1396 lines
45 KiB
Rust
1396 lines
45 KiB
Rust
use inkwell::{IntPredicate, OptimizationLevel, types::BasicType, values::{BasicValueEnum, IntValue, PointerValue}};
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use nac3parser::ast::{Operator, StrRef};
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use crate::{
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codegen::{
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classes::{
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ArrayLikeIndexer,
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ArrayLikeValue,
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ListValue,
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NDArrayValue,
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TypedArrayLikeAccessor,
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TypedArrayLikeAdapter,
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UntypedArrayLikeAccessor,
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UntypedArrayLikeMutator,
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},
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CodeGenContext,
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CodeGenerator,
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expr::gen_binop_expr_with_values,
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irrt::{
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call_ndarray_calc_broadcast,
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call_ndarray_calc_broadcast_index,
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call_ndarray_calc_nd_indices,
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call_ndarray_calc_size,
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},
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llvm_intrinsics,
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llvm_intrinsics::{call_memcpy_generic},
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stmt::{gen_for_callback_incrementing, gen_if_else_expr_callback},
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},
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symbol_resolver::ValueEnum,
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toplevel::{
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DefinitionId,
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numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
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},
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typecheck::typedef::{FunSignature, Type},
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};
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/// Creates an `NDArray` instance from a dynamic shape.
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///
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/// * `elem_ty` - The element type of the `NDArray`.
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/// * `shape` - The shape of the `NDArray`.
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/// * `shape_len_fn` - A function that retrieves the number of dimensions from `shape`.
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/// * `shape_data_fn` - A function that retrieves the size of a dimension from `shape`.
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fn create_ndarray_dyn_shape<'ctx, 'a, G, V, LenFn, DataFn>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, 'a>,
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elem_ty: Type,
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shape: &V,
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shape_len_fn: LenFn,
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shape_data_fn: DataFn,
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) -> Result<NDArrayValue<'ctx>, String>
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where
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G: CodeGenerator + ?Sized,
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LenFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>, &V) -> Result<IntValue<'ctx>, String>,
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DataFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>, &V, IntValue<'ctx>) -> Result<IntValue<'ctx>, String>,
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{
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let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
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let llvm_usize = generator.get_size_type(ctx.ctx);
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let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
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let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
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let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
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assert!(llvm_ndarray_data_t.is_sized());
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// Assert that all dimensions are non-negative
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let shape_len = shape_len_fn(generator, ctx, shape)?;
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gen_for_callback_incrementing(
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generator,
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ctx,
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llvm_usize.const_zero(),
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(shape_len, false),
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|generator, ctx, i| {
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let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
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debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
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let shape_dim_gez = ctx.builder
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.build_int_compare(IntPredicate::SGE, shape_dim, shape_dim.get_type().const_zero(), "")
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.unwrap();
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ctx.make_assert(
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generator,
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shape_dim_gez,
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"0:ValueError",
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"negative dimensions not supported",
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[None, None, None],
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ctx.current_loc,
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);
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// TODO: Disallow dim_sz > u32_MAX
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Ok(())
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},
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llvm_usize.const_int(1, false),
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)?;
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let ndarray = generator.gen_var_alloc(
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ctx,
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llvm_ndarray_t.into(),
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None,
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)?;
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let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
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let num_dims = shape_len_fn(generator, ctx, shape)?;
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ndarray.store_ndims(ctx, generator, num_dims);
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let ndarray_num_dims = ndarray.load_ndims(ctx);
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ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
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// Copy the dimension sizes from shape to ndarray.dims
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let shape_len = shape_len_fn(generator, ctx, shape)?;
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gen_for_callback_incrementing(
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generator,
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ctx,
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llvm_usize.const_zero(),
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(shape_len, false),
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|generator, ctx, i| {
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let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
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debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
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let shape_dim = ctx.builder
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.build_int_z_extend(shape_dim, llvm_usize, "")
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.unwrap();
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let ndarray_pdim = unsafe {
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ndarray.dim_sizes().ptr_offset_unchecked(ctx, generator, &i, None)
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};
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ctx.builder.build_store(ndarray_pdim, shape_dim).unwrap();
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Ok(())
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},
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llvm_usize.const_int(1, false),
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)?;
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let ndarray_num_elems = call_ndarray_calc_size(
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generator,
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ctx,
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&ndarray.dim_sizes().as_slice_value(ctx, generator),
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);
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ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
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Ok(ndarray)
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}
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/// Creates an `NDArray` instance from a constant shape.
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///
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/// * `elem_ty` - The element type of the `NDArray`.
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/// * `shape` - The shape of the `NDArray`, represented am array of [`IntValue`]s.
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fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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elem_ty: Type,
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shape: &[IntValue<'ctx>],
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) -> Result<NDArrayValue<'ctx>, String> {
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let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
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let llvm_usize = generator.get_size_type(ctx.ctx);
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let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
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let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
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let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
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assert!(llvm_ndarray_data_t.is_sized());
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for shape_dim in shape {
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let shape_dim_gez = ctx.builder
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.build_int_compare(IntPredicate::SGE, *shape_dim, llvm_usize.const_zero(), "")
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.unwrap();
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ctx.make_assert(
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generator,
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shape_dim_gez,
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"0:ValueError",
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"negative dimensions not supported",
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[None, None, None],
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ctx.current_loc,
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);
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// TODO: Disallow dim_sz > u32_MAX
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}
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let ndarray = generator.gen_var_alloc(
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ctx,
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llvm_ndarray_t.into(),
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None,
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)?;
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let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
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let num_dims = llvm_usize.const_int(shape.len() as u64, false);
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ndarray.store_ndims(ctx, generator, num_dims);
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let ndarray_num_dims = ndarray.load_ndims(ctx);
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ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
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for (i, shape_dim) in shape.iter().enumerate() {
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let ndarray_dim = unsafe {
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ndarray
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.dim_sizes()
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.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_int(i as u64, true), None)
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};
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ctx.builder.build_store(ndarray_dim, *shape_dim).unwrap();
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}
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let ndarray_num_elems = call_ndarray_calc_size(
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generator,
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ctx,
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&ndarray.dim_sizes().as_slice_value(ctx, generator),
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);
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ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
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Ok(ndarray)
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}
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fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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elem_ty: Type,
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) -> BasicValueEnum<'ctx> {
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if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
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ctx.ctx.i32_type().const_zero().into()
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} else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
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ctx.ctx.i64_type().const_zero().into()
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} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
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ctx.ctx.f64_type().const_zero().into()
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} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
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ctx.ctx.bool_type().const_zero().into()
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} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
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ctx.gen_string(generator, "")
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} else {
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unreachable!()
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}
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}
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fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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elem_ty: Type,
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) -> BasicValueEnum<'ctx> {
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if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
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let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
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ctx.ctx.i32_type().const_int(1, is_signed).into()
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} else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
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let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
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ctx.ctx.i64_type().const_int(1, is_signed).into()
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} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
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ctx.ctx.f64_type().const_float(1.0).into()
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} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
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ctx.ctx.bool_type().const_int(1, false).into()
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} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
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ctx.gen_string(generator, "1")
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} else {
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unreachable!()
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}
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}
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/// LLVM-typed implementation for generating the implementation for constructing an `NDArray`.
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///
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/// * `elem_ty` - The element type of the `NDArray`.
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/// * `shape` - The `shape` parameter used to construct the `NDArray`.
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fn call_ndarray_empty_impl<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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elem_ty: Type,
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shape: ListValue<'ctx>,
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) -> Result<NDArrayValue<'ctx>, String> {
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create_ndarray_dyn_shape(
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generator,
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ctx,
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elem_ty,
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&shape,
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|_, ctx, shape| {
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Ok(shape.load_size(ctx, None))
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},
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|generator, ctx, shape, idx| {
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Ok(shape.data().get(ctx, generator, &idx, None).into_int_value())
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},
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)
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}
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/// Generates LLVM IR for populating the entire `NDArray` using a lambda with its flattened index as
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/// its input.
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fn ndarray_fill_flattened<'ctx, 'a, G, ValueFn>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, 'a>,
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ndarray: NDArrayValue<'ctx>,
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value_fn: ValueFn,
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) -> Result<(), String>
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where
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G: CodeGenerator + ?Sized,
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ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
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{
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let llvm_usize = generator.get_size_type(ctx.ctx);
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let ndarray_num_elems = call_ndarray_calc_size(
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generator,
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ctx,
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&ndarray.dim_sizes().as_slice_value(ctx, generator),
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);
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gen_for_callback_incrementing(
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generator,
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ctx,
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llvm_usize.const_zero(),
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(ndarray_num_elems, false),
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|generator, ctx, i| {
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let elem = unsafe {
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ndarray.data().ptr_offset_unchecked(ctx, generator, &i, None)
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};
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let value = value_fn(generator, ctx, i)?;
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ctx.builder.build_store(elem, value).unwrap();
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Ok(())
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},
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llvm_usize.const_int(1, false),
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)
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}
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/// Generates LLVM IR for populating the entire `NDArray` using a lambda with the dimension-indices
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/// as its input.
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fn ndarray_fill_indexed<'ctx, G, ValueFn>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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ndarray: NDArrayValue<'ctx>,
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value_fn: ValueFn,
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) -> Result<(), String>
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where
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G: CodeGenerator + ?Sized,
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ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, &TypedArrayLikeAdapter<'ctx, IntValue<'ctx>>) -> Result<BasicValueEnum<'ctx>, String>,
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{
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ndarray_fill_flattened(
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generator,
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ctx,
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ndarray,
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|generator, ctx, idx| {
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let indices = call_ndarray_calc_nd_indices(
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generator,
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ctx,
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idx,
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ndarray,
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);
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value_fn(generator, ctx, &indices)
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}
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)
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}
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fn ndarray_fill_mapping<'ctx, G, MapFn>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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src: NDArrayValue<'ctx>,
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dest: NDArrayValue<'ctx>,
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map_fn: MapFn,
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) -> Result<(), String>
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where
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G: CodeGenerator + ?Sized,
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MapFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, BasicValueEnum<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
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{
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ndarray_fill_flattened(
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generator,
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ctx,
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dest,
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|generator, ctx, i| {
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let elem = unsafe {
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src.data().get_unchecked(ctx, generator, &i, None)
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};
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map_fn(generator, ctx, elem)
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},
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)
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}
|
|
|
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/// Generates the LLVM IR for checking whether the source `ndarray` can be broadcast to the shape of
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/// the target `ndarray`.
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fn ndarray_assert_is_broadcastable<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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target: NDArrayValue<'ctx>,
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source: NDArrayValue<'ctx>,
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) {
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let array_ndims = source.load_ndims(ctx);
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let broadcast_size = target.load_ndims(ctx);
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|
|
|
ctx.make_assert(
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generator,
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ctx.builder.build_int_compare(IntPredicate::ULE, array_ndims, broadcast_size, "").unwrap(),
|
|
"0:ValueError",
|
|
"operands cannot be broadcast together",
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|
[None, None, None],
|
|
ctx.current_loc,
|
|
);
|
|
}
|
|
|
|
/// Generates the LLVM IR for populating the entire `NDArray` from two `ndarray` or scalar value
|
|
/// with broadcast-compatible shapes.
|
|
fn ndarray_broadcast_fill<'ctx, G, ValueFn>(
|
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generator: &mut G,
|
|
ctx: &mut CodeGenContext<'ctx, '_>,
|
|
res: NDArrayValue<'ctx>,
|
|
lhs: (BasicValueEnum<'ctx>, bool),
|
|
rhs: (BasicValueEnum<'ctx>, bool),
|
|
value_fn: ValueFn,
|
|
) -> Result<NDArrayValue<'ctx>, String>
|
|
where
|
|
G: CodeGenerator + ?Sized,
|
|
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
|
|
{
|
|
let llvm_usize = generator.get_size_type(ctx.ctx);
|
|
|
|
let (lhs_val, lhs_scalar) = lhs;
|
|
let (rhs_val, rhs_scalar) = rhs;
|
|
|
|
assert!(!(lhs_scalar && rhs_scalar),
|
|
"One of the operands must be a ndarray instance: `{}`, `{}`",
|
|
lhs_val.get_type(),
|
|
rhs_val.get_type());
|
|
|
|
// Assert that all ndarray operands are broadcastable to the target size
|
|
if !lhs_scalar {
|
|
let lhs_val = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
|
|
ndarray_assert_is_broadcastable(generator, ctx, res, lhs_val);
|
|
}
|
|
|
|
if !rhs_scalar {
|
|
let rhs_val = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
|
|
ndarray_assert_is_broadcastable(generator, ctx, res, rhs_val);
|
|
}
|
|
|
|
ndarray_fill_indexed(
|
|
generator,
|
|
ctx,
|
|
res,
|
|
|generator, ctx, idx| {
|
|
let lhs_elem = if lhs_scalar {
|
|
lhs_val
|
|
} else {
|
|
let lhs = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
|
|
let lhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, lhs, idx);
|
|
|
|
unsafe {
|
|
lhs.data().get_unchecked(ctx, generator, &lhs_idx, None)
|
|
}
|
|
};
|
|
|
|
let rhs_elem = if rhs_scalar {
|
|
rhs_val
|
|
} else {
|
|
let rhs = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
|
|
let rhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, rhs, idx);
|
|
|
|
unsafe {
|
|
rhs.data().get_unchecked(ctx, generator, &rhs_idx, None)
|
|
}
|
|
};
|
|
|
|
debug_assert_eq!(lhs_elem.get_type(), rhs_elem.get_type());
|
|
|
|
value_fn(generator, ctx, (lhs_elem, rhs_elem))
|
|
},
|
|
)?;
|
|
|
|
Ok(res)
|
|
}
|
|
|
|
/// 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, G: CodeGenerator + ?Sized>(
|
|
generator: &mut G,
|
|
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, G: CodeGenerator + ?Sized>(
|
|
generator: &mut G,
|
|
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, G: CodeGenerator + ?Sized>(
|
|
generator: &mut G,
|
|
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, G: CodeGenerator + ?Sized>(
|
|
generator: &mut G,
|
|
ctx: &mut CodeGenContext<'ctx, '_>,
|
|
elem_ty: Type,
|
|
nrows: IntValue<'ctx>,
|
|
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 nrows = ctx.builder.build_int_z_extend_or_bit_cast(nrows, llvm_usize, "").unwrap();
|
|
let ncols = ctx.builder.build_int_z_extend_or_bit_cast(ncols, llvm_usize, "").unwrap();
|
|
|
|
let ndarray = create_ndarray_const_shape(
|
|
generator,
|
|
ctx,
|
|
elem_ty,
|
|
&[nrows, ncols],
|
|
)?;
|
|
|
|
ndarray_fill_indexed(
|
|
generator,
|
|
ctx,
|
|
ndarray,
|
|
|generator, ctx, indices| {
|
|
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_i32, "").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, G: CodeGenerator + ?Sized>(
|
|
generator: &mut G,
|
|
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| {
|
|
unsafe { Ok(shape.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None)) }
|
|
},
|
|
)?;
|
|
|
|
let len = call_ndarray_calc_size(
|
|
generator,
|
|
ctx,
|
|
&ndarray.dim_sizes().as_slice_value(ctx, generator),
|
|
);
|
|
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().base_ptr(ctx, generator),
|
|
this.data().base_ptr(ctx, generator),
|
|
len_bytes,
|
|
llvm_i1.const_zero(),
|
|
);
|
|
|
|
Ok(ndarray)
|
|
}
|
|
|
|
pub fn ndarray_elementwise_unaryop_impl<'ctx, G, MapFn>(
|
|
generator: &mut G,
|
|
ctx: &mut CodeGenContext<'ctx, '_>,
|
|
elem_ty: Type,
|
|
res: Option<NDArrayValue<'ctx>>,
|
|
operand: NDArrayValue<'ctx>,
|
|
map_fn: MapFn,
|
|
) -> Result<NDArrayValue<'ctx>, String>
|
|
where
|
|
G: CodeGenerator,
|
|
MapFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, BasicValueEnum<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
|
|
{
|
|
let res = res.unwrap_or_else(|| {
|
|
create_ndarray_dyn_shape(
|
|
generator,
|
|
ctx,
|
|
elem_ty,
|
|
&operand,
|
|
|_, ctx, v| {
|
|
Ok(v.load_ndims(ctx))
|
|
},
|
|
|generator, ctx, v, idx| {
|
|
unsafe {
|
|
Ok(v.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None))
|
|
}
|
|
},
|
|
).unwrap()
|
|
});
|
|
|
|
ndarray_fill_mapping(
|
|
generator,
|
|
ctx,
|
|
operand,
|
|
res,
|
|
|generator, ctx, elem| {
|
|
map_fn(generator, ctx, elem)
|
|
}
|
|
)?;
|
|
|
|
Ok(res)
|
|
}
|
|
|
|
/// LLVM-typed implementation for computing elementwise binary operations on two input operands.
|
|
///
|
|
/// If the operand is a `ndarray`, the broadcast index corresponding to each element in the output
|
|
/// is computed, the element accessed and used as an operand of the `value_fn` arguments tuple.
|
|
/// Otherwise, the operand is treated as a scalar value, and is used as an operand of the
|
|
/// `value_fn` arguments tuple for all output elements.
|
|
///
|
|
/// The second element of the tuple indicates whether to treat the operand value as a `ndarray`
|
|
/// (which would be accessed by its broadcast index) or as a scalar value (which would be
|
|
/// broadcast to all elements).
|
|
///
|
|
/// * `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.
|
|
///
|
|
/// # Panic
|
|
///
|
|
/// This function will panic if neither input operands (`lhs` or `rhs`) is a `ndarray`.
|
|
pub fn ndarray_elementwise_binop_impl<'ctx, G, ValueFn>(
|
|
generator: &mut G,
|
|
ctx: &mut CodeGenContext<'ctx, '_>,
|
|
elem_ty: Type,
|
|
res: Option<NDArrayValue<'ctx>>,
|
|
lhs: (BasicValueEnum<'ctx>, bool),
|
|
rhs: (BasicValueEnum<'ctx>, bool),
|
|
value_fn: ValueFn,
|
|
) -> Result<NDArrayValue<'ctx>, String>
|
|
where
|
|
G: CodeGenerator,
|
|
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
|
|
{
|
|
let llvm_usize = generator.get_size_type(ctx.ctx);
|
|
|
|
let (lhs_val, lhs_scalar) = lhs;
|
|
let (rhs_val, rhs_scalar) = rhs;
|
|
|
|
assert!(!(lhs_scalar && rhs_scalar),
|
|
"One of the operands must be a ndarray instance: `{}`, `{}`",
|
|
lhs_val.get_type(),
|
|
rhs_val.get_type());
|
|
|
|
let ndarray = res.unwrap_or_else(|| {
|
|
if lhs_scalar && rhs_scalar {
|
|
let lhs_val = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
|
|
let rhs_val = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
|
|
|
|
let ndarray_dims = call_ndarray_calc_broadcast(generator, ctx, lhs_val, rhs_val);
|
|
|
|
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()
|
|
} else {
|
|
let ndarray = NDArrayValue::from_ptr_val(
|
|
if lhs_scalar { rhs_val } else { lhs_val }.into_pointer_value(),
|
|
llvm_usize,
|
|
None,
|
|
);
|
|
|
|
create_ndarray_dyn_shape(
|
|
generator,
|
|
ctx,
|
|
elem_ty,
|
|
&ndarray,
|
|
|_, ctx, v| {
|
|
Ok(v.load_ndims(ctx))
|
|
},
|
|
|generator, ctx, v, idx| {
|
|
unsafe {
|
|
Ok(v.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None))
|
|
}
|
|
},
|
|
).unwrap()
|
|
}
|
|
});
|
|
|
|
ndarray_broadcast_fill(
|
|
generator,
|
|
ctx,
|
|
ndarray,
|
|
lhs,
|
|
rhs,
|
|
|generator, ctx, elems| {
|
|
value_fn(generator, ctx, elems)
|
|
},
|
|
)?;
|
|
|
|
Ok(ndarray)
|
|
}
|
|
|
|
/// LLVM-typed implementation for computing matrix multiplication between two 2D `ndarray`s.
|
|
///
|
|
/// * `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`.
|
|
pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
|
|
generator: &mut G,
|
|
ctx: &mut CodeGenContext<'ctx, '_>,
|
|
elem_ty: Type,
|
|
res: Option<NDArrayValue<'ctx>>,
|
|
lhs: NDArrayValue<'ctx>,
|
|
rhs: NDArrayValue<'ctx>,
|
|
) -> Result<NDArrayValue<'ctx>, String> {
|
|
let llvm_i32 = ctx.ctx.i32_type();
|
|
let llvm_usize = generator.get_size_type(ctx.ctx);
|
|
|
|
if cfg!(debug_assertions) {
|
|
let lhs_ndims = lhs.load_ndims(ctx);
|
|
let rhs_ndims = rhs.load_ndims(ctx);
|
|
|
|
// lhs.ndims == 2
|
|
ctx.make_assert(
|
|
generator,
|
|
ctx.builder.build_int_compare(
|
|
IntPredicate::EQ,
|
|
lhs_ndims,
|
|
llvm_usize.const_int(2, false),
|
|
"",
|
|
).unwrap(),
|
|
"0:ValueError",
|
|
"",
|
|
[None, None, None],
|
|
ctx.current_loc,
|
|
);
|
|
|
|
// rhs.ndims == 2
|
|
ctx.make_assert(
|
|
generator,
|
|
ctx.builder.build_int_compare(
|
|
IntPredicate::EQ,
|
|
rhs_ndims,
|
|
llvm_usize.const_int(2, false),
|
|
"",
|
|
).unwrap(),
|
|
"0:ValueError",
|
|
"",
|
|
[None, None, None],
|
|
ctx.current_loc,
|
|
);
|
|
|
|
if let Some(res) = res {
|
|
let res_ndims = res.load_ndims(ctx);
|
|
let res_dim0 = unsafe {
|
|
res.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
|
|
};
|
|
let res_dim1 = unsafe {
|
|
res.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
|
|
};
|
|
let lhs_dim0 = unsafe {
|
|
lhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
|
|
};
|
|
let rhs_dim1 = unsafe {
|
|
rhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
|
|
};
|
|
|
|
// res.ndims == 2
|
|
ctx.make_assert(
|
|
generator,
|
|
ctx.builder.build_int_compare(
|
|
IntPredicate::EQ,
|
|
res_ndims,
|
|
llvm_usize.const_int(2, false),
|
|
"",
|
|
).unwrap(),
|
|
"0:ValueError",
|
|
"",
|
|
[None, None, None],
|
|
ctx.current_loc,
|
|
);
|
|
|
|
// res.dims[0] == lhs.dims[0]
|
|
ctx.make_assert(
|
|
generator,
|
|
ctx.builder.build_int_compare(
|
|
IntPredicate::EQ,
|
|
lhs_dim0,
|
|
res_dim0,
|
|
"",
|
|
).unwrap(),
|
|
"0:ValueError",
|
|
"",
|
|
[None, None, None],
|
|
ctx.current_loc,
|
|
);
|
|
|
|
// res.dims[1] == rhs.dims[0]
|
|
ctx.make_assert(
|
|
generator,
|
|
ctx.builder.build_int_compare(
|
|
IntPredicate::EQ,
|
|
rhs_dim1,
|
|
res_dim1,
|
|
"",
|
|
).unwrap(),
|
|
"0:ValueError",
|
|
"",
|
|
[None, None, None],
|
|
ctx.current_loc,
|
|
);
|
|
}
|
|
}
|
|
|
|
if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
|
|
let lhs_dim1 = unsafe {
|
|
lhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
|
|
};
|
|
let rhs_dim0 = unsafe {
|
|
rhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
|
|
};
|
|
|
|
// lhs.dims[1] == rhs.dims[0]
|
|
ctx.make_assert(
|
|
generator,
|
|
ctx.builder.build_int_compare(
|
|
IntPredicate::EQ,
|
|
lhs_dim1,
|
|
rhs_dim0,
|
|
"",
|
|
).unwrap(),
|
|
"0:ValueError",
|
|
"",
|
|
[None, None, None],
|
|
ctx.current_loc,
|
|
);
|
|
}
|
|
|
|
let lhs = if res.is_some_and(|res| res.as_ptr_value() == lhs.as_ptr_value()) {
|
|
ndarray_copy_impl(generator, ctx, elem_ty, lhs)?
|
|
} else {
|
|
lhs
|
|
};
|
|
|
|
let ndarray = res.unwrap_or_else(|| {
|
|
create_ndarray_dyn_shape(
|
|
generator,
|
|
ctx,
|
|
elem_ty,
|
|
&(lhs, rhs),
|
|
|_, _, _| {
|
|
Ok(llvm_usize.const_int(2, false))
|
|
},
|
|
|generator, ctx, (lhs, rhs), idx| {
|
|
gen_if_else_expr_callback(
|
|
generator,
|
|
ctx,
|
|
|_, ctx| {
|
|
Ok(ctx.builder.build_int_compare(
|
|
IntPredicate::EQ,
|
|
idx,
|
|
llvm_usize.const_zero(),
|
|
"",
|
|
).unwrap())
|
|
},
|
|
|generator, ctx| {
|
|
Ok(Some(unsafe {
|
|
lhs.dim_sizes().get_typed_unchecked(
|
|
ctx,
|
|
generator,
|
|
&llvm_usize.const_zero(),
|
|
None,
|
|
)
|
|
}))
|
|
},
|
|
|generator, ctx| {
|
|
Ok(Some(unsafe {
|
|
rhs.dim_sizes().get_typed_unchecked(
|
|
ctx,
|
|
generator,
|
|
&llvm_usize.const_int(1, false),
|
|
None,
|
|
)
|
|
}))
|
|
},
|
|
).map(|v| v.map(BasicValueEnum::into_int_value).unwrap())
|
|
},
|
|
).unwrap()
|
|
});
|
|
|
|
let llvm_ndarray_ty = ctx.get_llvm_type(generator, elem_ty);
|
|
|
|
ndarray_fill_indexed(
|
|
generator,
|
|
ctx,
|
|
ndarray,
|
|
|generator, ctx, idx| {
|
|
llvm_intrinsics::call_expect(
|
|
ctx,
|
|
idx.size(ctx, generator).get_type().const_int(2, false),
|
|
idx.size(ctx, generator),
|
|
None,
|
|
);
|
|
|
|
let common_dim = {
|
|
let lhs_idx1 = unsafe {
|
|
lhs.dim_sizes().get_typed_unchecked(
|
|
ctx,
|
|
generator,
|
|
&llvm_usize.const_int(1, false),
|
|
None,
|
|
)
|
|
};
|
|
let rhs_idx0 = unsafe {
|
|
rhs.dim_sizes().get_typed_unchecked(
|
|
ctx,
|
|
generator,
|
|
&llvm_usize.const_zero(),
|
|
None,
|
|
)
|
|
};
|
|
|
|
let idx = llvm_intrinsics::call_expect(ctx, rhs_idx0, lhs_idx1, None);
|
|
|
|
ctx.builder.build_int_truncate(idx, llvm_i32, "").unwrap()
|
|
};
|
|
|
|
let idx0 = unsafe {
|
|
let idx0 = idx.get_typed_unchecked(
|
|
ctx,
|
|
generator,
|
|
&llvm_usize.const_zero(),
|
|
None,
|
|
);
|
|
|
|
ctx.builder.build_int_truncate(idx0, llvm_i32, "").unwrap()
|
|
};
|
|
let idx1 = unsafe {
|
|
let idx1 = idx.get_typed_unchecked(
|
|
ctx,
|
|
generator,
|
|
&llvm_usize.const_int(1, false),
|
|
None,
|
|
);
|
|
|
|
ctx.builder.build_int_truncate(idx1, llvm_i32, "").unwrap()
|
|
};
|
|
|
|
let result_addr = generator.gen_var_alloc(ctx, llvm_ndarray_ty, None)?;
|
|
let result_identity = ndarray_zero_value(generator, ctx, elem_ty);
|
|
ctx.builder.build_store(result_addr, result_identity).unwrap();
|
|
|
|
gen_for_callback_incrementing(
|
|
generator,
|
|
ctx,
|
|
llvm_i32.const_zero(),
|
|
(common_dim, false),
|
|
|generator, ctx, i| {
|
|
let i = ctx.builder.build_int_truncate(i, llvm_i32, "").unwrap();
|
|
|
|
let ab_idx = generator.gen_array_var_alloc(
|
|
ctx,
|
|
llvm_i32.into(),
|
|
llvm_usize.const_int(2, false),
|
|
None,
|
|
)?;
|
|
|
|
let a = unsafe {
|
|
ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_zero(), idx0.into());
|
|
ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_int(1, false), i.into());
|
|
|
|
lhs.data().get_unchecked(ctx, generator, &ab_idx, None)
|
|
};
|
|
let b = unsafe {
|
|
ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_zero(), i.into());
|
|
ab_idx.set_unchecked(ctx, generator, &llvm_usize.const_int(1, false), idx1.into());
|
|
|
|
rhs.data().get_unchecked(ctx, generator, &ab_idx, None)
|
|
};
|
|
|
|
let a_mul_b = gen_binop_expr_with_values(
|
|
generator,
|
|
ctx,
|
|
(&Some(elem_ty), a),
|
|
&Operator::Mult,
|
|
(&Some(elem_ty), b),
|
|
ctx.current_loc,
|
|
false,
|
|
)?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)?;
|
|
|
|
let result = ctx.builder.build_load(result_addr, "").unwrap();
|
|
let result = gen_binop_expr_with_values(
|
|
generator,
|
|
ctx,
|
|
(&Some(elem_ty), result),
|
|
&Operator::Add,
|
|
(&Some(elem_ty), a_mul_b),
|
|
ctx.current_loc,
|
|
false,
|
|
)?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)?;
|
|
ctx.builder.build_store(result_addr, result).unwrap();
|
|
|
|
Ok(())
|
|
},
|
|
llvm_usize.const_int(1, false),
|
|
)?;
|
|
|
|
let result = ctx.builder.build_load(result_addr, "").unwrap();
|
|
Ok(result)
|
|
}
|
|
)?;
|
|
|
|
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 = if let Some(arg) =
|
|
args.iter().find(|arg| arg.0.is_some_and(|name| name == fun.0.args[1].name)) {
|
|
arg.1.clone().to_basic_value_enum(context, generator, ncols_ty)
|
|
} else {
|
|
args[0].1.clone().to_basic_value_enum(context, generator, nrows_ty)
|
|
}?;
|
|
|
|
let offset_ty = fun.0.args[2].ty;
|
|
let offset_arg = if let Some(arg) =
|
|
args.iter().find(|arg| arg.0.is_some_and(|name| name == fun.0.args[2].name)) {
|
|
arg.1.clone().to_basic_value_enum(context, generator, offset_ty)
|
|
} 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_var_tys(&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(())
|
|
} |