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118 Commits

Author SHA1 Message Date
1531b6cc98 cargo: update dependencies 2024-12-13 19:42:01 +08:00
9bbc40bbfa flake: update dependencies 2024-12-13 19:41:52 +08:00
790e56d106 msys2: update 2024-12-13 19:39:39 +08:00
a00eb7969e [core] codegen: Implement matrix_power
Last of the functions that need to be ported over to strided-ndarray.
2024-12-13 15:23:31 +08:00
27a6f47330 [core] codegen: Implement construction of unsized ndarrays
Partially based on f731e604: core/ndstrides: add more ScalarOrNDArray
and NDArrayObject utils.
2024-12-13 15:23:31 +08:00
061747c67b [core] codegen: Implement NDArrayValue::atleast_nd
Based on 9cfa2622: core/ndstrides: add NDArrayObject::atleast_nd.
2024-12-13 15:23:31 +08:00
dc91d9e35a [core] codegen: Implement ScalarOrNDArray and use it in indexing
Based on 8f9d2d82: core/ndstrides: implement ndarray indexing.
2024-12-13 15:23:31 +08:00
438943ac6f [core] codegen: Implement indexing for NDArray
Based on 8f9d2d82: core/ndstrides: implement ndarray indexing

The functionality for `...` and `np.newaxis` is there in IRRT, but there
is no implementation of them for @kernel Python expressions because of
M-Labs/nac3#486.
2024-12-13 15:23:31 +08:00
678e56c95d [core] irrt: rename NDIndex to NDIndexInt
Unfortunately the name `NDIndex` is used in later commits. Renaming this
typedef to `NDIndexInt` to avoid amending. `NDIndexInt` will be removed
anyway when ndarray strides is completed.
2024-12-13 15:23:31 +08:00
fdfc80ca5f [core] codegen: Implement Slice{Type,Value}, RustSlice
Based on 01c96396: core/irrt: add Slice and Range and part of
8f9d2d82: core/ndstrides: implement ndarray indexing.

Needed for implementing general ndarray indexing.

Currently IRRT slice and range have nothing to do with NAC3's slice
and range. The IRRT slice and range are currently there to implement
ndarray specific features. However, in the future their definitions may
be used to replace that of NAC3's. (NAC3's range is a [i32 x 3], IRRT's
range is a proper struct. NAC3 does not have a slice struct).
2024-12-13 15:23:31 +08:00
8b3429d62a [artiq] Reimplement get_obj_value for strided ndarray
Based on 7ef93472: artiq: reimplement get_obj_value to use ndarray with
strides
2024-12-13 15:23:31 +08:00
f4c5038b95 [artiq] codegen: Reimplement polymorphic_print for strided ndarray
Based on 2a6ee503: artiq: reimplement polymorphic_print for ndarray
2024-12-13 15:23:31 +08:00
ddd16738a6 [core] codegen: implement ndarray iterator NDIter
Based on 50f960ab: core/ndstrides: implement ndarray iterator NDIter

A necessary utility to iterate through all elements in a possibly
strided ndarray.
2024-12-13 15:23:31 +08:00
44c49dc102 [artiq] codegen: Reimplement polymorphic_print for strided ndarray
Based on 2a6ee503: artiq: reimplement polymorphic_print for ndarray
2024-12-13 15:23:31 +08:00
e4bd376587 [core] codegen: Implement ContiguousNDArray
Fixes compatibility with linalg algorithms. matrix_power is missing due
to the need for indexing support.
2024-12-13 15:23:29 +08:00
44498f22f6 [core] codegen: Implement NDArray functions from a0a1f35b 2024-12-13 15:22:11 +08:00
110416d07a [core] codegen/irrt: Add IRRT functions for strided-ndarray 2024-12-13 15:22:11 +08:00
08a7d01a13 [core] Add itemsize and strides to NDArray struct
Temporarily disable linalg ndarray tests as they are not ported to work
with strided-ndarray.
2024-12-13 15:22:09 +08:00
3cd36fddc3 [core] codegen/types: Add check_struct_type_matches_fields
Shorthand for checking if a type is representable by a StructFields
instance.
2024-12-12 11:40:44 +08:00
56a7a9e03d [core] codegen: Add helper functions for create+call functions
Replacement for various FnCall methods from legacy ndstrides
implementation.
2024-12-12 11:30:36 +08:00
574ae40f97 [core] codegen: Add call_memcpy_generic_array
Replacement for Instance<Ptr>::copy_from from legacy ndstrides
implementation.
2024-12-12 11:30:36 +08:00
aa293b6bea [core] codegen: Add type_aligned_alloca 2024-12-12 11:30:35 +08:00
eb4b881690 [core] Expose {types,values}::ndarray modules
Allows better encapsulation of members in these modules rather than
allowing them to leak into types/values mod.
2024-12-12 11:30:14 +08:00
3d0a1d281c [core] Expose irrt::ndarray 2024-12-10 12:49:49 +08:00
ad67a99c8f [core] codegen: Cleanup builtin_fns.rs
- Unpack tuples directly in function argument
- Replace Vec parameters with slices
- Replace unwrap-transform with map-unwrap
2024-12-10 12:49:49 +08:00
8e2b50df21 [core] codegen/ndarray: Cleanup
- Remove redundant size param
- Add *_fields functions and docs
2024-12-09 13:01:08 +08:00
06092ad29b [core] Move alloca and map_value of ProxyType to implementations
These functions may not be invokable by the same set of parameters as
some classes has associated states.
2024-12-09 12:51:50 +08:00
d62c6b95fd [core] codegen/types: Rename StructField::set_from_value 2024-12-09 12:51:50 +08:00
95e29d9997 [core] codegen: Move ndarray type/value as a separate module 2024-12-09 12:51:46 +08:00
536ed2146c [meta] Remove all mentions of build_int_cast
build_int_cast performs signed extension or truncation depending on the
source and target int lengths. This is usually not what we want - We
want zero-extension instead.

Replace all instances of build_int_cast with
build_int_z_extend_or_bit_cast to fix this issue.
2024-12-09 12:51:39 +08:00
d484d44d95 [standalone] linalg: Fix function name in error message 2024-12-09 12:09:57 +08:00
ac978864f2 [meta] Apply clippy suggestions 2024-12-09 12:08:41 +08:00
95254f8464 [meta] Update Cargo dependencies 2024-12-09 12:08:41 +08:00
964945d244 string_store: update embedding map after compilation 2024-12-03 16:45:46 +08:00
ae09a0d444 exceptions: preallocate in NAC3 instead 2024-12-03 16:45:05 +08:00
01edd5af67 [meta] Apply rustfmt changes 2024-11-29 15:43:34 +08:00
015714eee1 copy constructor -> clone 2024-11-28 18:52:53 +08:00
71dec251e3 ld/dwarf: remove reader resets
DWARF reader never had to reverse. Readers are already copied to achieve this effect.
Plus the position that it reverses to might be questionable.
2024-11-28 18:52:53 +08:00
fce61f7b8c ld: fix dwarf sections offset calculations 2024-11-28 18:52:53 +08:00
babc081dbd core/toplevel: update tests 2024-11-27 14:31:57 +08:00
5337dbe23b core/toplevel: add python-like error messages for class definition 2024-11-27 14:31:57 +08:00
f862c01412 core/toplevel: refactor composer 2024-11-27 14:31:53 +08:00
0c9705f5f1 [meta] Apply clippy changes 2024-11-25 16:05:12 +08:00
5f940f86d9 [artiq] Fix obtaining ndarray struct from NDArrayType 2024-11-25 15:01:39 +08:00
5651e00688 flake: add platformdirs artiq dependency 2024-11-22 20:30:30 +08:00
f6745b987f bump sipyco and artiq used for profiling 2024-11-22 19:43:03 +08:00
e0dedc6580 nac3artiq: support kernels sent by content 2024-11-22 19:38:52 +08:00
28f574282c [core_derive] Ignore doctest in example
Causes linker errors for unknown reasons.
2024-11-22 00:00:05 +08:00
144f0922db [core] coregen/types: Implement StructFields for NDArray
Also rename some fields to better align with their naming in numpy.
2024-11-21 14:27:00 +08:00
c58ce9c3a9 [core] codegen/types: Implement NDArray in terms of i8*
Better aligns with the future implementation of ndstrides.
2024-11-21 14:27:00 +08:00
f7e296da53 [core] irrt: Break IRRT into several impl files
Each IRRT file is now mapped to one Rust file.
2024-11-21 14:27:00 +08:00
b58c99369e [core] irrt: Update some IRRT implementation
- Change CSlice to use `void*` for better pointer compatibility
- Only include impl *.hpp files in irrt.cpp
- Refactor typedef to using declaration
- Add missing ``// namespace`
2024-11-21 14:26:58 +08:00
1a535db558 [core] codegen: Add dtype to NDArrayType
We won't have this once NDArray is refactored to strided impl.
2024-11-20 15:35:57 +08:00
1ba2e287a6 [core] codegen: Add Self::llvm_type to all type abstractions 2024-11-20 15:35:57 +08:00
f95f979ad3 core/irrt: fix exception.hpp C++ castings 2024-11-20 15:35:57 +08:00
48e2148c0f core/toplevel/helper: add {extract,create}_ndims 2024-11-20 15:35:57 +08:00
88e57f7120 [core_derive] Initial implementation 2024-11-20 15:35:55 +08:00
d7633c42bc [core] codegen/types: Implement StructField{,s}
Loosely based on FieldTraversal by lyken.
2024-11-19 13:46:25 +08:00
a4f53b6e6b [core] codegen: Refactor ProxyType and ProxyValue
Accepts generator+context object for generic type checking. Also
implements more default trait impl for easier delegation.
2024-11-19 13:46:25 +08:00
9d9ead211e [core] Move Proxies to their own modules 2024-11-19 13:46:23 +08:00
26a1b85206 [core] codegen/classes: Remove Underlying type
This is confusing and we want a better abstraction than this.
2024-11-19 13:45:55 +08:00
2822074b2d [meta] Cleanup from upgrading Rust version
- Remove rust_2024_edition warnings, since it wouldn't be released for
another 3 months
- Fix new clippy warnings
2024-11-19 13:43:57 +08:00
fe67ed076c [meta] Update pre-commit configuration 2024-11-19 13:20:27 +08:00
94e2414df0 [meta] Update cargo dependencies 2024-11-19 13:20:26 +08:00
2cee760404 turn rust_2024_compatibility lints into warnings 2024-11-16 13:41:49 +08:00
230982dc84 update dependencies 2024-11-16 12:40:11 +08:00
2bd3f63991 boolop: terminate both branches with *_end_bb 2024-11-16 12:06:20 +08:00
b53266e9e6 artiq: use async RPC for attributes writeback 2024-11-12 12:04:01 +08:00
86eb22bbf3 artiq: main is always the last module 2024-11-12 12:03:38 +08:00
beaa38047d artiq: suppress main module debug warning 2024-11-12 12:03:08 +08:00
705dc4ff1c artiq: lump return value into attributes writeback RPC 2024-11-12 12:02:35 +08:00
979209a526 binop: expand not operator as loglcal not 2024-11-08 17:12:01 +08:00
c3927d0ef6 [ast] Refactor lazy_static to LazyLock
It is available in Rust 1.80 and reduces a dependency.
2024-10-30 12:29:51 +08:00
202a902cd0 [meta] Update dependencies 2024-10-30 12:29:51 +08:00
b6e2644391 [meta] Update cargo dependencies 2024-10-18 14:17:16 +08:00
45cd01556b [meta] Apply cargo fmt 2024-10-18 14:16:42 +08:00
b6cd2a6993 [meta] Reorganize order of use declarations - Phase 3 2024-10-17 16:25:52 +08:00
a98f33e6d1 [meta] Reorganize order of use declarations - Phase 2
Some more rules:

- For module-level imports, prefer no prefix > super > crate.
- Use crate instead of super if super refers to the crate-level module
2024-10-17 15:57:33 +08:00
5839badadd [standalone] Update globals.py with type-inferred global var 2024-10-07 20:44:08 +08:00
56c845aac4 [standalone] Add support for registering globals without type decl 2024-10-07 20:44:06 +08:00
65a12d9ab3 [core] Refactor registration of top-level variables 2024-10-07 17:05:48 +08:00
9c6685fa8f [core] typecheck/function_check: Fix lookup of defined ids in scope 2024-10-07 16:51:37 +08:00
2bb788e4bb [core] codegen/expr: Materialize implicit globals
Required for when globals are read without the use of a global
declaration.
2024-10-07 13:13:20 +08:00
42a2f243b5 [core] typecheck: Disallow redeclaration of var shadowing global 2024-10-07 13:11:00 +08:00
3ce2eddcdc [core] typecheck/type_inferencer: Infer whether variables are global 2024-10-07 13:10:46 +08:00
51bf126a32 [core] typecheck/type_inferencer: Differentiate global symbols
Required for analyzing use of global symbols before global declaration.
2024-10-07 12:25:00 +08:00
1a197c67f6 [core] toplevel/composer: Reduce lock scope while analyzing function 2024-10-05 15:53:20 +08:00
581b2f7bb2 [standalone] Add demo for global variables 2024-10-04 13:24:30 +08:00
746329ec5d [standalone] Implement symbol resolution for globals 2024-10-04 13:24:30 +08:00
e60e8e837f [core] Add support for global statements 2024-10-04 13:24:27 +08:00
9fdbe9695d [core] Add generator to SymbolResolver::get_symbol_value
Needed in a future commit.
2024-10-04 13:20:29 +08:00
8065e73598 [core] toplevel/composer: Add type analysis for global variables 2024-10-04 13:20:29 +08:00
192290889b [core] Add IdentifierInfo
Keeps track of whether an identifier refers to a global or local
variable.
2024-10-04 13:20:24 +08:00
1407553a2f [core] Implement parsing of global variables
Globals are now parsed into symbol resolver and top level definitions.
2024-10-04 13:18:29 +08:00
c7697606e1 [core] Add TopLevelDef::Variable 2024-10-04 13:09:25 +08:00
88d0ccbf69 [standalone] Explicit panic when encountering a compilation error
Otherwise scripts will continue to execute.
2024-10-04 13:00:16 +08:00
a43b59539c [meta] Move variables declarations closer to where they are first used 2024-10-04 13:00:16 +08:00
fe06b2806f [meta] Reorganize order of use declarations
Use declarations are now grouped into 4 groups:

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

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

The year is 2024 and compiler toolchains are still a trash fire like this.
2024-09-09 11:17:31 +08:00
6098b1b853 fix previous commit 2024-09-06 11:32:08 +08:00
668ccb1c95 nac3core: expose inkwell and nac3parser 2024-09-06 11:06:26 +08:00
a3c624d69d update all dependencies 2024-09-06 10:21:58 +08:00
bd06155f34 irrt: compatibility with pre-C23 compilers 2024-09-05 18:54:55 +08:00
9c33c4209c [core] Fix type of ndarray.element_type
Should be the element type of the NDArray itself, not the pointer to its
type.
2024-08-30 22:47:38 +08:00
122983f11c flake: update dependencies 2024-08-30 14:45:38 +08:00
148 changed files with 11504 additions and 10977 deletions

View File

@ -1,24 +1,24 @@
# See https://pre-commit.com for more information
# See https://pre-commit.com/hooks.html for more hooks
default_stages: [commit]
default_stages: [pre-commit]
repos:
- repo: local
hooks:
- id: nac3-cargo-fmt
name: nac3 cargo format
entry: cargo
entry: nix
language: system
types: [file, rust]
pass_filenames: false
description: Runs cargo fmt on the codebase.
args: [fmt]
args: [develop, -c, cargo, fmt, --all]
- id: nac3-cargo-clippy
name: nac3 cargo clippy
entry: cargo
entry: nix
language: system
types: [file, rust]
pass_filenames: false
description: Runs cargo clippy on the codebase.
args: [clippy, --tests]
args: [develop, -c, cargo, clippy, --tests]

525
Cargo.lock generated

File diff suppressed because it is too large Load Diff

View File

@ -4,6 +4,7 @@ members = [
"nac3ast",
"nac3parser",
"nac3core",
"nac3core/nac3core_derive",
"nac3standalone",
"nac3artiq",
"runkernel",

6
flake.lock generated
View File

@ -2,11 +2,11 @@
"nodes": {
"nixpkgs": {
"locked": {
"lastModified": 1723637854,
"narHash": "sha256-med8+5DSWa2UnOqtdICndjDAEjxr5D7zaIiK4pn0Q7c=",
"lastModified": 1733940404,
"narHash": "sha256-Pj39hSoUA86ZePPF/UXiYHHM7hMIkios8TYG29kQT4g=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "c3aa7b8938b17aebd2deecf7be0636000d62a2b9",
"rev": "5d67ea6b4b63378b9c13be21e2ec9d1afc921713",
"type": "github"
},
"original": {

View File

@ -107,18 +107,18 @@
(pkgs.fetchFromGitHub {
owner = "m-labs";
repo = "sipyco";
rev = "939f84f9b5eef7efbf7423c735d1834783b6140e";
sha256 = "sha256-15Nun4EY35j+6SPZkjzZtyH/ncxLS60KuGJjFh5kSTc=";
rev = "094a6cd63ffa980ef63698920170e50dc9ba77fd";
sha256 = "sha256-PPnAyDedUQ7Og/Cby9x5OT9wMkNGTP8GS53V6N/dk4w=";
})
(pkgs.fetchFromGitHub {
owner = "m-labs";
repo = "artiq";
rev = "923ca3377d42c815f979983134ec549dc39d3ca0";
sha256 = "sha256-oJoEeNEeNFSUyh6jXG8Tzp6qHVikeHS0CzfE+mODPgw=";
rev = "28c9de3e251daa89a8c9fd79d5ab64a3ec03bac6";
sha256 = "sha256-vAvpbHc5B+1wtG8zqN7j9dQE1ON+i22v+uqA+tw6Gak=";
})
];
buildInputs = [
(python3-mimalloc.withPackages(ps: [ ps.numpy ps.scipy ps.jsonschema ps.lmdb nac3artiq-instrumented ]))
(python3-mimalloc.withPackages(ps: [ ps.numpy ps.scipy ps.jsonschema ps.lmdb ps.platformdirs nac3artiq-instrumented ]))
pkgs.llvmPackages_14.llvm.out
];
phases = [ "buildPhase" "installPhase" ];

View File

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

View File

@ -7,33 +7,6 @@ class EmbeddingMap:
self.function_map = {}
self.attributes_writeback = []
# preallocate exception names
self.preallocate_runtime_exception_names(["RuntimeError",
"RTIOUnderflow",
"RTIOOverflow",
"RTIODestinationUnreachable",
"DMAError",
"I2CError",
"CacheError",
"SPIError",
"0:ZeroDivisionError",
"0:IndexError",
"0:ValueError",
"0:RuntimeError",
"0:AssertionError",
"0:KeyError",
"0:NotImplementedError",
"0:OverflowError",
"0:IOError",
"0:UnwrapNoneError"])
def preallocate_runtime_exception_names(self, names):
for i, name in enumerate(names):
if ":" not in name:
name = "0:artiq.coredevice.exceptions." + name
exn_id = self.store_str(name)
assert exn_id == i
def store_function(self, key, fun):
self.function_map[key] = fun
return key

View File

@ -112,10 +112,15 @@ def extern(function):
register_function(function)
return function
def rpc(function):
"""Decorates a function declaration defined by the core device runtime."""
register_function(function)
return function
def rpc(arg=None, flags={}):
"""Decorates a function or method to be executed on the host interpreter."""
if arg is None:
def inner_decorator(function):
return rpc(function, flags)
return inner_decorator
register_function(arg)
return arg
def kernel(function_or_method):
"""Decorates a function or method to be executed on the core device."""
@ -201,7 +206,7 @@ class Core:
embedding = EmbeddingMap()
if allow_registration:
compiler.analyze(registered_functions, registered_classes)
compiler.analyze(registered_functions, registered_classes, set())
allow_registration = False
if hasattr(method, "__self__"):

View File

@ -1,39 +1,3 @@
use nac3core::{
codegen::{
classes::{
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayType,
NDArrayValue, ProxyType, ProxyValue, RangeValue, UntypedArrayLikeAccessor,
},
expr::{destructure_range, gen_call},
irrt::call_ndarray_calc_size,
llvm_intrinsics::{call_int_smax, call_memcpy_generic, call_stackrestore, call_stacksave},
stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
CodeGenContext, CodeGenerator,
},
symbol_resolver::ValueEnum,
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, GenCall},
typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
};
use nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef};
use inkwell::{
context::Context,
module::Linkage,
types::{BasicType, IntType},
values::{BasicValueEnum, PointerValue, StructValue},
AddressSpace, IntPredicate, OptimizationLevel,
};
use pyo3::{
types::{PyDict, PyList},
PyObject, PyResult, Python,
};
use crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
use inkwell::values::IntValue;
use itertools::Itertools;
use std::{
collections::{hash_map::DefaultHasher, HashMap},
hash::{Hash, Hasher},
@ -42,6 +6,43 @@ use std::{
sync::Arc,
};
use itertools::Itertools;
use pyo3::{
types::{PyDict, PyList},
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},
stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
type_aligned_alloca,
types::ndarray::NDArrayType,
values::{
ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, ProxyValue, RangeValue,
UntypedArrayLikeAccessor,
},
CodeGenContext, CodeGenerator,
},
inkwell::{
context::Context,
module::Linkage,
types::{BasicType, IntType},
values::{BasicValueEnum, IntValue, PointerValue, StructValue},
AddressSpace, IntPredicate, OptimizationLevel,
},
nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef},
symbol_resolver::ValueEnum,
toplevel::{
helper::{extract_ndims, PrimDef},
numpy::unpack_ndarray_var_tys,
DefinitionId, GenCall,
},
typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
};
/// The parallelism mode within a block.
#[derive(Copy, Clone, Eq, PartialEq)]
enum ParallelMode {
@ -460,52 +461,49 @@ fn format_rpc_arg<'ctx>(
let llvm_i1 = ctx.ctx.bool_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
let llvm_arg_ty =
NDArrayType::new(generator, ctx.ctx, ctx.get_llvm_type(generator, elem_ty));
let llvm_arg = NDArrayValue::from_ptr_val(arg.into_pointer_value(), llvm_usize, None);
let (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, Some(ndims))
.map_value(arg.into_pointer_value(), None);
let llvm_usize_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(llvm_arg_ty.size_type().size_of(), llvm_usize, "")
.unwrap();
let llvm_pdata_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(
llvm_arg_ty.element_type().ptr_type(AddressSpace::default()).size_of(),
llvm_usize,
"",
)
.unwrap();
let ndims = llvm_usize.const_int(ndims, false);
let dims_buf_sz =
ctx.builder.build_int_mul(llvm_arg.load_ndims(ctx), llvm_usize_sizeof, "").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 buffer_size =
ctx.builder.build_int_add(dims_buf_sz, llvm_pdata_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 = 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_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();
call_memcpy_generic(
ctx,
buffer.base_ptr(ctx, generator),
llvm_arg.ptr_to_data(ctx),
llvm_pdata_sizeof,
llvm_i1.const_zero(),
);
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();
let pbuffer_dims_begin =
unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
call_memcpy_generic(
ctx,
pbuffer_dims_begin,
llvm_arg.dim_sizes().base_ptr(ctx, generator),
dims_buf_sz,
llvm_i1.const_zero(),
);
// 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) };
buffer.base_ptr(ctx, generator)
// 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)
}
_ => {
@ -515,7 +513,7 @@ fn format_rpc_arg<'ctx>(
ctx.builder.build_store(arg_slot, arg).unwrap();
ctx.builder
.build_bitcast(arg_slot, llvm_pi8, "rpc.arg")
.build_bit_cast(arg_slot, llvm_pi8, "rpc.arg")
.map(BasicValueEnum::into_pointer_value)
.unwrap()
}
@ -546,6 +544,8 @@ 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)
@ -566,8 +566,7 @@ 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 llvm_i1 = ctx.ctx.bool_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let num_0 = llvm_usize.const_zero();
// Round `val` up to its modulo `power_of_two`
let round_up = |ctx: &mut CodeGenContext<'ctx, '_>,
@ -593,79 +592,49 @@ 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 ndarray = llvm_ret_ty.new_value(generator, ctx, Some("rpc.result"));
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, Some(ndims))
.construct_uninitialized(generator, ctx, None);
// Setup ndims
let ndims =
if let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) {
assert_eq!(values.len(), 1);
// 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.
u64::try_from(values[0].clone()).unwrap()
} else {
unreachable!();
};
// Set `ndarray.ndims`
ndarray.store_ndims(ctx, generator, llvm_usize.const_int(ndims, false));
// Allocate `ndarray.shape` [size_t; ndims]
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray.load_ndims(ctx));
/*
ndarray now:
- .ndims: initialized
- .shape: allocated but uninitialized .shape
- .data: uninitialized
*/
let llvm_usize_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(llvm_usize.size_of(), llvm_usize, "")
.unwrap();
let llvm_pdata_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(
llvm_ret_ty.element_type().size_of().unwrap(),
llvm_usize,
"",
)
.unwrap();
let llvm_elem_sizeof = ctx
.builder
.build_int_truncate_or_bit_cast(llvm_elem_ty.size_of().unwrap(), llvm_usize, "")
.unwrap();
let itemsize = ndarray.load_itemsize(ctx); // Same as doing a `ctx.get_llvm_type` on `dtype` and get its `size_of()`.
// Allocates a buffer for the initial RPC'ed object, which is guaranteed to be
// (4 + 4 * ndims) bytes with 8-byte alignment
let sizeof_dims =
ctx.builder.build_int_mul(ndarray.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
let 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 unaligned_buffer_size =
ctx.builder.build_int_add(sizeof_dims, llvm_pdata_sizeof, "").unwrap();
let buffer_size = round_up(ctx, unaligned_buffer_size, llvm_usize.const_int(8, false));
ctx.builder.build_int_add(sizeof_ptr, sizeof_shape, "").unwrap();
let stackptr = call_stacksave(ctx, None);
// Just to be absolutely sure, alloca in [i8 x 8] slices to force 8-byte alignment
let buffer = ctx
.builder
.build_array_alloca(
llvm_i8_8,
ctx.builder
.build_int_unsigned_div(buffer_size, llvm_usize.const_int(8, false), "")
.unwrap(),
"rpc.buffer",
)
.unwrap();
let buffer = ctx
.builder
.build_bitcast(buffer, llvm_pi8, "")
.map(BasicValueEnum::into_pointer_value)
.unwrap();
let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, None);
let buffer = type_aligned_alloca(
generator,
ctx,
llvm_i8_8,
unaligned_buffer_size,
Some("rpc.buffer"),
);
let buffer = ArraySliceValue::from_ptr_val(buffer, unaligned_buffer_size, None);
// The first call to `rpc_recv` reads the top-level ndarray object: [pdata, shape]
//
@ -673,7 +642,7 @@ fn format_rpc_ret<'ctx>(
let ndarray_nbytes = ctx
.build_call_or_invoke(
rpc_recv,
&[buffer.base_ptr(ctx, generator).into()], // Reads [usize; ndims]. NOTE: We are allocated [size_t; ndims].
&[buffer.base_ptr(ctx, generator).into()], // Reads [usize; ndims]
"rpc.size.next",
)
.map(BasicValueEnum::into_int_value)
@ -681,16 +650,14 @@ 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,
ctx.builder
.build_int_compare(
IntPredicate::UGT,
ndarray_nbytes,
ndarray_nbytes.get_type().const_zero(),
"",
)
.unwrap(),
cmp,
"0:AssertionError",
"Unexpected RPC termination for ndarray - Expected data buffer next",
[None, None, None],
@ -699,49 +666,50 @@ fn format_rpc_ret<'ctx>(
}
// Copy shape from the buffer to `ndarray.shape`.
let pbuffer_dims =
unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
// 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);
call_memcpy_generic(
ctx,
ndarray.dim_sizes().base_ptr(ctx, generator),
pbuffer_dims,
sizeof_dims,
llvm_i1.const_zero(),
);
// Restore stack from before allocation of buffer
call_stackrestore(ctx, stackptr);
// Allocate `ndarray.data`.
// `ndarray.shape` must be initialized beforehand in this implementation
// (for ndarray.create_data() to know how many elements to allocate)
let num_elements =
call_ndarray_calc_size(generator, ctx, &ndarray.dim_sizes(), (None, None));
unsafe { ndarray.create_data(generator, ctx) }; // NOTE: the strides of `ndarray` has also been set to contiguous in `create_data`.
// debug_assert(nelems * sizeof(T) >= ndarray_nbytes)
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
let sizeof_data =
ctx.builder.build_int_mul(num_elements, llvm_elem_sizeof, "").unwrap();
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();
ctx.make_assert(
generator,
ctx.builder.build_int_compare(IntPredicate::UGE,
sizeof_data,
ndarray_nbytes,
"",
).unwrap(),
cmp,
"0:AssertionError",
"Unexpected allocation size request for ndarray data - Expected up to {0} bytes, got {1} bytes",
[Some(sizeof_data), Some(ndarray_nbytes), None],
[Some(expected_ndarray_nbytes), 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();
@ -750,7 +718,7 @@ 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_i8, prehead_bb)]);
phi.add_incoming(&[(&ndarray_data, prehead_bb)]);
let alloc_size = ctx
.build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
@ -765,12 +733,13 @@ 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, llvm_elem_sizeof);
let alloc_size = round_up(ctx, alloc_size, itemsize);
// TODO(Derppening): Candidate for refactor into type_aligned_alloca
let alloc_ptr = ctx
.builder
.build_array_alloca(
llvm_elem_ty,
ctx.builder.build_int_unsigned_div(alloc_size, llvm_elem_sizeof, "").unwrap(),
dtype_llvm,
ctx.builder.build_int_unsigned_div(alloc_size, itemsize, "").unwrap(),
"rpc.alloc",
)
.unwrap();
@ -785,7 +754,7 @@ fn format_rpc_ret<'ctx>(
_ => {
let slot = ctx.builder.build_alloca(llvm_ret_ty, "rpc.ret.slot").unwrap();
let slotgen = ctx.builder.build_bitcast(slot, llvm_pi8, "rpc.ret.ptr").unwrap();
let slotgen = ctx.builder.build_bit_cast(slot, llvm_pi8, "rpc.ret.ptr").unwrap();
ctx.builder.build_unconditional_branch(head_bb).unwrap();
ctx.builder.position_at_end(head_bb);
@ -806,7 +775,7 @@ fn format_rpc_ret<'ctx>(
let alloc_ptr =
ctx.builder.build_array_alloca(llvm_pi8, alloc_size, "rpc.alloc").unwrap();
let alloc_ptr =
ctx.builder.build_bitcast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
ctx.builder.build_bit_cast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
ctx.builder.build_unconditional_branch(head_bb).unwrap();
@ -824,6 +793,7 @@ fn rpc_codegen_callback_fn<'ctx>(
fun: (&FunSignature, DefinitionId),
args: Vec<(Option<StrRef>, ValueEnum<'ctx>)>,
generator: &mut dyn CodeGenerator,
is_async: bool,
) -> Result<Option<BasicValueEnum<'ctx>>, String> {
let int8 = ctx.ctx.i8_type();
let int32 = ctx.ctx.i32_type();
@ -932,42 +902,72 @@ fn rpc_codegen_callback_fn<'ctx>(
}
// call
let rpc_send = ctx.module.get_function("rpc_send").unwrap_or_else(|| {
ctx.module.add_function(
"rpc_send",
ctx.ctx.void_type().fn_type(
&[
int32.into(),
tag_ptr_type.ptr_type(AddressSpace::default()).into(),
ptr_type.ptr_type(AddressSpace::default()).into(),
],
false,
),
None,
)
});
ctx.builder
.build_call(rpc_send, &[service_id.into(), tag_ptr.into(), args_ptr.into()], "rpc.send")
.unwrap();
if is_async {
let rpc_send_async = ctx.module.get_function("rpc_send_async").unwrap_or_else(|| {
ctx.module.add_function(
"rpc_send_async",
ctx.ctx.void_type().fn_type(
&[
int32.into(),
tag_ptr_type.ptr_type(AddressSpace::default()).into(),
ptr_type.ptr_type(AddressSpace::default()).into(),
],
false,
),
None,
)
});
ctx.builder
.build_call(
rpc_send_async,
&[service_id.into(), tag_ptr.into(), args_ptr.into()],
"rpc.send",
)
.unwrap();
} else {
let rpc_send = ctx.module.get_function("rpc_send").unwrap_or_else(|| {
ctx.module.add_function(
"rpc_send",
ctx.ctx.void_type().fn_type(
&[
int32.into(),
tag_ptr_type.ptr_type(AddressSpace::default()).into(),
ptr_type.ptr_type(AddressSpace::default()).into(),
],
false,
),
None,
)
});
ctx.builder
.build_call(rpc_send, &[service_id.into(), tag_ptr.into(), args_ptr.into()], "rpc.send")
.unwrap();
}
// reclaim stack space used by arguments
call_stackrestore(ctx, stackptr);
let result = format_rpc_ret(generator, ctx, fun.0.ret);
if is_async {
// async RPCs do not return any values
Ok(None)
} else {
let result = format_rpc_ret(generator, ctx, fun.0.ret);
if !result.is_some_and(|res| res.get_type().is_pointer_type()) {
// An RPC returning an NDArray would not touch here.
call_stackrestore(ctx, stackptr);
if !result.is_some_and(|res| res.get_type().is_pointer_type()) {
// An RPC returning an NDArray would not touch here.
call_stackrestore(ctx, stackptr);
}
Ok(result)
}
Ok(result)
}
pub fn attributes_writeback(
ctx: &mut CodeGenContext<'_, '_>,
pub fn attributes_writeback<'ctx>(
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut dyn CodeGenerator,
inner_resolver: &InnerResolver,
host_attributes: &PyObject,
return_obj: Option<(Type, ValueEnum<'ctx>)>,
) -> Result<(), String> {
Python::with_gil(|py| -> PyResult<Result<(), String>> {
let host_attributes: &PyList = host_attributes.downcast(py)?;
@ -977,6 +977,11 @@ pub fn attributes_writeback(
let zero = int32.const_zero();
let mut values = Vec::new();
let mut scratch_buffer = Vec::new();
if let Some((ty, obj)) = return_obj {
values.push((ty, obj.to_basic_value_enum(ctx, generator, ty).unwrap()));
}
for val in (*globals).values() {
let val = val.as_ref(py);
let ty = inner_resolver.get_obj_type(
@ -1055,7 +1060,7 @@ pub fn attributes_writeback(
let args: Vec<_> =
values.into_iter().map(|(_, val)| (None, ValueEnum::Dynamic(val))).collect();
if let Err(e) =
rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator)
rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator, true)
{
return Ok(Err(e));
}
@ -1065,9 +1070,9 @@ pub fn attributes_writeback(
Ok(())
}
pub fn rpc_codegen_callback() -> Arc<GenCall> {
Arc::new(GenCall::new(Box::new(|ctx, obj, fun, args, generator| {
rpc_codegen_callback_fn(ctx, obj, fun, args, generator)
pub fn rpc_codegen_callback(is_async: bool) -> Arc<GenCall> {
Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
rpc_codegen_callback_fn(ctx, obj, fun, args, generator, is_async)
})))
}
@ -1281,7 +1286,8 @@ fn polymorphic_print<'ctx>(
fmt.push('[');
flush(ctx, generator, &mut fmt, &mut args);
let val = ListValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
let val =
ListValue::from_pointer_value(value.into_pointer_value(), llvm_usize, None);
let len = val.load_size(ctx, None);
let last =
ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
@ -1332,56 +1338,50 @@ fn polymorphic_print<'ctx>(
}
TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
fmt.push_str("array([");
flush(ctx, generator, &mut fmt, &mut args);
let val = NDArrayValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
let len = call_ndarray_calc_size(generator, ctx, &val.dim_sizes(), (None, None));
let last =
ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
let (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);
gen_for_callback_incrementing(
generator,
ctx,
None,
llvm_usize.const_zero(),
(len, false),
|generator, ctx, _, i| {
let elem = unsafe { val.data().get_unchecked(ctx, generator, &i, None) };
let num_0 = llvm_usize.const_zero();
polymorphic_print(
ctx,
generator,
&[(elem_ty, elem.into())],
"",
None,
true,
as_rtio,
)?;
// Print `ndarray` as a flat list delimited by interspersed with ", \0"
ndarray.foreach(generator, ctx, |generator, ctx, _, hdl| {
let i = hdl.get_index(ctx);
let scalar = hdl.get_scalar(ctx);
gen_if_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(IntPredicate::ULT, i, last, "")
.unwrap())
},
|generator, ctx| {
printf(ctx, generator, ", \0".into(), Vec::default());
// if (i != 0) puts(", ");
gen_if_callback(
generator,
ctx,
|_, ctx| {
let not_first = 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(()),
)?;
Ok(())
},
|_, _| Ok(()),
)?;
Ok(())
},
llvm_usize.const_int(1, false),
)?;
// Print element
polymorphic_print(
ctx,
generator,
&[(dtype, scalar.into())],
"",
None,
true,
as_rtio,
)?;
Ok(())
})?;
fmt.push_str(")]");
flush(ctx, generator, &mut fmt, &mut args);
@ -1391,7 +1391,7 @@ fn polymorphic_print<'ctx>(
fmt.push_str("range(");
flush(ctx, generator, &mut fmt, &mut args);
let val = RangeValue::from_ptr_val(value.into_pointer_value(), None);
let val = RangeValue::from_pointer_value(value.into_pointer_value(), None);
let (start, stop, step) = destructure_range(ctx, val);

View File

@ -1,10 +1,4 @@
#![deny(
future_incompatible,
let_underscore,
nonstandard_style,
rust_2024_compatibility,
clippy::all
)]
#![deny(future_incompatible, let_underscore, nonstandard_style, clippy::all)]
#![warn(clippy::pedantic)]
#![allow(
unsafe_op_in_unsafe_fn,
@ -16,64 +10,65 @@
clippy::wildcard_imports
)]
use std::collections::{HashMap, HashSet};
use std::fs;
use std::io::Write;
use std::process::Command;
use std::rc::Rc;
use std::sync::Arc;
use inkwell::{
context::Context,
memory_buffer::MemoryBuffer,
module::{Linkage, Module},
passes::PassBuilderOptions,
support::is_multithreaded,
targets::*,
OptimizationLevel,
use std::{
collections::{HashMap, HashSet},
fs,
io::Write,
process::Command,
rc::Rc,
sync::Arc,
};
use itertools::Itertools;
use nac3core::codegen::{gen_func_impl, CodeGenLLVMOptions, CodeGenTargetMachineOptions};
use nac3core::toplevel::builtins::get_exn_constructor;
use nac3core::typecheck::typedef::{into_var_map, TypeEnum, Unifier, VarMap};
use nac3parser::{
ast::{ExprKind, Stmt, StmtKind, StrRef},
parser::parse_program,
};
use pyo3::create_exception;
use pyo3::prelude::*;
use pyo3::{exceptions, types::PyBytes, types::PyDict, types::PySet};
use parking_lot::{Mutex, RwLock};
use pyo3::{
create_exception, exceptions,
prelude::*,
types::{PyBytes, PyDict, PyNone, PySet},
};
use tempfile::{self, TempDir};
use nac3core::{
codegen::irrt::load_irrt,
codegen::{concrete_type::ConcreteTypeStore, CodeGenTask, WithCall, WorkerRegistry},
codegen::{
concrete_type::ConcreteTypeStore, gen_func_impl, irrt::load_irrt, CodeGenLLVMOptions,
CodeGenTargetMachineOptions, CodeGenTask, CodeGenerator, WithCall, WorkerRegistry,
},
inkwell::{
context::Context,
memory_buffer::MemoryBuffer,
module::{FlagBehavior, Linkage, Module},
passes::PassBuilderOptions,
support::is_multithreaded,
targets::*,
OptimizationLevel,
},
nac3parser::{
ast::{Constant, ExprKind, Located, Stmt, StmtKind, StrRef},
parser::parse_program,
},
symbol_resolver::SymbolResolver,
toplevel::{
builtins::get_exn_constructor,
composer::{BuiltinFuncCreator, BuiltinFuncSpec, ComposerConfig, TopLevelComposer},
DefinitionId, GenCall, TopLevelDef,
},
typecheck::typedef::{FunSignature, FuncArg},
typecheck::{type_inferencer::PrimitiveStore, typedef::Type},
typecheck::{
type_inferencer::PrimitiveStore,
typedef::{into_var_map, FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
},
};
use nac3ld::Linker;
use crate::{
codegen::{
attributes_writeback, gen_core_log, gen_rtio_log, rpc_codegen_callback, ArtiqCodeGenerator,
},
symbol_resolver::{DeferredEvaluationStore, InnerResolver, PythonHelper, Resolver},
use codegen::{
attributes_writeback, gen_core_log, gen_rtio_log, rpc_codegen_callback, ArtiqCodeGenerator,
};
use tempfile::{self, TempDir};
use symbol_resolver::{DeferredEvaluationStore, InnerResolver, PythonHelper, Resolver};
use timeline::TimeFns;
mod codegen;
mod symbol_resolver;
mod timeline;
use timeline::TimeFns;
#[derive(PartialEq, Clone, Copy)]
enum Isa {
Host,
@ -147,14 +142,32 @@ impl Nac3 {
module: &PyObject,
registered_class_ids: &HashSet<u64>,
) -> PyResult<()> {
let (module_name, source_file) = Python::with_gil(|py| -> PyResult<(String, String)> {
let module: &PyAny = module.extract(py)?;
Ok((module.getattr("__name__")?.extract()?, module.getattr("__file__")?.extract()?))
})?;
let (module_name, source_file, source) =
Python::with_gil(|py| -> PyResult<(String, String, String)> {
let module: &PyAny = module.extract(py)?;
let source_file = module.getattr("__file__");
let (source_file, source) = if let Ok(source_file) = source_file {
let source_file = source_file.extract()?;
(
source_file,
fs::read_to_string(source_file).map_err(|e| {
exceptions::PyIOError::new_err(format!(
"failed to read input file: {e}"
))
})?,
)
} else {
// kernels submitted by content have no file
// but still can provide source by StringLoader
let get_src_fn = module
.getattr("__loader__")?
.extract::<PyObject>()?
.getattr(py, "get_source")?;
("<expcontent>", get_src_fn.call1(py, (PyNone::get(py),))?.extract(py)?)
};
Ok((module.getattr("__name__")?.extract()?, source_file.to_string(), source))
})?;
let source = fs::read_to_string(&source_file).map_err(|e| {
exceptions::PyIOError::new_err(format!("failed to read input file: {e}"))
})?;
let parser_result = parse_program(&source, source_file.into())
.map_err(|e| exceptions::PySyntaxError::new_err(format!("parse error: {e}")))?;
@ -194,10 +207,8 @@ impl Nac3 {
body.retain(|stmt| {
if let StmtKind::FunctionDef { ref decorator_list, .. } = stmt.node {
decorator_list.iter().any(|decorator| {
if let ExprKind::Name { id, .. } = decorator.node {
id.to_string() == "kernel"
|| id.to_string() == "portable"
|| id.to_string() == "rpc"
if let Some(id) = decorator_id_string(decorator) {
id == "kernel" || id == "portable" || id == "rpc"
} else {
false
}
@ -210,9 +221,8 @@ impl Nac3 {
}
StmtKind::FunctionDef { ref decorator_list, .. } => {
decorator_list.iter().any(|decorator| {
if let ExprKind::Name { id, .. } = decorator.node {
let id = id.to_string();
id == "extern" || id == "portable" || id == "kernel" || id == "rpc"
if let Some(id) = decorator_id_string(decorator) {
id == "extern" || id == "kernel" || id == "portable" || id == "rpc"
} else {
false
}
@ -478,9 +488,25 @@ impl Nac3 {
match &stmt.node {
StmtKind::FunctionDef { decorator_list, .. } => {
if decorator_list.iter().any(|decorator| matches!(decorator.node, ExprKind::Name { id, .. } if id == "rpc".into())) {
store_fun.call1(py, (def_id.0.into_py(py), module.getattr(py, name.to_string().as_str()).unwrap())).unwrap();
rpc_ids.push((None, def_id));
if decorator_list
.iter()
.any(|decorator| decorator_id_string(decorator) == Some("rpc".to_string()))
{
store_fun
.call1(
py,
(
def_id.0.into_py(py),
module.getattr(py, name.to_string().as_str()).unwrap(),
),
)
.unwrap();
let is_async = decorator_list.iter().any(|decorator| {
decorator_get_flags(decorator)
.iter()
.any(|constant| *constant == Constant::Str("async".into()))
});
rpc_ids.push((None, def_id, is_async));
}
}
StmtKind::ClassDef { name, body, .. } => {
@ -488,19 +514,26 @@ impl Nac3 {
let class_obj = module.getattr(py, class_name.as_str()).unwrap();
for stmt in body {
if let StmtKind::FunctionDef { name, decorator_list, .. } = &stmt.node {
if decorator_list.iter().any(|decorator| matches!(decorator.node, ExprKind::Name { id, .. } if id == "rpc".into())) {
if decorator_list.iter().any(|decorator| {
decorator_id_string(decorator) == Some("rpc".to_string())
}) {
let is_async = decorator_list.iter().any(|decorator| {
decorator_get_flags(decorator)
.iter()
.any(|constant| *constant == Constant::Str("async".into()))
});
if name == &"__init__".into() {
return Err(CompileError::new_err(format!(
"compilation failed\n----------\nThe constructor of class {} should not be decorated with rpc decorator (at {})",
class_name, stmt.location
)));
}
rpc_ids.push((Some((class_obj.clone(), *name)), def_id));
rpc_ids.push((Some((class_obj.clone(), *name)), def_id, is_async));
}
}
}
}
_ => ()
_ => (),
}
let id = *name_to_pyid.get(&name).unwrap();
@ -544,7 +577,7 @@ impl Nac3 {
field_to_val: RwLock::default(),
name_to_pyid,
module: module.to_object(py),
helper,
helper: helper.clone(),
string_store: self.string_store.clone(),
exception_ids: self.exception_ids.clone(),
deferred_eval_store: self.deferred_eval_store.clone(),
@ -556,7 +589,7 @@ impl Nac3 {
.unwrap();
// Process IRRT
let context = inkwell::context::Context::create();
let context = Context::create();
let irrt = load_irrt(&context, resolver.as_ref());
let fun_signature =
@ -596,13 +629,12 @@ impl Nac3 {
let top_level = Arc::new(composer.make_top_level_context());
{
let rpc_codegen = rpc_codegen_callback();
let defs = top_level.definitions.read();
for (class_data, id) in &rpc_ids {
for (class_data, id, is_async) in &rpc_ids {
let mut def = defs[id.0].write();
match &mut *def {
TopLevelDef::Function { codegen_callback, .. } => {
*codegen_callback = Some(rpc_codegen.clone());
*codegen_callback = Some(rpc_codegen_callback(*is_async));
}
TopLevelDef::Class { methods, .. } => {
let (class_def, method_name) = class_data.as_ref().unwrap();
@ -613,7 +645,7 @@ impl Nac3 {
if let TopLevelDef::Function { codegen_callback, .. } =
&mut *defs[id.0].write()
{
*codegen_callback = Some(rpc_codegen.clone());
*codegen_callback = Some(rpc_codegen_callback(*is_async));
store_fun
.call1(
py,
@ -628,6 +660,11 @@ impl Nac3 {
}
}
}
TopLevelDef::Variable { .. } => {
return Err(CompileError::new_err(String::from(
"Unsupported @rpc annotation on global variable",
)))
}
}
}
}
@ -648,33 +685,12 @@ impl Nac3 {
let task = CodeGenTask {
subst: Vec::default(),
symbol_name: "__modinit__".to_string(),
body: instance.body,
signature,
resolver: resolver.clone(),
store,
unifier_index: instance.unifier_id,
calls: instance.calls,
id: 0,
};
let mut store = ConcreteTypeStore::new();
let mut cache = HashMap::new();
let signature = store.from_signature(
&mut composer.unifier,
&self.primitive,
&fun_signature,
&mut cache,
);
let signature = store.add_cty(signature);
let attributes_writeback_task = CodeGenTask {
subst: Vec::default(),
symbol_name: "attributes_writeback".to_string(),
body: Arc::new(Vec::default()),
signature,
resolver,
store,
unifier_index: instance.unifier_id,
calls: Arc::new(HashMap::default()),
calls: instance.calls,
id: 0,
};
@ -687,7 +703,7 @@ impl Nac3 {
let buffer = buffer.as_slice().into();
membuffer.lock().push(buffer);
})));
let size_t = Context::create()
let size_t = context
.ptr_sized_int_type(&self.get_llvm_target_machine().get_target_data(), None)
.get_bit_width();
let num_threads = if is_multithreaded() { 4 } else { 1 };
@ -698,19 +714,27 @@ impl Nac3 {
.collect();
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);
registry.add_task(task);
registry.wait_tasks_complete(handles);
let mut generator =
ArtiqCodeGenerator::new("attributes_writeback".to_string(), size_t, self.time_fns);
let context = inkwell::context::Context::create();
let module = context.create_module("attributes_writeback");
let mut generator = ArtiqCodeGenerator::new("main".to_string(), size_t, self.time_fns);
let context = Context::create();
let module = context.create_module("main");
let target_machine = self.llvm_options.create_target_machine().unwrap();
module.set_data_layout(&target_machine.get_target_data().get_data_layout());
module.set_triple(&target_machine.get_triple());
module.add_basic_value_flag(
"Debug Info Version",
FlagBehavior::Warning,
context.i32_type().const_int(3, false),
);
module.add_basic_value_flag(
"Dwarf Version",
FlagBehavior::Warning,
context.i32_type().const_int(4, false),
);
let builder = context.create_builder();
let (_, module, _) = gen_func_impl(
&context,
@ -718,9 +742,27 @@ impl Nac3 {
&registry,
builder,
module,
attributes_writeback_task,
task,
|generator, ctx| {
attributes_writeback(ctx, generator, inner_resolver.as_ref(), &host_attributes)
assert_eq!(instance.body.len(), 1, "toplevel module should have 1 statement");
let StmtKind::Expr { value: ref expr, .. } = instance.body[0].node else {
unreachable!("toplevel statement must be an expression")
};
let ExprKind::Call { .. } = expr.node else {
unreachable!("toplevel expression must be a function call")
};
let return_obj =
generator.gen_expr(ctx, expr)?.map(|value| (expr.custom.unwrap(), value));
has_return = return_obj.is_some();
registry.wait_tasks_complete(handles);
attributes_writeback(
ctx,
generator,
inner_resolver.as_ref(),
&host_attributes,
return_obj,
)
},
)
.unwrap();
@ -729,35 +771,23 @@ impl Nac3 {
membuffer.lock().push(buffer);
});
embedding_map.setattr("expects_return", has_return).unwrap();
// Link all modules into `main`.
let buffers = membuffers.lock();
let main = context
.create_module_from_ir(MemoryBuffer::create_from_memory_range(&buffers[0], "main"))
.create_module_from_ir(MemoryBuffer::create_from_memory_range(
buffers.last().unwrap(),
"main",
))
.unwrap();
for buffer in buffers.iter().skip(1) {
for buffer in buffers.iter().rev().skip(1) {
let other = context
.create_module_from_ir(MemoryBuffer::create_from_memory_range(buffer, "main"))
.unwrap();
main.link_in_module(other).map_err(|err| CompileError::new_err(err.to_string()))?;
}
let builder = context.create_builder();
let modinit_return = main
.get_function("__modinit__")
.unwrap()
.get_last_basic_block()
.unwrap()
.get_terminator()
.unwrap();
builder.position_before(&modinit_return);
builder
.build_call(
main.get_function("attributes_writeback").unwrap(),
&[],
"attributes_writeback",
)
.unwrap();
main.link_in_module(irrt).map_err(|err| CompileError::new_err(err.to_string()))?;
let mut function_iter = main.get_first_function();
@ -792,6 +822,20 @@ impl Nac3 {
panic!("Failed to run optimization for module `main`: {}", err.to_string());
}
Python::with_gil(|py| {
let string_store = self.string_store.read();
let mut string_store_vec = string_store.iter().collect::<Vec<_>>();
string_store_vec.sort_by(|(_s1, key1), (_s2, key2)| key1.cmp(key2));
for (s, key) in string_store_vec {
let embed_key: i32 = helper.store_str.call1(py, (s,)).unwrap().extract(py).unwrap();
assert_eq!(
embed_key, *key,
"string {s} is out of sync between embedding map (key={embed_key}) and \
the internal string store (key={key})"
);
}
});
link_fn(&main)
}
@ -844,6 +888,41 @@ impl Nac3 {
}
}
/// Retrieves the Name.id from a decorator, supports decorators with arguments.
fn decorator_id_string(decorator: &Located<ExprKind>) -> Option<String> {
if let ExprKind::Name { id, .. } = decorator.node {
// Bare decorator
return Some(id.to_string());
} else if let ExprKind::Call { func, .. } = &decorator.node {
// Decorators that are calls (e.g. "@rpc()") have Call for the node,
// need to extract the id from within.
if let ExprKind::Name { id, .. } = func.node {
return Some(id.to_string());
}
}
None
}
/// Retrieves flags from a decorator, if any.
fn decorator_get_flags(decorator: &Located<ExprKind>) -> Vec<Constant> {
let mut flags = vec![];
if let ExprKind::Call { keywords, .. } = &decorator.node {
for keyword in keywords {
if keyword.node.arg != Some("flags".into()) {
continue;
}
if let ExprKind::Set { elts } = &keyword.node.value.node {
for elt in elts {
if let ExprKind::Constant { value, .. } = &elt.node {
flags.push(value.clone());
}
}
}
}
}
flags
}
fn link_with_lld(elf_filename: String, obj_filename: String) -> PyResult<()> {
let linker_args = vec![
"-shared".to_string(),
@ -1006,6 +1085,48 @@ impl Nac3 {
let working_directory = tempfile::Builder::new().prefix("nac3-").tempdir().unwrap();
fs::write(working_directory.path().join("kernel.ld"), include_bytes!("kernel.ld")).unwrap();
let mut string_store: HashMap<String, i32> = HashMap::default();
// Keep this list of exceptions in sync with `EXCEPTION_ID_LOOKUP` in `artiq::firmware::ksupport::eh_artiq`
// The exceptions declared here must be defined in `artiq.coredevice.exceptions`
// Verify synchronization by running the test cases in `artiq.test.coredevice.test_exceptions`
let runtime_exception_names = [
"RTIOUnderflow",
"RTIOOverflow",
"RTIODestinationUnreachable",
"DMAError",
"I2CError",
"CacheError",
"SPIError",
"SubkernelError",
"0:AssertionError",
"0:AttributeError",
"0:IndexError",
"0:IOError",
"0:KeyError",
"0:NotImplementedError",
"0:OverflowError",
"0:RuntimeError",
"0:TimeoutError",
"0:TypeError",
"0:ValueError",
"0:ZeroDivisionError",
"0:LinAlgError",
"UnwrapNoneError",
];
// Preallocate runtime exception names
for (i, name) in runtime_exception_names.iter().enumerate() {
let exn_name = if name.find(':').is_none() {
format!("0:artiq.coredevice.exceptions.{name}")
} else {
(*name).to_string()
};
let id = i32::try_from(i).unwrap();
string_store.insert(exn_name, id);
}
Ok(Nac3 {
isa,
time_fns,
@ -1015,7 +1136,7 @@ impl Nac3 {
top_levels: Vec::default(),
pyid_to_def: Arc::default(),
working_directory,
string_store: Arc::default(),
string_store: Arc::new(string_store.into()),
exception_ids: Arc::default(),
deferred_eval_store: DeferredEvaluationStore::new(),
llvm_options: CodeGenLLVMOptions {
@ -1025,7 +1146,12 @@ impl Nac3 {
})
}
fn analyze(&mut self, functions: &PySet, classes: &PySet) -> PyResult<()> {
fn analyze(
&mut self,
functions: &PySet,
classes: &PySet,
content_modules: &PySet,
) -> PyResult<()> {
let (modules, class_ids) =
Python::with_gil(|py| -> PyResult<(HashMap<u64, PyObject>, HashSet<u64>)> {
let mut modules: HashMap<u64, PyObject> = HashMap::new();
@ -1035,14 +1161,22 @@ impl Nac3 {
let getmodule_fn = PyModule::import(py, "inspect")?.getattr("getmodule")?;
for function in functions {
let module = getmodule_fn.call1((function,))?.extract()?;
modules.insert(id_fn.call1((&module,))?.extract()?, module);
let module: PyObject = getmodule_fn.call1((function,))?.extract()?;
if !module.is_none(py) {
modules.insert(id_fn.call1((&module,))?.extract()?, module);
}
}
for class in classes {
let module = getmodule_fn.call1((class,))?.extract()?;
modules.insert(id_fn.call1((&module,))?.extract()?, module);
let module: PyObject = getmodule_fn.call1((class,))?.extract()?;
if !module.is_none(py) {
modules.insert(id_fn.call1((&module,))?.extract()?, module);
}
class_ids.insert(id_fn.call1((class,))?.extract()?);
}
for module in content_modules {
let module: PyObject = module.extract()?;
modules.insert(id_fn.call1((&module,))?.extract()?, module);
}
Ok((modules, class_ids))
})?;

View File

@ -1,16 +1,32 @@
use crate::PrimitivePythonId;
use inkwell::{
module::Linkage,
types::{BasicType, BasicTypeEnum},
values::BasicValueEnum,
AddressSpace,
use std::{
collections::{HashMap, HashSet},
sync::{
atomic::{AtomicBool, Ordering::Relaxed},
Arc,
},
};
use itertools::Itertools;
use parking_lot::RwLock;
use pyo3::{
types::{PyDict, PyTuple},
PyAny, PyErr, PyObject, PyResult, Python,
};
use super::PrimitivePythonId;
use nac3core::{
codegen::{
classes::{NDArrayType, ProxyType},
types::{ndarray::NDArrayType, ProxyType},
values::ndarray::make_contiguous_strides,
CodeGenContext, CodeGenerator,
},
inkwell::{
module::Linkage,
types::{BasicType, BasicTypeEnum},
values::BasicValueEnum,
AddressSpace,
},
nac3parser::ast::{self, StrRef},
symbol_resolver::{StaticValue, SymbolResolver, SymbolValue, ValueEnum},
toplevel::{
helper::PrimDef,
@ -22,19 +38,6 @@ use nac3core::{
typedef::{into_var_map, iter_type_vars, Type, TypeEnum, TypeVar, Unifier, VarMap},
},
};
use nac3parser::ast::{self, StrRef};
use parking_lot::RwLock;
use pyo3::{
types::{PyDict, PyTuple},
PyAny, PyObject, PyResult, Python,
};
use std::{
collections::{HashMap, HashSet},
sync::{
atomic::{AtomicBool, Ordering::Relaxed},
Arc,
},
};
pub enum PrimitiveValue {
I32(i32),
@ -1082,18 +1085,19 @@ impl InnerResolver {
} else {
unreachable!("must be ndarray")
};
let (ndarray_dtype, ndarray_ndims) =
unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
let (ndarray_dtype, _) = 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 ndarray_dtype_llvm_ty = ctx.get_llvm_type(generator, ndarray_dtype);
let ndarray_llvm_ty = NDArrayType::new(generator, ctx.ctx, ndarray_dtype_llvm_ty);
let llvm_ndarray = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty);
let dtype = llvm_ndarray.element_type();
{
if self.global_value_ids.read().contains_key(&id) {
let global = ctx.module.get_global(&id_str).unwrap_or_else(|| {
ctx.module.add_global(
ndarray_llvm_ty.as_underlying_type(),
llvm_ndarray.as_base_type().get_element_type().into_struct_type(),
Some(AddressSpace::default()),
&id_str,
)
@ -1103,40 +1107,41 @@ impl InnerResolver {
self.global_value_ids.write().insert(id, obj.into());
}
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")
};
let ndims = llvm_ndarray.ndims().unwrap();
// Obtain the shape of the ndarray
let shape_tuple: &PyTuple = obj.getattr("shape")?.downcast()?;
assert_eq!(shape_tuple.len(), ndarray_ndims as usize);
let shape_values: Result<Option<Vec<_>>, _> = shape_tuple
assert_eq!(shape_tuple.len(), ndims as usize);
// The Rust type inferencer cannot figure this out
let shape_values = shape_tuple
.iter()
.enumerate()
.map(|(i, elem)| {
self.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize()).map_err(
|e| super::CompileError::new_err(format!("Error getting element {i}: {e}")),
)
let value = self
.get_obj_value(py, elem, ctx, generator, ctx.primitives.usize())
.map_err(|e| {
super::CompileError::new_err(format!("Error getting element {i}: {e}"))
})?
.unwrap();
let value = value.into_int_value();
Ok(value)
})
.collect();
let shape_values = shape_values?.unwrap();
let shape_values = llvm_usize.const_array(
&shape_values.into_iter().map(BasicValueEnum::into_int_value).collect_vec(),
);
.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);
// create a global for ndarray.shape and initialize it using the shape
let shape_global = ctx.module.add_global(
llvm_usize.array_type(ndarray_ndims as u32),
llvm_usize.array_type(ndims as u32),
Some(AddressSpace::default()),
&(id_str.clone() + ".shape"),
);
@ -1144,17 +1149,25 @@ impl InnerResolver {
// Obtain the (flattened) elements of the ndarray
let sz: usize = obj.getattr("size")?.extract()?;
let data: Result<Option<Vec<_>>, _> = (0..sz)
let data: Vec<_> = (0..sz)
.map(|i| {
obj.getattr("flat")?.get_item(i).and_then(|elem| {
self.get_obj_value(py, elem, ctx, generator, ndarray_dtype).map_err(|e| {
super::CompileError::new_err(format!("Error getting element {i}: {e}"))
})
let value = self
.get_obj_value(py, elem, ctx, generator, ndarray_dtype)
.map_err(|e| {
super::CompileError::new_err(format!(
"Error getting element {i}: {e}"
))
})?
.unwrap();
assert_eq!(value.get_type(), dtype);
Ok(value)
})
})
.collect();
let data = data?.unwrap().into_iter();
let data = match ndarray_dtype_llvm_ty {
.try_collect()?;
let data = data.into_iter();
let data = match dtype {
BasicTypeEnum::ArrayType(ty) => {
ty.const_array(&data.map(BasicValueEnum::into_array_value).collect_vec())
}
@ -1179,34 +1192,68 @@ 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(
ndarray_dtype_llvm_ty.array_type(sz as u32),
dtype.array_type(sz as u32),
Some(AddressSpace::default()),
&(id_str.clone() + ".data"),
);
data_global.set_initializer(&data);
// create a global for the ndarray object and initialize it
let value = ndarray_llvm_ty.as_underlying_type().const_named_struct(&[
llvm_usize.const_int(ndarray_ndims, false).into(),
shape_global
.as_pointer_value()
.const_cast(llvm_usize.ptr_type(AddressSpace::default()))
.into(),
data_global
.as_pointer_value()
.const_cast(ndarray_dtype_llvm_ty.ptr_type(AddressSpace::default()))
.into(),
]);
// Get the constant itemsize.
let itemsize = dtype.size_of().unwrap();
let itemsize = itemsize.get_zero_extended_constant().unwrap();
let ndarray = ctx.module.add_global(
ndarray_llvm_ty.as_underlying_type(),
// 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_i8.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);
let ndarray_shape = shape_global.as_pointer_value();
let ndarray_strides = strides_global.as_pointer_value();
let ndarray = llvm_ndarray
.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(),
]);
let ndarray_global = ctx.module.add_global(
llvm_ndarray.as_base_type().get_element_type().into_struct_type(),
Some(AddressSpace::default()),
&id_str,
);
ndarray.set_initializer(&value);
ndarray_global.set_initializer(&ndarray);
Ok(Some(ndarray.as_pointer_value().into()))
Ok(Some(ndarray_global.as_pointer_value().into()))
} else if ty_id == self.primitive_ids.tuple {
let expected_ty_enum = ctx.unifier.get_ty_immutable(expected_ty);
let TypeEnum::TTuple { ty, is_vararg_ctx: false } = expected_ty_enum.as_ref() else {
@ -1467,6 +1514,7 @@ impl SymbolResolver for Resolver {
&self,
id: StrRef,
_: &mut CodeGenContext<'ctx, '_>,
_: &mut dyn CodeGenerator,
) -> Option<ValueEnum<'ctx>> {
let sym_value = {
let id_to_val = self.0.id_to_pyval.read();
@ -1528,10 +1576,7 @@ impl SymbolResolver for Resolver {
if let Some(id) = string_store.get(s) {
*id
} else {
let id = Python::with_gil(|py| -> PyResult<i32> {
self.0.helper.store_str.call1(py, (s,))?.extract(py)
})
.unwrap();
let id = i32::try_from(string_store.len()).unwrap();
string_store.insert(s.into(), id);
id
}

View File

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

View File

@ -10,7 +10,6 @@ constant-optimization = ["fold"]
fold = []
[dependencies]
lazy_static = "1.5"
parking_lot = "0.12"
string-interner = "0.17"
fxhash = "0.2"

View File

@ -5,14 +5,12 @@ pub use crate::location::Location;
use fxhash::FxBuildHasher;
use parking_lot::{Mutex, MutexGuard};
use std::{cell::RefCell, collections::HashMap, fmt};
use std::{cell::RefCell, collections::HashMap, fmt, sync::LazyLock};
use string_interner::{symbol::SymbolU32, DefaultBackend, StringInterner};
pub type Interner = StringInterner<DefaultBackend, FxBuildHasher>;
lazy_static! {
static ref INTERNER: Mutex<Interner> =
Mutex::new(StringInterner::with_hasher(FxBuildHasher::default()));
}
static INTERNER: LazyLock<Mutex<Interner>> =
LazyLock::new(|| Mutex::new(StringInterner::with_hasher(FxBuildHasher::default())));
thread_local! {
static LOCAL_INTERNER: RefCell<HashMap<String, StrRef>> = RefCell::default();

View File

@ -1,10 +1,4 @@
#![deny(
future_incompatible,
let_underscore,
nonstandard_style,
rust_2024_compatibility,
clippy::all
)]
#![deny(future_incompatible, let_underscore, nonstandard_style, clippy::all)]
#![warn(clippy::pedantic)]
#![allow(
clippy::missing_errors_doc,
@ -14,9 +8,6 @@
clippy::wildcard_imports
)]
#[macro_use]
extern crate lazy_static;
mod ast_gen;
mod constant;
#[cfg(feature = "fold")]

View File

@ -5,22 +5,25 @@ authors = ["M-Labs"]
edition = "2021"
[features]
default = ["derive"]
derive = ["dep:nac3core_derive"]
no-escape-analysis = []
[dependencies]
itertools = "0.13"
crossbeam = "0.8"
indexmap = "2.2"
indexmap = "2.6"
parking_lot = "0.12"
rayon = "1.8"
rayon = "1.10"
nac3core_derive = { path = "nac3core_derive", optional = true }
nac3parser = { path = "../nac3parser" }
strum = "0.26"
strum_macros = "0.26"
[dependencies.inkwell]
version = "0.4"
version = "0.5"
default-features = false
features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
features = ["llvm14-0-prefer-dynamic", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
[dev-dependencies]
test-case = "1.2.0"

View File

@ -1,4 +1,3 @@
use regex::Regex;
use std::{
env,
fs::File,
@ -7,6 +6,8 @@ use std::{
process::{Command, Stdio},
};
use regex::Regex;
fn main() {
let out_dir = env::var("OUT_DIR").unwrap();
let out_dir = Path::new(&out_dir);
@ -55,9 +56,8 @@ fn main() {
let output = Command::new("clang-irrt")
.args(flags)
.output()
.map(|o| {
.inspect(|o| {
assert!(o.status.success(), "{}", std::str::from_utf8(&o.stderr).unwrap());
o
})
.unwrap();

View File

@ -1,5 +1,4 @@
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/list.hpp"
#include "irrt/math.hpp"
#include "irrt/ndarray.hpp"
@ -9,7 +8,3 @@
#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"

View File

@ -4,6 +4,6 @@
template<typename SizeT>
struct CSlice {
uint8_t* base;
void* base;
SizeT len;
};

View File

@ -6,7 +6,7 @@
/**
* @brief The int type of ARTIQ exception IDs.
*/
typedef int32_t ExceptionId;
using ExceptionId = int32_t;
/*
* Set of exceptions C++ IRRT can use.
@ -55,14 +55,14 @@ void _raise_exception_helper(ExceptionId id,
int64_t param2) {
Exception<SizeT> e = {
.id = id,
.filename = {.base = reinterpret_cast<uint8_t*>(const_cast<char*>(filename)),
.len = static_cast<int32_t>(__builtin_strlen(filename))},
.filename = {.base = reinterpret_cast<void*>(const_cast<char*>(filename)),
.len = static_cast<SizeT>(__builtin_strlen(filename))},
.line = line,
.column = 0,
.function = {.base = reinterpret_cast<uint8_t*>(const_cast<char*>(function)),
.len = static_cast<int32_t>(__builtin_strlen(function))},
.msg = {.base = reinterpret_cast<uint8_t*>(const_cast<char*>(msg)),
.len = static_cast<int32_t>(__builtin_strlen(msg))},
.function = {.base = reinterpret_cast<void*>(const_cast<char*>(function)),
.len = static_cast<SizeT>(__builtin_strlen(function))},
.msg = {.base = reinterpret_cast<void*>(const_cast<char*>(msg)),
.len = static_cast<SizeT>(__builtin_strlen(msg))},
};
e.params[0] = param0;
e.params[1] = param1;
@ -70,6 +70,7 @@ void _raise_exception_helper(ExceptionId id,
__nac3_raise(reinterpret_cast<void*>(&e));
__builtin_unreachable();
}
} // namespace
/**
* @brief Raise an exception with location details (location in the IRRT source files).
@ -82,4 +83,3 @@ void _raise_exception_helper(ExceptionId id,
*/
#define raise_exception(SizeT, id, msg, param0, param1, param2) \
_raise_exception_helper<SizeT>(id, __FILE__, __LINE__, __FUNCTION__, msg, param0, param1, param2)
} // namespace

View File

@ -1,13 +1,27 @@
#pragma once
#if __STDC_VERSION__ >= 202000
using int8_t = _BitInt(8);
using uint8_t = unsigned _BitInt(8);
using int32_t = _BitInt(32);
using uint32_t = unsigned _BitInt(32);
using int64_t = _BitInt(64);
using uint64_t = unsigned _BitInt(64);
#else
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wdeprecated-type"
using int8_t = _ExtInt(8);
using uint8_t = unsigned _ExtInt(8);
using int32_t = _ExtInt(32);
using uint32_t = unsigned _ExtInt(32);
using int64_t = _ExtInt(64);
using uint64_t = unsigned _ExtInt(64);
#pragma clang diagnostic pop
#endif
// NDArray indices are always `uint32_t`.
using NDIndexInt = uint32_t;
// The type of an index or a value describing the length of a range/slice is always `int32_t`.
using SliceIndex = int32_t;
using SliceIndex = int32_t;

View File

@ -2,21 +2,6 @@
#include "irrt/int_types.hpp"
#include "irrt/math_util.hpp"
#include "irrt/slice.hpp"
namespace {
/**
* @brief A list in NAC3.
*
* The `items` field is opaque. You must rely on external contexts to
* know how to interpret it.
*/
template<typename SizeT>
struct List {
uint8_t* items;
SizeT len;
};
} // namespace
extern "C" {
// Handle list assignment and dropping part of the list when
@ -28,12 +13,12 @@ extern "C" {
SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
SliceIndex dest_end,
SliceIndex dest_step,
uint8_t* dest_arr,
void* dest_arr,
SliceIndex dest_arr_len,
SliceIndex src_start,
SliceIndex src_end,
SliceIndex src_step,
uint8_t* src_arr,
void* src_arr,
SliceIndex src_arr_len,
const SliceIndex size) {
/* if dest_arr_len == 0, do nothing since we do not support extending list */
@ -44,11 +29,13 @@ SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
if (src_len > 0) {
__builtin_memmove(dest_arr + dest_start * size, src_arr + src_start * size, src_len * size);
__builtin_memmove(static_cast<uint8_t*>(dest_arr) + dest_start * size,
static_cast<uint8_t*>(src_arr) + src_start * size, src_len * size);
}
if (dest_len > 0) {
/* dropping */
__builtin_memmove(dest_arr + (dest_start + src_len) * size, dest_arr + (dest_end + 1) * size,
__builtin_memmove(static_cast<uint8_t*>(dest_arr) + (dest_start + src_len) * size,
static_cast<uint8_t*>(dest_arr) + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size);
}
/* shrink size */
@ -59,7 +46,7 @@ SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
&& !(max(dest_start, dest_end) < min(src_start, src_end)
|| max(src_start, src_end) < min(dest_start, dest_end));
if (need_alloca) {
uint8_t* tmp = reinterpret_cast<uint8_t*>(__builtin_alloca(src_arr_len * size));
void* tmp = __builtin_alloca(src_arr_len * size);
__builtin_memcpy(tmp, src_arr, src_arr_len * size);
src_arr = tmp;
}
@ -68,20 +55,24 @@ SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end); src_ind += src_step, dest_ind += dest_step) {
/* for constant optimization */
if (size == 1) {
__builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
__builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind, static_cast<uint8_t*>(src_arr) + src_ind, 1);
} else if (size == 4) {
__builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
__builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind * 4,
static_cast<uint8_t*>(src_arr) + src_ind * 4, 4);
} else if (size == 8) {
__builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
__builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind * 8,
static_cast<uint8_t*>(src_arr) + src_ind * 8, 8);
} else {
/* memcpy for var size, cannot overlap after previous alloca */
__builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
__builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind * size,
static_cast<uint8_t*>(src_arr) + src_ind * size, size);
}
}
/* only dest_step == 1 can we shrink the dest list. */
/* size should be ensured prior to calling this function */
if (dest_step == 1 && dest_end >= dest_start) {
__builtin_memmove(dest_arr + dest_ind * size, dest_arr + (dest_end + 1) * size,
__builtin_memmove(static_cast<uint8_t*>(dest_arr) + dest_ind * size,
static_cast<uint8_t*>(dest_arr) + (dest_end + 1) * size,
(dest_arr_len - dest_end - 1) * size);
return dest_arr_len - (dest_end - dest_ind) - 1;
}

View File

@ -90,4 +90,4 @@ double __nac3_j0(double x) {
return j0(x);
}
}
} // namespace

View File

@ -148,4 +148,4 @@ void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
NDIndexInt* out_idx) {
__nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
}
}
} // namespace

View File

@ -1,134 +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 {
namespace array {
/**
* @brief In the context of `np.array(<list>)`, deduce the ndarray's shape produced by `<list>` and raise
* an exception if there is anything wrong with `<shape>` (e.g., inconsistent dimensions `np.array([[1.0, 2.0],
* [3.0]])`)
*
* If this function finds no issues with `<list>`, the deduced shape is written to `shape`. The caller has the
* responsibility to allocate `[SizeT; ndims]` for `shape`. The caller must also initialize `shape` with `-1`s because
* of implementation details.
*/
template<typename SizeT>
void set_and_validate_list_shape_helper(SizeT axis, List<SizeT>* list, SizeT ndims, SizeT* shape) {
if (shape[axis] == -1) {
// Dimension is unspecified. Set it.
shape[axis] = list->len;
} else {
// Dimension is specified. Check.
if (shape[axis] != list->len) {
// Mismatch, throw an error.
// NOTE: NumPy's error message is more complex and needs more PARAMS to display.
raise_exception(SizeT, EXN_VALUE_ERROR,
"The requested array has an inhomogenous shape "
"after {0} dimension(s).",
axis, shape[axis], list->len);
}
}
if (axis + 1 == ndims) {
// `list` has type `list[ItemType]`
// Do nothing
} else {
// `list` has type `list[list[...]]`
List<SizeT>** lists = (List<SizeT>**)(list->items);
for (SizeT i = 0; i < list->len; i++) {
set_and_validate_list_shape_helper<SizeT>(axis + 1, lists[i], ndims, shape);
}
}
}
/**
* @brief See `set_and_validate_list_shape_helper`.
*/
template<typename SizeT>
void set_and_validate_list_shape(List<SizeT>* list, SizeT ndims, SizeT* shape) {
for (SizeT axis = 0; axis < ndims; axis++) {
shape[axis] = -1; // Sentinel to say this dimension is unspecified.
}
set_and_validate_list_shape_helper<SizeT>(0, list, ndims, shape);
}
/**
* @brief In the context of `np.array(<list>)`, copied the contents stored in `list` to `ndarray`.
*
* `list` is assumed to be "legal". (i.e., no inconsistent dimensions)
*
* # Notes on `ndarray`
* The caller is responsible for allocating space for `ndarray`.
* Here is what this function expects from `ndarray` when called:
* - `ndarray->data` has to be allocated, contiguous, and may contain uninitialized values.
* - `ndarray->itemsize` has to be initialized.
* - `ndarray->ndims` has to be initialized.
* - `ndarray->shape` has to be initialized.
* - `ndarray->strides` is ignored, but note that `ndarray->data` is contiguous.
* When this function call ends:
* - `ndarray->data` is written with contents from `<list>`.
*/
template<typename SizeT>
void write_list_to_array_helper(SizeT axis, SizeT* index, List<SizeT>* list, NDArray<SizeT>* ndarray) {
debug_assert_eq(SizeT, list->len, ndarray->shape[axis]);
if (IRRT_DEBUG_ASSERT_BOOL) {
if (!ndarray::basic::is_c_contiguous(ndarray)) {
raise_debug_assert(SizeT, "ndarray is not C-contiguous", ndarray->strides[0], ndarray->strides[1],
NO_PARAM);
}
}
if (axis + 1 == ndarray->ndims) {
// `list` has type `list[scalar]`
// `ndarray` is contiguous, so we can do this, and this is fast.
uint8_t* dst = ndarray->data + (ndarray->itemsize * (*index));
__builtin_memcpy(dst, list->items, ndarray->itemsize * list->len);
*index += list->len;
} else {
// `list` has type `list[list[...]]`
List<SizeT>** lists = (List<SizeT>**)(list->items);
for (SizeT i = 0; i < list->len; i++) {
write_list_to_array_helper<SizeT>(axis + 1, index, lists[i], ndarray);
}
}
}
/**
* @brief See `write_list_to_array_helper`.
*/
template<typename SizeT>
void write_list_to_array(List<SizeT>* list, NDArray<SizeT>* ndarray) {
SizeT index = 0;
write_list_to_array_helper<SizeT>((SizeT)0, &index, list, ndarray);
}
} // namespace array
} // namespace ndarray
} // namespace
extern "C" {
using namespace ndarray::array;
void __nac3_ndarray_array_set_and_validate_list_shape(List<int32_t>* list, int32_t ndims, int32_t* shape) {
set_and_validate_list_shape(list, ndims, shape);
}
void __nac3_ndarray_array_set_and_validate_list_shape64(List<int64_t>* list, int64_t ndims, int64_t* shape) {
set_and_validate_list_shape(list, ndims, shape);
}
void __nac3_ndarray_array_write_list_to_array(List<int32_t>* list, NDArray<int32_t>* ndarray) {
write_list_to_array(list, ndarray);
}
void __nac3_ndarray_array_write_list_to_array64(List<int64_t>* list, NDArray<int64_t>* ndarray) {
write_list_to_array(list, ndarray);
}
}

View File

@ -113,12 +113,13 @@ SizeT nbytes(const NDArray<SizeT>* ndarray) {
*/
template<typename SizeT>
SizeT len(const NDArray<SizeT>* ndarray) {
// numpy prohibits `__len__` on unsized objects
if (ndarray->ndims == 0) {
raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object", NO_PARAM, NO_PARAM, NO_PARAM);
} else {
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();
}
/**
@ -178,10 +179,10 @@ bool is_c_contiguous(const NDArray<SizeT>* ndarray) {
* This function does no bound check.
*/
template<typename SizeT>
uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray, const SizeT* indices) {
uint8_t* element = ndarray->data;
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 += indices[dim_i] * ndarray->strides[dim_i];
element = static_cast<uint8_t*>(element) + indices[dim_i] * ndarray->strides[dim_i];
return element;
}
@ -191,12 +192,12 @@ uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray, const SizeT* ind
* This function does no bound check.
*/
template<typename SizeT>
uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
uint8_t* element = ndarray->data;
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 += ndarray->strides[axis] * (nth % dim);
element = static_cast<uint8_t*>(element) + ndarray->strides[axis] * (nth % dim);
nth /= dim;
}
return element;
@ -224,7 +225,7 @@ void set_strides_by_shape(NDArray<SizeT>* ndarray) {
* @param pvalue Pointer to the value `pelement` will be set to.
*/
template<typename SizeT>
void set_pelement_value(NDArray<SizeT>* ndarray, uint8_t* pelement, const uint8_t* pvalue) {
void set_pelement_value(NDArray<SizeT>* ndarray, void* pelement, const void* pvalue) {
__builtin_memcpy(pelement, pvalue, ndarray->itemsize);
}
@ -307,19 +308,19 @@ bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
return is_c_contiguous(ndarray);
}
uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
void* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray, int32_t nth) {
return get_nth_pelement(ndarray, nth);
}
uint8_t* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray, int64_t nth) {
void* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray, int64_t nth) {
return get_nth_pelement(ndarray, nth);
}
uint8_t* __nac3_ndarray_get_pelement_by_indices(const NDArray<int32_t>* ndarray, int32_t* indices) {
void* __nac3_ndarray_get_pelement_by_indices(const NDArray<int32_t>* ndarray, int32_t* indices) {
return get_pelement_by_indices(ndarray, indices);
}
uint8_t* __nac3_ndarray_get_pelement_by_indices64(const NDArray<int64_t>* ndarray, int64_t* indices) {
void* __nac3_ndarray_get_pelement_by_indices64(const NDArray<int64_t>* ndarray, int64_t* indices) {
return get_pelement_by_indices(ndarray, indices);
}

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

View File

@ -7,15 +7,16 @@ namespace {
* @brief The NDArray object
*
* Official numpy implementation:
* https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst
* 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 underlying data this `ndarray` is pointing to.
*/
uint8_t* data;
/**
* @brief The number of bytes of a single element in `data`.
*/
@ -41,5 +42,10 @@ struct NDArray {
* Note that `strides` can have negative values or contain 0.
*/
SizeT* strides;
/**
* @brief The underlying data this `ndarray` is pointing to.
*/
void* data;
};
} // namespace

View File

@ -154,7 +154,7 @@ void index(SizeT num_indices, const NDIndex* indices, const NDArray<SizeT>* src_
input, src_axis, src_ndarray->shape[src_axis]);
}
dst_ndarray->data += k * src_ndarray->strides[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) {
@ -162,7 +162,7 @@ void index(SizeT num_indices, const NDIndex* indices, const NDArray<SizeT>* src_
Range<int32_t> range = slice->indices_checked<SizeT>(src_ndarray->shape[src_axis]);
dst_ndarray->data += (SizeT)range.start * src_ndarray->strides[src_axis];
dst_ndarray->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>();

View File

@ -58,7 +58,7 @@ struct NDIter {
*
* Initially this points to first element of the ndarray.
*/
uint8_t* element;
void* element;
/**
* @brief Cache for the product of shape.
@ -67,7 +67,7 @@ struct NDIter {
*/
SizeT size;
void initialize(SizeT ndims, SizeT* shape, SizeT* strides, uint8_t* element, SizeT* indices) {
void initialize(SizeT ndims, SizeT* shape, SizeT* strides, void* element, SizeT* indices) {
this->ndims = ndims;
this->shape = shape;
this->strides = strides;
@ -108,9 +108,9 @@ struct NDIter {
// TODO: There is something called backstrides to speedup iteration.
// See https://ajcr.net/stride-guide-part-1/, and
// https://docs.scipy.org/doc/numpy-1.13.0/reference/c-api.types-and-structures.html#c.PyArrayIterObject.PyArrayIterObject.backstrides.
element -= strides[axis] * (shape[axis] - 1);
element = static_cast<void*>(reinterpret_cast<uint8_t*>(element) - strides[axis] * (shape[axis] - 1));
} else {
element += strides[axis];
element = static_cast<void*>(reinterpret_cast<uint8_t*>(element) + strides[axis]);
break;
}
}

View File

@ -1,99 +0,0 @@
#pragma once
#include "irrt/exception.hpp"
#include "irrt/int_types.hpp"
#include "irrt/ndarray/def.hpp"
namespace {
namespace ndarray {
namespace reshape {
/**
* @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
*
* If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
* modified to contain the resolved dimension.
*
* To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
* `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
*
* @param size The `.size` of `<ndarray>`
* @param new_ndims Number of elements in `new_shape`
* @param new_shape Target shape to reshape to
*/
template<typename SizeT>
void resolve_and_check_new_shape(SizeT size, SizeT new_ndims, SizeT* new_shape) {
// Is there a -1 in `new_shape`?
bool neg1_exists = false;
// Location of -1, only initialized if `neg1_exists` is true
SizeT neg1_axis_i;
// The computed ndarray size of `new_shape`
SizeT new_size = 1;
for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
SizeT dim = new_shape[axis_i];
if (dim < 0) {
if (dim == -1) {
if (neg1_exists) {
// Multiple `-1` found. Throw an error.
raise_exception(SizeT, EXN_VALUE_ERROR, "can only specify one unknown dimension", NO_PARAM,
NO_PARAM, NO_PARAM);
} else {
neg1_exists = true;
neg1_axis_i = axis_i;
}
} else {
// TODO: What? In `np.reshape` any negative dimensions is
// treated like its `-1`.
//
// Try running `np.zeros((3, 4)).reshape((-999, 2))`
//
// It is not documented by numpy.
// Throw an error for now...
raise_exception(SizeT, EXN_VALUE_ERROR, "Found non -1 negative dimension {0} on axis {1}", dim, axis_i,
NO_PARAM);
}
} else {
new_size *= dim;
}
}
bool can_reshape;
if (neg1_exists) {
// Let `x` be the unknown dimension
// Solve `x * <new_size> = <size>`
if (new_size == 0 && size == 0) {
// `x` has infinitely many solutions
can_reshape = false;
} else if (new_size == 0 && size != 0) {
// `x` has no solutions
can_reshape = false;
} else if (size % new_size != 0) {
// `x` has no integer solutions
can_reshape = false;
} else {
can_reshape = true;
new_shape[neg1_axis_i] = size / new_size; // Resolve dimension
}
} else {
can_reshape = (new_size == size);
}
if (!can_reshape) {
raise_exception(SizeT, EXN_VALUE_ERROR, "cannot reshape array of size {0} into given shape", size, NO_PARAM,
NO_PARAM);
}
}
} // namespace reshape
} // namespace ndarray
} // namespace
extern "C" {
void __nac3_ndarray_reshape_resolve_and_check_new_shape(int32_t size, int32_t new_ndims, int32_t* new_shape) {
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
}
void __nac3_ndarray_reshape_resolve_and_check_new_shape64(int64_t size, int64_t new_ndims, int64_t* new_shape) {
ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
}
}

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

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@ -0,0 +1,21 @@
[package]
name = "nac3core_derive"
version = "0.1.0"
edition = "2021"
[lib]
proc-macro = true
[[test]]
name = "structfields_tests"
path = "tests/structfields_test.rs"
[dev-dependencies]
nac3core = { path = ".." }
trybuild = { version = "1.0", features = ["diff"] }
[dependencies]
proc-macro2 = "1.0"
proc-macro-error = "1.0"
syn = "2.0"
quote = "1.0"

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@ -0,0 +1,320 @@
use proc_macro::TokenStream;
use proc_macro_error::{abort, proc_macro_error};
use quote::quote;
use syn::{
parse_macro_input, spanned::Spanned, Data, DataStruct, Expr, ExprField, ExprMethodCall,
ExprPath, GenericArgument, Ident, LitStr, Path, PathArguments, Type, TypePath,
};
/// Extracts all generic arguments of a [`Type`] into a [`Vec`].
///
/// Returns [`Some`] of a possibly-empty [`Vec`] if the path of `ty` matches with
/// `expected_ty_name`, otherwise returns [`None`].
fn extract_generic_args(expected_ty_name: &'static str, ty: &Type) -> Option<Vec<GenericArgument>> {
let Type::Path(TypePath { qself: None, path, .. }) = ty else {
return None;
};
let segments = &path.segments;
if segments.len() != 1 {
return None;
};
let segment = segments.iter().next().unwrap();
if segment.ident != expected_ty_name {
return None;
}
let PathArguments::AngleBracketed(path_args) = &segment.arguments else {
return Some(Vec::new());
};
let args = &path_args.args;
Some(args.iter().cloned().collect::<Vec<_>>())
}
/// Maps a `path` matching one of the `target_idents` into the `replacement` [`Ident`].
fn map_path_to_ident(path: &Path, target_idents: &[&str], replacement: &str) -> Option<Ident> {
path.require_ident()
.ok()
.filter(|ident| target_idents.iter().any(|target| ident == target))
.map(|ident| Ident::new(replacement, ident.span()))
}
/// Extracts the left-hand side of a dot-expression.
fn extract_dot_operand(expr: &Expr) -> Option<&Expr> {
match expr {
Expr::MethodCall(ExprMethodCall { receiver: operand, .. })
| Expr::Field(ExprField { base: operand, .. }) => Some(operand),
_ => None,
}
}
/// Replaces the top-level receiver of a dot-expression with an [`Ident`], returning `Some(&mut expr)` if the
/// replacement is performed.
///
/// The top-level receiver is the left-most receiver expression, e.g. the top-level receiver of `a.b.c.foo()` is `a`.
fn replace_top_level_receiver(expr: &mut Expr, ident: Ident) -> Option<&mut Expr> {
if let Expr::MethodCall(ExprMethodCall { receiver: operand, .. })
| Expr::Field(ExprField { base: operand, .. }) = expr
{
return if extract_dot_operand(operand).is_some() {
if replace_top_level_receiver(operand, ident).is_some() {
Some(expr)
} else {
None
}
} else {
*operand = Box::new(Expr::Path(ExprPath {
attrs: Vec::default(),
qself: None,
path: ident.into(),
}));
Some(expr)
};
}
None
}
/// Iterates all operands to the left-hand side of the `.` of an [expression][`Expr`], i.e. the container operand of all
/// [`Expr::Field`] and the receiver operand of all [`Expr::MethodCall`].
///
/// The iterator will return the operand expressions in reverse order of appearance. For example, `a.b.c.func()` will
/// return `vec![c, b, a]`.
fn iter_dot_operands(expr: &Expr) -> impl Iterator<Item = &Expr> {
let mut o = extract_dot_operand(expr);
std::iter::from_fn(move || {
let this = o;
o = o.as_ref().and_then(|o| extract_dot_operand(o));
this
})
}
/// Normalizes a value expression for use when creating an instance of this structure, returning a
/// [`proc_macro2::TokenStream`] of tokens representing the normalized expression.
fn normalize_value_expr(expr: &Expr) -> proc_macro2::TokenStream {
match &expr {
Expr::Path(ExprPath { qself: None, path, .. }) => {
if let Some(ident) = map_path_to_ident(path, &["usize", "size_t"], "llvm_usize") {
quote! { #ident }
} else {
abort!(
path,
format!(
"Expected one of `size_t`, `usize`, or an implicit call expression in #[value_type(...)], found {}",
quote!(#expr).to_string(),
)
)
}
}
Expr::Call(_) => {
quote! { ctx.#expr }
}
Expr::MethodCall(_) => {
let base_receiver = iter_dot_operands(expr).last();
match base_receiver {
// `usize.{...}`, `size_t.{...}` -> Rewrite the identifiers to `llvm_usize`
Some(Expr::Path(ExprPath { qself: None, path, .. }))
if map_path_to_ident(path, &["usize", "size_t"], "llvm_usize").is_some() =>
{
let ident =
map_path_to_ident(path, &["usize", "size_t"], "llvm_usize").unwrap();
let mut expr = expr.clone();
let expr = replace_top_level_receiver(&mut expr, ident).unwrap();
quote!(#expr)
}
// `ctx.{...}`, `context.{...}` -> Rewrite the identifiers to `ctx`
Some(Expr::Path(ExprPath { qself: None, path, .. }))
if map_path_to_ident(path, &["ctx", "context"], "ctx").is_some() =>
{
let ident = map_path_to_ident(path, &["ctx", "context"], "ctx").unwrap();
let mut expr = expr.clone();
let expr = replace_top_level_receiver(&mut expr, ident).unwrap();
quote!(#expr)
}
// No reserved identifier prefix -> Prepend `ctx.` to the entire expression
_ => quote! { ctx.#expr },
}
}
_ => {
abort!(
expr,
format!(
"Expected one of `size_t`, `usize`, or an implicit call expression in #[value_type(...)], found {}",
quote!(#expr).to_string(),
)
)
}
}
}
/// Derives an implementation of `codegen::types::structure::StructFields`.
///
/// The benefit of using `#[derive(StructFields)]` is that all index- or order-dependent logic required by
/// `impl StructFields` is automatically generated by this implementation, including the field index as required by
/// `StructField::new` and the fields as returned by `StructFields::to_vec`.
///
/// # Prerequisites
///
/// In order to derive from [`StructFields`], you must implement (or derive) [`Eq`] and [`Copy`] as required by
/// `StructFields`.
///
/// Moreover, `#[derive(StructFields)]` can only be used for `struct`s with named fields, and may only contain fields
/// with either `StructField` or [`PhantomData`] types.
///
/// # Attributes for [`StructFields`]
///
/// Each `StructField` field must be declared with the `#[value_type(...)]` attribute. The argument of `value_type`
/// accepts one of the following:
///
/// - An expression returning an instance of `inkwell::types::BasicType` (with or without the receiver `ctx`/`context`).
/// For example, `context.i8_type()`, `ctx.i8_type()`, and `i8_type()` all refer to `i8`.
/// - The reserved identifiers `usize` and `size_t` referring to an `inkwell::types::IntType` of the platform-dependent
/// integer size. `usize` and `size_t` can also be used as the receiver to other method calls, e.g.
/// `usize.array_type(3)`.
///
/// # Example
///
/// The following is an example of an LLVM slice implemented using `#[derive(StructFields)]`.
///
/// ```rust,ignore
/// use nac3core::{
/// codegen::types::structure::StructField,
/// inkwell::{
/// values::{IntValue, PointerValue},
/// AddressSpace,
/// },
/// };
/// use nac3core_derive::StructFields;
///
/// // All classes that implement StructFields must also implement Eq and Copy
/// #[derive(PartialEq, Eq, Clone, Copy, StructFields)]
/// pub struct SliceValue<'ctx> {
/// // Declares ptr have a value type of i8*
/// //
/// // Can also be written as `ctx.i8_type().ptr_type(...)` or `context.i8_type().ptr_type(...)`
/// #[value_type(i8_type().ptr_type(AddressSpace::default()))]
/// ptr: StructField<'ctx, PointerValue<'ctx>>,
///
/// // Declares len have a value type of usize, depending on the target compilation platform
/// #[value_type(usize)]
/// len: StructField<'ctx, IntValue<'ctx>>,
/// }
/// ```
#[proc_macro_derive(StructFields, attributes(value_type))]
#[proc_macro_error]
pub fn derive(input: TokenStream) -> TokenStream {
let input = parse_macro_input!(input as syn::DeriveInput);
let ident = &input.ident;
let Data::Struct(DataStruct { fields, .. }) = &input.data else {
abort!(input, "Only structs with named fields are supported");
};
if let Err(err_span) =
fields
.iter()
.try_for_each(|field| if field.ident.is_some() { Ok(()) } else { Err(field.span()) })
{
abort!(err_span, "Only structs with named fields are supported");
};
// Check if struct<'ctx>
if input.generics.params.len() != 1 {
abort!(input.generics, "Expected exactly 1 generic parameter")
}
let phantom_info = fields
.iter()
.filter(|field| extract_generic_args("PhantomData", &field.ty).is_some())
.map(|field| field.ident.as_ref().unwrap())
.cloned()
.collect::<Vec<_>>();
let field_info = fields
.iter()
.filter(|field| extract_generic_args("PhantomData", &field.ty).is_none())
.map(|field| {
let ident = field.ident.as_ref().unwrap();
let ty = &field.ty;
let Some(_) = extract_generic_args("StructField", ty) else {
abort!(field, "Only StructField and PhantomData are allowed")
};
let attrs = &field.attrs;
let Some(value_type_attr) =
attrs.iter().find(|attr| attr.path().is_ident("value_type"))
else {
abort!(field, "Expected #[value_type(...)] attribute for field");
};
let Ok(value_type_expr) = value_type_attr.parse_args::<Expr>() else {
abort!(value_type_attr, "Expected expression in #[value_type(...)]");
};
let value_expr_toks = normalize_value_expr(&value_type_expr);
(ident.clone(), value_expr_toks)
})
.collect::<Vec<_>>();
// `<*>::new` impl of `StructField` and `PhantomData` for `StructFields::new`
let phantoms_create = phantom_info
.iter()
.map(|id| quote! { #id: ::std::marker::PhantomData })
.collect::<Vec<_>>();
let fields_create = field_info
.iter()
.map(|(id, ty)| {
let id_lit = LitStr::new(&id.to_string(), id.span());
quote! {
#id: ::nac3core::codegen::types::structure::StructField::create(
&mut counter,
#id_lit,
#ty,
)
}
})
.collect::<Vec<_>>();
// `.into()` impl of `StructField` for `StructFields::to_vec`
let fields_into =
field_info.iter().map(|(id, _)| quote! { self.#id.into() }).collect::<Vec<_>>();
let impl_block = quote! {
impl<'ctx> ::nac3core::codegen::types::structure::StructFields<'ctx> for #ident<'ctx> {
fn new(ctx: impl ::nac3core::inkwell::context::AsContextRef<'ctx>, llvm_usize: ::nac3core::inkwell::types::IntType<'ctx>) -> Self {
let ctx = unsafe { ::nac3core::inkwell::context::ContextRef::new(ctx.as_ctx_ref()) };
let mut counter = ::nac3core::codegen::types::structure::FieldIndexCounter::default();
#ident {
#(#fields_create),*
#(#phantoms_create),*
}
}
fn to_vec(&self) -> ::std::vec::Vec<(&'static str, ::nac3core::inkwell::types::BasicTypeEnum<'ctx>)> {
vec![
#(#fields_into),*
]
}
}
};
impl_block.into()
}

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@ -0,0 +1,9 @@
use nac3core_derive::StructFields;
use std::marker::PhantomData;
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
pub struct EmptyValue<'ctx> {
_phantom: PhantomData<&'ctx ()>,
}
fn main() {}

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@ -0,0 +1,20 @@
use nac3core::{
codegen::types::structure::StructField,
inkwell::{
values::{IntValue, PointerValue},
AddressSpace,
},
};
use nac3core_derive::StructFields;
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
pub struct NDArrayValue<'ctx> {
#[value_type(usize)]
ndims: StructField<'ctx, IntValue<'ctx>>,
#[value_type(usize.ptr_type(AddressSpace::default()))]
shape: StructField<'ctx, PointerValue<'ctx>>,
#[value_type(i8_type().ptr_type(AddressSpace::default()))]
data: StructField<'ctx, PointerValue<'ctx>>,
}
fn main() {}

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@ -0,0 +1,18 @@
use nac3core::{
codegen::types::structure::StructField,
inkwell::{
values::{IntValue, PointerValue},
AddressSpace,
},
};
use nac3core_derive::StructFields;
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
pub struct SliceValue<'ctx> {
#[value_type(i8_type().ptr_type(AddressSpace::default()))]
ptr: StructField<'ctx, PointerValue<'ctx>>,
#[value_type(usize)]
len: StructField<'ctx, IntValue<'ctx>>,
}
fn main() {}

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@ -0,0 +1,18 @@
use nac3core::{
codegen::types::structure::StructField,
inkwell::{
values::{IntValue, PointerValue},
AddressSpace,
},
};
use nac3core_derive::StructFields;
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
pub struct SliceValue<'ctx> {
#[value_type(context.i8_type().ptr_type(AddressSpace::default()))]
ptr: StructField<'ctx, PointerValue<'ctx>>,
#[value_type(usize)]
len: StructField<'ctx, IntValue<'ctx>>,
}
fn main() {}

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@ -0,0 +1,18 @@
use nac3core::{
codegen::types::structure::StructField,
inkwell::{
values::{IntValue, PointerValue},
AddressSpace,
},
};
use nac3core_derive::StructFields;
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
pub struct SliceValue<'ctx> {
#[value_type(ctx.i8_type().ptr_type(AddressSpace::default()))]
ptr: StructField<'ctx, PointerValue<'ctx>>,
#[value_type(usize)]
len: StructField<'ctx, IntValue<'ctx>>,
}
fn main() {}

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@ -0,0 +1,18 @@
use nac3core::{
codegen::types::structure::StructField,
inkwell::{
values::{IntValue, PointerValue},
AddressSpace,
},
};
use nac3core_derive::StructFields;
#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
pub struct SliceValue<'ctx> {
#[value_type(i8_type().ptr_type(AddressSpace::default()))]
ptr: StructField<'ctx, PointerValue<'ctx>>,
#[value_type(size_t)]
len: StructField<'ctx, IntValue<'ctx>>,
}
fn main() {}

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@ -0,0 +1,10 @@
#[test]
fn test_parse_empty() {
let t = trybuild::TestCases::new();
t.pass("tests/structfields_empty.rs");
t.pass("tests/structfields_slice.rs");
t.pass("tests/structfields_slice_ctx.rs");
t.pass("tests/structfields_slice_context.rs");
t.pass("tests/structfields_slice_sizet.rs");
t.pass("tests/structfields_ndarray.rs");
}

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

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View File

@ -1,8 +1,10 @@
use inkwell::attributes::{Attribute, AttributeLoc};
use inkwell::values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue};
use inkwell::{
attributes::{Attribute, AttributeLoc},
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
};
use itertools::Either;
use crate::codegen::CodeGenContext;
use super::CodeGenContext;
/// Macro to generate extern function
/// Both function return type and function parameter type are `FloatValue`

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@ -1,16 +1,18 @@
use crate::{
codegen::{bool_to_i1, bool_to_i8, classes::ArraySliceValue, expr::*, stmt::*, CodeGenContext},
symbol_resolver::ValueEnum,
toplevel::{DefinitionId, TopLevelDef},
typecheck::typedef::{FunSignature, Type},
};
use inkwell::{
context::Context,
types::{BasicTypeEnum, IntType},
values::{BasicValueEnum, IntValue, PointerValue},
};
use nac3parser::ast::{Expr, Stmt, StrRef};
use super::{bool_to_i1, bool_to_i8, expr::*, stmt::*, values::ArraySliceValue, CodeGenContext};
use crate::{
symbol_resolver::ValueEnum,
toplevel::{DefinitionId, TopLevelDef},
typecheck::typedef::{FunSignature, Type},
};
pub trait CodeGenerator {
/// Return the module name for the code generator.
fn get_name(&self) -> &str;

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@ -0,0 +1,162 @@
use inkwell::{
types::BasicTypeEnum,
values::{BasicValueEnum, CallSiteValue, IntValue},
AddressSpace, IntPredicate,
};
use itertools::Either;
use super::calculate_len_for_slice_range;
use crate::codegen::{
macros::codegen_unreachable,
values::{ArrayLikeValue, ListValue},
CodeGenContext, CodeGenerator,
};
/// This function handles 'end' **inclusively**.
/// Order of tuples `assign_idx` and `value_idx` is ('start', 'end', 'step').
/// Negative index should be handled before entering this function
pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ty: BasicTypeEnum<'ctx>,
dest_arr: ListValue<'ctx>,
dest_idx: (IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>),
src_arr: ListValue<'ctx>,
src_idx: (IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>),
) {
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![
int32.into(), // dest start idx
int32.into(), // dest end idx
int32.into(), // dest step
elem_ptr_type.into(), // dest arr ptr
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
int32.into(), // src arr len
int32.into(), // size
];
ctx.module.get_function(fun_symbol).unwrap_or_else(|| {
let fn_t = int32.fn_type(ty_vec.as_slice(), false);
ctx.module.add_function(fun_symbol, fn_t, None)
})
};
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, 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, 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
// throw exception if not satisfied
let src_end = ctx
.builder
.build_select(
ctx.builder.build_int_compare(IntPredicate::SLT, src_idx.2, zero, "is_neg").unwrap(),
ctx.builder.build_int_sub(src_idx.1, one, "e_min_one").unwrap(),
ctx.builder.build_int_add(src_idx.1, one, "e_add_one").unwrap(),
"final_e",
)
.map(BasicValueEnum::into_int_value)
.unwrap();
let dest_end = ctx
.builder
.build_select(
ctx.builder.build_int_compare(IntPredicate::SLT, dest_idx.2, zero, "is_neg").unwrap(),
ctx.builder.build_int_sub(dest_idx.1, one, "e_min_one").unwrap(),
ctx.builder.build_int_add(dest_idx.1, one, "e_add_one").unwrap(),
"final_e",
)
.map(BasicValueEnum::into_int_value)
.unwrap();
let src_slice_len =
calculate_len_for_slice_range(generator, ctx, src_idx.0, src_end, src_idx.2);
let dest_slice_len =
calculate_len_for_slice_range(generator, ctx, dest_idx.0, dest_end, dest_idx.2);
let src_eq_dest = ctx
.builder
.build_int_compare(IntPredicate::EQ, src_slice_len, dest_slice_len, "slice_src_eq_dest")
.unwrap();
let src_slt_dest = ctx
.builder
.build_int_compare(IntPredicate::SLT, src_slice_len, dest_slice_len, "slice_src_slt_dest")
.unwrap();
let dest_step_eq_one = ctx
.builder
.build_int_compare(
IntPredicate::EQ,
dest_idx.2,
dest_idx.2.get_type().const_int(1, false),
"slice_dest_step_eq_one",
)
.unwrap();
let cond_1 = ctx.builder.build_and(dest_step_eq_one, src_slt_dest, "slice_cond_1").unwrap();
let cond = ctx.builder.build_or(src_eq_dest, cond_1, "slice_cond").unwrap();
ctx.make_assert(
generator,
cond,
"0:ValueError",
"attempt to assign sequence of size {0} to slice of size {1} with step size {2}",
[Some(src_slice_len), Some(dest_slice_len), Some(dest_idx.2)],
ctx.current_loc,
);
let new_len = {
let args = vec![
dest_idx.0.into(), // dest start idx
dest_idx.1.into(), // dest end idx
dest_idx.2.into(), // dest step
dest_arr_ptr.into(), // dest arr ptr
dest_len.into(), // dest arr len
src_idx.0.into(), // src start idx
src_idx.1.into(), // src end idx
src_idx.2.into(), // src step
src_arr_ptr.into(), // src arr ptr
src_len.into(), // src arr len
{
let s = match ty {
BasicTypeEnum::FloatType(t) => t.size_of(),
BasicTypeEnum::IntType(t) => t.size_of(),
BasicTypeEnum::PointerType(t) => t.size_of(),
BasicTypeEnum::StructType(t) => t.size_of().unwrap(),
_ => codegen_unreachable!(ctx),
};
ctx.builder.build_int_truncate_or_bit_cast(s, int32, "size").unwrap()
}
.into(),
];
ctx.builder
.build_call(slice_assign_fun, args.as_slice(), "slice_assign")
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_int_value))
.map(Either::unwrap_left)
.unwrap()
};
// update length
let need_update =
ctx.builder.build_int_compare(IntPredicate::NE, new_len, dest_len, "need_update").unwrap();
let current = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
let update_bb = ctx.ctx.append_basic_block(current, "update");
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, 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);
}

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use inkwell::{
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
IntPredicate,
};
use itertools::Either;
use crate::codegen::{
macros::codegen_unreachable,
{CodeGenContext, CodeGenerator},
};
// repeated squaring method adapted from GNU Scientific Library:
// https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
pub fn integer_power<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
base: IntValue<'ctx>,
exp: IntValue<'ctx>,
signed: bool,
) -> IntValue<'ctx> {
let symbol = match (base.get_type().get_bit_width(), exp.get_type().get_bit_width(), signed) {
(32, 32, true) => "__nac3_int_exp_int32_t",
(64, 64, true) => "__nac3_int_exp_int64_t",
(32, 32, false) => "__nac3_int_exp_uint32_t",
(64, 64, false) => "__nac3_int_exp_uint64_t",
_ => codegen_unreachable!(ctx),
};
let base_type = base.get_type();
let pow_fun = ctx.module.get_function(symbol).unwrap_or_else(|| {
let fn_type = base_type.fn_type(&[base_type.into(), base_type.into()], false);
ctx.module.add_function(symbol, fn_type, None)
});
// throw exception when exp < 0
let ge_zero = ctx
.builder
.build_int_compare(
IntPredicate::SGE,
exp,
exp.get_type().const_zero(),
"assert_int_pow_ge_0",
)
.unwrap();
ctx.make_assert(
generator,
ge_zero,
"0:ValueError",
"integer power must be positive or zero",
[None, None, None],
ctx.current_loc,
);
ctx.builder
.build_call(pow_fun, &[base.into(), exp.into()], "call_int_pow")
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_int_value))
.map(Either::unwrap_left)
.unwrap()
}
/// Generates a call to `isinf` in IR. Returns an `i1` representing the result.
pub fn call_isinf<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
v: FloatValue<'ctx>,
) -> IntValue<'ctx> {
let intrinsic_fn = ctx.module.get_function("__nac3_isinf").unwrap_or_else(|| {
let fn_type = ctx.ctx.i32_type().fn_type(&[ctx.ctx.f64_type().into()], false);
ctx.module.add_function("__nac3_isinf", fn_type, None)
});
let ret = ctx
.builder
.build_call(intrinsic_fn, &[v.into()], "isinf")
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_int_value))
.map(Either::unwrap_left)
.unwrap();
generator.bool_to_i1(ctx, ret)
}
/// Generates a call to `isnan` in IR. Returns an `i1` representing the result.
pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &CodeGenContext<'ctx, '_>,
v: FloatValue<'ctx>,
) -> IntValue<'ctx> {
let intrinsic_fn = ctx.module.get_function("__nac3_isnan").unwrap_or_else(|| {
let fn_type = ctx.ctx.i32_type().fn_type(&[ctx.ctx.f64_type().into()], false);
ctx.module.add_function("__nac3_isnan", fn_type, None)
});
let ret = ctx
.builder
.build_call(intrinsic_fn, &[v.into()], "isnan")
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_int_value))
.map(Either::unwrap_left)
.unwrap();
generator.bool_to_i1(ctx, ret)
}
/// Generates a call to `gamma` in IR. Returns an `f64` representing the result.
pub fn call_gamma<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
let llvm_f64 = ctx.ctx.f64_type();
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)
});
ctx.builder
.build_call(intrinsic_fn, &[v.into()], "gamma")
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_float_value))
.map(Either::unwrap_left)
.unwrap()
}
/// Generates a call to `gammaln` in IR. Returns an `f64` representing the result.
pub fn call_gammaln<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
let llvm_f64 = ctx.ctx.f64_type();
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)
});
ctx.builder
.build_call(intrinsic_fn, &[v.into()], "gammaln")
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_float_value))
.map(Either::unwrap_left)
.unwrap()
}
/// Generates a call to `j0` in IR. Returns an `f64` representing the result.
pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
let llvm_f64 = ctx.ctx.f64_type();
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)
});
ctx.builder
.build_call(intrinsic_fn, &[v.into()], "j0")
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_float_value))
.map(Either::unwrap_left)
.unwrap()
}

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use inkwell::{
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},
CodeGenContext, CodeGenerator,
};
pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &CodeGenContext<'ctx, '_>,
ndims: IntValue<'ctx>,
shape: PointerValue<'ctx>,
) {
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
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(), ndims.into()), (llvm_pusize.into(), shape.into())],
None,
None,
);
}
pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &CodeGenContext<'ctx, '_>,
ndarray_ndims: IntValue<'ctx>,
ndarray_shape: PointerValue<'ctx>,
output_ndims: IntValue<'ctx>,
output_shape: IntValue<'ctx>,
) {
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
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_ndims.into()),
(llvm_pusize.into(), ndarray_shape.into()),
(llvm_usize.into(), output_ndims.into()),
(llvm_pusize.into(), output_shape.into()),
],
None,
None,
);
}
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()
}
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()
}
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()
}
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()
}
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();
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()
}
pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &CodeGenContext<'ctx, '_>,
ndarray: NDArrayValue<'ctx>,
indices: PointerValue<'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();
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.into()),
],
Some("pelement"),
None,
)
.map(BasicValueEnum::into_pointer_value)
.unwrap()
}
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,
);
}
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,
);
}

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@ -0,0 +1,29 @@
use crate::codegen::{
expr::infer_and_call_function,
irrt::get_usize_dependent_function_name,
values::{ndarray::NDArrayValue, ArrayLikeValue, ArraySliceValue, ProxyValue},
CodeGenContext, CodeGenerator,
};
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,
);
}

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use inkwell::{
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},
ArrayLikeValue, ArraySliceValue, ProxyValue,
},
CodeGenContext, CodeGenerator,
};
pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &CodeGenContext<'ctx, '_>,
iter: NDIterValue<'ctx>,
ndarray: NDArrayValue<'ctx>,
indices: ArraySliceValue<'ctx>,
) {
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
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,
);
}
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()
}
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);
}

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@ -0,0 +1,391 @@
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::{
ndarray::NDArrayValue, ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue,
TypedArrayLikeAccessor, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
},
CodeGenContext, CodeGenerator,
};
pub use basic::*;
pub use indexing::*;
pub use iter::*;
mod basic;
mod indexing;
mod iter;
/// 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: &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: &G,
ctx: &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()),
)
}

View File

@ -0,0 +1,42 @@
use inkwell::{
values::{BasicValueEnum, CallSiteValue, IntValue},
IntPredicate,
};
use itertools::Either;
use crate::codegen::{CodeGenContext, CodeGenerator};
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()
}

View File

@ -0,0 +1,39 @@
use inkwell::values::{BasicValueEnum, CallSiteValue, IntValue};
use itertools::Either;
use nac3parser::ast::Expr;
use crate::{
codegen::{CodeGenContext, CodeGenerator},
typecheck::typedef::Type,
};
/// this function allows index out of range, since python
/// allows index out of range in slice (`a = [1,2,3]; a[1:10] == [2,3]`).
pub fn handle_slice_index_bound<'ctx, G: CodeGenerator>(
i: &Expr<Option<Type>>,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
length: IntValue<'ctx>,
) -> Result<Option<IntValue<'ctx>>, String> {
const SYMBOL: &str = "__nac3_slice_index_bound";
let 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()], false);
ctx.module.add_function(SYMBOL, fn_t, None)
});
let i = if let Some(v) = generator.gen_expr(ctx, i)? {
v.to_basic_value_enum(ctx, generator, i.custom.unwrap())?
} else {
return Ok(None);
};
Ok(Some(
ctx.builder
.build_call(func, &[i.into(), length.into()], "bounded_ind")
.map(CallSiteValue::try_as_basic_value)
.map(|v| v.map_left(BasicValueEnum::into_int_value))
.map(Either::unwrap_left)
.unwrap(),
))
}

View File

@ -1,12 +1,14 @@
use crate::codegen::CodeGenContext;
use inkwell::context::Context;
use inkwell::intrinsics::Intrinsic;
use inkwell::types::AnyTypeEnum::IntType;
use inkwell::types::FloatType;
use inkwell::values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue};
use inkwell::AddressSpace;
use inkwell::{
context::Context,
intrinsics::Intrinsic,
types::{AnyTypeEnum::IntType, FloatType},
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
AddressSpace,
};
use itertools::Either;
use 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 {
@ -183,7 +185,7 @@ pub fn call_memcpy_generic<'ctx>(
dest
} else {
ctx.builder
.build_bitcast(dest, llvm_p0i8, "")
.build_bit_cast(dest, llvm_p0i8, "")
.map(BasicValueEnum::into_pointer_value)
.unwrap()
};
@ -191,7 +193,7 @@ pub fn call_memcpy_generic<'ctx>(
src
} else {
ctx.builder
.build_bitcast(src, llvm_p0i8, "")
.build_bit_cast(src, llvm_p0i8, "")
.map(BasicValueEnum::into_pointer_value)
.unwrap()
};
@ -199,6 +201,49 @@ 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:
@ -341,3 +386,25 @@ 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()
}

View File

@ -1,12 +1,12 @@
use crate::{
codegen::classes::{ListType, ProxyType, RangeType},
symbol_resolver::{StaticValue, SymbolResolver},
toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
typecheck::{
type_inferencer::{CodeLocation, PrimitiveStore},
typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
use std::{
collections::{HashMap, HashSet},
sync::{
atomic::{AtomicBool, Ordering},
Arc,
},
thread,
};
use crossbeam::channel::{unbounded, Receiver, Sender};
use inkwell::{
attributes::{Attribute, AttributeLoc},
@ -24,36 +24,41 @@ use inkwell::{
AddressSpace, IntPredicate, OptimizationLevel,
};
use itertools::Itertools;
use model::*;
use nac3parser::ast::{Location, Stmt, StrRef};
use object::ndarray::NDArray;
use parking_lot::{Condvar, Mutex};
use std::collections::{HashMap, HashSet};
use std::sync::{
atomic::{AtomicBool, Ordering},
Arc,
use nac3parser::ast::{Location, Stmt, StrRef};
use crate::{
symbol_resolver::{StaticValue, SymbolResolver},
toplevel::{
helper::{extract_ndims, PrimDef},
numpy::unpack_ndarray_var_tys,
TopLevelContext, TopLevelDef,
},
typecheck::{
type_inferencer::{CodeLocation, PrimitiveStore},
typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
},
};
use std::thread;
use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
pub use generator::{CodeGenerator, DefaultCodeGenerator};
use types::{ndarray::NDArrayType, ListType, ProxyType, RangeType};
pub mod builtin_fns;
pub mod classes;
pub mod concrete_type;
pub mod expr;
pub mod extern_fns;
mod generator;
pub mod irrt;
pub mod llvm_intrinsics;
pub mod model;
pub mod numpy;
pub mod object;
pub mod stmt;
pub mod types;
pub mod values;
#[cfg(test)]
mod test;
use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
pub use generator::{CodeGenerator, DefaultCodeGenerator};
mod macros {
/// Codegen-variant of [`std::unreachable`] which accepts an instance of [`CodeGenContext`] as
/// its first argument to provide Python source information to indicate the codegen location
@ -509,7 +514,13 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
}
TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
Ptr(Struct(NDArray)).llvm_type(generator, ctx).as_basic_type_enum()
let (dtype, ndims) = unpack_ndarray_var_tys(unifier, ty);
let ndims = extract_ndims(unifier, ndims);
let element_type = get_llvm_type(
ctx, module, generator, unifier, top_level, type_cache, dtype,
);
NDArrayType::new(generator, ctx, element_type, Some(ndims)).as_base_type().into()
}
_ => unreachable!(
@ -847,10 +858,9 @@ pub fn gen_func_impl<
builder.position_at_end(init_bb);
let body_bb = context.append_basic_block(fn_val, "body");
// Store non-vararg argument values into local variables
let mut var_assignment = HashMap::new();
let offset = u32::from(has_sret);
// Store non-vararg argument values into local variables
for (n, arg) in args.iter().enumerate().filter(|(_, arg)| !arg.is_vararg) {
let param = fn_val.get_nth_param((n as u32) + offset).unwrap();
let local_type = get_llvm_type(
@ -1114,3 +1124,106 @@ 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()
}

View File

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

View File

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

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

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

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

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

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

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

View File

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

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

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

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

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@ -1,5 +0,0 @@
pub mod any;
pub mod list;
pub mod ndarray;
pub mod tuple;
pub mod utils;

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

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

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

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

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

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@ -1,638 +0,0 @@
pub mod array;
pub mod broadcast;
pub mod factory;
pub mod indexing;
pub mod map;
pub mod nditer;
pub mod shape_util;
pub mod view;
use inkwell::{
context::Context,
types::BasicType,
values::{BasicValue, BasicValueEnum, PointerValue},
AddressSpace,
};
use crate::{
codegen::{
irrt::{
call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
call_nac3_ndarray_get_pelement_by_indices, call_nac3_ndarray_is_c_contiguous,
call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
call_nac3_ndarray_util_assert_output_shape_same,
},
model::*,
CodeGenContext, CodeGenerator,
},
toplevel::{helper::extract_ndims, numpy::unpack_ndarray_var_tys},
typecheck::typedef::{Type, TypeEnum},
};
use super::{any::AnyObject, tuple::TupleObject};
/// Fields of [`NDArray`]
pub struct NDArrayFields<'ctx, F: FieldTraversal<'ctx>> {
pub data: F::Output<Ptr<Int<Byte>>>,
pub itemsize: F::Output<Int<SizeT>>,
pub ndims: F::Output<Int<SizeT>>,
pub shape: F::Output<Ptr<Int<SizeT>>>,
pub strides: F::Output<Ptr<Int<SizeT>>>,
}
/// A strided ndarray in NAC3.
///
/// See IRRT implementation for details about its fields.
#[derive(Debug, Clone, Copy, Default)]
pub struct NDArray;
impl<'ctx> StructKind<'ctx> for NDArray {
type Fields<F: FieldTraversal<'ctx>> = NDArrayFields<'ctx, F>;
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
data: traversal.add_auto("data"),
itemsize: traversal.add_auto("itemsize"),
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
strides: traversal.add_auto("strides"),
}
}
}
/// A NAC3 Python ndarray object.
#[derive(Debug, Clone, Copy)]
pub struct NDArrayObject<'ctx> {
pub dtype: Type,
pub ndims: u64,
pub instance: Instance<'ctx, Ptr<Struct<NDArray>>>,
}
impl<'ctx> NDArrayObject<'ctx> {
/// Attempt to convert an [`AnyObject`] into an [`NDArrayObject`].
pub fn from_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> NDArrayObject<'ctx> {
let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
let ndims = extract_ndims(&ctx.unifier, ndims);
Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
}
/// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
/// `dtype` and `ndims`.
pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
value: V,
dtype: Type,
ndims: u64,
) -> Self {
let value = Ptr(Struct(NDArray)).check_value(generator, ctx.ctx, value).unwrap();
NDArrayObject { dtype, ndims, instance: value }
}
/// Get this ndarray's `ndims` as an LLVM constant.
pub fn ndims_llvm<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &'ctx Context,
) -> Instance<'ctx, Int<SizeT>> {
Int(SizeT).const_int(generator, ctx, self.ndims, false)
}
/// Allocate an ndarray on the stack given its `ndims` and `dtype`.
///
/// `shape` and `strides` will be automatically allocated onto the stack.
///
/// The returned ndarray's content will be:
/// - `data`: uninitialized.
/// - `itemsize`: set to the `sizeof()` of `dtype`.
/// - `ndims`: set to the value of `ndims`.
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
pub fn alloca<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
ndims: u64,
) -> Self {
let ndarray = Struct(NDArray).alloca(generator, ctx);
let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
let itemsize = Int(SizeT).z_extend_or_truncate(generator, ctx, itemsize);
ndarray.set(ctx, |f| f.itemsize, itemsize);
let ndims_val = Int(SizeT).const_int(generator, ctx.ctx, ndims, false);
ndarray.set(ctx, |f| f.ndims, ndims_val);
let shape = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
ndarray.set(ctx, |f| f.shape, shape);
let strides = Int(SizeT).array_alloca(generator, ctx, ndims_val.value);
ndarray.set(ctx, |f| f.strides, strides);
NDArrayObject { dtype, ndims, instance: ndarray }
}
/// Convenience function. Allocate an [`NDArrayObject`] with a statically known shape.
///
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
pub fn alloca_constant_shape<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
shape: &[u64],
) -> Self {
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
// Write shape
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
for (i, dim) in shape.iter().enumerate() {
let dim = Int(SizeT).const_int(generator, ctx.ctx, *dim, false);
dst_shape.offset_const(ctx, i64::try_from(i).unwrap()).store(ctx, dim);
}
ndarray
}
/// Convenience function. Allocate an [`NDArrayObject`] with a dynamically known shape.
///
/// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
pub fn alloca_dynamic_shape<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
dtype: Type,
shape: &[Instance<'ctx, Int<SizeT>>],
) -> Self {
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64);
// Write shape
let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape);
for (i, dim) in shape.iter().enumerate() {
dst_shape.offset_const(ctx, i64::try_from(i).unwrap()).store(ctx, *dim);
}
ndarray
}
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
/// The allocated data buffer is considered to be *owned* by the ndarray.
///
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
///
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
pub fn create_data<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
let nbytes = self.nbytes(generator, ctx);
let data = Int(Byte).array_alloca(generator, ctx, nbytes.value);
self.instance.set(ctx, |f| f.data, data);
self.set_strides_contiguous(generator, ctx);
}
/// Copy shape dimensions from an array.
pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let num_items = self.ndims_llvm(generator, ctx.ctx).value;
self.instance.get(generator, ctx, |f| f.shape).copy_from(generator, ctx, shape, num_items);
}
/// Copy shape dimensions from an ndarray.
/// Panics if `ndims` mismatches.
pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: NDArrayObject<'ctx>,
) {
assert_eq!(self.ndims, src_ndarray.ndims);
let src_shape = src_ndarray.instance.get(generator, ctx, |f| f.shape);
self.copy_shape_from_array(generator, ctx, src_shape);
}
/// Copy strides dimensions from an array.
pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
strides: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let num_items = self.ndims_llvm(generator, ctx.ctx).value;
self.instance
.get(generator, ctx, |f| f.strides)
.copy_from(generator, ctx, strides, num_items);
}
/// Copy strides dimensions from an ndarray.
/// Panics if `ndims` mismatches.
pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src_ndarray: NDArrayObject<'ctx>,
) {
assert_eq!(self.ndims, src_ndarray.ndims);
let src_strides = src_ndarray.instance.get(generator, ctx, |f| f.strides);
self.copy_strides_from_array(generator, ctx, src_strides);
}
/// Get the `np.size()` of this ndarray.
pub fn size<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_size(generator, ctx, self.instance)
}
/// Get the `ndarray.nbytes` of this ndarray.
pub fn nbytes<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_nbytes(generator, ctx, self.instance)
}
/// Get the `len()` of this ndarray.
pub fn len<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<SizeT>> {
call_nac3_ndarray_len(generator, ctx, self.instance)
}
/// Check if this ndarray is C-contiguous.
///
/// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Instance<'ctx, Int<Bool>> {
call_nac3_ndarray_is_c_contiguous(generator, ctx, self.instance)
}
/// Get the pointer to the n-th (0-based) element.
///
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
pub fn get_nth_pelement<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Instance<'ctx, Int<SizeT>>,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.instance, nth);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
.unwrap()
}
/// Get the n-th (0-based) scalar.
pub fn get_nth_scalar<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
nth: Instance<'ctx, Int<SizeT>>,
) -> AnyObject<'ctx> {
let ptr = self.get_nth_pelement(generator, ctx, nth);
let value = ctx.builder.build_load(ptr, "").unwrap();
AnyObject { ty: self.dtype, value }
}
/// Get the pointer to the element indexed by `indices`.
///
/// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
pub fn get_pelement_by_indices<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> PointerValue<'ctx> {
let elem_ty = ctx.get_llvm_type(generator, self.dtype);
let p = call_nac3_ndarray_get_pelement_by_indices(generator, ctx, self.instance, indices);
ctx.builder
.build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "")
.unwrap()
}
/// Get the scalar indexed by `indices`.
pub fn get_scalar_by_indices<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
indices: Instance<'ctx, Ptr<Int<SizeT>>>,
) -> AnyObject<'ctx> {
let ptr = self.get_pelement_by_indices(generator, ctx, indices);
let value = ctx.builder.build_load(ptr, "").unwrap();
AnyObject { ty: self.dtype, value }
}
/// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
///
/// Update the ndarray's strides to make the ndarray contiguous.
pub fn set_strides_contiguous<G: CodeGenerator + ?Sized>(
self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) {
call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.instance);
}
/// Clone/Copy this ndarray - Allocate a new ndarray with the same shape as this ndarray and copy the contents over.
///
/// The new ndarray will own its data and will be C-contiguous.
#[must_use]
pub fn make_copy<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> Self {
let clone = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
let shape = self.instance.gep(ctx, |f| f.shape).load(generator, ctx);
clone.copy_shape_from_array(generator, ctx, shape);
clone.create_data(generator, ctx);
clone.copy_data_from(generator, ctx, *self);
clone
}
/// Copy data from another ndarray.
///
/// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
/// do not matter. The copying order is determined by how their flattened views look.
///
/// Panics if the `dtype`s of ndarrays are different.
pub fn copy_data_from<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
src: NDArrayObject<'ctx>,
) {
assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
call_nac3_ndarray_copy_data(generator, ctx, src.instance, self.instance);
}
/// Returns true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
#[must_use]
pub fn is_unsized(&self) -> bool {
self.ndims == 0
}
/// If this ndarray is unsized, return its sole value as an [`AnyObject`].
/// Otherwise, do nothing and return the ndarray itself.
pub fn split_unsized<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> ScalarOrNDArray<'ctx> {
if self.is_unsized() {
// NOTE: `np.size(self) == 0` here is never possible.
let zero = Int(SizeT).const_0(generator, ctx.ctx);
let value = self.get_nth_scalar(generator, ctx, zero).value;
ScalarOrNDArray::Scalar(AnyObject { ty: self.dtype, value })
} else {
ScalarOrNDArray::NDArray(*self)
}
}
/// Fill the ndarray with a scalar.
///
/// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
pub fn fill<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
value: BasicValueEnum<'ctx>,
) {
// TODO: It is possible to optimize this by exploiting contiguous strides with memset.
// Probably best to implement in IRRT.
self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
let p = nditer.get_pointer(generator, ctx);
ctx.builder.build_store(p, value).unwrap();
Ok(())
})
.unwrap();
}
/// Create the shape tuple of this ndarray like `np.shape(<ndarray>)`.
///
/// The returned integers in the tuple are in int32.
pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> TupleObject<'ctx> {
// TODO: Return a tuple of SizeT
let mut objects = Vec::with_capacity(self.ndims as usize);
for i in 0..self.ndims {
let dim = self
.instance
.get(generator, ctx, |f| f.shape)
.get_index_const(generator, ctx, i64::try_from(i).unwrap())
.truncate_or_bit_cast(generator, ctx, Int32);
objects.push(AnyObject {
ty: ctx.primitives.int32,
value: dim.value.as_basic_value_enum(),
});
}
TupleObject::from_objects(generator, ctx, objects)
}
/// Create the strides tuple of this ndarray like `<ndarray>.strides`.
///
/// The returned integers in the tuple are in int32.
pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> TupleObject<'ctx> {
// TODO: Return a tuple of SizeT.
let mut objects = Vec::with_capacity(self.ndims as usize);
for i in 0..self.ndims {
let dim = self
.instance
.get(generator, ctx, |f| f.strides)
.get_index_const(generator, ctx, i64::try_from(i).unwrap())
.truncate_or_bit_cast(generator, ctx, Int32);
objects.push(AnyObject {
ty: ctx.primitives.int32,
value: dim.value.as_basic_value_enum(),
});
}
TupleObject::from_objects(generator, ctx, objects)
}
/// Create an unsized ndarray to contain `object`.
pub fn make_unsized<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> NDArrayObject<'ctx> {
// We have to put the value on the stack to get a data pointer.
let data = ctx.builder.build_alloca(object.value.get_type(), "make_unsized").unwrap();
ctx.builder.build_store(data, object.value).unwrap();
let data = Ptr(Int(Byte)).pointer_cast(generator, ctx, data);
let ndarray = NDArrayObject::alloca(generator, ctx, object.ty, 0);
ndarray.instance.set(ctx, |f| f.data, data);
ndarray
}
/// Check if this `NDArray` can be used as an `out` ndarray for an operation.
///
/// Raise an exception if the shapes do not match.
pub fn assert_can_be_written_by_out<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
out_ndims: u64,
out_shape: Instance<'ctx, Ptr<Int<SizeT>>>,
) {
let ndarray_ndims = self.ndims_llvm(generator, ctx.ctx);
let ndarray_shape = self.instance.get(generator, ctx, |f| f.shape);
let output_ndims = Int(SizeT).const_int(generator, ctx.ctx, out_ndims, false);
let output_shape = out_shape;
call_nac3_ndarray_util_assert_output_shape_same(
generator,
ctx,
ndarray_ndims,
ndarray_shape,
output_ndims,
output_shape,
);
}
}
/// A convenience enum for implementing functions that acts on scalars or ndarrays or both.
#[derive(Debug, Clone, Copy)]
pub enum ScalarOrNDArray<'ctx> {
Scalar(AnyObject<'ctx>),
NDArray(NDArrayObject<'ctx>),
}
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for AnyObject<'ctx> {
type Error = ();
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
match value {
ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
ScalarOrNDArray::NDArray(_ndarray) => Err(()),
}
}
}
impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
type Error = ();
fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
match value {
ScalarOrNDArray::Scalar(_scalar) => Err(()),
ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
}
}
}
impl<'ctx> ScalarOrNDArray<'ctx> {
/// Split on `object` either into a scalar or an ndarray.
///
/// If `object` is an ndarray, [`ScalarOrNDArray::NDArray`].
///
/// For everything else, it is wrapped with [`ScalarOrNDArray::Scalar`].
pub fn split_object<G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
object: AnyObject<'ctx>,
) -> ScalarOrNDArray<'ctx> {
match &*ctx.unifier.get_ty(object.ty) {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
let ndarray = NDArrayObject::from_object(generator, ctx, object);
ScalarOrNDArray::NDArray(ndarray)
}
_ => ScalarOrNDArray::Scalar(object),
}
}
/// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
#[must_use]
pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
match self {
ScalarOrNDArray::Scalar(scalar) => scalar.value,
ScalarOrNDArray::NDArray(ndarray) => ndarray.instance.value.as_basic_value_enum(),
}
}
/// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
/// - If this is an ndarray, the ndarray is returned.
/// - If this is a scalar, this function returns new ndarray created with [`NDArrayObject::make_unsized`].
pub fn to_ndarray<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
) -> NDArrayObject<'ctx> {
match self {
ScalarOrNDArray::NDArray(ndarray) => *ndarray,
ScalarOrNDArray::Scalar(scalar) => NDArrayObject::make_unsized(generator, ctx, *scalar),
}
}
/// Get the dtype of the ndarray created if this were called with [`ScalarOrNDArray::to_ndarray`].
#[must_use]
pub fn get_dtype(&self) -> Type {
match self {
ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
ScalarOrNDArray::Scalar(scalar) => scalar.ty,
}
}
}
/// An helper enum specifying how a function should produce its output.
///
/// Many functions in NumPy has an optional `out` parameter (e.g., `matmul`). If `out` is specified
/// with an ndarray, the result of a function will be written to `out`. If `out` is not specified, a function will
/// create a new ndarray and store the result in it.
#[derive(Debug, Clone, Copy)]
pub enum NDArrayOut<'ctx> {
/// Tell a function should create a new ndarray with the expected element type `dtype`.
NewNDArray { dtype: Type },
/// Tell a function to write the result to `ndarray`.
WriteToNDArray { ndarray: NDArrayObject<'ctx> },
}
impl<'ctx> NDArrayOut<'ctx> {
/// Get the dtype of this output.
#[must_use]
pub fn get_dtype(&self) -> Type {
match self {
NDArrayOut::NewNDArray { dtype } => *dtype,
NDArrayOut::WriteToNDArray { ndarray } => ndarray.dtype,
}
}
}

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

@ -1,15 +1,22 @@
use inkwell::{
attributes::{Attribute, AttributeLoc},
basic_block::BasicBlock,
types::{BasicType, BasicTypeEnum},
values::{BasicValue, BasicValueEnum, FunctionValue, IntValue, PointerValue},
IntPredicate,
};
use itertools::{izip, Itertools};
use nac3parser::ast::{
Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
};
use super::{
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
expr::{destructure_range, gen_binop_expr},
gen_in_range_check,
irrt::{handle_slice_indices, list_slice_assignment},
macros::codegen_unreachable,
object::{
any::AnyObject,
ndarray::{
indexing::util::gen_ndarray_subscript_ndindices, NDArrayObject, ScalarOrNDArray,
},
},
values::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
CodeGenContext, CodeGenerator,
};
use crate::{
@ -20,17 +27,6 @@ use crate::{
typedef::{iter_type_vars, FunSignature, Type, TypeEnum},
},
};
use inkwell::{
attributes::{Attribute, AttributeLoc},
basic_block::BasicBlock,
types::{BasicType, BasicTypeEnum},
values::{BasicValue, BasicValueEnum, FunctionValue, IntValue, PointerValue},
IntPredicate,
};
use itertools::{izip, Itertools};
use nac3parser::ast::{
Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
};
/// See [`CodeGenerator::gen_var_alloc`].
pub fn gen_var<'ctx>(
@ -314,7 +310,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
.unwrap()
.to_basic_value_enum(ctx, generator, target_ty)?
.into_pointer_value();
let target = ListValue::from_ptr_val(target, llvm_usize, None);
let target = ListValue::from_pointer_value(target, llvm_usize, None);
if let ExprKind::Slice { .. } = &key.node {
// Handle assigning to a slice
@ -335,7 +331,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
let value =
value.to_basic_value_enum(ctx, generator, value_ty)?.into_pointer_value();
let value = ListValue::from_ptr_val(value, llvm_usize, None);
let value = ListValue::from_pointer_value(value, llvm_usize, None);
let target_item_ty = ctx.get_llvm_type(generator, target_item_ty);
let Some(src_ind) = handle_slice_indices(
@ -415,47 +411,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
// Handle NDArray item assignment
// Process target
let target = generator
.gen_expr(ctx, target)?
.unwrap()
.to_basic_value_enum(ctx, generator, target_ty)?;
let target = AnyObject { value: target, ty: target_ty };
// Process key
let key = gen_ndarray_subscript_ndindices(generator, ctx, key)?;
// Process value
let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
let value = AnyObject { value, ty: value_ty };
/*
Reference code:
```python
target = target[key]
value = np.asarray(value)
shape = np.broadcast_shape((target, value))
target = np.broadcast_to(target, shape)
value = np.broadcast_to(value, shape)
...and finally copy 1-1 from value to target.
```
*/
let target = NDArrayObject::from_object(generator, ctx, target);
let target = target.index(generator, ctx, &key);
let value =
ScalarOrNDArray::split_object(generator, ctx, value).to_ndarray(generator, ctx);
let broadcast_result = NDArrayObject::broadcast(generator, ctx, &[target, value]);
let target = broadcast_result.ndarrays[0];
let value = broadcast_result.ndarrays[1];
target.copy_data_from(generator, ctx, value);
todo!("ndarray subscript assignment is not yet implemented");
}
_ => {
panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
@ -507,7 +463,8 @@ pub fn gen_for<G: CodeGenerator>(
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.range.obj_id(&ctx.unifier).unwrap() =>
{
let iter_val = RangeValue::from_ptr_val(iter_val.into_pointer_value(), Some("range"));
let iter_val =
RangeValue::from_pointer_value(iter_val.into_pointer_value(), Some("range"));
// Internal variable for loop; Cannot be assigned
let i = generator.gen_var_alloc(ctx, int32.into(), Some("for.i.addr"))?;
// Variable declared in "target" expression of the loop; Can be reassigned *or* shadowed
@ -1872,6 +1829,37 @@ pub fn gen_stmt<G: CodeGenerator>(
stmt.location,
);
}
StmtKind::Global { names, .. } => {
let registered_globals = ctx
.top_level
.definitions
.read()
.iter()
.filter_map(|def| {
if let TopLevelDef::Variable { simple_name, ty, .. } = &*def.read() {
Some((*simple_name, *ty))
} else {
None
}
})
.collect_vec();
for id in names {
let Some((_, ty)) = registered_globals.iter().find(|(name, _)| name == id) else {
return Err(format!("{id} is not a global at {}", stmt.location));
};
let resolver = ctx.resolver.clone();
let ptr = resolver
.get_symbol_value(*id, ctx, generator)
.map(|val| val.to_basic_value_enum(ctx, generator, *ty))
.transpose()?
.map(BasicValueEnum::into_pointer_value)
.unwrap();
ctx.var_assignment.insert(*id, (ptr, None, 0));
}
}
_ => unimplemented!(),
};
Ok(())

View File

@ -1,34 +1,37 @@
use crate::{
codegen::{
classes::{ListType, NDArrayType, ProxyType, RangeType},
concrete_type::ConcreteTypeStore,
CodeGenContext, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask,
CodeGenerator, DefaultCodeGenerator, WithCall, WorkerRegistry,
},
symbol_resolver::{SymbolResolver, ValueEnum},
toplevel::{
composer::{ComposerConfig, TopLevelComposer},
DefinitionId, FunInstance, TopLevelContext, TopLevelDef,
},
typecheck::{
type_inferencer::{FunctionData, Inferencer, PrimitiveStore},
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
},
use std::{
collections::{HashMap, HashSet},
sync::Arc,
};
use indexmap::IndexMap;
use indoc::indoc;
use inkwell::{
targets::{InitializationConfig, Target},
OptimizationLevel,
};
use nac3parser::ast::FileName;
use nac3parser::{
ast::{fold::Fold, StrRef},
ast::{fold::Fold, FileName, StrRef},
parser::parse_program,
};
use parking_lot::RwLock;
use std::collections::{HashMap, HashSet};
use std::sync::Arc;
use super::{
concrete_type::ConcreteTypeStore,
types::{ndarray::NDArrayType, ListType, ProxyType, RangeType},
CodeGenContext, CodeGenLLVMOptions, CodeGenTargetMachineOptions, CodeGenTask, CodeGenerator,
DefaultCodeGenerator, WithCall, WorkerRegistry,
};
use crate::{
symbol_resolver::{SymbolResolver, ValueEnum},
toplevel::{
composer::{ComposerConfig, TopLevelComposer},
DefinitionId, FunInstance, TopLevelContext, TopLevelDef,
},
typecheck::{
type_inferencer::{FunctionData, IdentifierInfo, Inferencer, PrimitiveStore},
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
},
};
struct Resolver {
id_to_type: HashMap<StrRef, Type>,
@ -64,6 +67,7 @@ impl SymbolResolver for Resolver {
&self,
_: StrRef,
_: &mut CodeGenContext<'ctx, '_>,
_: &mut dyn CodeGenerator,
) -> Option<ValueEnum<'ctx>> {
unimplemented!()
}
@ -138,7 +142,8 @@ fn test_primitives() {
};
let mut virtual_checks = Vec::new();
let mut calls = HashMap::new();
let mut identifiers: HashSet<_> = ["a".into(), "b".into()].into();
let mut identifiers: HashMap<_, _> =
["a".into(), "b".into()].map(|id| (id, IdentifierInfo::default())).into();
let mut inferencer = Inferencer {
top_level: &top_level,
function_data: &mut function_data,
@ -317,7 +322,8 @@ fn test_simple_call() {
};
let mut virtual_checks = Vec::new();
let mut calls = HashMap::new();
let mut identifiers: HashSet<_> = ["a".into(), "foo".into()].into();
let mut identifiers: HashMap<_, _> =
["a".into(), "foo".into()].map(|id| (id, IdentifierInfo::default())).into();
let mut inferencer = Inferencer {
top_level: &top_level,
function_data: &mut function_data,
@ -446,7 +452,7 @@ fn test_classes_list_type_new() {
let llvm_usize = generator.get_size_type(&ctx);
let llvm_list = ListType::new(&generator, &ctx, llvm_i32.into());
assert!(ListType::is_type(llvm_list.as_base_type(), llvm_usize).is_ok());
assert!(ListType::is_representable(llvm_list.as_base_type(), llvm_usize).is_ok());
}
#[test]
@ -454,7 +460,7 @@ fn test_classes_range_type_new() {
let ctx = inkwell::context::Context::create();
let llvm_range = RangeType::new(&ctx);
assert!(RangeType::is_type(llvm_range.as_base_type()).is_ok());
assert!(RangeType::is_representable(llvm_range.as_base_type()).is_ok());
}
#[test]
@ -465,6 +471,6 @@ 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());
assert!(NDArrayType::is_type(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into(), None);
assert!(NDArrayType::is_representable(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
}

View File

@ -0,0 +1,206 @@
use inkwell::{
context::Context,
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
values::IntValue,
AddressSpace,
};
use super::ProxyType;
use crate::codegen::{
values::{ArraySliceValue, ListValue, ProxyValue},
CodeGenContext, CodeGenerator,
};
/// Proxy type for a `list` type in LLVM.
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
pub struct ListType<'ctx> {
ty: PointerType<'ctx>,
llvm_usize: IntType<'ctx>,
}
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 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 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 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()
));
}
Ok(())
}
/// Creates an LLVM type corresponding to the expected structure of a `List`.
#[must_use]
fn llvm_type(
ctx: &'ctx Context,
element_type: BasicTypeEnum<'ctx>,
llvm_usize: IntType<'ctx>,
) -> PointerType<'ctx> {
// 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())
}
/// Creates an instance of [`ListType`].
#[must_use]
pub fn new<G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &'ctx Context,
element_type: BasicTypeEnum<'ctx>,
) -> Self {
let llvm_usize = generator.get_size_type(ctx);
let llvm_list = Self::llvm_type(ctx, element_type, llvm_usize);
ListType::from_type(llvm_list, llvm_usize)
}
/// Creates an [`ListType`] from a [`PointerType`].
#[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());
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.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) -> 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.
#[must_use]
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(generator, ctx, name),
self.llvm_usize,
name,
)
}
/// 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_pointer_value(value, self.llvm_usize, name)
}
}
impl<'ctx> ProxyType<'ctx> for ListType<'ctx> {
type Base = PointerType<'ctx>;
type Value = ListValue<'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 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 {
self.ty
}
}
impl<'ctx> From<ListType<'ctx>> for PointerType<'ctx> {
fn from(value: ListType<'ctx>) -> Self {
value.as_base_type()
}
}

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//! This module contains abstraction over all intrinsic composite types of NAC3.
//!
//! # `raw_alloca` vs `alloca` vs `construct`
//!
//! There are three ways of creating a new object instance using the abstractions provided by this
//! module.
//!
//! - `raw_alloca`: Allocates the object on the stack, returning an instance of
//! [`impl BasicValue`][inkwell::values::BasicValue]. This is similar to a `malloc` expression in
//! C++ but the object is allocated on the stack.
//! - `alloca`: Similar to `raw_alloca`, but also wraps the allocated object with
//! [`<Self as ProxyType<'ctx>>::Value`][ProxyValue], and returns the wrapped object. The returned
//! object will not initialize any value or fields. This is similar to a type-safe `malloc`
//! expression in C++ but the object is allocated on the stack.
//! - `construct`: Similar to `alloca`, but performs some initialization on the value or fields of
//! 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};
use super::{
values::{ArraySliceValue, ProxyValue},
{CodeGenContext, CodeGenerator},
};
pub use list::*;
pub use range::*;
mod list;
pub mod ndarray;
mod range;
pub mod structure;
pub mod utils;
/// A LLVM type that is used to represent a corresponding type in NAC3.
pub trait ProxyType<'ctx>: Into<Self::Base> {
/// The LLVM type of which values of this type possess. This is usually a
/// [LLVM pointer type][PointerType] for any non-primitive types.
type Base: BasicType<'ctx>;
/// The type of values represented by this type.
type Value: ProxyValue<'ctx, Type = Self>;
fn is_type<G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &'ctx Context,
llvm_ty: impl BasicType<'ctx>,
) -> Result<(), String>;
/// Checks whether `llvm_ty` can be represented by this [`ProxyType`].
fn is_representable<G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &'ctx Context,
llvm_ty: Self::Base,
) -> Result<(), String>;
/// 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>,
) -> <Self::Value as ProxyValue<'ctx>>::Base;
/// 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>;
/// Returns the [base type][Self::Base] of this proxy.
fn as_base_type(&self) -> Self::Base;
}

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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, ArraySliceValue, 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 ContiguousNDArrayFields<'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> ContiguousNDArrayFields<'ctx> {
#[must_use]
pub fn new_typed(item: BasicTypeEnum<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
let mut counter = FieldIndexCounter::default();
ContiguousNDArrayFields {
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 = ContiguousNDArrayFields::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>,
) -> ContiguousNDArrayFields<'ctx> {
ContiguousNDArrayFields::new_typed(item, llvm_usize)
}
/// See [`NDArrayType::fields`].
// TODO: Move this into e.g. StructProxyType
#[must_use]
pub fn get_fields(&self) -> ContiguousNDArrayFields<'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.
#[must_use]
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(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 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 {
self.ty
}
}
impl<'ctx> From<ContiguousNDArrayType<'ctx>> for PointerType<'ctx> {
fn from(value: ContiguousNDArrayType<'ctx>) -> Self {
value.as_base_type()
}
}

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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 }
}
#[must_use]
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(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(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 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 {
self.ty
}
}
impl<'ctx> From<NDIndexType<'ctx>> for PointerType<'ctx> {
fn from(value: NDIndexType<'ctx>) -> Self {
value.as_base_type()
}
}

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use inkwell::{
context::{AsContextRef, Context},
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
values::{BasicValue, IntValue, PointerValue},
AddressSpace,
};
use itertools::Itertools;
use nac3core_derive::StructFields;
use super::{
structure::{check_struct_type_matches_fields, StructField, StructFields},
ProxyType,
};
use crate::{
codegen::{
values::{ndarray::NDArrayValue, ArraySliceValue, ProxyValue, TypedArrayLikeMutator},
{CodeGenContext, CodeGenerator},
},
toplevel::{helper::extract_ndims, numpy::unpack_ndarray_var_tys},
typecheck::typedef::Type,
};
pub use contiguous::*;
pub use indexing::*;
pub use nditer::*;
mod contiguous;
mod indexing;
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: Option<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>>,
}
impl<'ctx> NDArrayType<'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_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}"));
};
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>,
) -> 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> {
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> {
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 [`NDArrayType`].
#[must_use]
pub fn new<G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &'ctx Context,
dtype: BasicTypeEnum<'ctx>,
ndims: Option<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`] 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: Some(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: Some(ndims),
llvm_usize,
}
}
/// Creates an [`NDArrayType`] from a [`PointerType`] representing an `NDArray`.
#[must_use]
pub fn from_type(
ptr_ty: PointerType<'ctx>,
dtype: BasicTypeEnum<'ctx>,
ndims: Option<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 }
}
/// Returns the type of the `size` field of this `ndarray` type.
#[must_use]
pub fn size_type(&self) -> IntType<'ctx> {
self.llvm_usize
}
/// Returns the element type of this `ndarray` type.
#[must_use]
pub fn element_type(&self) -> BasicTypeEnum<'ctx> {
self.dtype
}
/// Returns the number of dimensions of this `ndarray` type.
#[must_use]
pub fn ndims(&self) -> Option<u64> {
self.ndims
}
/// Allocates an instance of [`NDArrayValue`] as if by calling `alloca` on the base type.
#[must_use]
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(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(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 {
assert!(self.ndims.is_some(), "NDArrayType::construct can only be called on an instance with compile-time known ndims (self.ndims = Some(ndims))");
let Some(ndims) = self.ndims.map(|ndims| self.llvm_usize.const_int(ndims, false)) else {
unreachable!()
};
self.construct_impl(generator, ctx, ndims, name)
}
/// Allocate an [`NDArrayValue`] on the stack given its `ndims` and `dtype`.
///
/// `shape` and `strides` will be automatically allocated onto the stack.
///
/// The returned ndarray's content will be:
/// - `data`: uninitialized.
/// - `itemsize`: set to the size of `dtype`.
/// - `ndims`: set to the value of `ndims`.
/// - `shape`: allocated with an array of length `ndims` with uninitialized values.
/// - `strides`: allocated with an array of length `ndims` with uninitialized values.
#[deprecated = "Prefer construct_uninitialized or construct_*_shape."]
#[must_use]
pub fn construct_dyn_ndims<G: CodeGenerator + ?Sized>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndims: IntValue<'ctx>,
name: Option<&'ctx str>,
) -> <Self as ProxyType<'ctx>>::Value {
assert!(self.ndims.is_none(), "NDArrayType::construct_dyn_ndims can only be called on an instance with compile-time unknown ndims (self.ndims = None)");
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!(self.ndims.is_none_or(|ndims| shape.len() as u64 == ndims));
let ndarray = Self::new(generator, ctx.ctx, self.dtype, Some(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!(self.ndims.is_none_or(|ndims| shape.len() as u64 == ndims));
let ndarray = Self::new(generator, ctx.ctx, self.dtype, Some(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!(self.ndims.is_none_or(|ndims| 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(
&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.dtype,
self.ndims,
self.llvm_usize,
name,
)
}
}
impl<'ctx> ProxyType<'ctx> for NDArrayType<'ctx> {
type Base = PointerType<'ctx>;
type Value = NDArrayValue<'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 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 {
self.ty
}
}
impl<'ctx> From<NDArrayType<'ctx>> for PointerType<'ctx> {
fn from(value: NDArrayType<'ctx>) -> Self {
value.as_base_type()
}
}

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@ -0,0 +1,241 @@
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},
ArraySliceValue, ProxyValue,
},
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
}
#[must_use]
pub fn alloca<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(generator, ctx, name),
parent,
indices,
self.llvm_usize,
name,
)
}
/// Allocate an [`NDIter`] that iterates through the given `ndarray`.
#[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(generator, ctx, None);
let ndims = ndarray.load_ndims(ctx);
// 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 nditer = <Self as ProxyType<'ctx>>::Value::from_pointer_value(
nditer,
ndarray,
indices,
self.llvm_usize,
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 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 {
self.ty
}
}
impl<'ctx> From<NDIterType<'ctx>> for PointerType<'ctx> {
fn from(value: NDIterType<'ctx>) -> Self {
value.as_base_type()
}
}

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@ -0,0 +1,170 @@
use inkwell::{
context::Context,
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
values::IntValue,
AddressSpace,
};
use super::ProxyType;
use crate::codegen::{
values::{ArraySliceValue, ProxyValue, RangeValue},
{CodeGenContext, CodeGenerator},
};
/// Proxy type for a `range` type in LLVM.
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
pub struct RangeType<'ctx> {
ty: PointerType<'ctx>,
}
impl<'ctx> RangeType<'ctx> {
/// Checks whether `llvm_ty` represents a `range` type, returning [Err] if it does not.
pub fn is_representable(llvm_ty: PointerType<'ctx>) -> Result<(), String> {
let llvm_range_ty = llvm_ty.get_element_type();
let AnyTypeEnum::ArrayType(llvm_range_ty) = llvm_range_ty else {
return Err(format!("Expected array type for `range` type, got {llvm_range_ty}"));
};
if llvm_range_ty.len() != 3 {
return Err(format!(
"Expected 3 elements for `range` type, got {}",
llvm_range_ty.len()
));
}
let llvm_range_elem_ty = llvm_range_ty.get_element_type();
let Ok(llvm_range_elem_ty) = IntType::try_from(llvm_range_elem_ty) else {
return Err(format!(
"Expected int type for `range` element type, got {llvm_range_elem_ty}"
));
};
if llvm_range_elem_ty.get_bit_width() != 32 {
return Err(format!(
"Expected 32-bit int type for `range` element type, got {}",
llvm_range_elem_ty.get_bit_width()
));
}
Ok(())
}
/// Creates an LLVM type corresponding to the expected structure of a `Range`.
#[must_use]
fn llvm_type(ctx: &'ctx Context) -> PointerType<'ctx> {
// typedef int32_t Range[3];
let llvm_i32 = ctx.i32_type();
llvm_i32.array_type(3).ptr_type(AddressSpace::default())
}
/// Creates an instance of [`RangeType`].
#[must_use]
pub fn new(ctx: &'ctx Context) -> Self {
let llvm_range = Self::llvm_type(ctx);
RangeType::from_type(llvm_range)
}
/// Creates an [`RangeType`] from a [`PointerType`].
#[must_use]
pub fn from_type(ptr_ty: PointerType<'ctx>) -> Self {
debug_assert!(Self::is_representable(ptr_ty).is_ok());
RangeType { ty: ptr_ty }
}
/// Returns the type of all fields of this `range` type.
#[must_use]
pub fn value_type(&self) -> IntType<'ctx> {
self.as_base_type().get_element_type().into_array_type().get_element_type().into_int_type()
}
/// Allocates an instance of [`RangeValue`] as if by calling `alloca` on the base type.
#[must_use]
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(generator, ctx, name),
name,
)
}
/// Converts an existing value into a [`RangeValue`].
#[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, name)
}
}
impl<'ctx> ProxyType<'ctx> for RangeType<'ctx> {
type Base = PointerType<'ctx>;
type Value = RangeValue<'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>(
_: &G,
_: &'ctx Context,
llvm_ty: Self::Base,
) -> Result<(), String> {
Self::is_representable(llvm_ty)
}
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 {
self.ty
}
}
impl<'ctx> From<RangeType<'ctx>> for PointerType<'ctx> {
fn from(value: RangeType<'ctx>) -> Self {
value.as_base_type()
}
}

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@ -0,0 +1,255 @@
use std::marker::PhantomData;
use inkwell::{
context::AsContextRef,
types::{BasicTypeEnum, IntType, StructType},
values::{BasicValue, BasicValueEnum, IntValue, PointerValue, StructValue},
};
use crate::codegen::CodeGenContext;
/// Trait indicating that the structure is a field-wise representation of an LLVM structure.
///
/// # Usage
///
/// For example, for a simple C-slice LLVM structure:
///
/// ```ignore
/// struct CSliceFields<'ctx> {
/// ptr: StructField<'ctx, PointerValue<'ctx>>,
/// len: StructField<'ctx, IntValue<'ctx>>
/// }
/// ```
pub trait StructFields<'ctx>: Eq + Copy {
/// Creates an instance of [`StructFields`] using the given `ctx` and `size_t` types.
fn new(ctx: impl AsContextRef<'ctx>, llvm_usize: IntType<'ctx>) -> Self;
/// Returns a [`Vec`] that contains the fields of the structure in the order as they appear in
/// the type definition.
#[must_use]
fn to_vec(&self) -> Vec<(&'static str, BasicTypeEnum<'ctx>)>;
/// Returns a [`Iterator`] that contains the fields of the structure in the order as they appear
/// in the type definition.
#[must_use]
fn iter(&self) -> impl Iterator<Item = (&'static str, BasicTypeEnum<'ctx>)> {
self.to_vec().into_iter()
}
/// Returns a [`Vec`] that contains the fields of the structure in the order as they appear in
/// the type definition.
#[must_use]
fn into_vec(self) -> Vec<(&'static str, BasicTypeEnum<'ctx>)>
where
Self: Sized,
{
self.to_vec()
}
/// Returns a [`Iterator`] that contains the fields of the structure in the order as they appear
/// in the type definition.
#[must_use]
fn into_iter(self) -> impl Iterator<Item = (&'static str, BasicTypeEnum<'ctx>)>
where
Self: Sized,
{
self.into_vec().into_iter()
}
}
/// A single field of an LLVM structure.
#[derive(Debug, PartialEq, Eq, Clone, Copy)]
pub struct StructField<'ctx, Value>
where
Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>, Error = ()>,
{
/// The index of this field within the structure.
index: u32,
/// The name of this field.
name: &'static str,
/// The type of this field.
ty: BasicTypeEnum<'ctx>,
/// Instance of [`PhantomData`] containing [`Value`], used to implement automatic downcasts.
_value_ty: PhantomData<Value>,
}
impl<'ctx, Value> StructField<'ctx, Value>
where
Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>, Error = ()>,
{
/// Creates an instance of [`StructField`].
///
/// * `idx_counter` - The instance of [`FieldIndexCounter`] used to track the current field
/// index.
/// * `name` - Name of the field.
/// * `ty` - The type of this field.
pub fn create(
idx_counter: &mut FieldIndexCounter,
name: &'static str,
ty: impl Into<BasicTypeEnum<'ctx>>,
) -> Self {
StructField { index: idx_counter.increment(), name, ty: ty.into(), _value_ty: PhantomData }
}
/// Creates an instance of [`StructField`] with a given index.
///
/// * `index` - The index of this field within its enclosing structure.
/// * `name` - Name of the field.
/// * `ty` - The type of this field.
pub fn create_at(index: u32, name: &'static str, ty: impl Into<BasicTypeEnum<'ctx>>) -> Self {
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(
&self,
ctx: &CodeGenContext<'ctx, '_>,
pobj: PointerValue<'ctx>,
idx: &[IntValue<'ctx>],
) -> PointerValue<'ctx> {
unsafe {
ctx.builder.build_in_bounds_gep(
pobj,
&[idx, &[ctx.ctx.i32_type().const_int(u64::from(self.index), false)]].concat(),
"",
)
}
.unwrap()
}
/// Creates a pointer to this field in an arbitrary structure by performing the equivalent of
/// `getelementptr i32 0, i32 {self.index}`.
pub fn ptr_by_gep(
&self,
ctx: &CodeGenContext<'ctx, '_>,
pobj: PointerValue<'ctx>,
obj_name: Option<&'ctx str>,
) -> PointerValue<'ctx> {
ctx.builder
.build_struct_gep(
pobj,
self.index,
&obj_name.map(|name| format!("{name}.{}.addr", self.name)).unwrap_or_default(),
)
.unwrap()
}
/// Gets the value of this field for a given `obj`.
#[must_use]
pub fn get_from_value(&self, obj: StructValue<'ctx>) -> Value {
obj.get_field_at_index(self.index).and_then(|value| Value::try_from(value).ok()).unwrap()
}
/// Sets the value of this field for a given `obj`.
pub fn set_for_value(&self, obj: StructValue<'ctx>, value: Value) {
obj.set_field_at_index(self.index, value);
}
/// Gets the value of this field for a pointer-to-structure.
pub fn get(
&self,
ctx: &CodeGenContext<'ctx, '_>,
pobj: PointerValue<'ctx>,
obj_name: Option<&'ctx str>,
) -> Value {
ctx.builder
.build_load(
self.ptr_by_gep(ctx, pobj, obj_name),
&obj_name.map(|name| format!("{name}.{}", self.name)).unwrap_or_default(),
)
.map_err(|_| ())
.and_then(|value| Value::try_from(value))
.unwrap()
}
/// Sets the value of this field for a pointer-to-structure.
pub fn set(
&self,
ctx: &CodeGenContext<'ctx, '_>,
pobj: PointerValue<'ctx>,
value: Value,
obj_name: Option<&'ctx str>,
) {
ctx.builder.build_store(self.ptr_by_gep(ctx, pobj, obj_name), value).unwrap();
}
}
impl<'ctx, Value> From<StructField<'ctx, Value>> for (&'static str, BasicTypeEnum<'ctx>)
where
Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>, Error = ()>,
{
fn from(value: StructField<'ctx, Value>) -> Self {
(value.name, value.ty)
}
}
/// A counter that tracks the next index of a field using a monotonically increasing counter.
#[derive(Default, Debug, PartialEq, Eq, Clone, Copy)]
pub struct FieldIndexCounter(u32);
impl FieldIndexCounter {
/// Increments the number stored by this counter, returning the previous value.
///
/// Functionally equivalent to `i++` in C-based languages.
pub fn increment(&mut self) -> u32 {
let v = self.0;
self.0 += 1;
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(())
}

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@ -0,0 +1,3 @@
pub use slice::*;
mod slice;

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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, ArraySliceValue, 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 [`ContiguousNDArrayValue`] as if by calling `alloca` on the base type.
#[must_use]
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(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 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 {
self.ty
}
}
impl<'ctx> From<SliceType<'ctx>> for PointerType<'ctx> {
fn from(value: SliceType<'ctx>) -> Self {
value.as_base_type()
}
}

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@ -0,0 +1,426 @@
use inkwell::{
types::AnyTypeEnum,
values::{BasicValueEnum, IntValue, PointerValue},
IntPredicate,
};
use crate::codegen::{CodeGenContext, CodeGenerator};
/// An LLVM value that is array-like, i.e. it contains a contiguous, sequenced collection of
/// elements.
pub trait ArrayLikeValue<'ctx> {
/// Returns the element type of this array-like value.
fn element_type<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> AnyTypeEnum<'ctx>;
/// Returns the base pointer to the array.
fn base_ptr<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> PointerValue<'ctx>;
/// Returns the size of this array-like value.
fn size<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> IntValue<'ctx>;
/// Returns a [`ArraySliceValue`] representing this value.
fn as_slice_value<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> ArraySliceValue<'ctx> {
ArraySliceValue::from_ptr_val(
self.base_ptr(ctx, generator),
self.size(ctx, generator),
None,
)
}
}
/// An array-like value that can be indexed by memory offset.
pub trait ArrayLikeIndexer<'ctx, Index = IntValue<'ctx>>: ArrayLikeValue<'ctx> {
/// # Safety
///
/// This function should be called with a valid index.
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &Index,
name: Option<&str>,
) -> PointerValue<'ctx>;
/// Returns the pointer to the data at the `idx`-th index.
fn ptr_offset<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &Index,
name: Option<&str>,
) -> PointerValue<'ctx>;
}
/// An array-like value that can have its array elements accessed as a [`BasicValueEnum`].
pub trait UntypedArrayLikeAccessor<'ctx, Index = IntValue<'ctx>>:
ArrayLikeIndexer<'ctx, Index>
{
/// # Safety
///
/// This function should be called with a valid index.
unsafe fn get_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &Index,
name: Option<&str>,
) -> BasicValueEnum<'ctx> {
let ptr = unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) };
ctx.builder.build_load(ptr, name.unwrap_or_default()).unwrap()
}
/// Returns the data at the `idx`-th index.
fn get<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &Index,
name: Option<&str>,
) -> BasicValueEnum<'ctx> {
let ptr = self.ptr_offset(ctx, generator, idx, name);
ctx.builder.build_load(ptr, name.unwrap_or_default()).unwrap()
}
}
/// An array-like value that can have its array elements mutated as a [`BasicValueEnum`].
pub trait UntypedArrayLikeMutator<'ctx, Index = IntValue<'ctx>>:
ArrayLikeIndexer<'ctx, Index>
{
/// # Safety
///
/// This function should be called with a valid index.
unsafe fn set_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &Index,
value: BasicValueEnum<'ctx>,
) {
let ptr = unsafe { self.ptr_offset_unchecked(ctx, generator, idx, None) };
ctx.builder.build_store(ptr, value).unwrap();
}
/// Sets the data at the `idx`-th index.
fn set<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &Index,
value: BasicValueEnum<'ctx>,
) {
let ptr = self.ptr_offset(ctx, generator, idx, None);
ctx.builder.build_store(ptr, value).unwrap();
}
}
/// An array-like value that can have its array elements accessed as an arbitrary type `T`.
pub trait TypedArrayLikeAccessor<'ctx, T, Index = IntValue<'ctx>>:
UntypedArrayLikeAccessor<'ctx, Index>
{
/// Casts an element from [`BasicValueEnum`] into `T`.
fn downcast_to_type(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
value: BasicValueEnum<'ctx>,
) -> T;
/// # Safety
///
/// This function should be called with a valid index.
unsafe fn get_typed_unchecked<G: CodeGenerator + ?Sized>(
&self,
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, value)
}
/// Returns the data at the `idx`-th index.
fn get_typed<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &Index,
name: Option<&str>,
) -> T {
let value = self.get(ctx, generator, idx, name);
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, T, Index = IntValue<'ctx>>:
UntypedArrayLikeMutator<'ctx, Index>
{
/// Casts an element from T into [`BasicValueEnum`].
fn upcast_from_type(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
value: T,
) -> BasicValueEnum<'ctx>;
/// # Safety
///
/// This function should be called with a valid index.
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, 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 + 'ctx>;
/// 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.
pub struct TypedArrayLikeAdapter<'ctx, T, Adapted: ArrayLikeValue<'ctx> = ArraySliceValue<'ctx>> {
adapted: Adapted,
downcast_fn: ValueDowncastFn<'ctx, T>,
upcast_fn: ValueUpcastFn<'ctx, T>,
}
impl<'ctx, T, Adapted> TypedArrayLikeAdapter<'ctx, T, Adapted>
where
Adapted: ArrayLikeValue<'ctx>,
{
/// Creates a [`TypedArrayLikeAdapter`].
///
/// * `adapted` - The value to be adapted.
/// * `downcast_fn` - The function converting a [`BasicValueEnum`] into a `T`.
/// * `upcast_fn` - The function converting a T into a [`BasicValueEnum`].
pub fn from(
adapted: Adapted,
downcast_fn: ValueDowncastFn<'ctx, T>,
upcast_fn: ValueUpcastFn<'ctx, T>,
) -> Self {
TypedArrayLikeAdapter { adapted, downcast_fn, upcast_fn }
}
}
impl<'ctx, T, Adapted> ArrayLikeValue<'ctx> for TypedArrayLikeAdapter<'ctx, T, Adapted>
where
Adapted: ArrayLikeValue<'ctx>,
{
fn element_type<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> AnyTypeEnum<'ctx> {
self.adapted.element_type(ctx, generator)
}
fn base_ptr<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> PointerValue<'ctx> {
self.adapted.base_ptr(ctx, generator)
}
fn size<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
) -> IntValue<'ctx> {
self.adapted.size(ctx, generator)
}
}
impl<'ctx, T, Index, Adapted> ArrayLikeIndexer<'ctx, Index>
for TypedArrayLikeAdapter<'ctx, T, Adapted>
where
Adapted: ArrayLikeIndexer<'ctx, Index>,
{
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
&self,
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<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &Index,
name: Option<&str>,
) -> PointerValue<'ctx> {
self.adapted.ptr_offset(ctx, generator, idx, name)
}
}
impl<'ctx, T, Index, Adapted> UntypedArrayLikeAccessor<'ctx, Index>
for TypedArrayLikeAdapter<'ctx, T, Adapted>
where
Adapted: UntypedArrayLikeAccessor<'ctx, Index>,
{
}
impl<'ctx, T, Index, Adapted> UntypedArrayLikeMutator<'ctx, Index>
for TypedArrayLikeAdapter<'ctx, T, Adapted>
where
Adapted: UntypedArrayLikeMutator<'ctx, Index>,
{
}
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: &mut CodeGenContext<'ctx, '_>,
value: BasicValueEnum<'ctx>,
) -> T {
(self.downcast_fn)(ctx, value)
}
}
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: &mut CodeGenContext<'ctx, '_>,
value: T,
) -> BasicValueEnum<'ctx> {
(self.upcast_fn)(ctx, value)
}
}
/// An LLVM value representing an array slice, consisting of a pointer to the data and the size of
/// the slice.
#[derive(Copy, Clone)]
pub struct ArraySliceValue<'ctx>(PointerValue<'ctx>, IntValue<'ctx>, Option<&'ctx str>);
impl<'ctx> ArraySliceValue<'ctx> {
/// Creates an [`ArraySliceValue`] from a [`PointerValue`] and its size.
#[must_use]
pub fn from_ptr_val(
ptr: PointerValue<'ctx>,
size: IntValue<'ctx>,
name: Option<&'ctx str>,
) -> Self {
ArraySliceValue(ptr, size, name)
}
}
impl<'ctx> From<ArraySliceValue<'ctx>> for PointerValue<'ctx> {
fn from(value: ArraySliceValue<'ctx>) -> Self {
value.0
}
}
impl<'ctx> ArrayLikeValue<'ctx> for ArraySliceValue<'ctx> {
fn element_type<G: CodeGenerator + ?Sized>(
&self,
_: &CodeGenContext<'ctx, '_>,
_: &G,
) -> AnyTypeEnum<'ctx> {
self.0.get_type().get_element_type()
}
fn base_ptr<G: CodeGenerator + ?Sized>(
&self,
_: &CodeGenContext<'ctx, '_>,
_: &G,
) -> PointerValue<'ctx> {
self.0
}
fn size<G: CodeGenerator + ?Sized>(
&self,
_: &CodeGenContext<'ctx, '_>,
_: &G,
) -> IntValue<'ctx> {
self.1
}
}
impl<'ctx> ArrayLikeIndexer<'ctx> for ArraySliceValue<'ctx> {
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(self.base_ptr(ctx, generator), &[*idx], var_name.as_str())
.unwrap()
}
}
fn ptr_offset<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
debug_assert_eq!(idx.get_type(), generator.get_size_type(ctx.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",
"list index out of range",
[None, None, None],
ctx.current_loc,
);
unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
}
}
impl<'ctx> UntypedArrayLikeAccessor<'ctx> for ArraySliceValue<'ctx> {}
impl<'ctx> UntypedArrayLikeMutator<'ctx> for ArraySliceValue<'ctx> {}

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@ -0,0 +1,241 @@
use inkwell::{
types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType},
values::{BasicValueEnum, IntValue, PointerValue},
AddressSpace, IntPredicate,
};
use super::{
ArrayLikeIndexer, ArrayLikeValue, ProxyValue, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
};
use crate::codegen::{
types::ListType,
{CodeGenContext, CodeGenerator},
};
/// Proxy type for accessing a `list` value in LLVM.
#[derive(Copy, Clone)]
pub struct ListValue<'ctx> {
value: PointerValue<'ctx>,
llvm_usize: IntType<'ctx>,
name: Option<&'ctx str>,
}
impl<'ctx> ListValue<'ctx> {
/// Checks whether `value` is an instance of `list`, returning [Err] if `value` is not an
/// instance.
pub fn is_representable(
value: PointerValue<'ctx>,
llvm_usize: IntType<'ctx>,
) -> Result<(), String> {
ListType::is_representable(value.get_type(), llvm_usize)
}
/// Creates an [`ListValue`] 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());
ListValue { value: ptr, llvm_usize, name }
}
/// 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> {
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>) {
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.
pub fn create_data(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: BasicTypeEnum<'ctx>,
size: Option<IntValue<'ctx>>,
) {
let size = size.unwrap_or_else(|| self.load_size(ctx, None));
let data = ctx
.builder
.build_select(
ctx.builder
.build_int_compare(IntPredicate::NE, size, self.llvm_usize.const_zero(), "")
.unwrap(),
ctx.builder.build_array_alloca(elem_ty, size, "").unwrap(),
elem_ty.ptr_type(AddressSpace::default()).const_zero(),
"",
)
.map(BasicValueEnum::into_pointer_value)
.unwrap();
self.store_data(ctx, data);
}
/// Returns the double-indirection pointer to the `data` array, as if by calling `getelementptr`
/// on the field.
#[must_use]
pub fn data(&self) -> ListDataProxy<'ctx, '_> {
ListDataProxy(self)
}
/// Stores the `size` of this `list` into this instance.
pub fn store_size<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
generator: &G,
size: IntValue<'ctx>,
) {
debug_assert_eq!(size.get_type(), generator.get_size_type(ctx.ctx));
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<&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();
ctx.builder
.build_load(psize, var_name.as_str())
.map(BasicValueEnum::into_int_value)
.unwrap()
}
}
impl<'ctx> ProxyValue<'ctx> for ListValue<'ctx> {
type Base = PointerValue<'ctx>;
type Type = ListType<'ctx>;
fn get_type(&self) -> Self::Type {
ListType::from_type(self.as_base_value().get_type(), self.llvm_usize)
}
fn as_base_value(&self) -> Self::Base {
self.value
}
}
impl<'ctx> From<ListValue<'ctx>> for PointerValue<'ctx> {
fn from(value: ListValue<'ctx>) -> Self {
value.as_base_value()
}
}
/// Proxy type for accessing the `data` array of an `list` instance in LLVM.
#[derive(Copy, Clone)]
pub struct ListDataProxy<'ctx, 'a>(&'a ListValue<'ctx>);
impl<'ctx> ArrayLikeValue<'ctx> for ListDataProxy<'ctx, '_> {
fn element_type<G: CodeGenerator + ?Sized>(
&self,
_: &CodeGenContext<'ctx, '_>,
_: &G,
) -> AnyTypeEnum<'ctx> {
self.0.value.get_type().get_element_type()
}
fn base_ptr<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
_: &G,
) -> PointerValue<'ctx> {
let var_name = self.0.name.map(|v| format!("{v}.data")).unwrap_or_default();
ctx.builder
.build_load(self.0.pptr_to_data(ctx), var_name.as_str())
.map(BasicValueEnum::into_pointer_value)
.unwrap()
}
fn size<G: CodeGenerator + ?Sized>(
&self,
ctx: &CodeGenContext<'ctx, '_>,
_: &G,
) -> IntValue<'ctx> {
self.0.load_size(ctx, None)
}
}
impl<'ctx> ArrayLikeIndexer<'ctx> for ListDataProxy<'ctx, '_> {
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
unsafe {
ctx.builder
.build_in_bounds_gep(self.base_ptr(ctx, generator), &[*idx], var_name.as_str())
.unwrap()
}
}
fn ptr_offset<G: CodeGenerator + ?Sized>(
&self,
ctx: &mut CodeGenContext<'ctx, '_>,
generator: &mut G,
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
debug_assert_eq!(idx.get_type(), generator.get_size_type(ctx.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",
"list index out of range",
[None, None, None],
ctx.current_loc,
);
unsafe { self.ptr_offset_unchecked(ctx, generator, idx, name) }
}
}
impl<'ctx> UntypedArrayLikeAccessor<'ctx> for ListDataProxy<'ctx, '_> {}
impl<'ctx> UntypedArrayLikeMutator<'ctx> for ListDataProxy<'ctx, '_> {}

View File

@ -0,0 +1,47 @@
use inkwell::{context::Context, values::BasicValue};
use super::types::ProxyType;
use crate::codegen::CodeGenerator;
pub use array::*;
pub use list::*;
pub use range::*;
mod array;
mod list;
pub mod ndarray;
mod range;
pub mod utils;
/// A LLVM type that is used to represent a non-primitive value in NAC3.
pub trait ProxyValue<'ctx>: Into<Self::Base> {
/// The type of LLVM values represented by this instance. This is usually the
/// [LLVM pointer type][PointerValue].
type Base: BasicValue<'ctx>;
/// The type of this value.
type Type: ProxyType<'ctx, Value = Self>;
/// Checks whether `value` can be represented by this [`ProxyValue`].
fn is_instance<G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &'ctx Context,
value: impl BasicValue<'ctx>,
) -> Result<(), String> {
Self::Type::is_type(generator, ctx, value.as_basic_value_enum().get_type())
}
/// Checks whether `value` can be represented by this [`ProxyValue`].
fn is_representable<G: CodeGenerator + ?Sized>(
generator: &G,
ctx: &'ctx Context,
value: Self::Base,
) -> Result<(), String> {
Self::is_instance(generator, ctx, value.as_basic_value_enum())
}
/// Returns the [type][ProxyType] of this value.
fn get_type(&self) -> Self::Type;
/// Returns the [base value][Self::Base] of this proxy.
fn as_base_value(&self) -> Self::Base;
}

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@ -0,0 +1,202 @@
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(generator, ctx, self.name);
// Set ndims and shape.
let ndims = self
.ndims
.map_or_else(|| self.load_ndims(ctx), |ndims| self.llvm_usize.const_int(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, Some(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
}
}

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