724 lines
24 KiB
Rust
724 lines
24 KiB
Rust
use inkwell::{AddressSpace, IntPredicate, types::BasicType, values::{BasicValueEnum, PointerValue}};
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use inkwell::values::{AggregateValueEnum, ArrayValue, IntValue};
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use nac3parser::ast::StrRef;
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use crate::{
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codegen::{
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classes::{ListValue, NDArrayValue},
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CodeGenContext,
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CodeGenerator,
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irrt::{
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call_ndarray_calc_nd_indices,
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call_ndarray_calc_size,
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call_ndarray_init_dims,
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},
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stmt::gen_for_callback
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},
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symbol_resolver::ValueEnum,
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toplevel::DefinitionId,
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typecheck::typedef::{FunSignature, Type, TypeEnum},
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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 as an LLVM [ArrayValue].
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fn create_ndarray_const_shape<'ctx, 'a>(
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generator: &mut dyn CodeGenerator,
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ctx: &mut CodeGenContext<'ctx, 'a>,
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elem_ty: Type,
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shape: ArrayValue<'ctx>
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) -> Result<NDArrayValue<'ctx>, String> {
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let ndarray_ty_enum = TypeEnum::ndarray(&mut ctx.unifier, Some(elem_ty), None, &ctx.primitives);
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let ndarray_ty = ctx.unifier.add_ty(ndarray_ty_enum);
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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 i in 0..shape.get_type().len() {
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let shape_dim = ctx.builder
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.build_extract_value(shape, i, "")
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.map(BasicValueEnum::into_int_value)
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.unwrap();
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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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}
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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.get_type().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_dims(ctx, llvm_usize, ndarray_num_dims);
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for i in 0..shape.get_type().len() {
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let ndarray_dim = ndarray
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.get_dims()
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.ptr_offset(ctx, generator, llvm_usize.const_int(i as u64, true), None);
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let shape_dim = ctx.builder.build_extract_value(shape, i, "")
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.map(BasicValueEnum::into_int_value)
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.unwrap();
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ctx.builder.build_store(ndarray_dim, shape_dim).unwrap();
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}
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let ndarray_dims = ndarray.get_dims().get_ptr(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.load_ndims(ctx),
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ndarray_dims,
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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, 'a>(
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generator: &mut dyn CodeGenerator,
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ctx: &mut CodeGenContext<'ctx, 'a>,
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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, "").into()
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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, 'a>(
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generator: &mut dyn CodeGenerator,
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ctx: &mut CodeGenContext<'ctx, 'a>,
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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").into()
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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, 'a>(
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generator: &mut dyn CodeGenerator,
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ctx: &mut CodeGenContext<'ctx, 'a>,
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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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let ndarray_ty_enum = TypeEnum::ndarray(&mut ctx.unifier, Some(elem_ty), None, &ctx.primitives);
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let ndarray_ty = ctx.unifier.add_ty(ndarray_ty_enum);
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let llvm_i32 = ctx.ctx.i32_type();
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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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gen_for_callback(
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generator,
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ctx,
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|generator, ctx| {
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let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
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ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
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Ok(i)
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},
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|_, ctx, i_addr| {
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let i = ctx.builder
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.build_load(i_addr, "")
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.map(BasicValueEnum::into_int_value)
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.unwrap();
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let shape_len = shape.load_size(ctx, None);
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Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, shape_len, "").unwrap())
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},
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|generator, ctx, i_addr| {
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let i = ctx.builder
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.build_load(i_addr, "")
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.map(BasicValueEnum::into_int_value)
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.unwrap();
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let shape_dim = shape.get_data().get(ctx, generator, i, None).into_int_value();
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let shape_dim_gez = ctx.builder
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.build_int_compare(IntPredicate::SGE, shape_dim, llvm_i32.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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Ok(())
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},
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|_, ctx, i_addr| {
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let i = ctx.builder
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.build_load(i_addr, "")
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.map(BasicValueEnum::into_int_value)
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.unwrap();
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let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
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ctx.builder.build_store(i_addr, i).unwrap();
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Ok(())
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},
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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.load_size(ctx, None);
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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_dims(ctx, llvm_usize, ndarray_num_dims);
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call_ndarray_init_dims(generator, ctx, ndarray, shape);
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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.load_ndims(ctx),
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ndarray.get_dims().get_ptr(ctx),
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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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/// 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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///
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/// Note that this differs from `ndarray.fill`, which instead replaces all first-dimension elements
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/// with the given value (as opposed to all elements within the array).
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fn ndarray_fill_flattened<'ctx, 'a, ValueFn>(
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generator: &mut dyn CodeGenerator,
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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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ValueFn: Fn(&mut dyn CodeGenerator, &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.load_ndims(ctx),
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ndarray.get_dims().get_ptr(ctx),
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);
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gen_for_callback(
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generator,
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ctx,
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|generator, ctx| {
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let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
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ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
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Ok(i)
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},
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|_, ctx, i_addr| {
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let i = ctx.builder
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.build_load(i_addr, "")
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.map(BasicValueEnum::into_int_value)
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.unwrap();
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Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, ndarray_num_elems, "").unwrap())
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},
|
|
|generator, ctx, i_addr| {
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let i = ctx.builder
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.build_load(i_addr, "")
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.map(BasicValueEnum::into_int_value)
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.unwrap();
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let elem = unsafe {
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ndarray.get_data().ptr_to_data_flattened_unchecked(ctx, 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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|_, ctx, i_addr| {
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let i = ctx.builder
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.build_load(i_addr, "")
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.map(BasicValueEnum::into_int_value)
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.unwrap();
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let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
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ctx.builder.build_store(i_addr, i).unwrap();
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|
Ok(())
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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
|
|
/// as its input
|
|
///
|
|
/// Note that this differs from `ndarray.fill`, which instead replaces all first-dimension elements
|
|
/// with the given value (as opposed to all elements within the array).
|
|
fn ndarray_fill_indexed<'ctx, 'a, ValueFn>(
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generator: &mut dyn CodeGenerator,
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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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ValueFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, PointerValue<'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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|
}
|
|
|
|
/// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
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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_zeros_impl<'ctx, 'a>(
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generator: &mut dyn CodeGenerator,
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ctx: &mut CodeGenContext<'ctx, 'a>,
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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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let supported_types = [
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ctx.primitives.int32,
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ctx.primitives.int64,
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ctx.primitives.uint32,
|
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ctx.primitives.uint64,
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ctx.primitives.float,
|
|
ctx.primitives.bool,
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ctx.primitives.str,
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];
|
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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)?;
|
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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, _| {
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let value = ndarray_zero_value(generator, ctx, elem_ty);
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|
|
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, 'a>(
|
|
generator: &mut dyn CodeGenerator,
|
|
ctx: &mut CodeGenContext<'ctx, 'a>,
|
|
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.ones`.
|
|
///
|
|
/// * `elem_ty` - The element type of the NDArray.
|
|
/// * `shape` - The `shape` parameter used to construct the NDArray.
|
|
fn call_ndarray_full_impl<'ctx, 'a>(
|
|
generator: &mut dyn CodeGenerator,
|
|
ctx: &mut CodeGenContext<'ctx, 'a>,
|
|
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_void = ctx.ctx.void_type();
|
|
let llvm_i1 = ctx.ctx.bool_type();
|
|
let llvm_i8 = ctx.ctx.i8_type();
|
|
let llvm_usize = generator.get_size_type(ctx.ctx);
|
|
let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
|
|
|
|
let copy = generator.gen_var_alloc(ctx, fill_value.get_type(), None)?;
|
|
|
|
let memcpy_fn_name = format!(
|
|
"llvm.memcpy.p0i8.p0i8.i{}",
|
|
generator.get_size_type(ctx.ctx).get_bit_width(),
|
|
);
|
|
let memcpy_fn = ctx.module.get_function(memcpy_fn_name.as_str()).unwrap_or_else(|| {
|
|
let fn_type = llvm_void.fn_type(
|
|
&[
|
|
llvm_pi8.into(),
|
|
llvm_pi8.into(),
|
|
llvm_usize.into(),
|
|
llvm_i1.into(),
|
|
],
|
|
false,
|
|
);
|
|
|
|
ctx.module.add_function(memcpy_fn_name.as_str(), fn_type, None)
|
|
});
|
|
|
|
ctx.builder
|
|
.build_call(
|
|
memcpy_fn,
|
|
&[
|
|
copy.into(),
|
|
fill_value.into(),
|
|
fill_value.get_type().size_of().unwrap().into(),
|
|
llvm_i1.const_zero().into(),
|
|
],
|
|
"",
|
|
)
|
|
.unwrap();
|
|
|
|
copy.into()
|
|
} else if fill_value.is_int_value() || fill_value.is_float_value() {
|
|
fill_value.into()
|
|
} 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, 'a>(
|
|
generator: &mut dyn CodeGenerator,
|
|
ctx: &mut CodeGenContext<'ctx, 'a>,
|
|
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 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_i32.const_zero()],
|
|
None,
|
|
).into_int_value();
|
|
let col = ctx.build_gep_and_load(
|
|
indices,
|
|
&[llvm_i32.const_int(1, true)],
|
|
None,
|
|
).into_int_value();
|
|
|
|
let col_with_offset = ctx.builder
|
|
.build_int_add(
|
|
col,
|
|
ctx.builder.build_int_z_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)
|
|
}
|
|
|
|
/// Generates LLVM IR for `ndarray.empty`.
|
|
pub fn gen_ndarray_empty<'ctx, 'a>(
|
|
context: &mut CodeGenContext<'ctx, 'a>,
|
|
obj: Option<(Type, ValueEnum<'ctx>)>,
|
|
fun: (&FunSignature, DefinitionId),
|
|
args: Vec<(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, 'a>(
|
|
context: &mut CodeGenContext<'ctx, 'a>,
|
|
obj: Option<(Type, ValueEnum<'ctx>)>,
|
|
fun: (&FunSignature, DefinitionId),
|
|
args: Vec<(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, 'a>(
|
|
context: &mut CodeGenContext<'ctx, 'a>,
|
|
obj: Option<(Type, ValueEnum<'ctx>)>,
|
|
fun: (&FunSignature, DefinitionId),
|
|
args: Vec<(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, 'a>(
|
|
context: &mut CodeGenContext<'ctx, 'a>,
|
|
obj: Option<(Type, ValueEnum<'ctx>)>,
|
|
fun: (&FunSignature, DefinitionId),
|
|
args: Vec<(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, 'a>(
|
|
context: &mut CodeGenContext<'ctx, 'a>,
|
|
obj: Option<(Type, ValueEnum<'ctx>)>,
|
|
fun: (&FunSignature, DefinitionId),
|
|
args: Vec<(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.map(|name| name == fun.0.args[1].name).unwrap_or(false))
|
|
.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.map(|name| name == fun.0.args[2].name).unwrap_or(false))
|
|
.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, 'a>(
|
|
context: &mut CodeGenContext<'ctx, 'a>,
|
|
obj: Option<(Type, ValueEnum<'ctx>)>,
|
|
fun: (&FunSignature, DefinitionId),
|
|
args: Vec<(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)
|
|
} |