core/ndstrides: add ContiguousNDArray

Currently this is used to interop with nalgebra.
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lyken 2024-08-20 22:42:04 +08:00
parent 4baf1c64ed
commit 3426e0c9c2
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use crate::{
codegen::{model::*, CodeGenContext, CodeGenerator},
typecheck::typedef::Type,
};
use super::NDArrayObject;
/// Fields of [`ContiguousNDArray`]
pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
pub ndims: F::Output<Int<SizeT>>,
pub shape: F::Output<Ptr<Int<SizeT>>>,
pub data: F::Output<Ptr<Item>>,
}
/// An ndarray without strides and non-opaque `data` field in NAC3.
#[derive(Debug, Clone, Copy)]
pub struct ContiguousNDArray<M> {
/// [`Model`] of the items.
pub item: M,
}
impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
fn iter_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
Self::Fields {
ndims: traversal.add_auto("ndims"),
shape: traversal.add_auto("shape"),
data: traversal.add("data", Ptr(self.item)),
}
}
}
impl<'ctx> NDArrayObject<'ctx> {
/// Create a [`ContiguousNDArray`] from the contents of this ndarray.
///
/// This function may or may not be expensive depending on if this ndarray has contiguous data.
///
/// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
/// the returned [`ContiguousNDArray`] and copy contents of this ndarray to there.
///
/// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`ContiguousNDArray`]
/// will share memory with this ndarray.
///
/// The `item_model` sets the [`Model`] of the returned [`ContiguousNDArray`]'s `Item` model for type-safety, and
/// should match the `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics. Use model [`Any`]
/// if you don't care/cannot know the [`Model`] in advance.
pub fn make_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
&self,
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
item_model: Item,
) -> Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>> {
// Sanity check on `self.dtype` and `item_model`.
let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
let cdarray_model = Struct(ContiguousNDArray { item: item_model });
let current_bb = ctx.builder.get_insert_block().unwrap();
let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
// Allocate and setup the resulting [`ContiguousNDArray`].
let result = cdarray_model.alloca(generator, ctx);
// Set ndims and shape.
let ndims = self.ndims_llvm(generator, ctx.ctx);
result.set(ctx, |f| f.ndims, ndims);
let shape = self.instance.get(generator, ctx, |f| f.shape);
result.set(ctx, |f| f.shape, shape);
let is_contiguous = self.is_c_contiguous(generator, ctx);
ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
// Inserting into then_bb; This ndarray is contiguous.
ctx.builder.position_at_end(then_bb);
let data = self.instance.get(generator, ctx, |f| f.data);
let data = data.pointer_cast(generator, ctx, item_model);
result.set(ctx, |f| f.data, data);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
// `make_copy` produces an ndarray with contiguous `data`.
ctx.builder.position_at_end(else_bb);
let copied_ndarray = self.make_copy(generator, ctx);
let data = copied_ndarray.instance.get(generator, ctx, |f| f.data);
let data = data.pointer_cast(generator, ctx, item_model);
result.set(ctx, |f| f.data, data);
ctx.builder.build_unconditional_branch(end_bb).unwrap();
// Reposition to end_bb for continuation
ctx.builder.position_at_end(end_bb);
result
}
/// Create an [`NDArrayObject`] from a [`ContiguousNDArray`].
///
/// The operation is super cheap. The newly created [`NDArrayObject`] will share the
/// same memory as the [`ContiguousNDArray`].
///
/// `ndims` has to be provided as [`NDArrayObject`] requires a statically known `ndims` value, despite
/// the fact that the information should be contained within the [`ContiguousNDArray`].
pub fn from_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
carray: Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>>,
dtype: Type,
ndims: u64,
) -> Self {
// Sanity check on `dtype` and `contiguous_array`'s `Item` model.
let dtype_llvm = ctx.get_llvm_type(generator, dtype);
carray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
// TODO: Debug assert `ndims == carray.ndims` to catch bugs.
// Allocate the resulting ndarray.
let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
// Copy shape and update strides
let shape = carray.get(generator, ctx, |f| f.shape);
ndarray.copy_shape_from_array(generator, ctx, shape);
ndarray.set_strides_contiguous(generator, ctx);
// Share data
let data = carray.get(generator, ctx, |f| f.data).pointer_cast(generator, ctx, Int(Byte));
ndarray.instance.set(ctx, |f| f.data, data);
ndarray
}
}

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pub mod array;
pub mod broadcast;
pub mod contiguous;
pub mod factory;
pub mod indexing;
pub mod map;