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core: Split numpy into codegen and toplevel

This commit is contained in:
David Mak 2024-03-11 14:47:01 +08:00
parent fd44ee6887
commit 1b77e62901
4 changed files with 915 additions and 901 deletions

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@ -45,6 +45,7 @@ pub mod expr;
mod generator;
pub mod irrt;
pub mod llvm_intrinsics;
pub mod numpy;
pub mod stmt;
#[cfg(test)]

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@ -0,0 +1,907 @@
use inkwell::{
IntPredicate,
types::BasicType,
values::{AggregateValueEnum, ArrayValue, BasicValueEnum, IntValue, PointerValue}
};
use nac3parser::ast::StrRef;
use crate::{
codegen::{
classes::{ListValue, NDArrayValue},
CodeGenContext,
CodeGenerator,
irrt::{
call_ndarray_calc_nd_indices,
call_ndarray_calc_size,
},
llvm_intrinsics::call_memcpy_generic,
stmt::gen_for_callback
},
symbol_resolver::ValueEnum,
toplevel::{
DefinitionId,
numpy::{make_ndarray_ty, unpack_ndarray_tvars},
},
typecheck::typedef::{FunSignature, Type},
};
/// Creates an `NDArray` instance from a dynamic shape.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The shape of the `NDArray`.
/// * `shape_len_fn` - A function that retrieves the number of dimensions from `shape`.
/// * `shape_data_fn` - A function that retrieves the size of a dimension from `shape`.
fn create_ndarray_dyn_shape<'ctx, 'a, V, LenFn, DataFn>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, 'a>,
elem_ty: Type,
shape: &V,
shape_len_fn: LenFn,
shape_data_fn: DataFn,
) -> Result<NDArrayValue<'ctx>, String>
where
LenFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, &V) -> Result<IntValue<'ctx>, String>,
DataFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, &V, IntValue<'ctx>) -> Result<IntValue<'ctx>, String>,
{
let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
assert!(llvm_ndarray_data_t.is_sized());
// Assert that all dimensions are non-negative
gen_for_callback(
generator,
ctx,
|generator, ctx| {
let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
Ok(i)
},
|generator, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let shape_len = shape_len_fn(generator, ctx, shape)?;
debug_assert!(shape_len.get_type().get_bit_width() <= llvm_usize.get_bit_width());
Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, shape_len, "").unwrap())
},
|generator, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
let shape_dim_gez = ctx.builder
.build_int_compare(IntPredicate::SGE, shape_dim, shape_dim.get_type().const_zero(), "")
.unwrap();
ctx.make_assert(
generator,
shape_dim_gez,
"0:ValueError",
"negative dimensions not supported",
[None, None, None],
ctx.current_loc,
);
Ok(())
},
|_, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
ctx.builder.build_store(i_addr, i).unwrap();
Ok(())
},
)?;
let ndarray = generator.gen_var_alloc(
ctx,
llvm_ndarray_t.into(),
None,
)?;
let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
let num_dims = shape_len_fn(generator, ctx, shape)?;
ndarray.store_ndims(ctx, generator, num_dims);
let ndarray_num_dims = ndarray.load_ndims(ctx);
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
// Copy the dimension sizes from shape to ndarray.dims
gen_for_callback(
generator,
ctx,
|generator, ctx| {
let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
Ok(i)
},
|generator, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let shape_len = shape_len_fn(generator, ctx, shape)?;
debug_assert!(shape_len.get_type().get_bit_width() <= llvm_usize.get_bit_width());
Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, shape_len, "").unwrap())
},
|generator, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
let shape_dim = ctx.builder
.build_int_z_extend(shape_dim, llvm_usize, "")
.unwrap();
let ndarray_pdim = ndarray.dim_sizes().ptr_offset(ctx, generator, i, None);
ctx.builder.build_store(ndarray_pdim, shape_dim).unwrap();
Ok(())
},
|_, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
ctx.builder.build_store(i_addr, i).unwrap();
Ok(())
},
)?;
let ndarray_num_elems = call_ndarray_calc_size(
generator,
ctx,
ndarray.load_ndims(ctx),
ndarray.dim_sizes().as_ptr_value(ctx),
);
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
Ok(ndarray)
}
/// Creates an `NDArray` instance from a constant shape.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The shape of the `NDArray`, represented as an LLVM [`ArrayValue`].
fn create_ndarray_const_shape<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
shape: ArrayValue<'ctx>
) -> Result<NDArrayValue<'ctx>, String> {
let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
assert!(llvm_ndarray_data_t.is_sized());
for i in 0..shape.get_type().len() {
let shape_dim = ctx.builder
.build_extract_value(shape, i, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let shape_dim_gez = ctx.builder
.build_int_compare(IntPredicate::SGE, shape_dim, llvm_usize.const_zero(), "")
.unwrap();
ctx.make_assert(
generator,
shape_dim_gez,
"0:ValueError",
"negative dimensions not supported",
[None, None, None],
ctx.current_loc,
);
}
let ndarray = generator.gen_var_alloc(
ctx,
llvm_ndarray_t.into(),
None,
)?;
let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
let num_dims = llvm_usize.const_int(shape.get_type().len() as u64, false);
ndarray.store_ndims(ctx, generator, num_dims);
let ndarray_num_dims = ndarray.load_ndims(ctx);
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
for i in 0..shape.get_type().len() {
let ndarray_dim = ndarray
.dim_sizes()
.ptr_offset(ctx, generator, llvm_usize.const_int(i as u64, true), None);
let shape_dim = ctx.builder.build_extract_value(shape, i, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
ctx.builder.build_store(ndarray_dim, shape_dim).unwrap();
}
let ndarray_dims = ndarray.dim_sizes().as_ptr_value(ctx);
let ndarray_num_elems = call_ndarray_calc_size(
generator,
ctx,
ndarray.load_ndims(ctx),
ndarray_dims,
);
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
Ok(ndarray)
}
fn ndarray_zero_value<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
ctx.ctx.i32_type().const_zero().into()
} else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
ctx.ctx.i64_type().const_zero().into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
ctx.ctx.f64_type().const_zero().into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
ctx.ctx.bool_type().const_zero().into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
ctx.gen_string(generator, "")
} else {
unreachable!()
}
}
fn ndarray_one_value<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
ctx.ctx.i32_type().const_int(1, is_signed).into()
} else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
ctx.ctx.i64_type().const_int(1, is_signed).into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
ctx.ctx.f64_type().const_float(1.0).into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
ctx.ctx.bool_type().const_int(1, false).into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
ctx.gen_string(generator, "1")
} else {
unreachable!()
}
}
/// LLVM-typed implementation for generating the implementation for constructing an `NDArray`.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
fn call_ndarray_empty_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
shape: ListValue<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
create_ndarray_dyn_shape(
generator,
ctx,
elem_ty,
&shape,
|_, ctx, shape| {
Ok(shape.load_size(ctx, None))
},
|generator, ctx, shape, idx| {
Ok(shape.data().get(ctx, generator, idx, None).into_int_value())
},
)
}
/// Generates LLVM IR for populating the entire `NDArray` using a lambda with its flattened index as
/// its input.
fn ndarray_fill_flattened<'ctx, 'a, ValueFn>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, 'a>,
ndarray: NDArrayValue<'ctx>,
value_fn: ValueFn,
) -> Result<(), String>
where
ValueFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
{
let llvm_usize = generator.get_size_type(ctx.ctx);
let ndarray_num_elems = call_ndarray_calc_size(
generator,
ctx,
ndarray.load_ndims(ctx),
ndarray.dim_sizes().as_ptr_value(ctx),
);
gen_for_callback(
generator,
ctx,
|generator, ctx| {
let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
Ok(i)
},
|_, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, ndarray_num_elems, "").unwrap())
},
|generator, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let elem = unsafe {
ndarray.data().ptr_to_data_flattened_unchecked(ctx, i, None)
};
let value = value_fn(generator, ctx, i)?;
ctx.builder.build_store(elem, value).unwrap();
Ok(())
},
|_, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
ctx.builder.build_store(i_addr, i).unwrap();
Ok(())
},
)
}
/// Generates LLVM IR for populating the entire `NDArray` using a lambda with the dimension-indices
/// as its input.
fn ndarray_fill_indexed<'ctx, ValueFn>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: NDArrayValue<'ctx>,
value_fn: ValueFn,
) -> Result<(), String>
where
ValueFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, '_>, PointerValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
{
ndarray_fill_flattened(
generator,
ctx,
ndarray,
|generator, ctx, idx| {
let indices = call_ndarray_calc_nd_indices(
generator,
ctx,
idx,
ndarray,
);
value_fn(generator, ctx, indices)
}
)
}
/// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
fn call_ndarray_zeros_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
shape: ListValue<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
let supported_types = [
ctx.primitives.int32,
ctx.primitives.int64,
ctx.primitives.uint32,
ctx.primitives.uint64,
ctx.primitives.float,
ctx.primitives.bool,
ctx.primitives.str,
];
assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
ndarray_fill_flattened(
generator,
ctx,
ndarray,
|generator, ctx, _| {
let value = ndarray_zero_value(generator, ctx, elem_ty);
Ok(value)
}
)?;
Ok(ndarray)
}
/// LLVM-typed implementation for generating the implementation for `ndarray.ones`.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
fn call_ndarray_ones_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
shape: ListValue<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
let supported_types = [
ctx.primitives.int32,
ctx.primitives.int64,
ctx.primitives.uint32,
ctx.primitives.uint64,
ctx.primitives.float,
ctx.primitives.bool,
ctx.primitives.str,
];
assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
ndarray_fill_flattened(
generator,
ctx,
ndarray,
|generator, ctx, _| {
let value = ndarray_one_value(generator, ctx, elem_ty);
Ok(value)
}
)?;
Ok(ndarray)
}
/// LLVM-typed implementation for generating the implementation for `ndarray.full`.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
fn call_ndarray_full_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
shape: ListValue<'ctx>,
fill_value: BasicValueEnum<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
ndarray_fill_flattened(
generator,
ctx,
ndarray,
|generator, ctx, _| {
let value = if fill_value.is_pointer_value() {
let llvm_i1 = ctx.ctx.bool_type();
let copy = generator.gen_var_alloc(ctx, fill_value.get_type(), None)?;
call_memcpy_generic(
ctx,
copy,
fill_value.into_pointer_value(),
fill_value.get_type().size_of().map(Into::into).unwrap(),
llvm_i1.const_zero(),
);
copy.into()
} else if fill_value.is_int_value() || fill_value.is_float_value() {
fill_value
} else {
unreachable!()
};
Ok(value)
}
)?;
Ok(ndarray)
}
/// LLVM-typed implementation for generating the implementation for `ndarray.eye`.
///
/// * `elem_ty` - The element type of the `NDArray`.
fn call_ndarray_eye_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
nrows: IntValue<'ctx>,
ncols: IntValue<'ctx>,
offset: IntValue<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_usize_2 = llvm_usize.array_type(2);
let shape_addr = generator.gen_var_alloc(ctx, llvm_usize_2.into(), None)?;
let shape = ctx.builder.build_load(shape_addr, "")
.map(BasicValueEnum::into_array_value)
.unwrap();
let nrows = ctx.builder.build_int_z_extend_or_bit_cast(nrows, llvm_usize, "").unwrap();
let shape = ctx.builder
.build_insert_value(shape, nrows, 0, "")
.map(AggregateValueEnum::into_array_value)
.unwrap();
let ncols = ctx.builder.build_int_z_extend_or_bit_cast(ncols, llvm_usize, "").unwrap();
let shape = ctx.builder
.build_insert_value(shape, ncols, 1, "")
.map(AggregateValueEnum::into_array_value)
.unwrap();
let ndarray = create_ndarray_const_shape(generator, ctx, elem_ty, shape)?;
ndarray_fill_indexed(
generator,
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 col_with_offset = ctx.builder
.build_int_add(
col,
ctx.builder.build_int_s_extend_or_bit_cast(offset, llvm_usize, "").unwrap(),
"",
)
.unwrap();
let is_on_diag = ctx.builder
.build_int_compare(IntPredicate::EQ, row, col_with_offset, "")
.unwrap();
let zero = ndarray_zero_value(generator, ctx, elem_ty);
let one = ndarray_one_value(generator, ctx, elem_ty);
let value = ctx.builder.build_select(is_on_diag, one, zero, "").unwrap();
Ok(value)
},
)?;
Ok(ndarray)
}
/// LLVM-typed implementation for generating the implementation for `ndarray.copy`.
///
/// * `elem_ty` - The element type of the `NDArray`.
fn ndarray_copy_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
this: NDArrayValue<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
let llvm_i1 = ctx.ctx.bool_type();
let ndarray = create_ndarray_dyn_shape(
generator,
ctx,
elem_ty,
&this,
|_, ctx, shape| {
Ok(shape.load_ndims(ctx))
},
|generator, ctx, shape, idx| {
Ok(shape.dim_sizes().get(ctx, generator, idx, None))
},
)?;
let len = call_ndarray_calc_size(
generator,
ctx,
ndarray.load_ndims(ctx),
ndarray.dim_sizes().as_ptr_value(ctx),
);
let sizeof_ty = ctx.get_llvm_type(generator, elem_ty);
let len_bytes = ctx.builder
.build_int_mul(
len,
sizeof_ty.size_of().unwrap(),
"",
)
.unwrap();
call_memcpy_generic(
ctx,
ndarray.data().as_ptr_value(ctx),
this.data().as_ptr_value(ctx),
len_bytes,
llvm_i1.const_zero(),
);
Ok(ndarray)
}
/// Generates LLVM IR for `ndarray.empty`.
pub fn gen_ndarray_empty<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let llvm_usize = generator.get_size_type(context.ctx);
let shape_ty = fun.0.args[0].ty;
let shape_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, shape_ty)?;
call_ndarray_empty_impl(
generator,
context,
context.primitives.float,
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.zeros`.
pub fn gen_ndarray_zeros<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let llvm_usize = generator.get_size_type(context.ctx);
let shape_ty = fun.0.args[0].ty;
let shape_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, shape_ty)?;
call_ndarray_zeros_impl(
generator,
context,
context.primitives.float,
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.ones`.
pub fn gen_ndarray_ones<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let llvm_usize = generator.get_size_type(context.ctx);
let shape_ty = fun.0.args[0].ty;
let shape_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, shape_ty)?;
call_ndarray_ones_impl(
generator,
context,
context.primitives.float,
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.full`.
pub fn gen_ndarray_full<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 2);
let llvm_usize = generator.get_size_type(context.ctx);
let shape_ty = fun.0.args[0].ty;
let shape_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, shape_ty)?;
let fill_value_ty = fun.0.args[1].ty;
let fill_value_arg = args[1].1.clone()
.to_basic_value_enum(context, generator, fill_value_ty)?;
call_ndarray_full_impl(
generator,
context,
fill_value_ty,
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
fill_value_arg,
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.eye`.
pub fn gen_ndarray_eye<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert!(matches!(args.len(), 1..=3));
let nrows_ty = fun.0.args[0].ty;
let nrows_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, nrows_ty)?;
let ncols_ty = fun.0.args[1].ty;
let ncols_arg = args.iter()
.find(|arg| arg.0.is_some_and(|name| name == fun.0.args[1].name))
.map(|arg| arg.1.clone().to_basic_value_enum(context, generator, ncols_ty))
.unwrap_or_else(|| {
args[0].1.clone().to_basic_value_enum(context, generator, nrows_ty)
})?;
let offset_ty = fun.0.args[2].ty;
let offset_arg = args.iter()
.find(|arg| arg.0.is_some_and(|name| name == fun.0.args[2].name))
.map(|arg| arg.1.clone().to_basic_value_enum(context, generator, offset_ty))
.unwrap_or_else(|| {
Ok(context.gen_symbol_val(
generator,
fun.0.args[2].default_value.as_ref().unwrap(),
offset_ty
))
})?;
call_ndarray_eye_impl(
generator,
context,
context.primitives.float,
nrows_arg.into_int_value(),
ncols_arg.into_int_value(),
offset_arg.into_int_value(),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.identity`.
pub fn gen_ndarray_identity<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let llvm_usize = generator.get_size_type(context.ctx);
let n_ty = fun.0.args[0].ty;
let n_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, n_ty)?;
call_ndarray_eye_impl(
generator,
context,
context.primitives.float,
n_arg.into_int_value(),
n_arg.into_int_value(),
llvm_usize.const_zero(),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.copy`.
pub fn gen_ndarray_copy<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
_fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_some());
assert!(args.is_empty());
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_arg = obj
.as_ref()
.unwrap()
.1
.clone()
.to_basic_value_enum(context, generator, this_ty)?;
ndarray_copy_impl(
generator,
context,
this_elem_ty,
NDArrayValue::from_ptr_val(this_arg.into_pointer_value(), llvm_usize, None),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.fill`.
pub fn gen_ndarray_fill<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<(), String> {
assert!(obj.is_some());
assert_eq!(args.len(), 1);
let llvm_usize = generator.get_size_type(context.ctx);
let this_ty = obj.as_ref().unwrap().0;
let this_arg = obj.as_ref().unwrap().1.clone()
.to_basic_value_enum(context, generator, this_ty)?
.into_pointer_value();
let value_ty = fun.0.args[0].ty;
let value_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, value_ty)?;
ndarray_fill_flattened(
generator,
context,
NDArrayValue::from_ptr_val(this_arg, llvm_usize, None),
|generator, ctx, _| {
let value = if value_arg.is_pointer_value() {
let llvm_i1 = ctx.ctx.bool_type();
let copy = generator.gen_var_alloc(ctx, value_arg.get_type(), None)?;
call_memcpy_generic(
ctx,
copy,
value_arg.into_pointer_value(),
value_arg.get_type().size_of().map(Into::into).unwrap(),
llvm_i1.const_zero(),
);
copy.into()
} else if value_arg.is_int_value() || value_arg.is_float_value() {
value_arg
} else {
unreachable!()
};
Ok(value)
}
)?;
Ok(())
}

View File

@ -5,11 +5,14 @@ use crate::{
expr::destructure_range,
irrt::*,
llvm_intrinsics::*,
numpy::*,
stmt::exn_constructor,
},
symbol_resolver::SymbolValue,
toplevel::helper::PRIMITIVE_DEF_IDS,
toplevel::numpy::*,
toplevel::{
helper::PRIMITIVE_DEF_IDS,
numpy::make_ndarray_ty,
},
typecheck::typedef::VarMap,
};
use inkwell::{

View File

@ -1,24 +1,9 @@
use inkwell::{IntPredicate, types::BasicType, values::{BasicValueEnum, PointerValue}};
use inkwell::values::{AggregateValueEnum, ArrayValue, IntValue};
use itertools::Itertools;
use nac3parser::ast::StrRef;
use crate::{
codegen::{
classes::{ListValue, NDArrayValue},
CodeGenContext,
CodeGenerator,
irrt::{
call_ndarray_calc_nd_indices,
call_ndarray_calc_size,
},
llvm_intrinsics::call_memcpy_generic,
stmt::gen_for_callback
},
symbol_resolver::ValueEnum,
toplevel::{DefinitionId, helper::PRIMITIVE_DEF_IDS},
toplevel::helper::PRIMITIVE_DEF_IDS,
typecheck::{
type_inferencer::PrimitiveStore,
typedef::{FunSignature, Type, TypeEnum, Unifier, VarMap},
typedef::{Type, TypeEnum, Unifier, VarMap},
},
};
@ -76,885 +61,3 @@ pub fn unpack_ndarray_tvars(
.collect_tuple()
.unwrap()
}
/// Creates an `NDArray` instance from a dynamic shape.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The shape of the `NDArray`.
/// * `shape_len_fn` - A function that retrieves the number of dimensions from `shape`.
/// * `shape_data_fn` - A function that retrieves the size of a dimension from `shape`.
fn create_ndarray_dyn_shape<'ctx, 'a, V, LenFn, DataFn>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, 'a>,
elem_ty: Type,
shape: &V,
shape_len_fn: LenFn,
shape_data_fn: DataFn,
) -> Result<NDArrayValue<'ctx>, String>
where
LenFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, &V) -> Result<IntValue<'ctx>, String>,
DataFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, &V, IntValue<'ctx>) -> Result<IntValue<'ctx>, String>,
{
let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
assert!(llvm_ndarray_data_t.is_sized());
// Assert that all dimensions are non-negative
gen_for_callback(
generator,
ctx,
|generator, ctx| {
let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
Ok(i)
},
|generator, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let shape_len = shape_len_fn(generator, ctx, shape)?;
debug_assert!(shape_len.get_type().get_bit_width() <= llvm_usize.get_bit_width());
Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, shape_len, "").unwrap())
},
|generator, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
let shape_dim_gez = ctx.builder
.build_int_compare(IntPredicate::SGE, shape_dim, shape_dim.get_type().const_zero(), "")
.unwrap();
ctx.make_assert(
generator,
shape_dim_gez,
"0:ValueError",
"negative dimensions not supported",
[None, None, None],
ctx.current_loc,
);
Ok(())
},
|_, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
ctx.builder.build_store(i_addr, i).unwrap();
Ok(())
},
)?;
let ndarray = generator.gen_var_alloc(
ctx,
llvm_ndarray_t.into(),
None,
)?;
let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
let num_dims = shape_len_fn(generator, ctx, shape)?;
ndarray.store_ndims(ctx, generator, num_dims);
let ndarray_num_dims = ndarray.load_ndims(ctx);
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
// Copy the dimension sizes from shape to ndarray.dims
gen_for_callback(
generator,
ctx,
|generator, ctx| {
let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
Ok(i)
},
|generator, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let shape_len = shape_len_fn(generator, ctx, shape)?;
debug_assert!(shape_len.get_type().get_bit_width() <= llvm_usize.get_bit_width());
Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, shape_len, "").unwrap())
},
|generator, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
let shape_dim = ctx.builder
.build_int_z_extend(shape_dim, llvm_usize, "")
.unwrap();
let ndarray_pdim = ndarray.dim_sizes().ptr_offset(ctx, generator, i, None);
ctx.builder.build_store(ndarray_pdim, shape_dim).unwrap();
Ok(())
},
|_, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
ctx.builder.build_store(i_addr, i).unwrap();
Ok(())
},
)?;
let ndarray_num_elems = call_ndarray_calc_size(
generator,
ctx,
ndarray.load_ndims(ctx),
ndarray.dim_sizes().as_ptr_value(ctx),
);
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
Ok(ndarray)
}
/// Creates an `NDArray` instance from a constant shape.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The shape of the `NDArray`, represented as an LLVM [`ArrayValue`].
fn create_ndarray_const_shape<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
shape: ArrayValue<'ctx>
) -> Result<NDArrayValue<'ctx>, String> {
let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
assert!(llvm_ndarray_data_t.is_sized());
for i in 0..shape.get_type().len() {
let shape_dim = ctx.builder
.build_extract_value(shape, i, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let shape_dim_gez = ctx.builder
.build_int_compare(IntPredicate::SGE, shape_dim, llvm_usize.const_zero(), "")
.unwrap();
ctx.make_assert(
generator,
shape_dim_gez,
"0:ValueError",
"negative dimensions not supported",
[None, None, None],
ctx.current_loc,
);
}
let ndarray = generator.gen_var_alloc(
ctx,
llvm_ndarray_t.into(),
None,
)?;
let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
let num_dims = llvm_usize.const_int(shape.get_type().len() as u64, false);
ndarray.store_ndims(ctx, generator, num_dims);
let ndarray_num_dims = ndarray.load_ndims(ctx);
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
for i in 0..shape.get_type().len() {
let ndarray_dim = ndarray
.dim_sizes()
.ptr_offset(ctx, generator, llvm_usize.const_int(i as u64, true), None);
let shape_dim = ctx.builder.build_extract_value(shape, i, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
ctx.builder.build_store(ndarray_dim, shape_dim).unwrap();
}
let ndarray_dims = ndarray.dim_sizes().as_ptr_value(ctx);
let ndarray_num_elems = call_ndarray_calc_size(
generator,
ctx,
ndarray.load_ndims(ctx),
ndarray_dims,
);
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
Ok(ndarray)
}
fn ndarray_zero_value<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
ctx.ctx.i32_type().const_zero().into()
} else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
ctx.ctx.i64_type().const_zero().into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
ctx.ctx.f64_type().const_zero().into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
ctx.ctx.bool_type().const_zero().into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
ctx.gen_string(generator, "")
} else {
unreachable!()
}
}
fn ndarray_one_value<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
) -> BasicValueEnum<'ctx> {
if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
ctx.ctx.i32_type().const_int(1, is_signed).into()
} else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
ctx.ctx.i64_type().const_int(1, is_signed).into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
ctx.ctx.f64_type().const_float(1.0).into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
ctx.ctx.bool_type().const_int(1, false).into()
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
ctx.gen_string(generator, "1")
} else {
unreachable!()
}
}
/// LLVM-typed implementation for generating the implementation for constructing an `NDArray`.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
fn call_ndarray_empty_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
shape: ListValue<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
create_ndarray_dyn_shape(
generator,
ctx,
elem_ty,
&shape,
|_, ctx, shape| {
Ok(shape.load_size(ctx, None))
},
|generator, ctx, shape, idx| {
Ok(shape.data().get(ctx, generator, idx, None).into_int_value())
},
)
}
/// Generates LLVM IR for populating the entire `NDArray` using a lambda with its flattened index as
/// its input.
fn ndarray_fill_flattened<'ctx, 'a, ValueFn>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, 'a>,
ndarray: NDArrayValue<'ctx>,
value_fn: ValueFn,
) -> Result<(), String>
where
ValueFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
{
let llvm_usize = generator.get_size_type(ctx.ctx);
let ndarray_num_elems = call_ndarray_calc_size(
generator,
ctx,
ndarray.load_ndims(ctx),
ndarray.dim_sizes().as_ptr_value(ctx),
);
gen_for_callback(
generator,
ctx,
|generator, ctx| {
let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
Ok(i)
},
|_, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, ndarray_num_elems, "").unwrap())
},
|generator, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let elem = unsafe {
ndarray.data().ptr_to_data_flattened_unchecked(ctx, i, None)
};
let value = value_fn(generator, ctx, i)?;
ctx.builder.build_store(elem, value).unwrap();
Ok(())
},
|_, ctx, i_addr| {
let i = ctx.builder
.build_load(i_addr, "")
.map(BasicValueEnum::into_int_value)
.unwrap();
let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
ctx.builder.build_store(i_addr, i).unwrap();
Ok(())
},
)
}
/// Generates LLVM IR for populating the entire `NDArray` using a lambda with the dimension-indices
/// as its input.
fn ndarray_fill_indexed<'ctx, ValueFn>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
ndarray: NDArrayValue<'ctx>,
value_fn: ValueFn,
) -> Result<(), String>
where
ValueFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, '_>, PointerValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
{
ndarray_fill_flattened(
generator,
ctx,
ndarray,
|generator, ctx, idx| {
let indices = call_ndarray_calc_nd_indices(
generator,
ctx,
idx,
ndarray,
);
value_fn(generator, ctx, indices)
}
)
}
/// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
fn call_ndarray_zeros_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
shape: ListValue<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
let supported_types = [
ctx.primitives.int32,
ctx.primitives.int64,
ctx.primitives.uint32,
ctx.primitives.uint64,
ctx.primitives.float,
ctx.primitives.bool,
ctx.primitives.str,
];
assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
ndarray_fill_flattened(
generator,
ctx,
ndarray,
|generator, ctx, _| {
let value = ndarray_zero_value(generator, ctx, elem_ty);
Ok(value)
}
)?;
Ok(ndarray)
}
/// LLVM-typed implementation for generating the implementation for `ndarray.ones`.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
fn call_ndarray_ones_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
shape: ListValue<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
let supported_types = [
ctx.primitives.int32,
ctx.primitives.int64,
ctx.primitives.uint32,
ctx.primitives.uint64,
ctx.primitives.float,
ctx.primitives.bool,
ctx.primitives.str,
];
assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
ndarray_fill_flattened(
generator,
ctx,
ndarray,
|generator, ctx, _| {
let value = ndarray_one_value(generator, ctx, elem_ty);
Ok(value)
}
)?;
Ok(ndarray)
}
/// LLVM-typed implementation for generating the implementation for `ndarray.full`.
///
/// * `elem_ty` - The element type of the `NDArray`.
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
fn call_ndarray_full_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
shape: ListValue<'ctx>,
fill_value: BasicValueEnum<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
ndarray_fill_flattened(
generator,
ctx,
ndarray,
|generator, ctx, _| {
let value = if fill_value.is_pointer_value() {
let llvm_i1 = ctx.ctx.bool_type();
let copy = generator.gen_var_alloc(ctx, fill_value.get_type(), None)?;
call_memcpy_generic(
ctx,
copy,
fill_value.into_pointer_value(),
fill_value.get_type().size_of().map(Into::into).unwrap(),
llvm_i1.const_zero(),
);
copy.into()
} else if fill_value.is_int_value() || fill_value.is_float_value() {
fill_value
} else {
unreachable!()
};
Ok(value)
}
)?;
Ok(ndarray)
}
/// LLVM-typed implementation for generating the implementation for `ndarray.eye`.
///
/// * `elem_ty` - The element type of the `NDArray`.
fn call_ndarray_eye_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
nrows: IntValue<'ctx>,
ncols: IntValue<'ctx>,
offset: IntValue<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
let llvm_usize = generator.get_size_type(ctx.ctx);
let llvm_usize_2 = llvm_usize.array_type(2);
let shape_addr = generator.gen_var_alloc(ctx, llvm_usize_2.into(), None)?;
let shape = ctx.builder.build_load(shape_addr, "")
.map(BasicValueEnum::into_array_value)
.unwrap();
let nrows = ctx.builder.build_int_z_extend_or_bit_cast(nrows, llvm_usize, "").unwrap();
let shape = ctx.builder
.build_insert_value(shape, nrows, 0, "")
.map(AggregateValueEnum::into_array_value)
.unwrap();
let ncols = ctx.builder.build_int_z_extend_or_bit_cast(ncols, llvm_usize, "").unwrap();
let shape = ctx.builder
.build_insert_value(shape, ncols, 1, "")
.map(AggregateValueEnum::into_array_value)
.unwrap();
let ndarray = create_ndarray_const_shape(generator, ctx, elem_ty, shape)?;
ndarray_fill_indexed(
generator,
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 col_with_offset = ctx.builder
.build_int_add(
col,
ctx.builder.build_int_s_extend_or_bit_cast(offset, llvm_usize, "").unwrap(),
"",
)
.unwrap();
let is_on_diag = ctx.builder
.build_int_compare(IntPredicate::EQ, row, col_with_offset, "")
.unwrap();
let zero = ndarray_zero_value(generator, ctx, elem_ty);
let one = ndarray_one_value(generator, ctx, elem_ty);
let value = ctx.builder.build_select(is_on_diag, one, zero, "").unwrap();
Ok(value)
},
)?;
Ok(ndarray)
}
/// LLVM-typed implementation for generating the implementation for `ndarray.copy`.
///
/// * `elem_ty` - The element type of the `NDArray`.
fn ndarray_copy_impl<'ctx>(
generator: &mut dyn CodeGenerator,
ctx: &mut CodeGenContext<'ctx, '_>,
elem_ty: Type,
this: NDArrayValue<'ctx>,
) -> Result<NDArrayValue<'ctx>, String> {
let llvm_i1 = ctx.ctx.bool_type();
let ndarray = create_ndarray_dyn_shape(
generator,
ctx,
elem_ty,
&this,
|_, ctx, shape| {
Ok(shape.load_ndims(ctx))
},
|generator, ctx, shape, idx| {
Ok(shape.dim_sizes().get(ctx, generator, idx, None))
},
)?;
let len = call_ndarray_calc_size(
generator,
ctx,
ndarray.load_ndims(ctx),
ndarray.dim_sizes().as_ptr_value(ctx),
);
let sizeof_ty = ctx.get_llvm_type(generator, elem_ty);
let len_bytes = ctx.builder
.build_int_mul(
len,
sizeof_ty.size_of().unwrap(),
"",
)
.unwrap();
call_memcpy_generic(
ctx,
ndarray.data().as_ptr_value(ctx),
this.data().as_ptr_value(ctx),
len_bytes,
llvm_i1.const_zero(),
);
Ok(ndarray)
}
/// Generates LLVM IR for `ndarray.empty`.
pub fn gen_ndarray_empty<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let llvm_usize = generator.get_size_type(context.ctx);
let shape_ty = fun.0.args[0].ty;
let shape_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, shape_ty)?;
call_ndarray_empty_impl(
generator,
context,
context.primitives.float,
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.zeros`.
pub fn gen_ndarray_zeros<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let llvm_usize = generator.get_size_type(context.ctx);
let shape_ty = fun.0.args[0].ty;
let shape_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, shape_ty)?;
call_ndarray_zeros_impl(
generator,
context,
context.primitives.float,
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.ones`.
pub fn gen_ndarray_ones<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let llvm_usize = generator.get_size_type(context.ctx);
let shape_ty = fun.0.args[0].ty;
let shape_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, shape_ty)?;
call_ndarray_ones_impl(
generator,
context,
context.primitives.float,
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.full`.
pub fn gen_ndarray_full<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 2);
let llvm_usize = generator.get_size_type(context.ctx);
let shape_ty = fun.0.args[0].ty;
let shape_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, shape_ty)?;
let fill_value_ty = fun.0.args[1].ty;
let fill_value_arg = args[1].1.clone()
.to_basic_value_enum(context, generator, fill_value_ty)?;
call_ndarray_full_impl(
generator,
context,
fill_value_ty,
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
fill_value_arg,
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.eye`.
pub fn gen_ndarray_eye<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert!(matches!(args.len(), 1..=3));
let nrows_ty = fun.0.args[0].ty;
let nrows_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, nrows_ty)?;
let ncols_ty = fun.0.args[1].ty;
let ncols_arg = args.iter()
.find(|arg| arg.0.is_some_and(|name| name == fun.0.args[1].name))
.map(|arg| arg.1.clone().to_basic_value_enum(context, generator, ncols_ty))
.unwrap_or_else(|| {
args[0].1.clone().to_basic_value_enum(context, generator, nrows_ty)
})?;
let offset_ty = fun.0.args[2].ty;
let offset_arg = args.iter()
.find(|arg| arg.0.is_some_and(|name| name == fun.0.args[2].name))
.map(|arg| arg.1.clone().to_basic_value_enum(context, generator, offset_ty))
.unwrap_or_else(|| {
Ok(context.gen_symbol_val(
generator,
fun.0.args[2].default_value.as_ref().unwrap(),
offset_ty
))
})?;
call_ndarray_eye_impl(
generator,
context,
context.primitives.float,
nrows_arg.into_int_value(),
ncols_arg.into_int_value(),
offset_arg.into_int_value(),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.identity`.
pub fn gen_ndarray_identity<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_none());
assert_eq!(args.len(), 1);
let llvm_usize = generator.get_size_type(context.ctx);
let n_ty = fun.0.args[0].ty;
let n_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, n_ty)?;
call_ndarray_eye_impl(
generator,
context,
context.primitives.float,
n_arg.into_int_value(),
n_arg.into_int_value(),
llvm_usize.const_zero(),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.copy`.
pub fn gen_ndarray_copy<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
_fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<PointerValue<'ctx>, String> {
assert!(obj.is_some());
assert!(args.is_empty());
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_arg = obj
.as_ref()
.unwrap()
.1
.clone()
.to_basic_value_enum(context, generator, this_ty)?;
ndarray_copy_impl(
generator,
context,
this_elem_ty,
NDArrayValue::from_ptr_val(this_arg.into_pointer_value(), llvm_usize, None),
).map(NDArrayValue::into)
}
/// Generates LLVM IR for `ndarray.fill`.
pub fn gen_ndarray_fill<'ctx>(
context: &mut CodeGenContext<'ctx, '_>,
obj: &Option<(Type, ValueEnum<'ctx>)>,
fun: (&FunSignature, DefinitionId),
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
generator: &mut dyn CodeGenerator,
) -> Result<(), String> {
assert!(obj.is_some());
assert_eq!(args.len(), 1);
let llvm_usize = generator.get_size_type(context.ctx);
let this_ty = obj.as_ref().unwrap().0;
let this_arg = obj.as_ref().unwrap().1.clone()
.to_basic_value_enum(context, generator, this_ty)?
.into_pointer_value();
let value_ty = fun.0.args[0].ty;
let value_arg = args[0].1.clone()
.to_basic_value_enum(context, generator, value_ty)?;
ndarray_fill_flattened(
generator,
context,
NDArrayValue::from_ptr_val(this_arg, llvm_usize, None),
|generator, ctx, _| {
let value = if value_arg.is_pointer_value() {
let llvm_i1 = ctx.ctx.bool_type();
let copy = generator.gen_var_alloc(ctx, value_arg.get_type(), None)?;
call_memcpy_generic(
ctx,
copy,
value_arg.into_pointer_value(),
value_arg.get_type().size_of().map(Into::into).unwrap(),
llvm_i1.const_zero(),
);
copy.into()
} else if value_arg.is_int_value() || value_arg.is_float_value() {
value_arg
} else {
unreachable!()
};
Ok(value)
}
)?;
Ok(())
}