Compare commits
8 Commits
1cb9a90825
...
d2ce0679ed
Author | SHA1 | Date |
---|---|---|
|
d2ce0679ed | |
|
aa673fce4e | |
|
ddfd19d00c | |
|
4887cd8007 | |
|
876e850d71 | |
|
1de2e9a4be | |
|
2b0beea8c0 | |
|
5778de02fc |
|
@ -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!(
|
||||
|
|
|
@ -1,12 +1,12 @@
|
|||
use inkwell::{
|
||||
IntPredicate,
|
||||
types::{AnyTypeEnum, BasicTypeEnum, IntType, PointerType},
|
||||
values::{ArrayValue, BasicValueEnum, IntValue, PointerValue},
|
||||
values::{BasicValueEnum, IntValue, PointerValue},
|
||||
};
|
||||
use crate::codegen::{
|
||||
CodeGenContext,
|
||||
CodeGenerator,
|
||||
irrt::{call_ndarray_calc_size, call_ndarray_flatten_index, call_ndarray_flatten_index_const},
|
||||
irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
|
||||
llvm_intrinsics::call_int_umin,
|
||||
stmt::gen_for_callback_incrementing,
|
||||
};
|
||||
|
@ -1162,98 +1162,6 @@ impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
|
|||
impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
|
||||
impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
|
||||
|
||||
impl<'ctx> ArrayLikeIndexer<'ctx, ArrayValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
indices: ArrayValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let index = call_ndarray_flatten_index_const(
|
||||
generator,
|
||||
ctx,
|
||||
*self.0,
|
||||
indices,
|
||||
);
|
||||
|
||||
unsafe {
|
||||
ctx.builder.build_in_bounds_gep(
|
||||
self.base_ptr(ctx, generator),
|
||||
&[index],
|
||||
name.unwrap_or_default(),
|
||||
)
|
||||
}.unwrap()
|
||||
}
|
||||
|
||||
fn ptr_offset<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
generator: &mut G,
|
||||
indices: ArrayValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> PointerValue<'ctx> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let indices_elem_ty = indices.get_type().get_element_type();
|
||||
let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
|
||||
panic!("Expected [int32] but got [{indices_elem_ty}]")
|
||||
};
|
||||
assert_eq!(indices_elem_ty.get_bit_width(), 32, "Expected [int32] but got [{indices_elem_ty}]");
|
||||
|
||||
let nidx_leq_ndims = ctx.builder.build_int_compare(
|
||||
IntPredicate::SLE,
|
||||
llvm_usize.const_int(indices.get_type().len() as u64, false),
|
||||
self.0.load_ndims(ctx),
|
||||
""
|
||||
).unwrap();
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
nidx_leq_ndims,
|
||||
"0:IndexError",
|
||||
"invalid index to scalar variable",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
for idx in 0..indices.get_type().len() {
|
||||
let i = llvm_usize.const_int(idx as u64, false);
|
||||
|
||||
let dim_idx = ctx.builder
|
||||
.build_extract_value(indices, idx, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.map(|v| ctx.builder.build_int_z_extend_or_bit_cast(v, llvm_usize, "").unwrap())
|
||||
.unwrap();
|
||||
let dim_sz = unsafe {
|
||||
self.0.dim_sizes().get_typed_unchecked(ctx, generator, i, None)
|
||||
};
|
||||
|
||||
let dim_lt = ctx.builder.build_int_compare(
|
||||
IntPredicate::SLT,
|
||||
dim_idx,
|
||||
dim_sz,
|
||||
""
|
||||
).unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
dim_lt,
|
||||
"0:IndexError",
|
||||
"index {0} is out of bounds for axis 0 with size {1}",
|
||||
[Some(dim_idx), Some(dim_sz), None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
unsafe {
|
||||
self.ptr_offset_unchecked(ctx, generator, indices, name)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<'ctx> UntypedArrayLikeAccessor<'ctx, ArrayValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
|
||||
impl<'ctx> UntypedArrayLikeMutator<'ctx, ArrayValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
|
||||
|
||||
impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index> for NDArrayDataProxy<'ctx, '_> {
|
||||
unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
|
||||
&self,
|
||||
|
@ -1326,6 +1234,9 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
|
|||
self.0.dim_sizes().get_typed_unchecked(ctx, generator, i, None),
|
||||
)
|
||||
};
|
||||
let dim_idx = ctx.builder
|
||||
.build_int_z_extend_or_bit_cast(dim_idx, dim_sz.get_type(), "")
|
||||
.unwrap();
|
||||
|
||||
let dim_lt = ctx.builder.build_int_compare(
|
||||
IntPredicate::SLT,
|
||||
|
|
|
@ -16,6 +16,7 @@ use crate::{
|
|||
get_llvm_abi_type,
|
||||
irrt::*,
|
||||
llvm_intrinsics::{call_expect, call_float_floor, call_float_pow, call_float_powi},
|
||||
numpy,
|
||||
stmt::{gen_raise, gen_var},
|
||||
CodeGenContext, CodeGenTask,
|
||||
},
|
||||
|
@ -23,7 +24,7 @@ use crate::{
|
|||
toplevel::{
|
||||
DefinitionId,
|
||||
helper::PRIMITIVE_DEF_IDS,
|
||||
numpy::make_ndarray_ty,
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
TopLevelDef,
|
||||
},
|
||||
typecheck::{
|
||||
|
@ -42,6 +43,7 @@ use itertools::{chain, izip, Itertools, Either};
|
|||
use nac3parser::ast::{
|
||||
self, Boolop, Comprehension, Constant, Expr, ExprKind, Location, Operator, StrRef,
|
||||
};
|
||||
use crate::codegen::classes::ArraySliceValue;
|
||||
|
||||
use super::{CodeGenerator, llvm_intrinsics::call_memcpy_generic, need_sret};
|
||||
|
||||
|
@ -1089,34 +1091,22 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
|
|||
Ok(Some(list.as_ptr_value().into()))
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for a [binary operator expression][expr].
|
||||
///
|
||||
/// * `left` - The left-hand side of the binary operator.
|
||||
/// * `op` - The operator applied on the operands.
|
||||
/// * `right` - The right-hand side of the binary operator.
|
||||
/// * `loc` - The location of the full expression.
|
||||
/// * `is_aug_assign` - Whether the binary operator expression is also an assignment operator.
|
||||
pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
|
||||
/// Generates LLVM IR for a binary operator expression using the [`Type`] and
|
||||
/// [LLVM value][`BasicValueEnum`] of the operands.
|
||||
pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
left: &Expr<Option<Type>>,
|
||||
left: (&Option<Type>, BasicValueEnum<'ctx>),
|
||||
op: &Operator,
|
||||
right: &Expr<Option<Type>>,
|
||||
right: (&Option<Type>, BasicValueEnum<'ctx>),
|
||||
loc: Location,
|
||||
is_aug_assign: bool,
|
||||
) -> Result<Option<ValueEnum<'ctx>>, String> {
|
||||
let ty1 = ctx.unifier.get_representative(left.custom.unwrap());
|
||||
let ty2 = ctx.unifier.get_representative(right.custom.unwrap());
|
||||
let left_val = if let Some(v) = generator.gen_expr(ctx, left)? {
|
||||
v.to_basic_value_enum(ctx, generator, left.custom.unwrap())?
|
||||
} else {
|
||||
return Ok(None)
|
||||
};
|
||||
let right_val = if let Some(v) = generator.gen_expr(ctx, right)? {
|
||||
v.to_basic_value_enum(ctx, generator, right.custom.unwrap())?
|
||||
} else {
|
||||
return Ok(None)
|
||||
};
|
||||
let (left_ty, left_val) = left;
|
||||
let (right_ty, right_val) = right;
|
||||
|
||||
let ty1 = ctx.unifier.get_representative(left_ty.unwrap());
|
||||
let ty2 = ctx.unifier.get_representative(right_ty.unwrap());
|
||||
|
||||
// we can directly compare the types, because we've got their representatives
|
||||
// which would be unchanged until further unification, which we would never do
|
||||
|
@ -1140,8 +1130,46 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
|
|||
Some("f_pow_i")
|
||||
);
|
||||
Ok(Some(res.into()))
|
||||
} else if matches!(&*ctx.unifier.get_ty(ty1), TypeEnum::TObj { obj_id, .. } if obj_id == &PRIMITIVE_DEF_IDS.ndarray) && matches!(&*ctx.unifier.get_ty(ty2), TypeEnum::TObj { obj_id, .. } if obj_id == &PRIMITIVE_DEF_IDS.ndarray) {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
|
||||
let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
|
||||
|
||||
assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
|
||||
|
||||
let left_val = NDArrayValue::from_ptr_val(
|
||||
left_val.into_pointer_value(),
|
||||
llvm_usize,
|
||||
None
|
||||
);
|
||||
let right_val = NDArrayValue::from_ptr_val(
|
||||
right_val.into_pointer_value(),
|
||||
llvm_usize,
|
||||
None
|
||||
);
|
||||
let res = numpy::ndarray_elementwise_binop_impl(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray_dtype1,
|
||||
if is_aug_assign { Some(left_val) } else { None },
|
||||
left_val,
|
||||
right_val,
|
||||
|generator, ctx, elem_ty, (lhs, rhs)| {
|
||||
gen_binop_expr_with_values(
|
||||
generator,
|
||||
ctx,
|
||||
(&Some(elem_ty), lhs),
|
||||
op,
|
||||
(&Some(elem_ty), rhs),
|
||||
ctx.current_loc,
|
||||
is_aug_assign,
|
||||
)?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)
|
||||
},
|
||||
)?;
|
||||
|
||||
Ok(Some(res.as_ptr_value().into()))
|
||||
} else {
|
||||
let left_ty_enum = ctx.unifier.get_ty_immutable(left.custom.unwrap());
|
||||
let left_ty_enum = ctx.unifier.get_ty_immutable(left_ty.unwrap());
|
||||
let TypeEnum::TObj { fields, obj_id, .. } = left_ty_enum.as_ref() else {
|
||||
unreachable!("must be tobj")
|
||||
};
|
||||
|
@ -1161,7 +1189,7 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
|
|||
let signature = if let Some(call) = ctx.calls.get(&loc.into()) {
|
||||
ctx.unifier.get_call_signature(*call).unwrap()
|
||||
} else {
|
||||
let left_enum_ty = ctx.unifier.get_ty_immutable(left.custom.unwrap());
|
||||
let left_enum_ty = ctx.unifier.get_ty_immutable(left_ty.unwrap());
|
||||
let TypeEnum::TObj { fields, .. } = left_enum_ty.as_ref() else {
|
||||
unreachable!("must be tobj")
|
||||
};
|
||||
|
@ -1186,13 +1214,51 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
|
|||
generator
|
||||
.gen_call(
|
||||
ctx,
|
||||
Some((left.custom.unwrap(), left_val.into())),
|
||||
Some((left_ty.unwrap(), left_val.into())),
|
||||
(&signature, fun_id),
|
||||
vec![(None, right_val.into())],
|
||||
).map(|f| f.map(Into::into))
|
||||
}
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for a [binary operator expression][expr].
|
||||
///
|
||||
/// * `left` - The left-hand side of the binary operator.
|
||||
/// * `op` - The operator applied on the operands.
|
||||
/// * `right` - The right-hand side of the binary operator.
|
||||
/// * `loc` - The location of the full expression.
|
||||
/// * `is_aug_assign` - Whether the binary operator expression is also an assignment operator.
|
||||
pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
left: &Expr<Option<Type>>,
|
||||
op: &Operator,
|
||||
right: &Expr<Option<Type>>,
|
||||
loc: Location,
|
||||
is_aug_assign: bool,
|
||||
) -> Result<Option<ValueEnum<'ctx>>, String> {
|
||||
let left_val = if let Some(v) = generator.gen_expr(ctx, left)? {
|
||||
v.to_basic_value_enum(ctx, generator, left.custom.unwrap())?
|
||||
} else {
|
||||
return Ok(None)
|
||||
};
|
||||
let right_val = if let Some(v) = generator.gen_expr(ctx, right)? {
|
||||
v.to_basic_value_enum(ctx, generator, right.custom.unwrap())?
|
||||
} else {
|
||||
return Ok(None)
|
||||
};
|
||||
|
||||
gen_binop_expr_with_values(
|
||||
generator,
|
||||
ctx,
|
||||
(&left.custom, left_val),
|
||||
op,
|
||||
(&right.custom, right_val),
|
||||
loc,
|
||||
is_aug_assign,
|
||||
)
|
||||
}
|
||||
|
||||
/// Generates code for a subscript expression on an `ndarray`.
|
||||
///
|
||||
/// * `ty` - The `Type` of the `NDArray` elements.
|
||||
|
@ -1265,12 +1331,14 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
|
|||
} else {
|
||||
return Ok(None)
|
||||
};
|
||||
let index_addr = generator.gen_var_alloc(ctx, index.get_type().into(), None)?;
|
||||
ctx.builder.build_store(index_addr, index).unwrap();
|
||||
|
||||
Ok(Some(v.data()
|
||||
.get(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.ctx.i32_type().const_array(&[index]),
|
||||
ArraySliceValue::from_ptr_val(index_addr, llvm_usize.const_int(1, false), None),
|
||||
None,
|
||||
)
|
||||
.into()))
|
||||
|
@ -1286,6 +1354,8 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
|
|||
} else {
|
||||
return Ok(None)
|
||||
};
|
||||
let index_addr = generator.gen_var_alloc(ctx, index.get_type().into(), None)?;
|
||||
ctx.builder.build_store(index_addr, index).unwrap();
|
||||
|
||||
// Create a new array, remove the top dimension from the dimension-size-list, and copy the
|
||||
// elements over
|
||||
|
@ -1340,7 +1410,7 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
|
|||
let v_data_src_ptr = v.data().ptr_offset(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.ctx.i32_type().const_array(&[index]),
|
||||
ArraySliceValue::from_ptr_val(index_addr, llvm_usize.const_int(1, false), None),
|
||||
None
|
||||
);
|
||||
call_memcpy_generic(
|
||||
|
|
|
@ -8,6 +8,8 @@ typedef unsigned _BitInt(64) uint64_t;
|
|||
# define MAX(a, b) (a > b ? a : b)
|
||||
# define MIN(a, b) (a > b ? b : a)
|
||||
|
||||
# define NULL ((void *) 0)
|
||||
|
||||
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
|
||||
// need to make sure `exp >= 0` before calling this function
|
||||
#define DEF_INT_EXP(T) T __nac3_int_exp_##T( \
|
||||
|
@ -243,13 +245,13 @@ void __nac3_ndarray_calc_nd_indices64(
|
|||
uint64_t index,
|
||||
const uint64_t* dims,
|
||||
uint64_t num_dims,
|
||||
uint64_t* idxs
|
||||
uint32_t* idxs
|
||||
) {
|
||||
uint64_t stride = 1;
|
||||
for (uint64_t dim = 0; dim < num_dims; dim++) {
|
||||
uint64_t i = num_dims - dim - 1;
|
||||
__builtin_assume(dims[i] > 0);
|
||||
idxs[i] = (index / stride) % dims[i];
|
||||
idxs[i] = (uint32_t) ((index / stride) % dims[i]);
|
||||
stride *= dims[i];
|
||||
}
|
||||
}
|
||||
|
@ -293,3 +295,87 @@ uint64_t __nac3_ndarray_flatten_index64(
|
|||
}
|
||||
return idx;
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast(
|
||||
const uint32_t *lhs_dims,
|
||||
uint32_t lhs_ndims,
|
||||
const uint32_t *rhs_dims,
|
||||
uint32_t rhs_ndims,
|
||||
uint32_t *out_dims
|
||||
) {
|
||||
uint32_t max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
|
||||
|
||||
for (uint32_t i = 0; i < max_ndims; ++i) {
|
||||
uint32_t *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : NULL;
|
||||
uint32_t *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : NULL;
|
||||
uint32_t *out_dim = &out_dims[max_ndims - i - 1];
|
||||
|
||||
if (lhs_dim_sz == NULL) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (rhs_dim_sz == NULL) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == 1) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (*rhs_dim_sz == 1) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == *rhs_dim_sz) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast64(
|
||||
const uint64_t *lhs_dims,
|
||||
uint64_t lhs_ndims,
|
||||
const uint64_t *rhs_dims,
|
||||
uint64_t rhs_ndims,
|
||||
uint64_t *out_dims
|
||||
) {
|
||||
uint64_t max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
|
||||
|
||||
for (uint64_t i = 0; i < max_ndims; ++i) {
|
||||
uint64_t *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : NULL;
|
||||
uint64_t *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : NULL;
|
||||
uint64_t *out_dim = &out_dims[max_ndims - i - 1];
|
||||
|
||||
if (lhs_dim_sz == NULL) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (rhs_dim_sz == NULL) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == 1) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (*rhs_dim_sz == 1) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == *rhs_dim_sz) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx(
|
||||
const uint32_t *src_dims,
|
||||
uint32_t src_ndims,
|
||||
const uint32_t *in_idx,
|
||||
uint32_t *out_idx
|
||||
) {
|
||||
for (uint32_t i = 0; i < src_ndims; ++i) {
|
||||
uint32_t src_i = src_ndims - i - 1;
|
||||
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
|
||||
}
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx64(
|
||||
const uint64_t *src_dims,
|
||||
uint64_t src_ndims,
|
||||
const uint32_t *in_idx,
|
||||
uint32_t *out_idx
|
||||
) {
|
||||
for (uint64_t i = 0; i < src_ndims; ++i) {
|
||||
uint64_t src_i = src_ndims - i - 1;
|
||||
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : (uint32_t) in_idx[src_i];
|
||||
}
|
||||
}
|
||||
|
|
|
@ -1,9 +1,18 @@
|
|||
use crate::typecheck::typedef::Type;
|
||||
|
||||
use super::{
|
||||
classes::{ArrayLikeIndexer, ArrayLikeValue, ListValue, NDArrayValue, UntypedArrayLikeMutator},
|
||||
classes::{
|
||||
ArrayLikeIndexer,
|
||||
ArrayLikeValue,
|
||||
ArraySliceValue,
|
||||
ListValue,
|
||||
NDArrayValue,
|
||||
TypedArrayLikeAdapter,
|
||||
UntypedArrayLikeAccessor,
|
||||
},
|
||||
CodeGenContext,
|
||||
CodeGenerator,
|
||||
llvm_intrinsics,
|
||||
};
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
|
@ -11,7 +20,7 @@ use inkwell::{
|
|||
memory_buffer::MemoryBuffer,
|
||||
module::Module,
|
||||
types::{BasicTypeEnum, IntType},
|
||||
values::{ArrayValue, BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
|
||||
values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
|
||||
AddressSpace, IntPredicate,
|
||||
};
|
||||
use itertools::Either;
|
||||
|
@ -619,7 +628,8 @@ pub fn call_ndarray_calc_size<'ctx, G, Dims>(
|
|||
.unwrap()
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_nd_indices`.
|
||||
/// 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
|
||||
|
@ -629,10 +639,11 @@ pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
|
|||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
index: IntValue<'ctx>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
) -> PointerValue<'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() {
|
||||
|
@ -646,7 +657,7 @@ pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
|
|||
llvm_usize.into(),
|
||||
llvm_pusize.into(),
|
||||
llvm_usize.into(),
|
||||
llvm_pusize.into(),
|
||||
llvm_pi32.into(),
|
||||
],
|
||||
false,
|
||||
);
|
||||
|
@ -658,7 +669,7 @@ pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
|
|||
let ndarray_dims = ndarray.dim_sizes();
|
||||
|
||||
let indices = ctx.builder.build_array_alloca(
|
||||
llvm_usize,
|
||||
llvm_i32,
|
||||
ndarray_num_dims,
|
||||
"",
|
||||
).unwrap();
|
||||
|
@ -676,7 +687,11 @@ pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
|
|||
)
|
||||
.unwrap();
|
||||
|
||||
indices
|
||||
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>(
|
||||
|
@ -771,46 +786,152 @@ pub fn call_ndarray_flatten_index<'ctx, G, Index>(
|
|||
indices,
|
||||
)
|
||||
}
|
||||
/// 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_const<'ctx, G: CodeGenerator + ?Sized>(
|
||||
|
||||
/// 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, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
indices: ArrayValue<'ctx>,
|
||||
) -> IntValue<'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 indices_size = indices.get_type().len();
|
||||
let indices_alloca = generator.gen_array_var_alloc(
|
||||
ctx,
|
||||
indices.get_type().get_element_type(),
|
||||
llvm_usize.const_int(indices_size as u64, false),
|
||||
None,
|
||||
).unwrap();
|
||||
for i in 0..indices_size {
|
||||
let v = ctx.builder.build_extract_value(indices, i, "")
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
|
||||
unsafe {
|
||||
indices_alloca.set_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.ctx.i32_type().const_int(i as u64, false),
|
||||
v.into(),
|
||||
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_broadcast",
|
||||
64 => "__nac3_ndarray_calc_broadcast64",
|
||||
bw => unreachable!("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,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
call_ndarray_flatten_index_impl(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
&indices_alloca,
|
||||
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 max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
|
||||
|
||||
// TODO: Generate assertion checks for whether each dimension is compatible
|
||||
// gen_for_callback_incrementing(
|
||||
// generator,
|
||||
// ctx,
|
||||
// llvm_usize.const_zero(),
|
||||
// (max_ndims, false),
|
||||
// |generator, ctx, idx| {
|
||||
// let lhs_dim_sz =
|
||||
//
|
||||
// let lhs_elem = lhs.get_dims().get(ctx, generator, idx, None);
|
||||
// let rhs_elem = rhs.get_dims().get(ctx, generator, idx, None);
|
||||
//
|
||||
//
|
||||
// },
|
||||
// llvm_usize.const_int(1, false),
|
||||
// ).unwrap();
|
||||
|
||||
let lhs_dims = lhs.dim_sizes().base_ptr(ctx, generator);
|
||||
let lhs_ndims = lhs.load_ndims(ctx);
|
||||
let rhs_dims = rhs.dim_sizes().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 => unreachable!("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)
|
||||
});
|
||||
|
||||
// TODO: Assertions
|
||||
|
||||
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.dim_sizes().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()),
|
||||
)
|
||||
}
|
|
@ -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,
|
||||
|
|
|
@ -12,11 +12,14 @@ use crate::{
|
|||
ListValue,
|
||||
NDArrayValue,
|
||||
TypedArrayLikeAccessor,
|
||||
TypedArrayLikeAdapter,
|
||||
UntypedArrayLikeAccessor,
|
||||
},
|
||||
CodeGenContext,
|
||||
CodeGenerator,
|
||||
irrt::{
|
||||
call_ndarray_calc_broadcast,
|
||||
call_ndarray_calc_broadcast_index,
|
||||
call_ndarray_calc_nd_indices,
|
||||
call_ndarray_calc_size,
|
||||
},
|
||||
|
@ -26,7 +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},
|
||||
};
|
||||
|
@ -324,7 +327,7 @@ fn ndarray_fill_indexed<'ctx, G, ValueFn>(
|
|||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, PointerValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, TypedArrayLikeAdapter<'ctx, IntValue<'ctx>>) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
|
@ -343,6 +346,45 @@ 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_broadcast_fill<'ctx, G, ValueFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
res: NDArrayValue<'ctx>,
|
||||
lhs: NDArrayValue<'ctx>,
|
||||
rhs: NDArrayValue<'ctx>,
|
||||
value_fn: ValueFn,
|
||||
) -> Result<NDArrayValue<'ctx>, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, Type, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
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, lhs_idx, None),
|
||||
rhs.data().get_unchecked(ctx, generator, rhs_idx, None),
|
||||
)
|
||||
};
|
||||
|
||||
debug_assert_eq!(elem.0.get_type(), elem.1.get_type());
|
||||
|
||||
value_fn(generator, ctx, elem_ty, elem)
|
||||
},
|
||||
)?;
|
||||
|
||||
Ok(res)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
|
@ -470,6 +512,7 @@ fn call_ndarray_eye_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
ncols: IntValue<'ctx>,
|
||||
offset: IntValue<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_usize_2 = llvm_usize.array_type(2);
|
||||
|
||||
|
@ -498,21 +541,17 @@ fn call_ndarray_eye_impl<'ctx, G: CodeGenerator + ?Sized>(
|
|||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, indices| {
|
||||
let row = ctx.build_gep_and_load(
|
||||
indices,
|
||||
&[llvm_usize.const_int(0, false)],
|
||||
None,
|
||||
).into_int_value();
|
||||
let col = ctx.build_gep_and_load(
|
||||
indices,
|
||||
&[llvm_usize.const_int(1, false)],
|
||||
None,
|
||||
).into_int_value();
|
||||
let (row, col) = unsafe {
|
||||
(
|
||||
indices.get_typed_unchecked(ctx, generator, llvm_usize.const_zero(), None),
|
||||
indices.get_typed_unchecked(ctx, generator, llvm_usize.const_int(1, false), None),
|
||||
)
|
||||
};
|
||||
|
||||
let col_with_offset = ctx.builder
|
||||
.build_int_add(
|
||||
col,
|
||||
ctx.builder.build_int_s_extend_or_bit_cast(offset, llvm_usize, "").unwrap(),
|
||||
ctx.builder.build_int_s_extend_or_bit_cast(offset, llvm_i32, "").unwrap(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
@ -581,6 +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, '_>,
|
||||
|
@ -767,7 +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()
|
||||
|
|
|
@ -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!(),
|
||||
};
|
||||
|
|
|
@ -299,6 +299,8 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
|
|||
Some("N".into()),
|
||||
None,
|
||||
);
|
||||
let size_t = primitives.0.usize();
|
||||
|
||||
let var_map: VarMap = vec![(num_ty.1, num_ty.0)].into_iter().collect();
|
||||
let exception_fields = vec![
|
||||
("__name__".into(), int32, true),
|
||||
|
@ -345,8 +347,27 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
|
|||
.nth(1)
|
||||
.map(|(var_id, ty)| (*ty, *var_id))
|
||||
.unwrap();
|
||||
let ndarray_usized_ndims_tvar = primitives.1.get_fresh_const_generic_var(
|
||||
size_t,
|
||||
Some("ndarray_ndims".into()),
|
||||
None,
|
||||
);
|
||||
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 +545,20 @@ 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 +597,216 @@ 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, ndarray_usized_ndims_tvar.1],
|
||||
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(),
|
||||
|
|
|
@ -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),
|
||||
|
@ -238,7 +285,16 @@ impl TopLevelComposer {
|
|||
]),
|
||||
});
|
||||
|
||||
let ndarray_usized_ndims_tvar = unifier.get_fresh_const_generic_var(size_t_ty, Some("ndarray_ndims".into()), None);
|
||||
let ndarray_unsized = subst_ndarray_tvars(&mut unifier, ndarray, Some(ndarray_usized_ndims_tvar.0), None);
|
||||
|
||||
unifier.unify(ndarray_copy_fun_ret_ty.0, ndarray).unwrap();
|
||||
unifier.unify(ndarray_binop_fun_other_ty.0, ndarray_unsized).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,
|
||||
|
|
|
@ -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()
|
||||
}
|
||||
|
|
|
@ -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`
|
||||
|
@ -299,8 +306,10 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
|
|||
bool: bool_t,
|
||||
uint32: uint32_t,
|
||||
uint64: uint64_t,
|
||||
ndarray: ndarray_t,
|
||||
..
|
||||
} = *store;
|
||||
let size_t = store.usize();
|
||||
|
||||
/* int ======== */
|
||||
for t in [int32_t, int64_t, uint32_t, uint64_t] {
|
||||
|
@ -308,7 +317,7 @@ 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 +332,7 @@ 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 +343,14 @@ 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);
|
||||
let ndarray_usized_ndims_tvar = unifier.get_fresh_const_generic_var(size_t, Some("ndarray_ndims".into()), None);
|
||||
let ndarray_unsized_t = make_ndarray_ty(unifier, store, None, Some(ndarray_usized_ndims_tvar.0));
|
||||
impl_basic_arithmetic(unifier, store, ndarray_t, &[ndarray_unsized_t], ndarray_t);
|
||||
impl_pow(unifier, store, ndarray_t, &[ndarray_unsized_t], ndarray_t);
|
||||
impl_div(unifier, store, ndarray_t, &[ndarray_t], ndarray_float_t);
|
||||
impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t], ndarray_t);
|
||||
impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t], ndarray_t);
|
||||
}
|
||||
|
|
|
@ -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)
|
||||
|
|
|
@ -67,6 +67,181 @@ 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_add_broadcast():
|
||||
# x = np_identity(2)
|
||||
# y: ndarray[float, 2] = x + np_ones([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 +252,17 @@ 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
|
||||
|
|
Loading…
Reference in New Issue