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[core] Refactor/Remove redundant and unused constructs

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- Use ProxyValue.name where necessary
- Remove NDArrayValue::ptr_to_{shape,strides}
- Remove functions made obsolete by ndstrides
- Remove use statement for ndarray::views as it only contain an impl
block.
- Remove class_names field in Resolvers of test sources
This commit is contained in:
David Mak 2024-12-17 13:58:02 +08:00
parent 08b717d640
commit f62babbace
15 changed files with 23 additions and 573 deletions
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324
nac3core/src/codegen/expr.rs
View File

@ -34,19 +34,14 @@ use super::{
},
types::{ndarray::NDArrayType, ListType},
values::{
ndarray::{NDArrayValue, RustNDIndex},
ArrayLikeIndexer, ArrayLikeValue, ListValue, ProxyValue, RangeValue,
TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
ndarray::RustNDIndex, ArrayLikeIndexer, ArrayLikeValue, ListValue, ProxyValue, RangeValue,
UntypedArrayLikeAccessor,
},
CodeGenContext, CodeGenTask, CodeGenerator,
};
use crate::{
symbol_resolver::{SymbolValue, ValueEnum},
toplevel::{
helper::{extract_ndims, PrimDef},
numpy::unpack_ndarray_var_tys,
DefinitionId, TopLevelDef,
},
toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, TopLevelDef},
typecheck::{
magic_methods::{Binop, BinopVariant, HasOpInfo},
typedef::{FunSignature, FuncArg, Type, TypeEnum, TypeVarId, Unifier, VarMap},
@ -2512,319 +2507,6 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
)
}
/// Generates code for a subscript expression on an `ndarray`.
///
/// * `ty` - The `Type` of the `NDArray` elements.
/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
/// * `v` - The `NDArray` value.
/// * `slice` - The slice expression used to subscript into the `ndarray`.
fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ty: Type,
ndims_ty: Type,
v: NDArrayValue<'ctx>,
slice: &Expr<Option<Type>>,
) -> Result<Option<ValueEnum<'ctx>>, String> {
let llvm_i1 = ctx.ctx.bool_type();
let llvm_i32 = ctx.ctx.i32_type();
let llvm_usize = generator.get_size_type(ctx.ctx);
let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims_ty) else {
codegen_unreachable!(ctx)
};
let ndims = values
.iter()
.map(|ndim| u64::try_from(ndim.clone()).map_err(|()| ndim.clone()))
.collect::<Result<Vec<_>, _>>()
.map_err(|val| {
format!(
"Expected non-negative literal for ndarray.ndims, got {}",
i128::try_from(val).unwrap()
)
})?;
assert!(!ndims.is_empty());
// The number of dimensions subscripted by the index expression.
// Slicing a ndarray will yield the same number of dimensions, whereas indexing into a
// dimension will remove a dimension.
let subscripted_dims = match &slice.node {
ExprKind::Tuple { elts, .. } => elts.iter().fold(0, |acc, value_subexpr| {
if let ExprKind::Slice { .. } = &value_subexpr.node {
acc
} else {
acc + 1
}
}),
ExprKind::Slice { .. } => 0,
_ => 1,
};
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, ty).as_basic_type_enum();
let sizeof_elem = llvm_ndarray_data_t.size_of().unwrap();
// Check that len is non-zero
let len = v.load_ndims(ctx);
ctx.make_assert(
generator,
ctx.builder.build_int_compare(IntPredicate::SGT, len, llvm_usize.const_zero(), "").unwrap(),
"0:IndexError",
"too many indices for array: array is {0}-dimensional but 1 were indexed",
[Some(len), None, None],
slice.location,
);
// Normalizes a possibly-negative index to its corresponding positive index
let normalize_index = |generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
index: IntValue<'ctx>,
dim: u64| {
gen_if_else_expr_callback(
generator,
ctx,
|_, ctx| {
Ok(ctx
.builder
.build_int_compare(IntPredicate::SGE, index, index.get_type().const_zero(), "")
.unwrap())
},
|_, _| Ok(Some(index)),
|generator, ctx| {
let llvm_i32 = ctx.ctx.i32_type();
let len = unsafe {
v.shape().get_typed_unchecked(
ctx,
generator,
&llvm_usize.const_int(dim, true),
None,
)
};
let index = ctx
.builder
.build_int_add(
len,
ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
"",
)
.unwrap();
Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
},
)
.map(|v| v.map(BasicValueEnum::into_int_value))
};
// Converts a slice expression into a slice-range tuple
let expr_to_slice = |generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
node: &ExprKind<Option<Type>>,
dim: u64| {
match node {
ExprKind::Constant { value: Constant::Int(v), .. } => {
let Some(index) =
normalize_index(generator, ctx, llvm_i32.const_int(*v as u64, true), dim)?
else {
return Ok(None);
};
Ok(Some((index, index, llvm_i32.const_int(1, true))))
}
ExprKind::Slice { lower, upper, step } => {
let dim_sz = unsafe {
v.shape().get_typed_unchecked(
ctx,
generator,
&llvm_usize.const_int(dim, false),
None,
)
};
handle_slice_indices(lower, upper, step, ctx, generator, dim_sz)
}
_ => {
let Some(index) = generator.gen_expr(ctx, slice)? else { return Ok(None) };
let index = index
.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
.into_int_value();
let Some(index) = normalize_index(generator, ctx, index, dim)? else {
return Ok(None);
};
Ok(Some((index, index, llvm_i32.const_int(1, true))))
}
}
};
let make_indices_arr = |generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>|
-> Result<_, String> {
Ok(if let ExprKind::Tuple { elts, .. } = &slice.node {
let llvm_int_ty = ctx.get_llvm_type(generator, elts[0].custom.unwrap());
let index_addr = generator.gen_array_var_alloc(
ctx,
llvm_int_ty,
llvm_usize.const_int(elts.len() as u64, false),
None,
)?;
for (i, elt) in elts.iter().enumerate() {
let Some(index) = generator.gen_expr(ctx, elt)? else {
return Ok(None);
};
let index = index
.to_basic_value_enum(ctx, generator, elt.custom.unwrap())?
.into_int_value();
let Some(index) = normalize_index(generator, ctx, index, 0)? else {
return Ok(None);
};
let store_ptr = unsafe {
index_addr.ptr_offset_unchecked(
ctx,
generator,
&llvm_usize.const_int(i as u64, false),
None,
)
};
ctx.builder.build_store(store_ptr, index).unwrap();
}
Some(index_addr)
} else if let Some(index) = generator.gen_expr(ctx, slice)? {
let llvm_int_ty = ctx.get_llvm_type(generator, slice.custom.unwrap());
let index_addr = generator.gen_array_var_alloc(
ctx,
llvm_int_ty,
llvm_usize.const_int(1u64, false),
None,
)?;
let index =
index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
let store_ptr = unsafe {
index_addr.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
};
ctx.builder.build_store(store_ptr, index).unwrap();
Some(index_addr)
} else {
None
})
};
Ok(Some(if ndims.len() == 1 && ndims[0] - subscripted_dims == 0 {
let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
v.data().get(ctx, generator, &index_addr, None).into()
} else {
match &slice.node {
ExprKind::Tuple { elts, .. } => {
let slices = elts
.iter()
.enumerate()
.map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
.take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
.collect::<Result<Vec<_>, _>>()?;
if slices.len() < elts.len() {
return Ok(None);
}
let slices = slices.into_iter().map(Option::unwrap).collect_vec();
numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
}
ExprKind::Slice { .. } => {
let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
return Ok(None);
};
numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
}
_ => {
// Accessing an element from a multi-dimensional `ndarray`
let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
let num_dims = extract_ndims(&ctx.unifier, ndims_ty) - 1;
// Create a new array, remove the top dimension from the dimension-size-list, and copy the
// elements over
let ndarray =
NDArrayType::new(generator, ctx.ctx, llvm_ndarray_data_t, Some(num_dims))
.construct_uninitialized(generator, ctx, None);
let ndarray_num_dims = ctx
.builder
.build_int_z_extend_or_bit_cast(
ndarray.load_ndims(ctx),
llvm_usize.size_of().get_type(),
"",
)
.unwrap();
let v_dims_src_ptr = unsafe {
v.shape().ptr_offset_unchecked(
ctx,
generator,
&llvm_usize.const_int(1, false),
None,
)
};
call_memcpy_generic(
ctx,
ndarray.shape().base_ptr(ctx, generator),
v_dims_src_ptr,
ctx.builder
.build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
.map(Into::into)
.unwrap(),
llvm_i1.const_zero(),
);
let ndarray_num_elems = ndarray::call_ndarray_calc_size(
generator,
ctx,
&ndarray.shape().as_slice_value(ctx, generator),
(None, None),
);
let ndarray_num_elems = ctx
.builder
.build_int_z_extend_or_bit_cast(ndarray_num_elems, sizeof_elem.get_type(), "")
.unwrap();
unsafe { ndarray.create_data(generator, ctx) };
let v_data_src_ptr = v.data().ptr_offset(ctx, generator, &index_addr, None);
call_memcpy_generic(
ctx,
ndarray.data().base_ptr(ctx, generator),
v_data_src_ptr,
ctx.builder
.build_int_mul(
ndarray_num_elems,
llvm_ndarray_data_t.size_of().unwrap(),
"",
)
.map(Into::into)
.unwrap(),
llvm_i1.const_zero(),
);
ndarray.as_base_value().into()
}
}
}))
}
/// See [`CodeGenerator::gen_expr`].
pub fn gen_expr<'ctx, G: CodeGenerator>(
generator: &mut G,

33
nac3core/src/codegen/llvm_intrinsics.rs
View File

@ -1,7 +1,6 @@
use inkwell::{
context::Context,
intrinsics::Intrinsic,
types::{AnyTypeEnum::IntType, FloatType},
types::AnyTypeEnum::IntType,
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
AddressSpace,
};
@ -9,34 +8,6 @@ use itertools::Either;
use super::CodeGenContext;
/// Returns the string representation for the floating-point type `ft` when used in intrinsic
/// functions.
fn get_float_intrinsic_repr(ctx: &Context, ft: FloatType) -> &'static str {
// Standard LLVM floating-point types
if ft == ctx.f16_type() {
return "f16";
}
if ft == ctx.f32_type() {
return "f32";
}
if ft == ctx.f64_type() {
return "f64";
}
if ft == ctx.f128_type() {
return "f128";
}
// Non-standard floating-point types
if ft == ctx.x86_f80_type() {
return "f80";
}
if ft == ctx.ppc_f128_type() {
return "ppcf128";
}
unreachable!()
}
/// Invokes the [`llvm.va_start`](https://llvm.org/docs/LangRef.html#llvm-va-start-intrinsic)
/// intrinsic.
pub fn call_va_start<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue<'ctx>) {
@ -54,7 +25,7 @@ pub fn call_va_start<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue
ctx.builder.build_call(intrinsic_fn, &[arglist.into()], "").unwrap();
}
/// Invokes the [`llvm.va_start`](https://llvm.org/docs/LangRef.html#llvm-va-start-intrinsic)
/// Invokes the [`llvm.va_end`](https://llvm.org/docs/LangRef.html#llvm-va-end-intrinsic)
/// intrinsic.
pub fn call_va_end<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue<'ctx>) {
const FN_NAME: &str = "llvm.va_end";

23
nac3core/src/codegen/numpy.rs
View File

@ -604,29 +604,6 @@ fn llvm_ndlist_get_ndims<'ctx, G: CodeGenerator + ?Sized>(
}
}
/// Returns the number of dimensions for an array-like object as an [`IntValue`].
fn llvm_arraylike_get_ndims<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
(ty, value): (Type, BasicValueEnum<'ctx>),
) -> IntValue<'ctx> {
let llvm_usize = generator.get_size_type(ctx.ctx);
match value {
BasicValueEnum::PointerValue(v)
if NDArrayValue::is_representable(v, llvm_usize).is_ok() =>
{
NDArrayType::from_unifier_type(generator, ctx, ty).map_value(v, None).load_ndims(ctx)
}
BasicValueEnum::PointerValue(v) if ListValue::is_representable(v, llvm_usize).is_ok() => {
llvm_ndlist_get_ndims(generator, ctx, v.get_type())
}
_ => llvm_usize.const_zero(),
}
}
/// Flattens and copies the values from a multidimensional list into an [`NDArrayValue`].
fn ndarray_from_ndlist_impl<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,

15
nac3core/src/codegen/test.rs
View File

@ -36,7 +36,6 @@ use crate::{
struct Resolver {
id_to_type: HashMap<StrRef, Type>,
id_to_def: RwLock<HashMap<StrRef, DefinitionId>>,
class_names: HashMap<StrRef, Type>,
}
impl Resolver {
@ -104,11 +103,9 @@ fn test_primitives() {
let top_level = Arc::new(composer.make_top_level_context());
unifier.top_level = Some(top_level.clone());
let resolver = Arc::new(Resolver {
id_to_type: HashMap::new(),
id_to_def: RwLock::new(HashMap::new()),
class_names: HashMap::default(),
}) as Arc<dyn SymbolResolver + Send + Sync>;
let resolver =
Arc::new(Resolver { id_to_type: HashMap::new(), id_to_def: RwLock::new(HashMap::new()) })
as Arc<dyn SymbolResolver + Send + Sync>;
let threads = vec![DefaultCodeGenerator::new("test".into(), 32).into()];
let signature = FunSignature {
@ -298,11 +295,7 @@ fn test_simple_call() {
loc: None,
})));
let resolver = Resolver {
id_to_type: HashMap::new(),
id_to_def: RwLock::new(HashMap::new()),
class_names: HashMap::default(),
};
let resolver = Resolver { id_to_type: HashMap::new(), id_to_def: RwLock::new(HashMap::new()) };
resolver.add_id_def("foo".into(), DefinitionId(foo_id));
let resolver = Arc::new(resolver) as Arc<dyn SymbolResolver + Send + Sync>;

2
nac3core/src/codegen/values/array.rs
View File

@ -389,7 +389,7 @@ impl<'ctx> ArrayLikeIndexer<'ctx> for ArraySliceValue<'ctx> {
idx: &IntValue<'ctx>,
name: Option<&str>,
) -> PointerValue<'ctx> {
let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
let var_name = name.or(self.2).map(|v| format!("{v}.addr")).unwrap_or_default();
unsafe {
ctx.builder

13
nac3core/src/codegen/values/ndarray/mod.rs
View File

@ -19,7 +19,6 @@ use crate::codegen::{
pub use contiguous::*;
pub use indexing::*;
pub use nditer::*;
pub use view::*;
mod contiguous;
mod indexing;
@ -113,12 +112,6 @@ impl<'ctx> NDArrayValue<'ctx> {
self.get_type().get_fields(ctx.ctx).shape
}
/// Returns the double-indirection pointer to the `shape` array, as if by calling
/// `getelementptr` on the field.
fn ptr_to_shape(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
self.shape_field(ctx).ptr_by_gep(ctx, self.value, self.name)
}
/// Stores the array of dimension sizes `dims` into this instance.
fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, dims: PointerValue<'ctx>) {
self.shape_field(ctx).set(ctx, self.as_base_value(), dims, self.name);
@ -147,12 +140,6 @@ impl<'ctx> NDArrayValue<'ctx> {
self.get_type().get_fields(ctx.ctx).strides
}
/// Returns the double-indirection pointer to the `strides` array, as if by calling
/// `getelementptr` on the field.
fn ptr_to_strides(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
self.strides_field(ctx).ptr_by_gep(ctx, self.value, self.name)
}
/// Stores the array of stride sizes `strides` into this instance.
fn store_strides(&self, ctx: &CodeGenContext<'ctx, '_>, strides: PointerValue<'ctx>) {
self.strides_field(ctx).set(ctx, self.as_base_value(), strides, self.name);

4
nac3core/src/codegen/values/ndarray/nditer.rs
View File

@ -78,7 +78,7 @@ impl<'ctx> NDIterValue<'ctx> {
pub fn get_pointer(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
let elem_ty = self.parent.dtype;
let p = self.element(ctx).get(ctx, self.as_base_value(), None);
let p = self.element(ctx).get(ctx, self.as_base_value(), self.name);
ctx.builder
.build_pointer_cast(p, elem_ty.ptr_type(AddressSpace::default()), "element")
.unwrap()
@ -98,7 +98,7 @@ impl<'ctx> NDIterValue<'ctx> {
/// Get the index of the current element if this ndarray were a flat ndarray.
#[must_use]
pub fn get_index(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
self.nth(ctx).get(ctx, self.as_base_value(), None)
self.nth(ctx).get(ctx, self.as_base_value(), self.name)
}
/// Get the indices of the current element.

146
nac3core/src/toplevel/builtins.rs
View File

@ -1,13 +1,7 @@
use std::iter::once;
use indexmap::IndexMap;
use inkwell::{
attributes::{Attribute, AttributeLoc},
types::{BasicMetadataTypeEnum, BasicType},
values::{BasicMetadataValueEnum, BasicValue, CallSiteValue},
IntPredicate,
};
use itertools::Either;
use inkwell::{values::BasicValue, IntPredicate};
use strum::IntoEnumIterator;
use super::{
@ -148,144 +142,6 @@ fn create_fn_by_codegen(
}
}
/// Creates a NumPy [`TopLevelDef`] function using an LLVM intrinsic.
///
/// * `name`: The name of the implemented NumPy function.
/// * `ret_ty`: The return type of this function.
/// * `param_ty`: The parameters accepted by this function, represented by a tuple of the
/// [parameter type][Type] and the parameter symbol name.
/// * `intrinsic_fn`: The fully-qualified name of the LLVM intrinsic function.
fn create_fn_by_intrinsic(
unifier: &mut Unifier,
var_map: &VarMap,
name: &'static str,
ret_ty: Type,
params: &[(Type, &'static str)],
intrinsic_fn: &'static str,
) -> TopLevelDef {
let param_tys = params.iter().map(|p| p.0).collect_vec();
create_fn_by_codegen(
unifier,
var_map,
name,
ret_ty,
params,
Box::new(move |ctx, _, fun, args, generator| {
let args_ty = fun.0.args.iter().map(|a| a.ty).collect_vec();
assert!(param_tys
.iter()
.zip(&args_ty)
.all(|(expected, actual)| ctx.unifier.unioned(*expected, *actual)));
let args_val = args_ty
.iter()
.zip_eq(args.iter())
.map(|(ty, arg)| arg.1.clone().to_basic_value_enum(ctx, generator, *ty).unwrap())
.map_into::<BasicMetadataValueEnum>()
.collect_vec();
let intrinsic_fn = ctx.module.get_function(intrinsic_fn).unwrap_or_else(|| {
let ret_llvm_ty = ctx.get_llvm_abi_type(generator, ret_ty);
let param_llvm_ty = param_tys
.iter()
.map(|p| ctx.get_llvm_abi_type(generator, *p))
.map_into::<BasicMetadataTypeEnum>()
.collect_vec();
let fn_type = ret_llvm_ty.fn_type(param_llvm_ty.as_slice(), false);
ctx.module.add_function(intrinsic_fn, fn_type, None)
});
let val = ctx
.builder
.build_call(intrinsic_fn, args_val.as_slice(), name)
.map(CallSiteValue::try_as_basic_value)
.map(Either::unwrap_left)
.unwrap();
Ok(val.into())
}),
)
}
/// Creates a unary NumPy [`TopLevelDef`] function using an extern function (e.g. from `libc` or
/// `libm`).
///
/// * `name`: The name of the implemented NumPy function.
/// * `ret_ty`: The return type of this function.
/// * `param_ty`: The parameters accepted by this function, represented by a tuple of the
/// [parameter type][Type] and the parameter symbol name.
/// * `extern_fn`: The fully-qualified name of the extern function used as the implementation.
/// * `attrs`: The list of attributes to apply to this function declaration. Note that `nounwind` is
/// already implied by the C ABI.
fn create_fn_by_extern(
unifier: &mut Unifier,
var_map: &VarMap,
name: &'static str,
ret_ty: Type,
params: &[(Type, &'static str)],
extern_fn: &'static str,
attrs: &'static [&str],
) -> TopLevelDef {
let param_tys = params.iter().map(|p| p.0).collect_vec();
create_fn_by_codegen(
unifier,
var_map,
name,
ret_ty,
params,
Box::new(move |ctx, _, fun, args, generator| {
let args_ty = fun.0.args.iter().map(|a| a.ty).collect_vec();
assert!(param_tys
.iter()
.zip(&args_ty)
.all(|(expected, actual)| ctx.unifier.unioned(*expected, *actual)));
let args_val = args_ty
.iter()
.zip_eq(args.iter())
.map(|(ty, arg)| arg.1.clone().to_basic_value_enum(ctx, generator, *ty).unwrap())
.map_into::<BasicMetadataValueEnum>()
.collect_vec();
let intrinsic_fn = ctx.module.get_function(extern_fn).unwrap_or_else(|| {
let ret_llvm_ty = ctx.get_llvm_abi_type(generator, ret_ty);
let param_llvm_ty = param_tys
.iter()
.map(|p| ctx.get_llvm_abi_type(generator, *p))
.map_into::<BasicMetadataTypeEnum>()
.collect_vec();
let fn_type = ret_llvm_ty.fn_type(param_llvm_ty.as_slice(), false);
let func = ctx.module.add_function(extern_fn, fn_type, None);
func.add_attribute(
AttributeLoc::Function,
ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id("nounwind"), 0),
);
for attr in attrs {
func.add_attribute(
AttributeLoc::Function,
ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
);
}
func
});
let val = ctx
.builder
.build_call(intrinsic_fn, &args_val, name)
.map(CallSiteValue::try_as_basic_value)
.map(Either::unwrap_left)
.unwrap();
Ok(val.into())
}),
)
}
pub fn get_builtins(unifier: &mut Unifier, primitives: &PrimitiveStore) -> BuiltinInfo {
BuiltinBuilder::new(unifier, primitives)
.build_all_builtins()

2
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap
View File

@ -8,5 +8,5 @@ expression: res_vec
"Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",
"Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
"Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(246)]\n}\n",
"Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(245)]\n}\n",
]

2
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap
View File

@ -7,7 +7,7 @@ expression: res_vec
"Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B[typevar230]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar230\"]\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B[typevar229]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar229\"]\n}\n",
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
"Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",

4
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap
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@ -5,8 +5,8 @@ expression: res_vec
[
"Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
"Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(243)]\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(242)]\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(247)]\n}\n",
"Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",

2
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap
View File

@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
expression: res_vec
---
[
"Class {\nname: \"A\",\nancestors: [\"A[typevar229, typevar230]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar229\", \"typevar230\"]\n}\n",
"Class {\nname: \"A\",\nancestors: [\"A[typevar228, typevar229]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar228\", \"typevar229\"]\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",

4
nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap
View File

@ -6,12 +6,12 @@ expression: res_vec
"Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(249)]\n}\n",
"Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(248)]\n}\n",
"Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
"Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
"Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(256)]\n}\n",
]

18
nac3core/src/toplevel/test.rs
View File

@ -15,14 +15,13 @@ use crate::{
symbol_resolver::{SymbolResolver, ValueEnum},
typecheck::{
type_inferencer::PrimitiveStore,
typedef::{into_var_map, Type, Unifier},
typedef::{Type, Unifier},
},
};
struct ResolverInternal {
id_to_type: Mutex<HashMap<StrRef, Type>>,
id_to_def: Mutex<HashMap<StrRef, DefinitionId>>,
class_names: Mutex<HashMap<StrRef, Type>>,
}
impl ResolverInternal {
@ -179,11 +178,8 @@ fn test_simple_function_analyze(source: &[&str], tys: &[&str], names: &[&str]) {
let mut composer =
TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 64).0;
let internal_resolver = Arc::new(ResolverInternal {
id_to_def: Mutex::default(),
id_to_type: Mutex::default(),
class_names: Mutex::default(),
});
let internal_resolver =
Arc::new(ResolverInternal { id_to_def: Mutex::default(), id_to_type: Mutex::default() });
let resolver =
Arc::new(Resolver(internal_resolver.clone())) as Arc<dyn SymbolResolver + Send + Sync>;
@ -784,13 +780,6 @@ fn make_internal_resolver_with_tvar(
unifier: &mut Unifier,
print: bool,
) -> Arc<ResolverInternal> {
let list_elem_tvar = unifier.get_fresh_var(Some("list_elem".into()), None);
let list = unifier.add_ty(TypeEnum::TObj {
obj_id: PrimDef::List.id(),
fields: HashMap::new(),
params: into_var_map([list_elem_tvar]),
});
let res: Arc<ResolverInternal> = ResolverInternal {
id_to_def: Mutex::new(HashMap::from([("list".into(), PrimDef::List.id())])),
id_to_type: tvars
@ -806,7 +795,6 @@ fn make_internal_resolver_with_tvar(
})
.collect::<HashMap<_, _>>()
.into(),
class_names: Mutex::new(HashMap::from([("list".into(), list)])),
}
.into();
if print {

4
nac3core/src/typecheck/type_inferencer/test.rs
View File

@ -18,7 +18,6 @@ use crate::{
struct Resolver {
id_to_type: HashMap<StrRef, Type>,
id_to_def: HashMap<StrRef, DefinitionId>,
class_names: HashMap<StrRef, Type>,
}
impl SymbolResolver for Resolver {
@ -198,7 +197,6 @@ impl TestEnvironment {
let resolver = Arc::new(Resolver {
id_to_type: identifier_mapping.clone(),
id_to_def: HashMap::default(),
class_names: HashMap::default(),
}) as Arc<dyn SymbolResolver + Send + Sync>;
TestEnvironment {
@ -454,7 +452,6 @@ impl TestEnvironment {
vars: IndexMap::default(),
})),
);
let class_names: HashMap<_, _> = [("Bar".into(), bar), ("Bar2".into(), bar2)].into();
let id_to_name = [
"int32".into(),
@ -492,7 +489,6 @@ impl TestEnvironment {
("Bar2".into(), DefinitionId(defs + 3)),
]
.into(),
class_names,
}) as Arc<dyn SymbolResolver + Send + Sync>;
TestEnvironment {