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core: Implement elementwise binary operators 

Including immediate variants of these operators.
This commit is contained in:
David Mak 2024-03-11 14:58:03 +08:00
parent ddfd19d00c
commit aa673fce4e
11 changed files with 606 additions and 28 deletions
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4
nac3artiq/src/symbol_resolver.rs
View File

@ -5,7 +5,7 @@ use nac3core::{
toplevel::{
DefinitionId,
helper::PRIMITIVE_DEF_IDS,
numpy::{make_ndarray_ty, unpack_ndarray_tvars},
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
TopLevelDef,
},
typecheck::{
@ -654,7 +654,7 @@ impl InnerResolver {
}
}
(TypeEnum::TObj { obj_id, .. }, false) if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
let (ty, ndims) = unpack_ndarray_tvars(unifier, extracted_ty);
let (ty, ndims) = unpack_ndarray_var_tys(unifier, extracted_ty);
let len: usize = self.helper.len_fn.call1(py, (obj,))?.extract(py)?;
if len == 0 {
assert!(matches!(

4
nac3core/src/codegen/expr.rs
View File

@ -24,7 +24,7 @@ use crate::{
toplevel::{
DefinitionId,
helper::PRIMITIVE_DEF_IDS,
numpy::{make_ndarray_ty, unpack_ndarray_tvars},
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
TopLevelDef,
},
typecheck::{
@ -1132,7 +1132,7 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
Ok(Some(res.into()))
} else if ty1 == ty2 && matches!(&*ctx.unifier.get_ty(ty1), TypeEnum::TObj { obj_id, .. } if obj_id == &PRIMITIVE_DEF_IDS.ndarray) {
let llvm_usize = generator.get_size_type(ctx.ctx);
let (ndarray_dtype, _) = unpack_ndarray_tvars(&mut ctx.unifier, ty1);
let (ndarray_dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
let left_val = NDArrayValue::from_ptr_val(
left_val.into_pointer_value(),

4
nac3core/src/codegen/mod.rs
View File

@ -2,7 +2,7 @@ use crate::{
symbol_resolver::{StaticValue, SymbolResolver},
toplevel::{
helper::PRIMITIVE_DEF_IDS,
numpy::unpack_ndarray_tvars,
numpy::unpack_ndarray_var_tys,
TopLevelContext,
TopLevelDef,
},
@ -451,7 +451,7 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
let llvm_usize = generator.get_size_type(ctx);
let (dtype, _) = unpack_ndarray_tvars(unifier, ty);
let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
let element_type = get_llvm_type(
ctx,
module,

69
nac3core/src/codegen/numpy.rs
View File

@ -18,6 +18,8 @@ use crate::{
CodeGenContext,
CodeGenerator,
irrt::{
call_ndarray_calc_broadcast,
call_ndarray_calc_broadcast_index,
call_ndarray_calc_nd_indices,
call_ndarray_calc_size,
},
@ -27,7 +29,7 @@ use crate::{
symbol_resolver::ValueEnum,
toplevel::{
DefinitionId,
numpy::{make_ndarray_ty, unpack_ndarray_tvars},
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
},
typecheck::typedef::{FunSignature, Type},
};
@ -346,7 +348,7 @@ fn ndarray_fill_indexed<'ctx, G, ValueFn>(
/// Generates the LLVM IR for populating the entire `NDArray` using a lambda with the same-indexed
/// element from two other `NDArray` as its input.
fn ndarray_fill_zip_map_flattened<'ctx, G, ValueFn>(
fn ndarray_broadcast_fill<'ctx, G, ValueFn>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
@ -359,15 +361,18 @@ fn ndarray_fill_zip_map_flattened<'ctx, G, ValueFn>(
G: CodeGenerator + ?Sized,
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, Type, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
{
ndarray_fill_flattened(
ndarray_fill_indexed(
generator,
ctx,
res,
|generator, ctx, idx| {
let lhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, lhs, &idx);
let rhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, rhs, &idx);
let elem = unsafe {
(
lhs.data().get_unchecked(ctx, generator, idx, None),
rhs.data().get_unchecked(ctx, generator, idx, None),
lhs.data().get_unchecked(ctx, generator, lhs_idx, None),
rhs.data().get_unchecked(ctx, generator, rhs_idx, None),
)
};
@ -615,6 +620,58 @@ fn ndarray_copy_impl<'ctx, G: CodeGenerator + ?Sized>(
Ok(ndarray)
}
/// LLVM-typed implementation for computing elementwise binary operations.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `res` - The `ndarray` instance to write results into, or [`None`] if the result should be
/// written to a new `ndarray`.
/// * `value_fn` - Function mapping the two input elements into the result.
pub fn ndarray_elementwise_binop_impl<'ctx, G, ValueFn>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
res: Option<NDArrayValue<'ctx>>,
this: NDArrayValue<'ctx>,
other: NDArrayValue<'ctx>,
value_fn: ValueFn,
) -> Result<NDArrayValue<'ctx>, String>
where
G: CodeGenerator,
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, Type, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
{
let ndarray_dims = call_ndarray_calc_broadcast(generator, ctx, this, other);
let ndarray = res.unwrap_or_else(|| {
create_ndarray_dyn_shape(
generator,
ctx,
elem_ty,
&ndarray_dims,
|generator, ctx, v| {
Ok(v.size(ctx, generator))
},
|generator, ctx, v, idx| {
unsafe {
Ok(v.get_typed_unchecked(ctx, generator, idx, None))
}
},
).unwrap()
});
ndarray_broadcast_fill(
generator,
ctx,
elem_ty,
ndarray,
this,
other,
|generator, ctx, elem_ty, elems| {
value_fn(generator, ctx, elem_ty, elems)
},
)?;
Ok(ndarray)
}
/// Generates LLVM IR for `ndarray.empty`.
pub fn gen_ndarray_empty<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
@ -801,7 +858,7 @@ pub fn gen_ndarray_copy<'ctx>(
let llvm_usize = generator.get_size_type(context.ctx);
let this_ty = obj.as_ref().unwrap().0;
let (this_elem_ty, _) = unpack_ndarray_tvars(&mut context.unifier, this_ty);
let (this_elem_ty, _) = unpack_ndarray_var_tys(&mut context.unifier, this_ty);
let this_arg = obj
.as_ref()
.unwrap()

4
nac3core/src/codegen/stmt.rs
View File

@ -13,7 +13,7 @@ use crate::{
toplevel::{
DefinitionId,
helper::PRIMITIVE_DEF_IDS,
numpy::unpack_ndarray_tvars,
numpy::unpack_ndarray_var_tys,
TopLevelDef,
},
typecheck::typedef::{FunSignature, Type, TypeEnum},
@ -251,7 +251,7 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
let ty = match &*ctx.unifier.get_ty_immutable(target.custom.unwrap()) {
TypeEnum::TList { ty } => *ty,
TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
unpack_ndarray_tvars(&mut ctx.unifier, target.custom.unwrap()).0
unpack_ndarray_var_tys(&mut ctx.unifier, target.custom.unwrap()).0
}
_ => unreachable!(),
};

238
nac3core/src/toplevel/builtins.rs
View File

@ -347,6 +347,20 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
.unwrap();
let ndarray_copy_ty = *ndarray_fields.get(&"copy".into()).unwrap();
let ndarray_fill_ty = *ndarray_fields.get(&"fill".into()).unwrap();
let ndarray_add_ty = *ndarray_fields.get(&"__add__".into()).unwrap();
let ndarray_sub_ty = *ndarray_fields.get(&"__sub__".into()).unwrap();
let ndarray_mul_ty = *ndarray_fields.get(&"__mul__".into()).unwrap();
let ndarray_truediv_ty = *ndarray_fields.get(&"__truediv__".into()).unwrap();
let ndarray_floordiv_ty = *ndarray_fields.get(&"__floordiv__".into()).unwrap();
let ndarray_mod_ty = *ndarray_fields.get(&"__mod__".into()).unwrap();
let ndarray_pow_ty = *ndarray_fields.get(&"__pow__".into()).unwrap();
let ndarray_iadd_ty = *ndarray_fields.get(&"__iadd__".into()).unwrap();
let ndarray_isub_ty = *ndarray_fields.get(&"__isub__".into()).unwrap();
let ndarray_imul_ty = *ndarray_fields.get(&"__imul__".into()).unwrap();
let ndarray_itruediv_ty = *ndarray_fields.get(&"__itruediv__".into()).unwrap();
let ndarray_ifloordiv_ty = *ndarray_fields.get(&"__ifloordiv__".into()).unwrap();
let ndarray_imod_ty = *ndarray_fields.get(&"__imod__".into()).unwrap();
let ndarray_ipow_ty = *ndarray_fields.get(&"__ipow__".into()).unwrap();
let top_level_def_list = vec![
Arc::new(RwLock::new(TopLevelComposer::make_top_level_class_def(
@ -524,6 +538,20 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
methods: vec![
("copy".into(), ndarray_copy_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 1)),
("fill".into(), ndarray_fill_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 2)),
("__add__".into(), ndarray_add_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 3)),
("__sub__".into(), ndarray_sub_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 4)),
("__mul__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 5)),
("__truediv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 6)),
("__floordiv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 7)),
("__mod__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 8)),
("__pow__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 9)),
("__iadd__".into(), ndarray_iadd_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 10)),
("__isub__".into(), ndarray_isub_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 11)),
("__imul__".into(), ndarray_imul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 12)),
("__itruediv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 13)),
("__ifloordiv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 14)),
("__imod__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 15)),
("__ipow__".into(), ndarray_imul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 16)),
],
ancestors: Vec::default(),
constructor: None,
@ -562,6 +590,216 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__add__".into(),
simple_name: "__add__".into(),
signature: ndarray_add_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__sub__".into(),
simple_name: "__sub__".into(),
signature: ndarray_sub_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__mul__".into(),
simple_name: "__mul__".into(),
signature: ndarray_mul_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__truediv__".into(),
simple_name: "__truediv__".into(),
signature: ndarray_truediv_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__floordiv__".into(),
simple_name: "__floordiv__".into(),
signature: ndarray_floordiv_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__mod__".into(),
simple_name: "__mod__".into(),
signature: ndarray_mod_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__pow__".into(),
simple_name: "__pow__".into(),
signature: ndarray_pow_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__iadd__".into(),
simple_name: "__iadd__".into(),
signature: ndarray_iadd_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__isub__".into(),
simple_name: "__isub__".into(),
signature: ndarray_isub_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__imul__".into(),
simple_name: "__imul__".into(),
signature: ndarray_imul_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__itruediv__".into(),
simple_name: "__itruediv__".into(),
signature: ndarray_itruediv_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__ifloordiv__".into(),
simple_name: "__ifloordiv__".into(),
signature: ndarray_ifloordiv_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__imod__".into(),
simple_name: "__imod__".into(),
signature: ndarray_imod_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "ndarray.__ipow__".into(),
simple_name: "__ipow__".into(),
signature: ndarray_ipow_ty.0,
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
instance_to_symbol: HashMap::default(),
instance_to_stmt: HashMap::default(),
resolver: None,
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|_, _, _, _, _| {
unreachable!("handled in gen_expr")
},
)))),
loc: None,
})),
Arc::new(RwLock::new(TopLevelDef::Function {
name: "int32".into(),
simple_name: "int32".into(),

53
nac3core/src/toplevel/helper.rs
View File

@ -1,6 +1,7 @@
use std::convert::TryInto;
use crate::symbol_resolver::SymbolValue;
use crate::toplevel::numpy::subst_ndarray_tvars;
use crate::typecheck::typedef::{Mapping, VarMap};
use nac3parser::ast::{Constant, Location};
@ -226,11 +227,57 @@ impl TopLevelComposer {
(ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
]),
}));
let ndarray_binop_fun_other_ty = unifier.get_fresh_var(None, None);
let ndarray_binop_fun_ret_ty = unifier.get_fresh_var(None, None);
let ndarray_binop_fun_ty = unifier.add_ty(TypeEnum::TFunc(FunSignature {
args: vec![
FuncArg {
name: "other".into(),
ty: ndarray_binop_fun_other_ty.0,
default_value: None,
},
],
ret: ndarray_binop_fun_ret_ty.0,
vars: VarMap::from([
(ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
(ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
]),
}));
let ndarray_truediv_fun_other_ty = unifier.get_fresh_var(None, None);
let ndarray_truediv_fun_ret_ty = unifier.get_fresh_var(None, None);
let ndarray_truediv_fun_ty = unifier.add_ty(TypeEnum::TFunc(FunSignature {
args: vec![
FuncArg {
name: "other".into(),
ty: ndarray_truediv_fun_other_ty.0,
default_value: None,
},
],
ret: ndarray_truediv_fun_ret_ty.0,
vars: VarMap::from([
(ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
(ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
]),
}));
let ndarray = unifier.add_ty(TypeEnum::TObj {
obj_id: PRIMITIVE_DEF_IDS.ndarray,
fields: Mapping::from([
("copy".into(), (ndarray_copy_fun_ty, true)),
("fill".into(), (ndarray_fill_fun_ty, true)),
("__add__".into(), (ndarray_binop_fun_ty, true)),
("__sub__".into(), (ndarray_binop_fun_ty, true)),
("__mul__".into(), (ndarray_binop_fun_ty, true)),
("__truediv__".into(), (ndarray_truediv_fun_ty, true)),
("__floordiv__".into(), (ndarray_binop_fun_ty, true)),
("__mod__".into(), (ndarray_binop_fun_ty, true)),
("__pow__".into(), (ndarray_binop_fun_ty, true)),
("__iadd__".into(), (ndarray_binop_fun_ty, true)),
("__isub__".into(), (ndarray_binop_fun_ty, true)),
("__imul__".into(), (ndarray_binop_fun_ty, true)),
("__itruediv__".into(), (ndarray_truediv_fun_ty, true)),
("__ifloordiv__".into(), (ndarray_binop_fun_ty, true)),
("__imod__".into(), (ndarray_binop_fun_ty, true)),
("__ipow__".into(), (ndarray_binop_fun_ty, true)),
]),
params: VarMap::from([
(ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
@ -239,6 +286,12 @@ impl TopLevelComposer {
});
unifier.unify(ndarray_copy_fun_ret_ty.0, ndarray).unwrap();
unifier.unify(ndarray_binop_fun_other_ty.0, ndarray).unwrap();
unifier.unify(ndarray_binop_fun_ret_ty.0, ndarray).unwrap();
let ndarray_float = subst_ndarray_tvars(&mut unifier, ndarray, Some(float), None);
unifier.unify(ndarray_truediv_fun_other_ty.0, ndarray).unwrap();
unifier.unify(ndarray_truediv_fun_ret_ty.0, ndarray_float).unwrap();
let primitives = PrimitiveStore {
int32,

53
nac3core/src/toplevel/numpy.rs
View File

@ -19,13 +19,30 @@ pub fn make_ndarray_ty(
dtype: Option<Type>,
ndims: Option<Type>,
) -> Type {
let ndarray = primitives.ndarray;
subst_ndarray_tvars(unifier, primitives.ndarray, dtype, ndims)
}
/// Substitutes type variables in `ndarray`.
///
/// * `dtype` - The element type of the `ndarray`, or [`None`] if the type variable is not
/// specialized.
/// * `ndims` - The number of dimensions of the `ndarray`, or [`None`] if the type variable is not
/// specialized.
pub fn subst_ndarray_tvars(
unifier: &mut Unifier,
ndarray: Type,
dtype: Option<Type>,
ndims: Option<Type>,
) -> Type {
let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(ndarray) else {
panic!("Expected `ndarray` to be TObj, but got {}", unifier.stringify(ndarray))
};
debug_assert_eq!(*obj_id, PRIMITIVE_DEF_IDS.ndarray);
if dtype.is_none() && ndims.is_none() {
return ndarray
}
let tvar_ids = params.iter()
.map(|(obj_id, _)| *obj_id)
.collect_vec();
@ -42,12 +59,10 @@ pub fn make_ndarray_ty(
unifier.subst(ndarray, &tvar_subst).unwrap_or(ndarray)
}
/// Unpacks the type variables of `ndarray` into a tuple. The elements of the tuple corresponds to
/// `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray` respectively.
pub fn unpack_ndarray_tvars(
fn unpack_ndarray_tvars(
unifier: &mut Unifier,
ndarray: Type,
) -> (Type, Type) {
) -> Vec<(u32, Type)> {
let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(ndarray) else {
panic!("Expected `ndarray` to be TObj, but got {}", unifier.stringify(ndarray))
};
@ -56,7 +71,33 @@ pub fn unpack_ndarray_tvars(
params.iter()
.sorted_by_key(|(obj_id, _)| *obj_id)
.map(|(_, ty)| *ty)
.map(|(var_id, ty)| (*var_id, *ty))
.collect_vec()
}
/// Unpacks the type variable IDs of `ndarray` into a tuple. The elements of the tuple corresponds
/// to `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray`
/// respectively.
pub fn unpack_ndarray_var_ids(
unifier: &mut Unifier,
ndarray: Type,
) -> (u32, u32) {
unpack_ndarray_tvars(unifier, ndarray)
.into_iter()
.map(|v| v.0)
.collect_tuple()
.unwrap()
}
/// Unpacks the type variables of `ndarray` into a tuple. The elements of the tuple corresponds to
/// `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray` respectively.
pub fn unpack_ndarray_var_tys(
unifier: &mut Unifier,
ndarray: Type,
) -> (Type, Type) {
unpack_ndarray_tvars(unifier, ndarray)
.into_iter()
.map(|v| v.1)
.collect_tuple()
.unwrap()
}

24
nac3core/src/typecheck/magic_methods.rs
View File

@ -1,3 +1,4 @@
use crate::toplevel::numpy::make_ndarray_ty;
use crate::typecheck::{
type_inferencer::*,
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
@ -234,8 +235,14 @@ pub fn impl_bitwise_shift(unifier: &mut Unifier, store: &PrimitiveStore, ty: Typ
}
/// `Div`
pub fn impl_div(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type, other_ty: &[Type]) {
impl_binop(unifier, store, ty, other_ty, store.float, &[Operator::Div]);
pub fn impl_div(
unifier: &mut Unifier,
store: &PrimitiveStore,
ty: Type,
other_ty: &[Type],
ret_ty: Type,
) {
impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::Div]);
}
/// `FloorDiv`
@ -299,6 +306,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
bool: bool_t,
uint32: uint32_t,
uint64: uint64_t,
ndarray: ndarray_t,
..
} = *store;
@ -308,7 +316,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
impl_pow(unifier, store, t, &[t], t);
impl_bitwise_arithmetic(unifier, store, t);
impl_bitwise_shift(unifier, store, t);
impl_div(unifier, store, t, &[t]);
impl_div(unifier, store, t, &[t], float_t);
impl_floordiv(unifier, store, t, &[t], t);
impl_mod(unifier, store, t, &[t], t);
impl_invert(unifier, store, t);
@ -323,7 +331,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
/* float ======== */
impl_basic_arithmetic(unifier, store, float_t, &[float_t], float_t);
impl_pow(unifier, store, float_t, &[int32_t, float_t], float_t);
impl_div(unifier, store, float_t, &[float_t]);
impl_div(unifier, store, float_t, &[float_t], float_t);
impl_floordiv(unifier, store, float_t, &[float_t], float_t);
impl_mod(unifier, store, float_t, &[float_t], float_t);
impl_sign(unifier, store, float_t);
@ -334,4 +342,12 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
/* bool ======== */
impl_not(unifier, store, bool_t);
impl_eq(unifier, store, bool_t);
/* ndarray ===== */
let ndarray_float_t = make_ndarray_ty(unifier, store, Some(float_t), None);
impl_basic_arithmetic(unifier, store, ndarray_t, &[ndarray_t], ndarray_t);
impl_pow(unifier, store, ndarray_t, &[ndarray_t], ndarray_t);
impl_div(unifier, store, ndarray_t, &[ndarray_t], ndarray_float_t);
impl_floordiv(unifier, store, ndarray_t, &[ndarray_t], ndarray_t);
impl_mod(unifier, store, ndarray_t, &[ndarray_t], ndarray_t);
}

8
nac3core/src/typecheck/type_inferencer/mod.rs
View File

@ -9,7 +9,7 @@ use crate::{
symbol_resolver::{SymbolResolver, SymbolValue},
toplevel::{
helper::PRIMITIVE_DEF_IDS,
numpy::{make_ndarray_ty, unpack_ndarray_tvars},
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
TopLevelContext,
},
};
@ -1334,7 +1334,7 @@ impl<'a> Inferencer<'a> {
let list_like_ty = match &*self.unifier.get_ty(value.custom.unwrap()) {
TypeEnum::TList { .. } => self.unifier.add_ty(TypeEnum::TList { ty }),
TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
make_ndarray_ty(self.unifier, self.primitives, Some(ty), Some(ndims))
}
@ -1347,7 +1347,7 @@ impl<'a> Inferencer<'a> {
ExprKind::Constant { value: ast::Constant::Int(val), .. } => {
match &*self.unifier.get_ty(value.custom.unwrap()) {
TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
self.infer_subscript_ndarray(value, ty, ndims)
}
_ => {
@ -1379,7 +1379,7 @@ impl<'a> Inferencer<'a> {
Ok(ty)
}
TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
self.constrain(slice.custom.unwrap(), self.primitives.usize(), &slice.location)?;
self.infer_subscript_ndarray(value, ty, ndims)

173
nac3standalone/demo/src/ndarray.py
View File

@ -67,6 +67,167 @@ def test_ndarray_copy():
output_float64(y[1][0])
output_float64(y[1][1])
def test_ndarray_add():
x = np_identity(2)
y = x + np_ones([2, 2])
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
output_float64(y[0][0])
output_float64(y[0][1])
output_float64(y[1][0])
output_float64(y[1][1])
def test_ndarray_iadd():
x = np_identity(2)
x += np_ones([2, 2])
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
def test_ndarray_sub():
x = np_ones([2, 2])
y = x - np_identity(2)
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
output_float64(y[0][0])
output_float64(y[0][1])
output_float64(y[1][0])
output_float64(y[1][1])
def test_ndarray_isub():
x = np_ones([2, 2])
x -= np_identity(2)
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
def test_ndarray_mul():
x = np_ones([2, 2])
y = x * np_identity(2)
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
output_float64(y[0][0])
output_float64(y[0][1])
output_float64(y[1][0])
output_float64(y[1][1])
def test_ndarray_imul():
x = np_ones([2, 2])
x *= np_identity(2)
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
def test_ndarray_truediv():
x = np_identity(2)
y = x / np_ones([2, 2])
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
output_float64(y[0][0])
output_float64(y[0][1])
output_float64(y[1][0])
output_float64(y[1][1])
def test_ndarray_itruediv():
x = np_identity(2)
x /= np_ones([2, 2])
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
def test_ndarray_floordiv():
x = np_identity(2)
y = x // np_ones([2, 2])
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
output_float64(y[0][0])
output_float64(y[0][1])
output_float64(y[1][0])
output_float64(y[1][1])
def test_ndarray_ifloordiv():
x = np_identity(2)
x //= np_ones([2, 2])
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
def test_ndarray_mod():
x = np_identity(2)
y = x % np_full([2, 2], 2.0)
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
output_float64(y[0][0])
output_float64(y[0][1])
output_float64(y[1][0])
output_float64(y[1][1])
def test_ndarray_imod():
x = np_identity(2)
x %= np_full([2, 2], 2.0)
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
def test_ndarray_pow():
x = np_identity(2)
y = x ** np_full([2, 2], 2.0)
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
output_float64(y[0][0])
output_float64(y[0][1])
output_float64(y[1][0])
output_float64(y[1][1])
def test_ndarray_ipow():
x = np_identity(2)
x **= np_full([2, 2], 2.0)
output_float64(x[0][0])
output_float64(x[0][1])
output_float64(x[1][0])
output_float64(x[1][1])
def run() -> int32:
test_ndarray_ctor()
test_ndarray_empty()
@ -77,5 +238,17 @@ def run() -> int32:
test_ndarray_identity()
test_ndarray_fill()
test_ndarray_copy()
test_ndarray_add()
test_ndarray_iadd()
test_ndarray_sub()
test_ndarray_isub()
test_ndarray_mul()
test_ndarray_imul()
test_ndarray_truediv()
test_ndarray_itruediv()
test_ndarray_floordiv()
test_ndarray_ifloordiv()
test_ndarray_mod()
test_ndarray_imod()
return 0