(ctx, generator), &[*idx], var_name.as_str())
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
fn ptr_offset<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let size = self.size(ctx, generator);
|
||||
let in_range = ctx.builder.build_int_compare(IntPredicate::ULT, *idx, size, "").unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
in_range,
|
||||
"0:IndexError",
|
||||
"index {0} is out of bounds for axis 0 with size {1}",
|
||||
[Some(*idx), Some(self.0.load_ndims(ctx)), None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {}
|
||||
impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
|
||||
for NDArrayStridesProxy<'ctx, '_>
|
||||
{
|
||||
fn downcast_to_type(
|
||||
&self,
|
||||
_: &CodeGenContext<'ctx, '_>,
|
||||
_: &G,
|
||||
value: BasicValueEnum<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
value.into_int_value()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>
|
||||
for NDArrayStridesProxy<'ctx, '_>
|
||||
{
|
||||
fn upcast_from_type(
|
||||
&self,
|
||||
_: &CodeGenContext<'ctx, '_>,
|
||||
_: &G,
|
||||
_: &mut CodeGenContext<'ctx, '_>,
|
||||
value: IntValue<'ctx>,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
value.into()
|
||||
@ -758,19 +291,36 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
) -> IntValue<'ctx> {
|
||||
irrt::ndarray::call_nac3_ndarray_len(generator, ctx, *self.0)
|
||||
call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
idx: &IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let ptr = irrt::ndarray::call_nac3_ndarray_get_nth_pelement(generator, ctx, *self.0, *idx);
|
||||
let sizeof_elem = ctx
|
||||
.builder
|
||||
.build_int_truncate_or_bit_cast(
|
||||
self.element_type(ctx, generator).size_of().unwrap(),
|
||||
idx.get_type(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let idx = ctx.builder.build_int_mul(*idx, sizeof_elem, "").unwrap();
|
||||
let ptr = unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.base_ptr(ctx, generator),
|
||||
&[idx],
|
||||
name.unwrap_or_default(),
|
||||
)
|
||||
.unwrap()
|
||||
};
|
||||
|
||||
// Current implementation is transparent - The returned pointer type is
|
||||
// already cast into the expected type, allowing for immediately
|
||||
@ -781,7 +331,7 @@ impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
|
||||
BasicTypeEnum::try_from(self.element_type(ctx, generator))
|
||||
.unwrap()
|
||||
.ptr_type(AddressSpace::default()),
|
||||
name.unwrap_or_default(),
|
||||
"",
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
@ -829,33 +379,55 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
|
||||
{
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
indices: &Index,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
assert_eq!(indices.element_type(ctx, generator), generator.get_size_type(ctx.ctx).into());
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let indices = TypedArrayLikeAdapter::from(
|
||||
indices.as_slice_value(ctx, generator),
|
||||
|_, _, v| v.into_int_value(),
|
||||
|_, _, v| v.into(),
|
||||
let indices_elem_ty = indices
|
||||
.ptr_offset(ctx, generator, &llvm_usize.const_zero(), None)
|
||||
.get_type()
|
||||
.get_element_type();
|
||||
let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
|
||||
panic!("Expected list[int32] but got {indices_elem_ty}")
|
||||
};
|
||||
assert_eq!(
|
||||
indices_elem_ty.get_bit_width(),
|
||||
32,
|
||||
"Expected list[int32] but got list[int{}]",
|
||||
indices_elem_ty.get_bit_width()
|
||||
);
|
||||
|
||||
let ptr = irrt::ndarray::call_nac3_ndarray_get_pelement_by_indices(
|
||||
generator, ctx, *self.0, &indices,
|
||||
);
|
||||
let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
|
||||
let sizeof_elem = ctx
|
||||
.builder
|
||||
.build_int_truncate_or_bit_cast(
|
||||
self.element_type(ctx, generator).size_of().unwrap(),
|
||||
index.get_type(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let index = ctx.builder.build_int_mul(index, sizeof_elem, "").unwrap();
|
||||
|
||||
// Current implementation is transparent - The returned pointer type is
|
||||
// already cast into the expected type, allowing for immediately
|
||||
// load/store.
|
||||
let ptr = unsafe {
|
||||
ctx.builder
|
||||
.build_in_bounds_gep(
|
||||
self.base_ptr(ctx, generator),
|
||||
&[index],
|
||||
name.unwrap_or_default(),
|
||||
)
|
||||
.unwrap()
|
||||
};
|
||||
// TODO: Current implementation is transparent
|
||||
ctx.builder
|
||||
.build_pointer_cast(
|
||||
ptr,
|
||||
BasicTypeEnum::try_from(self.element_type(ctx, generator))
|
||||
.unwrap()
|
||||
.ptr_type(AddressSpace::default()),
|
||||
name.unwrap_or_default(),
|
||||
"",
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
@ -944,133 +516,3 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeMutator<'ctx,
|
||||
for NDArrayDataProxy<'ctx, '_>
|
||||
{
|
||||
}
|
||||
|
||||
/// A version of [`call_nac3_ndarray_set_strides_by_shape`] in Rust.
|
||||
///
|
||||
/// This function is used generating strides for globally defined contiguous ndarrays.
|
||||
#[must_use]
|
||||
pub fn make_contiguous_strides(itemsize: u64, ndims: u64, shape: &[u64]) -> Vec<u64> {
|
||||
let mut strides = vec![0u64; ndims as usize];
|
||||
let mut stride_product = 1u64;
|
||||
for axis in (0..ndims).rev() {
|
||||
strides[axis as usize] = stride_product * itemsize;
|
||||
stride_product *= shape[axis as usize];
|
||||
}
|
||||
strides
|
||||
}
|
||||
|
||||
/// A convenience enum for implementing functions that acts on scalars or ndarrays or both.
|
||||
#[derive(Clone, Copy)]
|
||||
pub enum ScalarOrNDArray<'ctx> {
|
||||
Scalar(BasicValueEnum<'ctx>),
|
||||
NDArray(NDArrayValue<'ctx>),
|
||||
}
|
||||
|
||||
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for BasicValueEnum<'ctx> {
|
||||
type Error = ();
|
||||
|
||||
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
|
||||
match value {
|
||||
ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
|
||||
ScalarOrNDArray::NDArray(_) => Err(()),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayValue<'ctx> {
|
||||
type Error = ();
|
||||
|
||||
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
|
||||
match value {
|
||||
ScalarOrNDArray::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 from_value<G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
(object_ty, object): (Type, BasicValueEnum<'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 = NDArrayType::from_unifier_type(generator, ctx, object_ty)
|
||||
.map_value(object.into_pointer_value(), None);
|
||||
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,
|
||||
ScalarOrNDArray::NDArray(ndarray) => ndarray.as_base_value().into(),
|
||||
}
|
||||
}
|
||||
|
||||
/// 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
|
||||
/// [`NDArrayType::construct_unsized`].
|
||||
pub fn to_ndarray<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
) -> NDArrayValue<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
|
||||
ScalarOrNDArray::Scalar(scalar) => {
|
||||
NDArrayType::new_unsized(generator, ctx.ctx, scalar.get_type())
|
||||
.construct_unsized(generator, ctx, scalar, None)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Get the dtype of the ndarray created if this were called with
|
||||
/// [`ScalarOrNDArray::to_ndarray`].
|
||||
#[must_use]
|
||||
pub fn get_dtype(&self) -> BasicTypeEnum<'ctx> {
|
||||
match self {
|
||||
ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
|
||||
ScalarOrNDArray::Scalar(scalar) => scalar.get_type(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// 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(Clone, Copy)]
|
||||
pub enum NDArrayOut<'ctx> {
|
||||
/// Tell a function should create a new ndarray with the expected element type `dtype`.
|
||||
NewNDArray { dtype: BasicTypeEnum<'ctx> },
|
||||
/// Tell a function to write the result to `ndarray`.
|
||||
WriteToNDArray { ndarray: NDArrayValue<'ctx> },
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayOut<'ctx> {
|
||||
/// Get the dtype of this output.
|
||||
#[must_use]
|
||||
pub fn get_dtype(&self) -> BasicTypeEnum<'ctx> {
|
||||
match self {
|
||||
NDArrayOut::NewNDArray { dtype } => *dtype,
|
||||
NDArrayOut::WriteToNDArray { ndarray } => ndarray.dtype,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,178 +0,0 @@
|
||||
use inkwell::{
|
||||
types::{BasicType, IntType},
|
||||
values::{BasicValueEnum, IntValue, PointerValue},
|
||||
AddressSpace,
|
||||
};
|
||||
|
||||
use super::{NDArrayValue, ProxyValue};
|
||||
use crate::codegen::{
|
||||
irrt,
|
||||
stmt::{gen_for_callback, BreakContinueHooks},
|
||||
types::{ndarray::NDIterType, structure::StructField},
|
||||
values::{ArraySliceValue, TypedArrayLikeAdapter},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct NDIterValue<'ctx> {
|
||||
value: PointerValue<'ctx>,
|
||||
parent: NDArrayValue<'ctx>,
|
||||
indices: ArraySliceValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
}
|
||||
|
||||
impl<'ctx> NDIterValue<'ctx> {
|
||||
/// Checks whether `value` is an instance of `NDArray`, returning [Err] if `value` is not an
|
||||
/// instance.
|
||||
pub fn is_representable(
|
||||
value: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
<Self as ProxyValue>::Type::is_representable(value.get_type(), llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an [`NDArrayValue`] from a [`PointerValue`].
|
||||
#[must_use]
|
||||
pub fn from_pointer_value(
|
||||
ptr: PointerValue<'ctx>,
|
||||
parent: NDArrayValue<'ctx>,
|
||||
indices: ArraySliceValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
|
||||
|
||||
Self { value: ptr, parent, indices, llvm_usize, name }
|
||||
}
|
||||
|
||||
/// 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: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> IntValue<'ctx> {
|
||||
irrt::ndarray::call_nac3_nditer_has_element(generator, ctx, *self)
|
||||
}
|
||||
|
||||
/// 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: &G, ctx: &CodeGenContext<'ctx, '_>) {
|
||||
irrt::ndarray::call_nac3_nditer_next(generator, ctx, *self);
|
||||
}
|
||||
|
||||
fn element_field(
|
||||
&self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
) -> StructField<'ctx, PointerValue<'ctx>> {
|
||||
self.get_type().get_fields(ctx.ctx).element
|
||||
}
|
||||
|
||||
/// Get pointer to the current element.
|
||||
#[must_use]
|
||||
pub fn get_pointer(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
|
||||
let elem_ty = self.parent.dtype;
|
||||
|
||||
let p = self.element_field(ctx).get(ctx, self.as_base_value(), self.name);
|
||||
ctx.builder
|
||||
.build_pointer_cast(p, elem_ty.ptr_type(AddressSpace::default()), "element")
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Get the value of the current element.
|
||||
#[must_use]
|
||||
pub fn get_scalar(&self, ctx: &CodeGenContext<'ctx, '_>) -> BasicValueEnum<'ctx> {
|
||||
let p = self.get_pointer(ctx);
|
||||
ctx.builder.build_load(p, "value").unwrap()
|
||||
}
|
||||
|
||||
fn nth_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields(ctx.ctx).nth
|
||||
}
|
||||
|
||||
/// Get the index of the current element if this ndarray were a flat ndarray.
|
||||
#[must_use]
|
||||
pub fn get_index(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
self.nth_field(ctx).get(ctx, self.as_base_value(), self.name)
|
||||
}
|
||||
|
||||
/// Get the indices of the current element.
|
||||
#[must_use]
|
||||
pub fn get_indices<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
) -> TypedArrayLikeAdapter<'ctx, G, IntValue<'ctx>> {
|
||||
TypedArrayLikeAdapter::from(
|
||||
self.indices,
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyValue<'ctx> for NDIterValue<'ctx> {
|
||||
type Base = PointerValue<'ctx>;
|
||||
type Type = NDIterType<'ctx>;
|
||||
|
||||
fn get_type(&self) -> Self::Type {
|
||||
NDIterType::from_type(self.as_base_value().get_type(), self.llvm_usize)
|
||||
}
|
||||
|
||||
fn as_base_value(&self) -> Self::Base {
|
||||
self.value
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<NDIterValue<'ctx>> for PointerValue<'ctx> {
|
||||
fn from(value: NDIterValue<'ctx>) -> Self {
|
||||
value.as_base_value()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> NDArrayValue<'ctx> {
|
||||
/// Iterate through every element in the ndarray.
|
||||
///
|
||||
/// `body` has access to [`BreakContinueHooks`] to short-circuit and [`NDIterValue`] to
|
||||
/// get properties of the current iteration (e.g., the current element, indices, etc.)
|
||||
///
|
||||
/// Note: The caller is recommended to call `llvm.stacksave` and `llvm.stackrestore` before and
|
||||
/// after invoking this function respectively. See [`NDIterType::construct`] for an explanation
|
||||
/// on why this is suggested.
|
||||
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>,
|
||||
NDIterValue<'ctx>,
|
||||
) -> Result<(), String>,
|
||||
{
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
Some("ndarray_foreach"),
|
||||
|generator, ctx| {
|
||||
Ok(NDIterType::new(generator, ctx.ctx).construct(generator, ctx, *self))
|
||||
},
|
||||
|generator, ctx, nditer| Ok(nditer.has_element(generator, ctx)),
|
||||
|generator, ctx, hooks, nditer| body(generator, ctx, hooks, nditer),
|
||||
|generator, ctx, nditer| {
|
||||
nditer.next(generator, ctx);
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
}
|
||||
}
|
||||
@ -1,152 +0,0 @@
|
||||
use inkwell::values::{BasicValueEnum, IntValue};
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
stmt::gen_for_callback_incrementing,
|
||||
types::{ListType, TupleType},
|
||||
values::{
|
||||
ArraySliceValue, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
|
||||
TypedArrayLikeMutator, UntypedArrayLikeAccessor,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::{Type, 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_seq_ty, input_seq): (Type, BasicValueEnum<'ctx>),
|
||||
) -> impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let zero = llvm_usize.const_zero();
|
||||
let one = llvm_usize.const_int(1, false);
|
||||
|
||||
// The result `list` to return.
|
||||
match &*ctx.unifier.get_ty_immutable(input_seq_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])`
|
||||
|
||||
let input_seq = ListType::from_unifier_type(generator, ctx, input_seq_ty)
|
||||
.map_value(input_seq.into_pointer_value(), None);
|
||||
|
||||
let len = input_seq.load_size(ctx, None);
|
||||
// TODO: Find a way to remove this mid-BB allocation
|
||||
let result = ctx.builder.build_array_alloca(llvm_usize, len, "").unwrap();
|
||||
let result = TypedArrayLikeAdapter::from(
|
||||
ArraySliceValue::from_ptr_val(result, len, None),
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
);
|
||||
|
||||
// Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
None,
|
||||
zero,
|
||||
(len, false),
|
||||
|generator, ctx, _, i| {
|
||||
// Load the i-th int32 in the input sequence
|
||||
let int = unsafe {
|
||||
input_seq.data().get_unchecked(ctx, generator, &i, None).into_int_value()
|
||||
};
|
||||
|
||||
// Cast to SizeT
|
||||
let int =
|
||||
ctx.builder.build_int_s_extend_or_bit_cast(int, llvm_usize, "").unwrap();
|
||||
|
||||
// Store
|
||||
unsafe { result.set_typed_unchecked(ctx, generator, &i, int) };
|
||||
|
||||
Ok(())
|
||||
},
|
||||
one,
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
TypeEnum::TTuple { .. } => {
|
||||
// 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
|
||||
|
||||
let input_seq = TupleType::from_unifier_type(generator, ctx, input_seq_ty)
|
||||
.map_value(input_seq.into_struct_value(), None);
|
||||
|
||||
let len = input_seq.get_type().num_elements();
|
||||
|
||||
let result = generator
|
||||
.gen_array_var_alloc(
|
||||
ctx,
|
||||
llvm_usize.into(),
|
||||
llvm_usize.const_int(u64::from(len), false),
|
||||
None,
|
||||
)
|
||||
.unwrap();
|
||||
let result = TypedArrayLikeAdapter::from(
|
||||
result,
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
);
|
||||
|
||||
for i in 0..input_seq.get_type().num_elements() {
|
||||
// Get the i-th element off of the tuple and load it into `result`.
|
||||
let int = input_seq.load_element(ctx, i).into_int_value();
|
||||
let int = ctx.builder.build_int_s_extend_or_bit_cast(int, llvm_usize, "").unwrap();
|
||||
|
||||
unsafe {
|
||||
result.set_typed_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
&llvm_usize.const_int(u64::from(i), false),
|
||||
int,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
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_seq.into_int_value();
|
||||
|
||||
let len = one;
|
||||
let result = generator.gen_array_var_alloc(ctx, llvm_usize.into(), len, None).unwrap();
|
||||
let result = TypedArrayLikeAdapter::from(
|
||||
result,
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
);
|
||||
let int =
|
||||
ctx.builder.build_int_s_extend_or_bit_cast(input_int, llvm_usize, "").unwrap();
|
||||
|
||||
// Storing into result[0]
|
||||
unsafe {
|
||||
result.set_typed_unchecked(ctx, generator, &zero, int);
|
||||
}
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
_ => panic!("encountered unknown sequence type: {}", ctx.unifier.stringify(input_seq_ty)),
|
||||
}
|
||||
}
|
||||
@ -1,154 +0,0 @@
|
||||
use std::iter::{once, repeat_n};
|
||||
|
||||
use inkwell::values::{IntValue, PointerValue};
|
||||
use itertools::Itertools;
|
||||
|
||||
use crate::codegen::{
|
||||
irrt,
|
||||
stmt::gen_if_callback,
|
||||
types::ndarray::NDArrayType,
|
||||
values::{
|
||||
ndarray::{NDArrayValue, RustNDIndex},
|
||||
ArrayLikeValue, ArraySliceValue, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
|
||||
},
|
||||
CodeGenContext, CodeGenerator,
|
||||
};
|
||||
|
||||
impl<'ctx> NDArrayValue<'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. Otherwise, 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 {
|
||||
let ndims = self.ndims;
|
||||
|
||||
if ndims < ndmin {
|
||||
// Extend the dimensions with np.newaxis.
|
||||
let indices = repeat_n(RustNDIndex::NewAxis, (ndmin - ndims) as usize)
|
||||
.chain(once(RustNDIndex::Ellipsis))
|
||||
.collect_vec();
|
||||
self.index(generator, ctx, &indices)
|
||||
} else {
|
||||
*self
|
||||
}
|
||||
}
|
||||
|
||||
/// Create a reshaped view on this ndarray like
|
||||
/// [`np.reshape()`](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html).
|
||||
///
|
||||
/// 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: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
|
||||
) -> Self {
|
||||
assert_eq!(new_shape.element_type(ctx, generator), self.llvm_usize.into());
|
||||
|
||||
// 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 dst_ndarray = NDArrayType::new(generator, ctx.ctx, self.dtype, new_ndims)
|
||||
.construct_uninitialized(generator, ctx, None);
|
||||
dst_ndarray.copy_shape_from_array(generator, ctx, new_shape.base_ptr(ctx, generator));
|
||||
|
||||
// Resolve negative indices
|
||||
let size = self.size(generator, ctx);
|
||||
let dst_ndims = self.llvm_usize.const_int(dst_ndarray.get_type().ndims(), false);
|
||||
let dst_shape = dst_ndarray.shape();
|
||||
irrt::ndarray::call_nac3_ndarray_reshape_resolve_and_check_new_shape(
|
||||
generator,
|
||||
ctx,
|
||||
size,
|
||||
dst_ndims,
|
||||
dst_shape.as_slice_value(ctx, generator),
|
||||
);
|
||||
|
||||
gen_if_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|generator, ctx| Ok(self.is_c_contiguous(generator, ctx)),
|
||||
|generator, ctx| {
|
||||
// Reshape is possible without copying
|
||||
dst_ndarray.set_strides_contiguous(generator, ctx);
|
||||
dst_ndarray.store_data(ctx, self.data().base_ptr(ctx, generator));
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|generator, ctx| {
|
||||
// Reshape is impossible without copying
|
||||
unsafe {
|
||||
dst_ndarray.create_data(generator, ctx);
|
||||
}
|
||||
dst_ndarray.copy_data_from(generator, ctx, *self);
|
||||
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
dst_ndarray
|
||||
}
|
||||
|
||||
/// Create a transposed view on this ndarray like
|
||||
/// [`np.transpose(<ndarray>, <axes> = None)`](https://numpy.org/doc/stable/reference/generated/numpy.transpose.html).
|
||||
///
|
||||
/// * `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<PointerValue<'ctx>>,
|
||||
) -> Self {
|
||||
assert!(
|
||||
axes.is_none_or(|axes| axes.get_type().get_element_type() == self.llvm_usize.into())
|
||||
);
|
||||
|
||||
// Define models
|
||||
let transposed_ndarray = self.get_type().construct_uninitialized(generator, ctx, None);
|
||||
|
||||
let axes = if let Some(axes) = axes {
|
||||
let num_axes = self.llvm_usize.const_int(self.ndims, false);
|
||||
|
||||
// `axes = nullptr` if `axes` is unspecified.
|
||||
let axes = ArraySliceValue::from_ptr_val(axes, num_axes, None);
|
||||
|
||||
Some(TypedArrayLikeAdapter::from(
|
||||
axes,
|
||||
|_, _, val| val.into_int_value(),
|
||||
|_, _, val| val.into(),
|
||||
))
|
||||
} else {
|
||||
None
|
||||
};
|
||||
|
||||
irrt::ndarray::call_nac3_ndarray_transpose(
|
||||
generator,
|
||||
ctx,
|
||||
*self,
|
||||
transposed_ndarray,
|
||||
axes.as_ref(),
|
||||
);
|
||||
|
||||
transposed_ndarray
|
||||
}
|
||||
}
|
||||
@ -1,85 +0,0 @@
|
||||
use inkwell::{
|
||||
types::IntType,
|
||||
values::{BasicValue, BasicValueEnum, StructValue},
|
||||
};
|
||||
|
||||
use super::ProxyValue;
|
||||
use crate::codegen::{types::TupleType, CodeGenContext};
|
||||
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct TupleValue<'ctx> {
|
||||
value: StructValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
}
|
||||
|
||||
impl<'ctx> TupleValue<'ctx> {
|
||||
/// Checks whether `value` is an instance of `tuple`, returning [Err] if `value` is not an
|
||||
/// instance.
|
||||
pub fn is_representable(
|
||||
value: StructValue<'ctx>,
|
||||
_llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
TupleType::is_representable(value.get_type())
|
||||
}
|
||||
|
||||
/// Creates an [`TupleValue`] from a [`StructValue`].
|
||||
#[must_use]
|
||||
pub fn from_struct_value(
|
||||
value: StructValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_representable(value, llvm_usize).is_ok());
|
||||
|
||||
Self { value, llvm_usize, name }
|
||||
}
|
||||
|
||||
/// Stores a value into the tuple element at the given `index`.
|
||||
pub fn store_element(
|
||||
&mut self,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
index: u32,
|
||||
element: impl BasicValue<'ctx>,
|
||||
) {
|
||||
assert_eq!(element.as_basic_value_enum().get_type(), unsafe {
|
||||
self.get_type().type_at_index_unchecked(index)
|
||||
});
|
||||
|
||||
let new_value = ctx
|
||||
.builder
|
||||
.build_insert_value(self.value, element, index, self.name.unwrap_or_default())
|
||||
.unwrap();
|
||||
self.value = new_value.into_struct_value();
|
||||
}
|
||||
|
||||
/// Loads a value from the tuple element at the given `index`.
|
||||
pub fn load_element(&self, ctx: &CodeGenContext<'ctx, '_>, index: u32) -> BasicValueEnum<'ctx> {
|
||||
ctx.builder
|
||||
.build_extract_value(
|
||||
self.value,
|
||||
index,
|
||||
&format!("{}[{{i}}]", self.name.unwrap_or("tuple")),
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyValue<'ctx> for TupleValue<'ctx> {
|
||||
type Base = StructValue<'ctx>;
|
||||
type Type = TupleType<'ctx>;
|
||||
|
||||
fn get_type(&self) -> Self::Type {
|
||||
TupleType::from_type(self.as_base_value().get_type(), self.llvm_usize)
|
||||
}
|
||||
|
||||
fn as_base_value(&self) -> Self::Base {
|
||||
self.value
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<TupleValue<'ctx>> for StructValue<'ctx> {
|
||||
fn from(value: TupleValue<'ctx>) -> Self {
|
||||
value.as_base_value()
|
||||
}
|
||||
}
|
||||
@ -1,3 +0,0 @@
|
||||
pub use slice::*;
|
||||
|
||||
mod slice;
|
||||
@ -1,231 +0,0 @@
|
||||
use inkwell::{
|
||||
types::IntType,
|
||||
values::{IntValue, PointerValue},
|
||||
};
|
||||
|
||||
use nac3parser::ast::Expr;
|
||||
|
||||
use crate::{
|
||||
codegen::{
|
||||
types::{structure::StructField, utils::SliceType},
|
||||
values::ProxyValue,
|
||||
CodeGenContext, CodeGenerator,
|
||||
},
|
||||
typecheck::typedef::Type,
|
||||
};
|
||||
|
||||
/// An IRRT representation of an (unresolved) slice.
|
||||
#[derive(Copy, Clone)]
|
||||
pub struct SliceValue<'ctx> {
|
||||
value: PointerValue<'ctx>,
|
||||
int_ty: IntType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
}
|
||||
|
||||
impl<'ctx> SliceValue<'ctx> {
|
||||
/// Checks whether `value` is an instance of `ContiguousNDArray`, returning [Err] if `value` is
|
||||
/// not an instance.
|
||||
pub fn is_representable(
|
||||
value: PointerValue<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
) -> Result<(), String> {
|
||||
<Self as ProxyValue<'ctx>>::Type::is_representable(value.get_type(), llvm_usize)
|
||||
}
|
||||
|
||||
/// Creates an [`SliceValue`] from a [`PointerValue`].
|
||||
#[must_use]
|
||||
pub fn from_pointer_value(
|
||||
ptr: PointerValue<'ctx>,
|
||||
int_ty: IntType<'ctx>,
|
||||
llvm_usize: IntType<'ctx>,
|
||||
name: Option<&'ctx str>,
|
||||
) -> Self {
|
||||
debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
|
||||
|
||||
Self { value: ptr, int_ty, llvm_usize, name }
|
||||
}
|
||||
|
||||
fn start_defined_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields().start_defined
|
||||
}
|
||||
|
||||
pub fn load_start_defined(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
self.start_defined_field().get(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
fn start_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields().start
|
||||
}
|
||||
|
||||
pub fn load_start(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
self.start_field().get(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
pub fn store_start(&self, ctx: &CodeGenContext<'ctx, '_>, value: Option<IntValue<'ctx>>) {
|
||||
match value {
|
||||
Some(start) => {
|
||||
self.start_defined_field().set(
|
||||
ctx,
|
||||
self.value,
|
||||
ctx.ctx.bool_type().const_all_ones(),
|
||||
self.name,
|
||||
);
|
||||
self.start_field().set(ctx, self.value, start, self.name);
|
||||
}
|
||||
|
||||
None => self.start_defined_field().set(
|
||||
ctx,
|
||||
self.value,
|
||||
ctx.ctx.bool_type().const_zero(),
|
||||
self.name,
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
fn stop_defined_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields().stop_defined
|
||||
}
|
||||
|
||||
pub fn load_stop_defined(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
self.stop_defined_field().get(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
fn stop_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields().stop
|
||||
}
|
||||
|
||||
pub fn load_stop(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
self.stop_field().get(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
pub fn store_stop(&self, ctx: &CodeGenContext<'ctx, '_>, value: Option<IntValue<'ctx>>) {
|
||||
match value {
|
||||
Some(stop) => {
|
||||
self.stop_defined_field().set(
|
||||
ctx,
|
||||
self.value,
|
||||
ctx.ctx.bool_type().const_all_ones(),
|
||||
self.name,
|
||||
);
|
||||
self.stop_field().set(ctx, self.value, stop, self.name);
|
||||
}
|
||||
|
||||
None => self.stop_defined_field().set(
|
||||
ctx,
|
||||
self.value,
|
||||
ctx.ctx.bool_type().const_zero(),
|
||||
self.name,
|
||||
),
|
||||
}
|
||||
}
|
||||
|
||||
fn step_defined_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields().step_defined
|
||||
}
|
||||
|
||||
pub fn load_step_defined(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
self.step_defined_field().get(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
fn step_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
|
||||
self.get_type().get_fields().step
|
||||
}
|
||||
|
||||
pub fn load_step(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
|
||||
self.step_field().get(ctx, self.value, self.name)
|
||||
}
|
||||
|
||||
pub fn store_step(&self, ctx: &CodeGenContext<'ctx, '_>, value: Option<IntValue<'ctx>>) {
|
||||
match value {
|
||||
Some(step) => {
|
||||
self.step_defined_field().set(
|
||||
ctx,
|
||||
self.value,
|
||||
ctx.ctx.bool_type().const_all_ones(),
|
||||
self.name,
|
||||
);
|
||||
self.step_field().set(ctx, self.value, step, self.name);
|
||||
}
|
||||
|
||||
None => self.step_defined_field().set(
|
||||
ctx,
|
||||
self.value,
|
||||
ctx.ctx.bool_type().const_zero(),
|
||||
self.name,
|
||||
),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> ProxyValue<'ctx> for SliceValue<'ctx> {
|
||||
type Base = PointerValue<'ctx>;
|
||||
type Type = SliceType<'ctx>;
|
||||
|
||||
fn get_type(&self) -> Self::Type {
|
||||
Self::Type::from_type(self.value.get_type(), self.int_ty, self.llvm_usize)
|
||||
}
|
||||
|
||||
fn as_base_value(&self) -> Self::Base {
|
||||
self.value
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> From<SliceValue<'ctx>> for PointerValue<'ctx> {
|
||||
fn from(value: SliceValue<'ctx>) -> Self {
|
||||
value.as_base_value()
|
||||
}
|
||||
}
|
||||
|
||||
/// A slice represented in compile-time by `start`, `stop` and `step`, all held as LLVM values.
|
||||
// TODO: Rename this to CTConstSlice
|
||||
#[derive(Debug, Copy, Clone)]
|
||||
pub struct RustSlice<'ctx> {
|
||||
int_ty: IntType<'ctx>,
|
||||
start: Option<IntValue<'ctx>>,
|
||||
stop: Option<IntValue<'ctx>>,
|
||||
step: Option<IntValue<'ctx>>,
|
||||
}
|
||||
|
||||
impl<'ctx> RustSlice<'ctx> {
|
||||
/// Generate LLVM IR for an [`ExprKind::Slice`] and convert it into a [`RustSlice`].
|
||||
#[allow(clippy::type_complexity)]
|
||||
pub fn from_slice_expr<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>, String> {
|
||||
let mut value_mapper = |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)?
|
||||
.map(|value| {
|
||||
value.to_basic_value_enum(ctx, generator, ctx.primitives.int32)
|
||||
})
|
||||
.unwrap()?;
|
||||
|
||||
Some(value_expr.into_int_value())
|
||||
}
|
||||
})
|
||||
};
|
||||
|
||||
let start = value_mapper(lower)?;
|
||||
let stop = value_mapper(upper)?;
|
||||
let step = value_mapper(step)?;
|
||||
|
||||
Ok(RustSlice { int_ty: ctx.ctx.i32_type(), start, stop, step })
|
||||
}
|
||||
|
||||
/// Write the contents to an LLVM [`SliceValue`].
|
||||
pub fn write_to_slice(&self, ctx: &CodeGenContext<'ctx, '_>, dst_slice_ptr: SliceValue<'ctx>) {
|
||||
assert_eq!(self.int_ty, dst_slice_ptr.int_ty);
|
||||
|
||||
dst_slice_ptr.store_start(ctx, self.start);
|
||||
dst_slice_ptr.store_stop(ctx, self.stop);
|
||||
dst_slice_ptr.store_step(ctx, self.step);
|
||||
}
|
||||
}
|
||||
@ -6,7 +6,7 @@ use std::{
|
||||
};
|
||||
|
||||
use inkwell::values::{BasicValueEnum, FloatValue, IntValue, PointerValue, StructValue};
|
||||
use itertools::{izip, Itertools};
|
||||
use itertools::{chain, izip, Itertools};
|
||||
use parking_lot::RwLock;
|
||||
|
||||
use nac3parser::ast::{Constant, Expr, Location, StrRef};
|
||||
@ -452,10 +452,10 @@ pub fn parse_type_annotation<T>(
|
||||
type_vars.len()
|
||||
)]));
|
||||
}
|
||||
let fields = fields
|
||||
.iter()
|
||||
.map(|(k, v, m)| (*k, (*v, *m)))
|
||||
.chain(methods.iter().map(|(k, v, _)| (*k, (*v, false))))
|
||||
let fields = chain(
|
||||
fields.iter().map(|(k, v, m)| (*k, (*v, *m))),
|
||||
methods.iter().map(|(k, v, _)| (*k, (*v, false))),
|
||||
)
|
||||
.collect();
|
||||
Ok(unifier.add_ty(TypeEnum::TObj { obj_id, fields, params: VarMap::default() }))
|
||||
} else {
|
||||
|
||||
@ -1,14 +1,18 @@
|
||||
use std::iter::once;
|
||||
|
||||
use indexmap::IndexMap;
|
||||
use inkwell::{values::BasicValue, IntPredicate};
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
types::{BasicMetadataTypeEnum, BasicType},
|
||||
values::{BasicMetadataValueEnum, BasicValue, CallSiteValue},
|
||||
IntPredicate,
|
||||
};
|
||||
use itertools::Either;
|
||||
use strum::IntoEnumIterator;
|
||||
|
||||
use super::{
|
||||
helper::{
|
||||
debug_assert_prim_is_allowed, extract_ndims, make_exception_fields, PrimDef, PrimDefDetails,
|
||||
},
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDef, PrimDefDetails},
|
||||
numpy::make_ndarray_ty,
|
||||
*,
|
||||
};
|
||||
use crate::{
|
||||
@ -16,8 +20,7 @@ use crate::{
|
||||
builtin_fns,
|
||||
numpy::*,
|
||||
stmt::exn_constructor,
|
||||
types::ndarray::NDArrayType,
|
||||
values::{ndarray::shape::parse_numpy_int_sequence, ProxyValue, RangeValue},
|
||||
values::{ProxyValue, RangeValue},
|
||||
},
|
||||
symbol_resolver::SymbolValue,
|
||||
typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
|
||||
@ -145,6 +148,144 @@ fn create_fn_by_codegen(
|
||||
}
|
||||
}
|
||||
|
||||
/// Creates a NumPy [`TopLevelDef`] function using an LLVM intrinsic.
|
||||
///
|
||||
/// * `name`: The name of the implemented NumPy function.
|
||||
/// * `ret_ty`: The return type of this function.
|
||||
/// * `param_ty`: The parameters accepted by this function, represented by a tuple of the
|
||||
/// [parameter type][Type] and the parameter symbol name.
|
||||
/// * `intrinsic_fn`: The fully-qualified name of the LLVM intrinsic function.
|
||||
fn create_fn_by_intrinsic(
|
||||
unifier: &mut Unifier,
|
||||
var_map: &VarMap,
|
||||
name: &'static str,
|
||||
ret_ty: Type,
|
||||
params: &[(Type, &'static str)],
|
||||
intrinsic_fn: &'static str,
|
||||
) -> TopLevelDef {
|
||||
let param_tys = params.iter().map(|p| p.0).collect_vec();
|
||||
|
||||
create_fn_by_codegen(
|
||||
unifier,
|
||||
var_map,
|
||||
name,
|
||||
ret_ty,
|
||||
params,
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let args_ty = fun.0.args.iter().map(|a| a.ty).collect_vec();
|
||||
|
||||
assert!(param_tys
|
||||
.iter()
|
||||
.zip(&args_ty)
|
||||
.all(|(expected, actual)| ctx.unifier.unioned(*expected, *actual)));
|
||||
|
||||
let args_val = args_ty
|
||||
.iter()
|
||||
.zip_eq(args.iter())
|
||||
.map(|(ty, arg)| arg.1.clone().to_basic_value_enum(ctx, generator, *ty).unwrap())
|
||||
.map_into::<BasicMetadataValueEnum>()
|
||||
.collect_vec();
|
||||
|
||||
let intrinsic_fn = ctx.module.get_function(intrinsic_fn).unwrap_or_else(|| {
|
||||
let ret_llvm_ty = ctx.get_llvm_abi_type(generator, ret_ty);
|
||||
let param_llvm_ty = param_tys
|
||||
.iter()
|
||||
.map(|p| ctx.get_llvm_abi_type(generator, *p))
|
||||
.map_into::<BasicMetadataTypeEnum>()
|
||||
.collect_vec();
|
||||
let fn_type = ret_llvm_ty.fn_type(param_llvm_ty.as_slice(), false);
|
||||
|
||||
ctx.module.add_function(intrinsic_fn, fn_type, None)
|
||||
});
|
||||
|
||||
let val = ctx
|
||||
.builder
|
||||
.build_call(intrinsic_fn, args_val.as_slice(), name)
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap();
|
||||
Ok(val.into())
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
/// Creates a unary NumPy [`TopLevelDef`] function using an extern function (e.g. from `libc` or
|
||||
/// `libm`).
|
||||
///
|
||||
/// * `name`: The name of the implemented NumPy function.
|
||||
/// * `ret_ty`: The return type of this function.
|
||||
/// * `param_ty`: The parameters accepted by this function, represented by a tuple of the
|
||||
/// [parameter type][Type] and the parameter symbol name.
|
||||
/// * `extern_fn`: The fully-qualified name of the extern function used as the implementation.
|
||||
/// * `attrs`: The list of attributes to apply to this function declaration. Note that `nounwind` is
|
||||
/// already implied by the C ABI.
|
||||
fn create_fn_by_extern(
|
||||
unifier: &mut Unifier,
|
||||
var_map: &VarMap,
|
||||
name: &'static str,
|
||||
ret_ty: Type,
|
||||
params: &[(Type, &'static str)],
|
||||
extern_fn: &'static str,
|
||||
attrs: &'static [&str],
|
||||
) -> TopLevelDef {
|
||||
let param_tys = params.iter().map(|p| p.0).collect_vec();
|
||||
|
||||
create_fn_by_codegen(
|
||||
unifier,
|
||||
var_map,
|
||||
name,
|
||||
ret_ty,
|
||||
params,
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let args_ty = fun.0.args.iter().map(|a| a.ty).collect_vec();
|
||||
|
||||
assert!(param_tys
|
||||
.iter()
|
||||
.zip(&args_ty)
|
||||
.all(|(expected, actual)| ctx.unifier.unioned(*expected, *actual)));
|
||||
|
||||
let args_val = args_ty
|
||||
.iter()
|
||||
.zip_eq(args.iter())
|
||||
.map(|(ty, arg)| arg.1.clone().to_basic_value_enum(ctx, generator, *ty).unwrap())
|
||||
.map_into::<BasicMetadataValueEnum>()
|
||||
.collect_vec();
|
||||
|
||||
let intrinsic_fn = ctx.module.get_function(extern_fn).unwrap_or_else(|| {
|
||||
let ret_llvm_ty = ctx.get_llvm_abi_type(generator, ret_ty);
|
||||
let param_llvm_ty = param_tys
|
||||
.iter()
|
||||
.map(|p| ctx.get_llvm_abi_type(generator, *p))
|
||||
.map_into::<BasicMetadataTypeEnum>()
|
||||
.collect_vec();
|
||||
let fn_type = ret_llvm_ty.fn_type(param_llvm_ty.as_slice(), false);
|
||||
let func = ctx.module.add_function(extern_fn, fn_type, None);
|
||||
func.add_attribute(
|
||||
AttributeLoc::Function,
|
||||
ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id("nounwind"), 0),
|
||||
);
|
||||
|
||||
for attr in attrs {
|
||||
func.add_attribute(
|
||||
AttributeLoc::Function,
|
||||
ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
|
||||
);
|
||||
}
|
||||
|
||||
func
|
||||
});
|
||||
|
||||
let val = ctx
|
||||
.builder
|
||||
.build_call(intrinsic_fn, &args_val, name)
|
||||
.map(CallSiteValue::try_as_basic_value)
|
||||
.map(Either::unwrap_left)
|
||||
.unwrap();
|
||||
Ok(val.into())
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
pub fn get_builtins(unifier: &mut Unifier, primitives: &PrimitiveStore) -> BuiltinInfo {
|
||||
BuiltinBuilder::new(unifier, primitives)
|
||||
.build_all_builtins()
|
||||
@ -195,6 +336,7 @@ struct BuiltinBuilder<'a> {
|
||||
|
||||
ndarray_float: Type,
|
||||
ndarray_float_2d: Type,
|
||||
ndarray_num_ty: Type,
|
||||
|
||||
float_or_ndarray_ty: TypeVar,
|
||||
float_or_ndarray_var_map: VarMap,
|
||||
@ -308,6 +450,7 @@ impl<'a> BuiltinBuilder<'a> {
|
||||
|
||||
ndarray_float,
|
||||
ndarray_float_2d,
|
||||
ndarray_num_ty,
|
||||
|
||||
float_or_ndarray_ty,
|
||||
float_or_ndarray_var_map,
|
||||
@ -369,14 +512,6 @@ impl<'a> BuiltinBuilder<'a> {
|
||||
| PrimDef::FunNpEye
|
||||
| PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
|
||||
|
||||
PrimDef::FunNpSize | PrimDef::FunNpShape | PrimDef::FunNpStrides => {
|
||||
self.build_ndarray_property_getter_function(prim)
|
||||
}
|
||||
|
||||
PrimDef::FunNpBroadcastTo | PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
|
||||
self.build_ndarray_view_function(prim)
|
||||
}
|
||||
|
||||
PrimDef::FunStr => self.build_str_function(),
|
||||
|
||||
PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
|
||||
@ -442,6 +577,10 @@ impl<'a> BuiltinBuilder<'a> {
|
||||
| PrimDef::FunNpHypot
|
||||
| PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
|
||||
|
||||
PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
|
||||
self.build_np_sp_ndarray_function(prim)
|
||||
}
|
||||
|
||||
PrimDef::FunNpDot
|
||||
| PrimDef::FunNpLinalgCholesky
|
||||
| PrimDef::FunNpLinalgQr
|
||||
@ -1247,176 +1386,6 @@ impl<'a> BuiltinBuilder<'a> {
|
||||
}
|
||||
}
|
||||
|
||||
fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(
|
||||
prim,
|
||||
&[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
|
||||
);
|
||||
|
||||
let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
|
||||
&[self.primitives.ndarray],
|
||||
Some("T".into()),
|
||||
None,
|
||||
);
|
||||
|
||||
match prim {
|
||||
PrimDef::FunNpSize => create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&into_var_map([in_ndarray_ty]),
|
||||
prim.name(),
|
||||
self.primitives.int32,
|
||||
&[(in_ndarray_ty.ty, "a")],
|
||||
Box::new(|ctx, obj, fun, args, generator| {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let ndarray_ty = fun.0.args[0].ty;
|
||||
let ndarray =
|
||||
args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
|
||||
let ndarray = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty)
|
||||
.map_value(ndarray.into_pointer_value(), None);
|
||||
|
||||
let size = ctx
|
||||
.builder
|
||||
.build_int_truncate_or_bit_cast(
|
||||
ndarray.size(generator, ctx),
|
||||
ctx.ctx.i32_type(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
Ok(Some(size.into()))
|
||||
}),
|
||||
),
|
||||
|
||||
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 = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty)
|
||||
.map_value(ndarray.into_pointer_value(), None);
|
||||
|
||||
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.as_base_value().into()))
|
||||
}),
|
||||
)
|
||||
}
|
||||
_ => 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 ndarray = NDArrayType::from_unifier_type(generator, ctx, arg_ty)
|
||||
.map_value(arg_val.into_pointer_value(), None);
|
||||
|
||||
let ndarray = ndarray.transpose(generator, ctx, None); // TODO: Add axes argument
|
||||
Ok(Some(ndarray.as_base_value().into()))
|
||||
}),
|
||||
),
|
||||
|
||||
// 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 = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty)
|
||||
.map_value(ndarray_val.into_pointer_value(), None);
|
||||
|
||||
let shape = parse_numpy_int_sequence(generator, ctx, (shape_ty, shape_val));
|
||||
|
||||
// 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.as_base_value().as_basic_value_enum()))
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Build the `str()` function.
|
||||
fn build_str_function(&mut self) -> TopLevelDef {
|
||||
let prim = PrimDef::FunStr;
|
||||
@ -1904,6 +1873,57 @@ impl<'a> BuiltinBuilder<'a> {
|
||||
}
|
||||
}
|
||||
|
||||
/// Build np/sp functions that take as input `NDArray` only
|
||||
fn build_np_sp_ndarray_function(&mut self, prim: PrimDef) -> TopLevelDef {
|
||||
debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
|
||||
|
||||
match prim {
|
||||
PrimDef::FunNpTranspose => {
|
||||
let ndarray_ty = self.unifier.get_fresh_var_with_range(
|
||||
&[self.ndarray_num_ty],
|
||||
Some("T".into()),
|
||||
None,
|
||||
);
|
||||
create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&into_var_map([ndarray_ty]),
|
||||
prim.name(),
|
||||
ndarray_ty.ty,
|
||||
&[(ndarray_ty.ty, "x")],
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let arg_ty = fun.0.args[0].ty;
|
||||
let arg_val =
|
||||
args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
|
||||
Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
|
||||
}),
|
||||
)
|
||||
}
|
||||
|
||||
// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
|
||||
// the `param_ty` for `create_fn_by_codegen`.
|
||||
//
|
||||
// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
|
||||
// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
|
||||
// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
|
||||
PrimDef::FunNpReshape => create_fn_by_codegen(
|
||||
self.unifier,
|
||||
&VarMap::new(),
|
||||
prim.name(),
|
||||
self.ndarray_num_ty,
|
||||
&[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let x1_ty = fun.0.args[0].ty;
|
||||
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
|
||||
let x2_ty = fun.0.args[1].ty;
|
||||
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
|
||||
Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
|
||||
}),
|
||||
),
|
||||
|
||||
_ => unreachable!(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Build `np_linalg` and `sp_linalg` functions
|
||||
///
|
||||
/// The input to these functions must be floating point `NDArray`
|
||||
@ -1935,12 +1955,10 @@ impl<'a> BuiltinBuilder<'a> {
|
||||
Box::new(move |ctx, _, fun, args, generator| {
|
||||
let x1_ty = fun.0.args[0].ty;
|
||||
let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
|
||||
|
||||
let x2_ty = fun.0.args[1].ty;
|
||||
let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
|
||||
|
||||
let result = ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?;
|
||||
Ok(Some(result))
|
||||
Ok(Some(ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
|
||||
}),
|
||||
),
|
||||
|
||||
|
||||
@ -1052,7 +1052,7 @@ impl TopLevelComposer {
|
||||
}
|
||||
let mut result = Vec::new();
|
||||
let no_defaults = args.args.len() - args.defaults.len() - 1;
|
||||
for (idx, x) in args.args.iter().skip(1).enumerate() {
|
||||
for (idx, x) in itertools::enumerate(args.args.iter().skip(1)) {
|
||||
let type_ann = {
|
||||
let Some(annotation_expr) = x.node.annotation.as_ref() else {return Err(HashSet::from([format!("type annotation needed for `{}` (at {})", x.node.arg, x.location)]));};
|
||||
parse_ast_to_type_annotation_kinds(
|
||||
|
||||
@ -54,16 +54,6 @@ pub enum PrimDef {
|
||||
FunNpEye,
|
||||
FunNpIdentity,
|
||||
|
||||
// NumPy ndarray property getters
|
||||
FunNpSize,
|
||||
FunNpShape,
|
||||
FunNpStrides,
|
||||
|
||||
// NumPy ndarray view functions
|
||||
FunNpBroadcastTo,
|
||||
FunNpTranspose,
|
||||
FunNpReshape,
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
FunNpRound,
|
||||
FunNpFloor,
|
||||
@ -111,6 +101,8 @@ pub enum PrimDef {
|
||||
FunNpLdExp,
|
||||
FunNpHypot,
|
||||
FunNpNextAfter,
|
||||
FunNpTranspose,
|
||||
FunNpReshape,
|
||||
|
||||
// Linalg functions
|
||||
FunNpDot,
|
||||
@ -248,16 +240,6 @@ impl PrimDef {
|
||||
PrimDef::FunNpEye => fun("np_eye", None),
|
||||
PrimDef::FunNpIdentity => fun("np_identity", None),
|
||||
|
||||
// NumPy NDArray property getters,
|
||||
PrimDef::FunNpSize => fun("np_size", None),
|
||||
PrimDef::FunNpShape => fun("np_shape", None),
|
||||
PrimDef::FunNpStrides => fun("np_strides", None),
|
||||
|
||||
// NumPy NDArray view functions
|
||||
PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
|
||||
PrimDef::FunNpTranspose => fun("np_transpose", None),
|
||||
PrimDef::FunNpReshape => fun("np_reshape", None),
|
||||
|
||||
// Miscellaneous NumPy & SciPy functions
|
||||
PrimDef::FunNpRound => fun("np_round", None),
|
||||
PrimDef::FunNpFloor => fun("np_floor", None),
|
||||
@ -305,6 +287,8 @@ impl PrimDef {
|
||||
PrimDef::FunNpLdExp => fun("np_ldexp", None),
|
||||
PrimDef::FunNpHypot => fun("np_hypot", None),
|
||||
PrimDef::FunNpNextAfter => fun("np_nextafter", None),
|
||||
PrimDef::FunNpTranspose => fun("np_transpose", None),
|
||||
PrimDef::FunNpReshape => fun("np_reshape", None),
|
||||
|
||||
// Linalg functions
|
||||
PrimDef::FunNpDot => fun("np_dot", None),
|
||||
|
||||
@ -8,5 +8,5 @@ expression: res_vec
|
||||
"Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
|
||||
"Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(261)]\n}\n",
|
||||
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(246)]\n}\n",
|
||||
]
|
||||
|
||||
@ -7,7 +7,7 @@ expression: res_vec
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B[typevar245]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar245\"]\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B[typevar230]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar230\"]\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",
|
||||
|
||||
@ -5,8 +5,8 @@ expression: res_vec
|
||||
[
|
||||
"Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(258)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(263)]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(243)]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
|
||||
"Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
|
||||
@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
|
||||
expression: res_vec
|
||||
---
|
||||
[
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[typevar244, typevar245]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar244\", \"typevar245\"]\n}\n",
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[typevar229, typevar230]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar229\", \"typevar230\"]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",
|
||||
|
||||
@ -6,12 +6,12 @@ expression: res_vec
|
||||
"Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(264)]\n}\n",
|
||||
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(249)]\n}\n",
|
||||
"Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(272)]\n}\n",
|
||||
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
|
||||
]
|
||||
|
||||
@ -15,13 +15,14 @@ use crate::{
|
||||
symbol_resolver::{SymbolResolver, ValueEnum},
|
||||
typecheck::{
|
||||
type_inferencer::PrimitiveStore,
|
||||
typedef::{Type, Unifier},
|
||||
typedef::{into_var_map, Type, Unifier},
|
||||
},
|
||||
};
|
||||
|
||||
struct ResolverInternal {
|
||||
id_to_type: Mutex<HashMap<StrRef, Type>>,
|
||||
id_to_def: Mutex<HashMap<StrRef, DefinitionId>>,
|
||||
class_names: Mutex<HashMap<StrRef, Type>>,
|
||||
}
|
||||
|
||||
impl ResolverInternal {
|
||||
@ -178,8 +179,11 @@ fn test_simple_function_analyze(source: &[&str], tys: &[&str], names: &[&str]) {
|
||||
let mut composer =
|
||||
TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 64).0;
|
||||
|
||||
let internal_resolver =
|
||||
Arc::new(ResolverInternal { id_to_def: Mutex::default(), id_to_type: Mutex::default() });
|
||||
let internal_resolver = Arc::new(ResolverInternal {
|
||||
id_to_def: Mutex::default(),
|
||||
id_to_type: Mutex::default(),
|
||||
class_names: Mutex::default(),
|
||||
});
|
||||
let resolver =
|
||||
Arc::new(Resolver(internal_resolver.clone())) as Arc<dyn SymbolResolver + Send + Sync>;
|
||||
|
||||
@ -780,6 +784,13 @@ fn make_internal_resolver_with_tvar(
|
||||
unifier: &mut Unifier,
|
||||
print: bool,
|
||||
) -> Arc<ResolverInternal> {
|
||||
let list_elem_tvar = unifier.get_fresh_var(Some("list_elem".into()), None);
|
||||
let list = unifier.add_ty(TypeEnum::TObj {
|
||||
obj_id: PrimDef::List.id(),
|
||||
fields: HashMap::new(),
|
||||
params: into_var_map([list_elem_tvar]),
|
||||
});
|
||||
|
||||
let res: Arc<ResolverInternal> = ResolverInternal {
|
||||
id_to_def: Mutex::new(HashMap::from([("list".into(), PrimDef::List.id())])),
|
||||
id_to_type: tvars
|
||||
@ -795,6 +806,7 @@ fn make_internal_resolver_with_tvar(
|
||||
})
|
||||
.collect::<HashMap<_, _>>()
|
||||
.into(),
|
||||
class_names: Mutex::new(HashMap::from([("list".into(), list)])),
|
||||
}
|
||||
.into();
|
||||
if print {
|
||||
@ -807,7 +819,7 @@ struct TypeToStringFolder<'a> {
|
||||
unifier: &'a mut Unifier,
|
||||
}
|
||||
|
||||
impl Fold<Option<Type>> for TypeToStringFolder<'_> {
|
||||
impl<'a> Fold<Option<Type>> for TypeToStringFolder<'a> {
|
||||
type TargetU = String;
|
||||
type Error = String;
|
||||
fn map_user(&mut self, user: Option<Type>) -> Result<Self::TargetU, Self::Error> {
|
||||
|
||||
@ -15,7 +15,7 @@ use super::{
|
||||
};
|
||||
use crate::toplevel::helper::PrimDef;
|
||||
|
||||
impl Inferencer<'_> {
|
||||
impl<'a> Inferencer<'a> {
|
||||
fn should_have_value(&mut self, expr: &Expr<Option<Type>>) -> Result<(), HashSet<String>> {
|
||||
if matches!(expr.custom, Some(ty) if self.unifier.unioned(ty, self.primitives.none)) {
|
||||
Err(HashSet::from([format!("Error at {}: cannot have value none", expr.location)]))
|
||||
@ -94,7 +94,7 @@ impl Inferencer<'_> {
|
||||
// there are some cases where the custom field is None
|
||||
if let Some(ty) = &expr.custom {
|
||||
if !matches!(&expr.node, ExprKind::Constant { value: Constant::Ellipsis, .. })
|
||||
&& ty.obj_id(self.unifier).is_none_or(|id| id != PrimDef::List.id())
|
||||
&& !ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::List.id())
|
||||
&& !self.unifier.is_concrete(*ty, &self.function_data.bound_variables)
|
||||
{
|
||||
return Err(HashSet::from([format!(
|
||||
|
||||
@ -7,12 +7,12 @@ use nac3parser::ast::{Cmpop, Operator, StrRef, Unaryop};
|
||||
|
||||
use super::{
|
||||
type_inferencer::*,
|
||||
typedef::{into_var_map, FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
|
||||
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
|
||||
};
|
||||
use crate::{
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::{
|
||||
helper::{extract_ndims, PrimDef},
|
||||
helper::PrimDef,
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
},
|
||||
};
|
||||
@ -175,8 +175,19 @@ pub fn impl_binop(
|
||||
ops: &[Operator],
|
||||
) {
|
||||
with_fields(unifier, ty, |unifier, fields| {
|
||||
let other_tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
|
||||
let function_vars = into_var_map([other_tvar]);
|
||||
let (other_ty, other_var_id) = if other_ty.len() == 1 {
|
||||
(other_ty[0], None)
|
||||
} else {
|
||||
let tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
|
||||
(tvar.ty, Some(tvar.id))
|
||||
};
|
||||
|
||||
let function_vars = if let Some(var_id) = other_var_id {
|
||||
vec![(var_id, other_ty)].into_iter().collect::<VarMap>()
|
||||
} else {
|
||||
VarMap::new()
|
||||
};
|
||||
|
||||
let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).ty);
|
||||
|
||||
for (base_op, variant) in iproduct!(ops, [BinopVariant::Normal, BinopVariant::AugAssign]) {
|
||||
@ -187,7 +198,7 @@ pub fn impl_binop(
|
||||
ret: ret_ty,
|
||||
vars: function_vars.clone(),
|
||||
args: vec![FuncArg {
|
||||
ty: other_tvar.ty,
|
||||
ty: other_ty,
|
||||
default_value: None,
|
||||
name: "other".into(),
|
||||
is_vararg: false,
|
||||
@ -530,43 +541,36 @@ pub fn typeof_binop(
|
||||
}
|
||||
}
|
||||
|
||||
let (lhs_dtype, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
|
||||
let lhs_ndims = extract_ndims(unifier, lhs_ndims);
|
||||
|
||||
let (rhs_dtype, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
|
||||
let rhs_ndims = extract_ndims(unifier, rhs_ndims);
|
||||
|
||||
if !(unifier.unioned(lhs_dtype, primitives.float)
|
||||
&& unifier.unioned(rhs_dtype, primitives.float))
|
||||
{
|
||||
return Err(format!(
|
||||
"ndarray.__matmul__ only supports float64 operations, but LHS has type {} and RHS has type {}",
|
||||
unifier.stringify(lhs),
|
||||
unifier.stringify(rhs)
|
||||
));
|
||||
let (_, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
|
||||
let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
|
||||
TypeEnum::TLiteral { values, .. } => {
|
||||
assert_eq!(values.len(), 1);
|
||||
u64::try_from(values[0].clone()).unwrap()
|
||||
}
|
||||
|
||||
// Deduce the ndims of the resulting ndarray.
|
||||
// If this is 0 (an unsized ndarray), matmul returns a scalar just like NumPy.
|
||||
let result_ndims = match (lhs_ndims, rhs_ndims) {
|
||||
(0, _) | (_, 0) => {
|
||||
return Err(
|
||||
"ndarray.__matmul__ does not allow unsized ndarray input".to_string()
|
||||
)
|
||||
_ => unreachable!(),
|
||||
};
|
||||
let (_, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
|
||||
let rhs_ndims = match &*unifier.get_ty_immutable(rhs_ndims) {
|
||||
TypeEnum::TLiteral { values, .. } => {
|
||||
assert_eq!(values.len(), 1);
|
||||
u64::try_from(values[0].clone()).unwrap()
|
||||
}
|
||||
(1, 1) => 0,
|
||||
(1, _) => rhs_ndims - 1,
|
||||
(_, 1) => lhs_ndims - 1,
|
||||
(m, n) => max(m, n),
|
||||
_ => unreachable!(),
|
||||
};
|
||||
|
||||
if result_ndims == 0 {
|
||||
// If the result is unsized, NumPy returns a scalar.
|
||||
primitives.float
|
||||
} else {
|
||||
let result_ndims_ty =
|
||||
unifier.get_fresh_literal(vec![SymbolValue::U64(result_ndims)], None);
|
||||
make_ndarray_ty(unifier, primitives, Some(primitives.float), Some(result_ndims_ty))
|
||||
match (lhs_ndims, rhs_ndims) {
|
||||
(2, 2) => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
|
||||
(lhs, rhs) if lhs == 0 || rhs == 0 => {
|
||||
return Err(format!(
|
||||
"Input operand {} does not have enough dimensions (has {lhs}, requires {rhs})",
|
||||
u8::from(rhs == 0)
|
||||
))
|
||||
}
|
||||
(lhs, rhs) => {
|
||||
return Err(format!(
|
||||
"ndarray.__matmul__ on {lhs}D and {rhs}D operands not supported"
|
||||
))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -769,7 +773,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
|
||||
impl_div(unifier, store, ndarray_t, &[ndarray_t, ndarray_dtype_t], None);
|
||||
impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
|
||||
impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
|
||||
impl_matmul(unifier, store, ndarray_t, &[ndarray_unsized_t], None);
|
||||
impl_matmul(unifier, store, ndarray_t, &[ndarray_t], Some(ndarray_t));
|
||||
impl_sign(unifier, store, ndarray_t, Some(ndarray_t));
|
||||
impl_invert(unifier, store, ndarray_t, Some(ndarray_t));
|
||||
impl_eq(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
|
||||
|
||||
@ -94,7 +94,7 @@ fn loc_to_str(loc: Option<Location>) -> String {
|
||||
}
|
||||
}
|
||||
|
||||
impl Display for DisplayTypeError<'_> {
|
||||
impl<'a> Display for DisplayTypeError<'a> {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
|
||||
use TypeErrorKind::*;
|
||||
let mut notes = Some(HashMap::new());
|
||||
|
||||
@ -3,7 +3,7 @@ use std::{
|
||||
cmp::max,
|
||||
collections::{HashMap, HashSet},
|
||||
convert::{From, TryInto},
|
||||
iter::{once, repeat_n},
|
||||
iter::once,
|
||||
sync::Arc,
|
||||
};
|
||||
|
||||
@ -187,7 +187,7 @@ fn fix_assignment_target_context(node: &mut ast::Located<ExprKind>) {
|
||||
}
|
||||
}
|
||||
|
||||
impl Fold<()> for Inferencer<'_> {
|
||||
impl<'a> Fold<()> for Inferencer<'a> {
|
||||
type TargetU = Option<Type>;
|
||||
type Error = InferenceError;
|
||||
|
||||
@ -657,7 +657,7 @@ impl Fold<()> for Inferencer<'_> {
|
||||
|
||||
type InferenceResult = Result<Type, InferenceError>;
|
||||
|
||||
impl Inferencer<'_> {
|
||||
impl<'a> Inferencer<'a> {
|
||||
/// Constrain a <: b
|
||||
/// Currently implemented as unification
|
||||
fn constrain(&mut self, a: Type, b: Type, location: &Location) -> Result<(), InferenceError> {
|
||||
@ -1234,45 +1234,6 @@ impl Inferencer<'_> {
|
||||
}));
|
||||
}
|
||||
|
||||
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: repeat_n(self.primitives.int32, 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))?;
|
||||
@ -1594,7 +1555,7 @@ impl Inferencer<'_> {
|
||||
}));
|
||||
}
|
||||
// 2-argument ndarray n-dimensional factory functions
|
||||
if ["np_reshape".into(), "np_broadcast_to".into()].contains(id) && args.len() == 2 {
|
||||
if id == &"np_reshape".into() && args.len() == 2 {
|
||||
let arg0 = self.fold_expr(args.remove(0))?;
|
||||
|
||||
let shape_expr = args.remove(0);
|
||||
|
||||
@ -18,6 +18,7 @@ use crate::{
|
||||
struct Resolver {
|
||||
id_to_type: HashMap<StrRef, Type>,
|
||||
id_to_def: HashMap<StrRef, DefinitionId>,
|
||||
class_names: HashMap<StrRef, Type>,
|
||||
}
|
||||
|
||||
impl SymbolResolver for Resolver {
|
||||
@ -197,6 +198,7 @@ impl TestEnvironment {
|
||||
let resolver = Arc::new(Resolver {
|
||||
id_to_type: identifier_mapping.clone(),
|
||||
id_to_def: HashMap::default(),
|
||||
class_names: HashMap::default(),
|
||||
}) as Arc<dyn SymbolResolver + Send + Sync>;
|
||||
|
||||
TestEnvironment {
|
||||
@ -452,6 +454,7 @@ impl TestEnvironment {
|
||||
vars: IndexMap::default(),
|
||||
})),
|
||||
);
|
||||
let class_names: HashMap<_, _> = [("Bar".into(), bar), ("Bar2".into(), bar2)].into();
|
||||
|
||||
let id_to_name = [
|
||||
"int32".into(),
|
||||
@ -489,6 +492,7 @@ impl TestEnvironment {
|
||||
("Bar2".into(), DefinitionId(defs + 3)),
|
||||
]
|
||||
.into(),
|
||||
class_names,
|
||||
}) as Arc<dyn SymbolResolver + Send + Sync>;
|
||||
|
||||
TestEnvironment {
|
||||
|
||||
@ -3,13 +3,13 @@ use std::{
|
||||
cell::RefCell,
|
||||
collections::{HashMap, HashSet},
|
||||
fmt::{self, Display},
|
||||
iter::{repeat, repeat_n, zip},
|
||||
iter::{repeat, zip},
|
||||
rc::Rc,
|
||||
sync::{Arc, Mutex},
|
||||
};
|
||||
|
||||
use indexmap::IndexMap;
|
||||
use itertools::Itertools;
|
||||
use itertools::{repeat_n, Itertools};
|
||||
|
||||
use nac3parser::ast::{Cmpop, Location, StrRef, Unaryop};
|
||||
|
||||
|
||||
@ -30,7 +30,7 @@ pub struct DwarfReader<'a> {
|
||||
pub virt_addr: u32,
|
||||
}
|
||||
|
||||
impl DwarfReader<'_> {
|
||||
impl<'a> DwarfReader<'a> {
|
||||
pub fn new(slice: &[u8], virt_addr: u32) -> DwarfReader {
|
||||
DwarfReader { slice, virt_addr }
|
||||
}
|
||||
@ -113,7 +113,7 @@ pub struct DwarfWriter<'a> {
|
||||
pub offset: usize,
|
||||
}
|
||||
|
||||
impl DwarfWriter<'_> {
|
||||
impl<'a> DwarfWriter<'a> {
|
||||
pub fn new(slice: &mut [u8]) -> DwarfWriter {
|
||||
DwarfWriter { slice, offset: 0 }
|
||||
}
|
||||
@ -375,7 +375,7 @@ pub struct FDE_Records<'a> {
|
||||
available: usize,
|
||||
}
|
||||
|
||||
impl Iterator for FDE_Records<'_> {
|
||||
impl<'a> Iterator for FDE_Records<'a> {
|
||||
type Item = (u32, u32);
|
||||
|
||||
fn next(&mut self) -> Option<Self::Item> {
|
||||
@ -423,7 +423,7 @@ pub struct EH_Frame_Hdr<'a> {
|
||||
fdes: Vec<(u32, u32)>,
|
||||
}
|
||||
|
||||
impl EH_Frame_Hdr<'_> {
|
||||
impl<'a> EH_Frame_Hdr<'a> {
|
||||
/// Create a [EH_Frame_Hdr] object, and write out the fixed fields of `.eh_frame_hdr` to memory.
|
||||
///
|
||||
/// Load address is not known at this point.
|
||||
|
||||
@ -159,7 +159,7 @@ struct SymbolTableReader<'a> {
|
||||
strtab: &'a [u8],
|
||||
}
|
||||
|
||||
impl SymbolTableReader<'_> {
|
||||
impl<'a> SymbolTableReader<'a> {
|
||||
pub fn find_index_by_name(&self, sym_name: &[u8]) -> Option<usize> {
|
||||
self.symtab.iter().position(|sym| {
|
||||
if let Ok(dynsym_name) = name_starting_at_slice(self.strtab, sym.st_name as usize) {
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user