forked from M-Labs/nac3
core/ndstrides: add ContiguousNDArray
Currently this is used to interop with nalgebra.
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nac3core/src/codegen/object/ndarray/contiguous.rs
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134
nac3core/src/codegen/object/ndarray/contiguous.rs
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use crate::{
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codegen::{model::*, CodeGenContext, CodeGenerator},
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typecheck::typedef::Type,
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};
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use super::NDArrayObject;
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/// Fields of [`ContiguousNDArray`]
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pub struct ContiguousNDArrayFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>> {
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pub ndims: F::Out<Int<SizeT>>,
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pub shape: F::Out<Ptr<Int<SizeT>>>,
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pub data: F::Out<Ptr<Item>>,
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}
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/// An ndarray without strides and non-opaque `data` field in NAC3.
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#[derive(Debug, Clone, Copy)]
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pub struct ContiguousNDArray<M> {
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/// [`Model`] of the items.
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pub item: M,
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}
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impl<'ctx, Item: Model<'ctx>> StructKind<'ctx> for ContiguousNDArray<Item> {
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type Fields<F: FieldTraversal<'ctx>> = ContiguousNDArrayFields<'ctx, F, Item>;
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fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
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Self::Fields {
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ndims: traversal.add_auto("ndims"),
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shape: traversal.add_auto("shape"),
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data: traversal.add("data", Ptr(self.item)),
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}
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}
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}
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impl<'ctx> NDArrayObject<'ctx> {
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/// Create a [`ContiguousNDArray`] from the contents of this ndarray.
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///
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/// This function may or may not be expensive depending on if this ndarray has contiguous data.
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///
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/// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
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/// the returned [`ContiguousNDArray`] and copy contents of this ndarray to there.
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///
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/// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`ContiguousNDArray`]
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/// will share memory with this ndarray.
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///
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/// The `item_model` sets the [`Model`] of the returned [`ContiguousNDArray`]'s `Item` model for type-safety, and
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/// should match the `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics. Use model [`Any`]
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/// if you don't care/cannot know the [`Model`] in advance.
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pub fn make_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
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&self,
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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item_model: Item,
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) -> Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>> {
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// Sanity check on `self.dtype` and `item_model`.
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let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
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item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
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let cdarray_model = Struct(ContiguousNDArray { item: item_model });
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let current_bb = ctx.builder.get_insert_block().unwrap();
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let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
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let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
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let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
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// Allocate and setup the resulting [`ContiguousNDArray`].
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let result = cdarray_model.alloca(generator, ctx);
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// Set ndims and shape.
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let ndims = self.ndims_llvm(generator, ctx.ctx);
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result.set(ctx, |f| f.ndims, ndims);
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let shape = self.instance.get(generator, ctx, |f| f.shape);
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result.set(ctx, |f| f.shape, shape);
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let is_contiguous = self.is_c_contiguous(generator, ctx);
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ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
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// Inserting into then_bb; This ndarray is contiguous.
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ctx.builder.position_at_end(then_bb);
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let data = self.instance.get(generator, ctx, |f| f.data);
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let data = data.pointer_cast(generator, ctx, item_model);
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result.set(ctx, |f| f.data, data);
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ctx.builder.build_unconditional_branch(end_bb).unwrap();
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// Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
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// `make_copy` produces an ndarray with contiguous `data`.
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ctx.builder.position_at_end(else_bb);
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let copied_ndarray = self.make_copy(generator, ctx);
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let data = copied_ndarray.instance.get(generator, ctx, |f| f.data);
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let data = data.pointer_cast(generator, ctx, item_model);
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result.set(ctx, |f| f.data, data);
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ctx.builder.build_unconditional_branch(end_bb).unwrap();
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// Reposition to end_bb for continuation
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ctx.builder.position_at_end(end_bb);
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result
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}
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/// Create an [`NDArrayObject`] from a [`ContiguousNDArray`].
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///
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/// The operation is super cheap. The newly created [`NDArrayObject`] will share the
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/// same memory as the [`ContiguousNDArray`].
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///
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/// `ndims` has to be provided as [`NDArrayObject`] requires a statically known `ndims` value, despite
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/// the fact that the information should be contained within the [`ContiguousNDArray`].
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pub fn from_contiguous_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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carray: Instance<'ctx, Ptr<Struct<ContiguousNDArray<Item>>>>,
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dtype: Type,
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ndims: u64,
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) -> Self {
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// Sanity check on `dtype` and `contiguous_array`'s `Item` model.
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let dtype_llvm = ctx.get_llvm_type(generator, dtype);
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carray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
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// TODO: Debug assert `ndims == carray.ndims` to catch bugs.
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// Allocate the resulting ndarray.
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let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims);
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// Copy shape and update strides
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let shape = carray.get(generator, ctx, |f| f.shape);
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ndarray.copy_shape_from_array(generator, ctx, shape);
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ndarray.set_strides_contiguous(generator, ctx);
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// Share data
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let data = carray.get(generator, ctx, |f| f.data).pointer_cast(generator, ctx, Int(Byte));
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ndarray.instance.set(ctx, |f| f.data, data);
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ndarray
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}
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}
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@ -1,5 +1,6 @@
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pub mod array;
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pub mod broadcast;
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pub mod contiguous;
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pub mod factory;
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pub mod indexing;
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pub mod map;
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