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7 Commits
f7fbc629aa
...
eb8faaece6
Author | SHA1 | Date |
---|---|---|
David Mak | eb8faaece6 | |
David Mak | 2c4bf3ce59 | |
David Mak | e980f19c93 | |
David Mak | cfbc37c1ed | |
David Mak | 50264e8750 | |
David Mak | 1b77e62901 | |
David Mak | fd44ee6887 |
|
@ -21,7 +21,7 @@ fn main() {
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match env::var("PROFILE").as_deref() {
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Ok("debug") => "-O0",
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Ok("release") => "-O3",
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flavor => panic!("Unknown or missing build flavor {:?}", flavor),
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flavor => panic!("Unknown or missing build flavor {flavor:?}"),
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},
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"-emit-llvm",
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"-S",
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File diff suppressed because it is too large
Load Diff
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@ -2,7 +2,14 @@ use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
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use crate::{
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codegen::{
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classes::{ListValue, NDArrayValue, RangeValue},
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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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RangeValue,
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UntypedArrayLikeAccessor,
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},
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concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
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gen_in_range_check,
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get_llvm_type,
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@ -103,9 +110,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
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index
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}
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pub fn gen_symbol_val(
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pub fn gen_symbol_val<G: CodeGenerator + ?Sized>(
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&mut self,
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generator: &mut dyn CodeGenerator,
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generator: &mut G,
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val: &SymbolValue,
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ty: Type,
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) -> BasicValueEnum<'ctx> {
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@ -174,9 +181,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
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}
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/// See [`get_llvm_type`].
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pub fn get_llvm_type(
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pub fn get_llvm_type<G: CodeGenerator + ?Sized>(
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&mut self,
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generator: &mut dyn CodeGenerator,
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generator: &mut G,
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ty: Type,
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) -> BasicTypeEnum<'ctx> {
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get_llvm_type(
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@ -191,9 +198,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
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}
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/// See [`get_llvm_abi_type`].
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pub fn get_llvm_abi_type(
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pub fn get_llvm_abi_type<G: CodeGenerator + ?Sized>(
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&mut self,
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generator: &mut dyn CodeGenerator,
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generator: &mut G,
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ty: Type,
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) -> BasicTypeEnum<'ctx> {
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get_llvm_abi_type(
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@ -209,9 +216,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
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}
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/// Generates an LLVM variable for a [constant value][value] with a given [type][ty].
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pub fn gen_const(
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pub fn gen_const<G: CodeGenerator + ?Sized>(
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&mut self,
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generator: &mut dyn CodeGenerator,
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generator: &mut G,
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value: &Constant,
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ty: Type,
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) -> Option<BasicValueEnum<'ctx>> {
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@ -291,9 +298,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
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}
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/// Generates a binary operation `op` between two integral operands `lhs` and `rhs`.
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pub fn gen_int_ops(
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pub fn gen_int_ops<G: CodeGenerator + ?Sized>(
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&mut self,
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generator: &mut dyn CodeGenerator,
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generator: &mut G,
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op: &Operator,
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lhs: BasicValueEnum<'ctx>,
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rhs: BasicValueEnum<'ctx>,
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@ -492,17 +499,21 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
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}
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/// Helper function for generating a LLVM variable storing a [String].
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pub fn gen_string<S: Into<String>>(
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pub fn gen_string<G, S>(
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&mut self,
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generator: &mut dyn CodeGenerator,
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generator: &mut G,
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s: S,
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) -> BasicValueEnum<'ctx> {
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) -> BasicValueEnum<'ctx>
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where
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G: CodeGenerator + ?Sized,
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S: Into<String>,
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{
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self.gen_const(generator, &Constant::Str(s.into()), self.primitives.str).unwrap()
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}
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pub fn raise_exn(
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pub fn raise_exn<G: CodeGenerator + ?Sized>(
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&mut self,
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generator: &mut dyn CodeGenerator,
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generator: &mut G,
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name: &str,
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msg: BasicValueEnum<'ctx>,
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params: [Option<IntValue<'ctx>>; 3],
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|
@ -546,9 +557,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
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gen_raise(generator, self, Some(&zelf.into()), loc);
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}
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pub fn make_assert(
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pub fn make_assert<G: CodeGenerator + ?Sized>(
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&mut self,
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generator: &mut dyn CodeGenerator,
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generator: &mut G,
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cond: IntValue<'ctx>,
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err_name: &str,
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err_msg: &str,
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|
@ -559,9 +570,9 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
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self.make_assert_impl(generator, cond, err_name, err_msg, params, loc);
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}
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pub fn make_assert_impl(
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pub fn make_assert_impl<G: CodeGenerator + ?Sized>(
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&mut self,
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generator: &mut dyn CodeGenerator,
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generator: &mut G,
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cond: IntValue<'ctx>,
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err_name: &str,
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err_msg: BasicValueEnum<'ctx>,
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|
@ -878,7 +889,7 @@ pub fn destructure_range<'ctx>(
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/// Returns an instance of [`PointerValue`] pointing to the List structure. The List structure is
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/// defined as `type { ty*, size_t }` in LLVM, where the first element stores the pointer to the
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/// data, and the second element stores the size of the List.
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pub fn allocate_list<'ctx, G: CodeGenerator>(
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pub fn allocate_list<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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ty: BasicTypeEnum<'ctx>,
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@ -978,7 +989,7 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
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list_alloc_size.into_int_value(),
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Some("listcomp.addr")
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);
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list_content = list.data().as_ptr_value(ctx);
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list_content = list.data().base_ptr(ctx, generator);
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let i = generator.gen_store_target(ctx, target, Some("i.addr"))?.unwrap();
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ctx.builder
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|
@ -1011,7 +1022,7 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
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)
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.into_int_value();
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list = allocate_list(generator, ctx, elem_ty, length, Some("listcomp"));
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list_content = list.data().as_ptr_value(ctx);
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list_content = list.data().base_ptr(ctx, generator);
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let counter = generator.gen_var_alloc(ctx, size_t.into(), Some("counter.addr"))?;
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// counter = -1
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ctx.builder.build_store(counter, size_t.const_int(u64::MAX, true)).unwrap();
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@ -1256,7 +1267,7 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
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};
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Ok(Some(v.data()
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.get_const(
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.get(
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ctx,
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generator,
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ctx.ctx.i32_type().const_array(&[index]),
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@ -1300,15 +1311,17 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
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ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
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let ndarray_num_dims = ndarray.load_ndims(ctx);
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let v_dims_src_ptr = v.dim_sizes().ptr_offset(
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let v_dims_src_ptr = unsafe {
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v.dim_sizes().ptr_offset_unchecked(
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ctx,
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generator,
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llvm_usize.const_int(1, false),
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None,
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);
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)
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};
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call_memcpy_generic(
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ctx,
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ndarray.dim_sizes().as_ptr_value(ctx),
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ndarray.dim_sizes().base_ptr(ctx, generator),
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v_dims_src_ptr,
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ctx.builder
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.build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
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@ -1320,12 +1333,11 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
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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.dim_sizes().as_ptr_value(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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let v_data_src_ptr = v.data().ptr_offset_const(
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let v_data_src_ptr = v.data().ptr_offset(
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ctx,
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generator,
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ctx.ctx.i32_type().const_array(&[index]),
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@ -1333,7 +1345,7 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
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);
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call_memcpy_generic(
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ctx,
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ndarray.data().as_ptr_value(ctx),
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ndarray.data().base_ptr(ctx, generator),
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v_data_src_ptr,
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ctx.builder
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.build_int_mul(ndarray_num_elems, llvm_ndarray_data_t.size_of().unwrap(), "")
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|
|
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@ -1,5 +1,5 @@
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use crate::{
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codegen::{expr::*, stmt::*, bool_to_i1, bool_to_i8, CodeGenContext},
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codegen::{classes::ArraySliceValue, expr::*, stmt::*, bool_to_i1, bool_to_i8, CodeGenContext},
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symbol_resolver::ValueEnum,
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toplevel::{DefinitionId, TopLevelDef},
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typecheck::typedef::{FunSignature, Type},
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|
@ -99,8 +99,8 @@ pub trait CodeGenerator {
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ctx: &mut CodeGenContext<'ctx, '_>,
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ty: BasicTypeEnum<'ctx>,
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size: IntValue<'ctx>,
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name: Option<&str>,
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) -> Result<PointerValue<'ctx>, String> {
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name: Option<&'ctx str>,
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) -> Result<ArraySliceValue<'ctx>, String> {
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gen_array_var(ctx, ty, size, name)
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}
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|
|
|
@ -1,7 +1,7 @@
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use crate::typecheck::typedef::Type;
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use super::{
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classes::{ListValue, NDArrayValue},
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classes::{ArrayLikeIndexer, ArrayLikeValue, ListValue, NDArrayValue, UntypedArrayLikeMutator},
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CodeGenContext,
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CodeGenerator,
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};
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|
@ -39,8 +39,8 @@ pub fn load_irrt(ctx: &Context) -> Module {
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// repeated squaring method adapted from GNU Scientific Library:
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// https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
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pub fn integer_power<'ctx>(
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generator: &mut dyn CodeGenerator,
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pub fn integer_power<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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base: IntValue<'ctx>,
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exp: IntValue<'ctx>,
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|
@ -81,8 +81,8 @@ pub fn integer_power<'ctx>(
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.unwrap()
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}
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pub fn calculate_len_for_slice_range<'ctx>(
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generator: &mut dyn CodeGenerator,
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pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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start: IntValue<'ctx>,
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end: IntValue<'ctx>,
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|
@ -303,8 +303,8 @@ pub fn handle_slice_index_bound<'ctx, G: CodeGenerator>(
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/// This function handles 'end' **inclusively**.
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/// Order of tuples `assign_idx` and `value_idx` is ('start', 'end', 'step').
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/// Negative index should be handled before entering this function
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pub fn list_slice_assignment<'ctx>(
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generator: &mut dyn CodeGenerator,
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pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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ty: BasicTypeEnum<'ctx>,
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dest_arr: ListValue<'ctx>,
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|
@ -338,7 +338,7 @@ pub fn list_slice_assignment<'ctx>(
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let zero = int32.const_zero();
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let one = int32.const_int(1, false);
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let dest_arr_ptr = dest_arr.data().as_ptr_value(ctx);
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let dest_arr_ptr = dest_arr.data().base_ptr(ctx, generator);
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let dest_arr_ptr = ctx.builder.build_pointer_cast(
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dest_arr_ptr,
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elem_ptr_type,
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|
@ -346,7 +346,7 @@ pub fn list_slice_assignment<'ctx>(
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|||
).unwrap();
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let dest_len = dest_arr.load_size(ctx, Some("dest.len"));
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let dest_len = ctx.builder.build_int_truncate_or_bit_cast(dest_len, int32, "srclen32").unwrap();
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let src_arr_ptr = src_arr.data().as_ptr_value(ctx);
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let src_arr_ptr = src_arr.data().base_ptr(ctx, generator);
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let src_arr_ptr = ctx.builder.build_pointer_cast(
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src_arr_ptr,
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elem_ptr_type,
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|
@ -468,8 +468,8 @@ pub fn list_slice_assignment<'ctx>(
|
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}
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|
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/// Generates a call to `isinf` in IR. Returns an `i1` representing the result.
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pub fn call_isinf<'ctx>(
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generator: &mut dyn CodeGenerator,
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pub fn call_isinf<'ctx, G: CodeGenerator + ?Sized>(
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generator: &mut G,
|
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ctx: &CodeGenContext<'ctx, '_>,
|
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v: FloatValue<'ctx>,
|
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) -> IntValue<'ctx> {
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||||
|
@ -489,8 +489,8 @@ pub fn call_isinf<'ctx>(
|
|||
}
|
||||
|
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/// Generates a call to `isnan` in IR. Returns an `i1` representing the result.
|
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pub fn call_isnan<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
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pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
v: FloatValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
|
@ -574,12 +574,14 @@ pub fn call_j0<'ctx>(
|
|||
///
|
||||
/// * `num_dims` - An [`IntValue`] containing the number of dimensions.
|
||||
/// * `dims` - A [`PointerValue`] to an array containing the size of each dimension.
|
||||
pub fn call_ndarray_calc_size<'ctx>(
|
||||
generator: &dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
num_dims: IntValue<'ctx>,
|
||||
dims: PointerValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
pub fn call_ndarray_calc_size<'ctx, G, Dims>(
|
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generator: &G,
|
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ctx: &CodeGenContext<'ctx, '_>,
|
||||
dims: &Dims,
|
||||
) -> IntValue<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Dims: ArrayLikeIndexer<'ctx>, {
|
||||
let llvm_i64 = ctx.ctx.i64_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
|
@ -606,8 +608,8 @@ pub fn call_ndarray_calc_size<'ctx>(
|
|||
.build_call(
|
||||
ndarray_calc_size_fn,
|
||||
&[
|
||||
dims.into(),
|
||||
num_dims.into(),
|
||||
dims.base_ptr(ctx, generator).into(),
|
||||
dims.size(ctx, generator).into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
|
@ -622,8 +624,8 @@ pub fn call_ndarray_calc_size<'ctx>(
|
|||
/// * `index` - The index to compute the multidimensional index for.
|
||||
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
|
||||
/// `NDArray`.
|
||||
pub fn call_ndarray_calc_nd_indices<'ctx>(
|
||||
generator: &dyn CodeGenerator,
|
||||
pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
index: IntValue<'ctx>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
|
@ -666,7 +668,7 @@ pub fn call_ndarray_calc_nd_indices<'ctx>(
|
|||
ndarray_calc_nd_indices_fn,
|
||||
&[
|
||||
index.into(),
|
||||
ndarray_dims.as_ptr_value(ctx).into(),
|
||||
ndarray_dims.base_ptr(ctx, generator).into(),
|
||||
ndarray_num_dims.into(),
|
||||
indices.into(),
|
||||
],
|
||||
|
@ -677,13 +679,15 @@ pub fn call_ndarray_calc_nd_indices<'ctx>(
|
|||
indices
|
||||
}
|
||||
|
||||
fn call_ndarray_flatten_index_impl<'ctx>(
|
||||
generator: &dyn CodeGenerator,
|
||||
fn call_ndarray_flatten_index_impl<'ctx, G, Indices>(
|
||||
generator: &G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
indices: PointerValue<'ctx>,
|
||||
indices_size: IntValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
indices: &Indices,
|
||||
) -> IntValue<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Indices: ArrayLikeIndexer<'ctx>, {
|
||||
let llvm_i32 = ctx.ctx.i32_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
|
@ -691,14 +695,14 @@ fn call_ndarray_flatten_index_impl<'ctx>(
|
|||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
debug_assert_eq!(
|
||||
IntType::try_from(indices.get_type().get_element_type())
|
||||
IntType::try_from(indices.element_type(ctx, generator))
|
||||
.map(IntType::get_bit_width)
|
||||
.unwrap_or_default(),
|
||||
llvm_i32.get_bit_width(),
|
||||
"Expected i32 value for argument `indices` to `call_ndarray_flatten_index_impl`"
|
||||
);
|
||||
debug_assert_eq!(
|
||||
indices_size.get_type().get_bit_width(),
|
||||
indices.size(ctx, generator).get_type().get_bit_width(),
|
||||
llvm_usize.get_bit_width(),
|
||||
"Expected usize integer value for argument `indices_size` to `call_ndarray_flatten_index_impl`"
|
||||
);
|
||||
|
@ -729,10 +733,10 @@ fn call_ndarray_flatten_index_impl<'ctx>(
|
|||
.build_call(
|
||||
ndarray_flatten_index_fn,
|
||||
&[
|
||||
ndarray_dims.as_ptr_value(ctx).into(),
|
||||
ndarray_dims.base_ptr(ctx, generator).into(),
|
||||
ndarray_num_dims.into(),
|
||||
indices.into(),
|
||||
indices_size.into(),
|
||||
indices.base_ptr(ctx, generator).into(),
|
||||
indices.size(ctx, generator).into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
|
@ -750,21 +754,21 @@ fn call_ndarray_flatten_index_impl<'ctx>(
|
|||
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
|
||||
/// `NDArray`.
|
||||
/// * `indices` - The multidimensional index to compute the flattened index for.
|
||||
pub fn call_ndarray_flatten_index<'ctx>(
|
||||
generator: &dyn CodeGenerator,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
pub fn call_ndarray_flatten_index<'ctx, G, Index>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
indices: ListValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
let indices_size = indices.load_size(ctx, None);
|
||||
let indices_data = indices.data();
|
||||
indices: &Index,
|
||||
) -> IntValue<'ctx>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
Index: ArrayLikeIndexer<'ctx>, {
|
||||
|
||||
call_ndarray_flatten_index_impl(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
indices_data.as_ptr_value(ctx),
|
||||
indices_size,
|
||||
indices,
|
||||
)
|
||||
}
|
||||
/// Generates a call to `__nac3_ndarray_flatten_index`. Returns the flattened index for the
|
||||
|
@ -773,8 +777,8 @@ pub fn call_ndarray_flatten_index<'ctx>(
|
|||
/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
|
||||
/// `NDArray`.
|
||||
/// * `indices` - The multidimensional index to compute the flattened index for.
|
||||
pub fn call_ndarray_flatten_index_const<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
pub fn call_ndarray_flatten_index_const<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
indices: ArrayValue<'ctx>,
|
||||
|
@ -786,27 +790,27 @@ pub fn call_ndarray_flatten_index_const<'ctx>(
|
|||
ctx,
|
||||
indices.get_type().get_element_type(),
|
||||
llvm_usize.const_int(indices_size as u64, false),
|
||||
None
|
||||
None,
|
||||
).unwrap();
|
||||
for i in 0..indices_size {
|
||||
let v = ctx.builder.build_extract_value(indices, i, "")
|
||||
.unwrap()
|
||||
.into_int_value();
|
||||
let elem_ptr = unsafe {
|
||||
ctx.builder.build_in_bounds_gep(
|
||||
indices_alloca,
|
||||
&[ctx.ctx.i32_type().const_int(i as u64, false)],
|
||||
""
|
||||
)
|
||||
}.unwrap();
|
||||
ctx.builder.build_store(elem_ptr, v).unwrap();
|
||||
|
||||
unsafe {
|
||||
indices_alloca.set_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.ctx.i32_type().const_int(i as u64, false),
|
||||
v.into(),
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
call_ndarray_flatten_index_impl(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
indices_alloca,
|
||||
llvm_usize.const_int(indices_size as u64, false),
|
||||
&indices_alloca,
|
||||
)
|
||||
}
|
|
@ -45,6 +45,7 @@ pub mod expr;
|
|||
mod generator;
|
||||
pub mod irrt;
|
||||
pub mod llvm_intrinsics;
|
||||
pub mod numpy;
|
||||
pub mod stmt;
|
||||
|
||||
#[cfg(test)]
|
||||
|
@ -415,10 +416,10 @@ pub struct CodeGenTask {
|
|||
/// This function is used to obtain the in-memory representation of `ty`, e.g. a `bool` variable
|
||||
/// would be represented by an `i8`.
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
fn get_llvm_type<'ctx>(
|
||||
fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
|
||||
ctx: &'ctx Context,
|
||||
module: &Module<'ctx>,
|
||||
generator: &mut dyn CodeGenerator,
|
||||
generator: &mut G,
|
||||
unifier: &mut Unifier,
|
||||
top_level: &TopLevelContext,
|
||||
type_cache: &mut HashMap<Type, BasicTypeEnum<'ctx>>,
|
||||
|
@ -553,10 +554,10 @@ fn get_llvm_type<'ctx>(
|
|||
/// be byte-aligned for the variable to be addressable in memory, whereas there is no such
|
||||
/// restriction for ABI representations.
|
||||
#[allow(clippy::too_many_arguments)]
|
||||
fn get_llvm_abi_type<'ctx>(
|
||||
fn get_llvm_abi_type<'ctx, G: CodeGenerator + ?Sized>(
|
||||
ctx: &'ctx Context,
|
||||
module: &Module<'ctx>,
|
||||
generator: &mut dyn CodeGenerator,
|
||||
generator: &mut G,
|
||||
unifier: &mut Unifier,
|
||||
top_level: &TopLevelContext,
|
||||
type_cache: &mut HashMap<Type, BasicTypeEnum<'ctx>>,
|
||||
|
|
|
@ -0,0 +1,837 @@
|
|||
use inkwell::{
|
||||
IntPredicate,
|
||||
types::BasicType,
|
||||
values::{AggregateValueEnum, ArrayValue, BasicValueEnum, IntValue, PointerValue}
|
||||
};
|
||||
use nac3parser::ast::StrRef;
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{
|
||||
ArrayLikeIndexer,
|
||||
ArrayLikeValue,
|
||||
ListValue,
|
||||
NDArrayValue,
|
||||
TypedArrayLikeAccessor,
|
||||
UntypedArrayLikeAccessor,
|
||||
},
|
||||
CodeGenContext,
|
||||
CodeGenerator,
|
||||
irrt::{
|
||||
call_ndarray_calc_nd_indices,
|
||||
call_ndarray_calc_size,
|
||||
},
|
||||
llvm_intrinsics::call_memcpy_generic,
|
||||
stmt::gen_for_callback_incrementing,
|
||||
},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{
|
||||
DefinitionId,
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_tvars},
|
||||
},
|
||||
typecheck::typedef::{FunSignature, Type},
|
||||
};
|
||||
|
||||
/// Creates an `NDArray` instance from a dynamic shape.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The shape of the `NDArray`.
|
||||
/// * `shape_len_fn` - A function that retrieves the number of dimensions from `shape`.
|
||||
/// * `shape_data_fn` - A function that retrieves the size of a dimension from `shape`.
|
||||
fn create_ndarray_dyn_shape<'ctx, 'a, G, V, LenFn, DataFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
elem_ty: Type,
|
||||
shape: &V,
|
||||
shape_len_fn: LenFn,
|
||||
shape_data_fn: DataFn,
|
||||
) -> Result<NDArrayValue<'ctx>, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
LenFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>, &V) -> Result<IntValue<'ctx>, String>,
|
||||
DataFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>, &V, IntValue<'ctx>) -> Result<IntValue<'ctx>, String>,
|
||||
{
|
||||
let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
|
||||
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
|
||||
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
|
||||
assert!(llvm_ndarray_data_t.is_sized());
|
||||
|
||||
// Assert that all dimensions are non-negative
|
||||
let shape_len = shape_len_fn(generator, ctx, shape)?;
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
llvm_usize.const_zero(),
|
||||
(shape_len, false),
|
||||
|generator, ctx, i| {
|
||||
let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
|
||||
debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
|
||||
|
||||
let shape_dim_gez = ctx.builder
|
||||
.build_int_compare(IntPredicate::SGE, shape_dim, shape_dim.get_type().const_zero(), "")
|
||||
.unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
shape_dim_gez,
|
||||
"0:ValueError",
|
||||
"negative dimensions not supported",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
|
||||
let ndarray = generator.gen_var_alloc(
|
||||
ctx,
|
||||
llvm_ndarray_t.into(),
|
||||
None,
|
||||
)?;
|
||||
let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
|
||||
|
||||
let num_dims = shape_len_fn(generator, ctx, shape)?;
|
||||
ndarray.store_ndims(ctx, generator, num_dims);
|
||||
|
||||
let ndarray_num_dims = ndarray.load_ndims(ctx);
|
||||
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
|
||||
|
||||
// Copy the dimension sizes from shape to ndarray.dims
|
||||
let shape_len = shape_len_fn(generator, ctx, shape)?;
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
llvm_usize.const_zero(),
|
||||
(shape_len, false),
|
||||
|generator, ctx, i| {
|
||||
let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
|
||||
debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
|
||||
let shape_dim = ctx.builder
|
||||
.build_int_z_extend(shape_dim, llvm_usize, "")
|
||||
.unwrap();
|
||||
|
||||
let ndarray_pdim = unsafe {
|
||||
ndarray.dim_sizes().ptr_offset_unchecked(ctx, generator, i, None)
|
||||
};
|
||||
|
||||
ctx.builder.build_store(ndarray_pdim, shape_dim).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)?;
|
||||
|
||||
let ndarray_num_elems = call_ndarray_calc_size(
|
||||
generator,
|
||||
ctx,
|
||||
&ndarray.dim_sizes().as_slice_value(ctx, generator),
|
||||
);
|
||||
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// Creates an `NDArray` instance from a constant shape.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The shape of the `NDArray`, represented as an LLVM [`ArrayValue`].
|
||||
fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: ArrayValue<'ctx>
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
|
||||
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
|
||||
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
|
||||
assert!(llvm_ndarray_data_t.is_sized());
|
||||
|
||||
for i in 0..shape.get_type().len() {
|
||||
let shape_dim = ctx.builder
|
||||
.build_extract_value(shape, i, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
|
||||
let shape_dim_gez = ctx.builder
|
||||
.build_int_compare(IntPredicate::SGE, shape_dim, llvm_usize.const_zero(), "")
|
||||
.unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
shape_dim_gez,
|
||||
"0:ValueError",
|
||||
"negative dimensions not supported",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
let ndarray = generator.gen_var_alloc(
|
||||
ctx,
|
||||
llvm_ndarray_t.into(),
|
||||
None,
|
||||
)?;
|
||||
let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
|
||||
|
||||
let num_dims = llvm_usize.const_int(shape.get_type().len() as u64, false);
|
||||
ndarray.store_ndims(ctx, generator, num_dims);
|
||||
|
||||
let ndarray_num_dims = ndarray.load_ndims(ctx);
|
||||
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
|
||||
|
||||
for i in 0..shape.get_type().len() {
|
||||
let ndarray_dim = ndarray
|
||||
.dim_sizes()
|
||||
.ptr_offset(ctx, generator, llvm_usize.const_int(i as u64, true), None);
|
||||
let shape_dim = ctx.builder.build_extract_value(shape, i, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
|
||||
ctx.builder.build_store(ndarray_dim, shape_dim).unwrap();
|
||||
}
|
||||
|
||||
let ndarray_num_elems = call_ndarray_calc_size(
|
||||
generator,
|
||||
ctx,
|
||||
&ndarray.dim_sizes().as_slice_value(ctx, generator),
|
||||
);
|
||||
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
|
||||
ctx.ctx.i32_type().const_zero().into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
|
||||
ctx.ctx.i64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "")
|
||||
} else {
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
|
||||
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
|
||||
ctx.ctx.i32_type().const_int(1, is_signed).into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
|
||||
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
|
||||
ctx.ctx.i64_type().const_int(1, is_signed).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_float(1.0).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_int(1, false).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "1")
|
||||
} else {
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for constructing an `NDArray`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
|
||||
fn call_ndarray_empty_impl<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: ListValue<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
create_ndarray_dyn_shape(
|
||||
generator,
|
||||
ctx,
|
||||
elem_ty,
|
||||
&shape,
|
||||
|_, ctx, shape| {
|
||||
Ok(shape.load_size(ctx, None))
|
||||
},
|
||||
|generator, ctx, shape, idx| {
|
||||
Ok(shape.data().get(ctx, generator, idx, None).into_int_value())
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for populating the entire `NDArray` using a lambda with its flattened index as
|
||||
/// its input.
|
||||
fn ndarray_fill_flattened<'ctx, 'a, G, ValueFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
value_fn: ValueFn,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let ndarray_num_elems = call_ndarray_calc_size(
|
||||
generator,
|
||||
ctx,
|
||||
&ndarray.dim_sizes().as_slice_value(ctx, generator),
|
||||
);
|
||||
|
||||
gen_for_callback_incrementing(
|
||||
generator,
|
||||
ctx,
|
||||
llvm_usize.const_zero(),
|
||||
(ndarray_num_elems, false),
|
||||
|generator, ctx, i| {
|
||||
let elem = unsafe {
|
||||
ndarray.data().ptr_offset_unchecked(ctx, generator, i, None)
|
||||
};
|
||||
|
||||
let value = value_fn(generator, ctx, i)?;
|
||||
ctx.builder.build_store(elem, value).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
llvm_usize.const_int(1, false),
|
||||
)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for populating the entire `NDArray` using a lambda with the dimension-indices
|
||||
/// as its input.
|
||||
fn ndarray_fill_indexed<'ctx, G, ValueFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
value_fn: ValueFn,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, PointerValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, idx| {
|
||||
let indices = call_ndarray_calc_nd_indices(
|
||||
generator,
|
||||
ctx,
|
||||
idx,
|
||||
ndarray,
|
||||
);
|
||||
|
||||
value_fn(generator, ctx, indices)
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
|
||||
fn call_ndarray_zeros_impl<'ctx, 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_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_usize_2 = llvm_usize.array_type(2);
|
||||
|
||||
let shape_addr = generator.gen_var_alloc(ctx, llvm_usize_2.into(), None)?;
|
||||
|
||||
let shape = ctx.builder.build_load(shape_addr, "")
|
||||
.map(BasicValueEnum::into_array_value)
|
||||
.unwrap();
|
||||
|
||||
let nrows = ctx.builder.build_int_z_extend_or_bit_cast(nrows, llvm_usize, "").unwrap();
|
||||
let shape = ctx.builder
|
||||
.build_insert_value(shape, nrows, 0, "")
|
||||
.map(AggregateValueEnum::into_array_value)
|
||||
.unwrap();
|
||||
|
||||
let ncols = ctx.builder.build_int_z_extend_or_bit_cast(ncols, llvm_usize, "").unwrap();
|
||||
let shape = ctx.builder
|
||||
.build_insert_value(shape, ncols, 1, "")
|
||||
.map(AggregateValueEnum::into_array_value)
|
||||
.unwrap();
|
||||
|
||||
let ndarray = create_ndarray_const_shape(generator, ctx, elem_ty, shape)?;
|
||||
|
||||
ndarray_fill_indexed(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, indices| {
|
||||
let row = ctx.build_gep_and_load(
|
||||
indices,
|
||||
&[llvm_usize.const_int(0, false)],
|
||||
None,
|
||||
).into_int_value();
|
||||
let col = ctx.build_gep_and_load(
|
||||
indices,
|
||||
&[llvm_usize.const_int(1, false)],
|
||||
None,
|
||||
).into_int_value();
|
||||
|
||||
let col_with_offset = ctx.builder
|
||||
.build_int_add(
|
||||
col,
|
||||
ctx.builder.build_int_s_extend_or_bit_cast(offset, llvm_usize, "").unwrap(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let is_on_diag = ctx.builder
|
||||
.build_int_compare(IntPredicate::EQ, row, col_with_offset, "")
|
||||
.unwrap();
|
||||
|
||||
let zero = ndarray_zero_value(generator, ctx, elem_ty);
|
||||
let one = ndarray_one_value(generator, ctx, elem_ty);
|
||||
|
||||
let value = ctx.builder.build_select(is_on_diag, one, zero, "").unwrap();
|
||||
|
||||
Ok(value)
|
||||
},
|
||||
)?;
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.copy`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
fn ndarray_copy_impl<'ctx, 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)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.empty`.
|
||||
pub fn gen_ndarray_empty<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, shape_ty)?;
|
||||
|
||||
call_ndarray_empty_impl(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.zeros`.
|
||||
pub fn gen_ndarray_zeros<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, shape_ty)?;
|
||||
|
||||
call_ndarray_zeros_impl(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.ones`.
|
||||
pub fn gen_ndarray_ones<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, shape_ty)?;
|
||||
|
||||
call_ndarray_ones_impl(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.full`.
|
||||
pub fn gen_ndarray_full<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, shape_ty)?;
|
||||
let fill_value_ty = fun.0.args[1].ty;
|
||||
let fill_value_arg = args[1].1.clone()
|
||||
.to_basic_value_enum(context, generator, fill_value_ty)?;
|
||||
|
||||
call_ndarray_full_impl(
|
||||
generator,
|
||||
context,
|
||||
fill_value_ty,
|
||||
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
|
||||
fill_value_arg,
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.eye`.
|
||||
pub fn gen_ndarray_eye<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert!(matches!(args.len(), 1..=3));
|
||||
|
||||
let nrows_ty = fun.0.args[0].ty;
|
||||
let nrows_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, nrows_ty)?;
|
||||
|
||||
let ncols_ty = fun.0.args[1].ty;
|
||||
let ncols_arg = args.iter()
|
||||
.find(|arg| arg.0.is_some_and(|name| name == fun.0.args[1].name))
|
||||
.map(|arg| arg.1.clone().to_basic_value_enum(context, generator, ncols_ty))
|
||||
.unwrap_or_else(|| {
|
||||
args[0].1.clone().to_basic_value_enum(context, generator, nrows_ty)
|
||||
})?;
|
||||
|
||||
let offset_ty = fun.0.args[2].ty;
|
||||
let offset_arg = args.iter()
|
||||
.find(|arg| arg.0.is_some_and(|name| name == fun.0.args[2].name))
|
||||
.map(|arg| arg.1.clone().to_basic_value_enum(context, generator, offset_ty))
|
||||
.unwrap_or_else(|| {
|
||||
Ok(context.gen_symbol_val(
|
||||
generator,
|
||||
fun.0.args[2].default_value.as_ref().unwrap(),
|
||||
offset_ty
|
||||
))
|
||||
})?;
|
||||
|
||||
call_ndarray_eye_impl(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
nrows_arg.into_int_value(),
|
||||
ncols_arg.into_int_value(),
|
||||
offset_arg.into_int_value(),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.identity`.
|
||||
pub fn gen_ndarray_identity<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
|
||||
let n_ty = fun.0.args[0].ty;
|
||||
let n_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, n_ty)?;
|
||||
|
||||
call_ndarray_eye_impl(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
n_arg.into_int_value(),
|
||||
n_arg.into_int_value(),
|
||||
llvm_usize.const_zero(),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.copy`.
|
||||
pub fn gen_ndarray_copy<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
_fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_some());
|
||||
assert!(args.is_empty());
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
|
||||
let this_ty = obj.as_ref().unwrap().0;
|
||||
let (this_elem_ty, _) = unpack_ndarray_tvars(&mut context.unifier, this_ty);
|
||||
let this_arg = obj
|
||||
.as_ref()
|
||||
.unwrap()
|
||||
.1
|
||||
.clone()
|
||||
.to_basic_value_enum(context, generator, this_ty)?;
|
||||
|
||||
ndarray_copy_impl(
|
||||
generator,
|
||||
context,
|
||||
this_elem_ty,
|
||||
NDArrayValue::from_ptr_val(this_arg.into_pointer_value(), llvm_usize, None),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.fill`.
|
||||
pub fn gen_ndarray_fill<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<(), String> {
|
||||
assert!(obj.is_some());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
|
||||
let this_ty = obj.as_ref().unwrap().0;
|
||||
let this_arg = obj.as_ref().unwrap().1.clone()
|
||||
.to_basic_value_enum(context, generator, this_ty)?
|
||||
.into_pointer_value();
|
||||
let value_ty = fun.0.args[0].ty;
|
||||
let value_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, value_ty)?;
|
||||
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
context,
|
||||
NDArrayValue::from_ptr_val(this_arg, llvm_usize, None),
|
||||
|generator, ctx, _| {
|
||||
let value = if value_arg.is_pointer_value() {
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
|
||||
let copy = generator.gen_var_alloc(ctx, value_arg.get_type(), None)?;
|
||||
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
copy,
|
||||
value_arg.into_pointer_value(),
|
||||
value_arg.get_type().size_of().map(Into::into).unwrap(),
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
copy.into()
|
||||
} else if value_arg.is_int_value() || value_arg.is_float_value() {
|
||||
value_arg
|
||||
} else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
Ok(value)
|
||||
}
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
}
|
|
@ -6,7 +6,7 @@ use super::{
|
|||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{ListValue, RangeValue},
|
||||
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
|
||||
expr::gen_binop_expr,
|
||||
gen_in_range_check,
|
||||
},
|
||||
|
@ -65,8 +65,8 @@ pub fn gen_array_var<'ctx, 'a, T: BasicType<'ctx>>(
|
|||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ty: T,
|
||||
size: IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
name: Option<&'ctx str>,
|
||||
) -> Result<ArraySliceValue<'ctx>, String> {
|
||||
// Restore debug location
|
||||
let di_loc = ctx.debug_info.0.create_debug_location(
|
||||
ctx.ctx,
|
||||
|
@ -84,6 +84,7 @@ pub fn gen_array_var<'ctx, 'a, T: BasicType<'ctx>>(
|
|||
ctx.builder.set_current_debug_location(di_loc);
|
||||
|
||||
let ptr = ctx.builder.build_array_alloca(ty, size, name.unwrap_or("")).unwrap();
|
||||
let ptr = ArraySliceValue::from_ptr_val(ptr, size, name);
|
||||
|
||||
ctx.builder.position_at_end(current);
|
||||
ctx.builder.set_current_debug_location(di_loc);
|
||||
|
@ -478,8 +479,8 @@ pub fn gen_for<G: CodeGenerator>(
|
|||
/// executing. The result value must be an `i1` indicating if the loop should continue.
|
||||
/// * `body` - A lambda containing IR statements within the loop body.
|
||||
/// * `update` - A lambda containing IR statements updating loop variables.
|
||||
pub fn gen_for_callback<'ctx, 'a, I, InitFn, CondFn, BodyFn, UpdateFn>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
pub fn gen_for_callback<'ctx, 'a, G, I, InitFn, CondFn, BodyFn, UpdateFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
init: InitFn,
|
||||
cond: CondFn,
|
||||
|
@ -487,11 +488,12 @@ pub fn gen_for_callback<'ctx, 'a, I, InitFn, CondFn, BodyFn, UpdateFn>(
|
|||
update: UpdateFn,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
I: Clone,
|
||||
InitFn: FnOnce(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>) -> Result<I, String>,
|
||||
CondFn: FnOnce(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, I) -> Result<IntValue<'ctx>, String>,
|
||||
BodyFn: FnOnce(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
|
||||
UpdateFn: FnOnce(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
|
||||
InitFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<I, String>,
|
||||
CondFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<IntValue<'ctx>, String>,
|
||||
BodyFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
|
||||
UpdateFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
|
||||
{
|
||||
let current = ctx.builder.get_insert_block().and_then(BasicBlock::get_parent).unwrap();
|
||||
let init_bb = ctx.ctx.append_basic_block(current, "for.init");
|
||||
|
@ -536,6 +538,85 @@ pub fn gen_for_callback<'ctx, 'a, I, InitFn, CondFn, BodyFn, UpdateFn>(
|
|||
Ok(())
|
||||
}
|
||||
|
||||
/// Generates a C-style monotonically-increasing `for` construct using lambdas, similar to the
|
||||
/// following C code:
|
||||
///
|
||||
/// ```c
|
||||
/// for (int x = init_val; x /* < or <= ; see `max_val` */ max_val; x += incr_val) {
|
||||
/// body(x);
|
||||
/// }
|
||||
/// ```
|
||||
///
|
||||
/// * `init_val` - The initial value of the loop variable. The type of this value will also be used
|
||||
/// as the type of the loop variable.
|
||||
/// * `max_val` - A tuple containing the maximum value of the loop variable, and whether the maximum
|
||||
/// value should be treated as inclusive (as opposed to exclusive).
|
||||
/// * `body` - A lambda containing IR statements within the loop body.
|
||||
/// * `incr_val` - The value to increment the loop variable on each iteration.
|
||||
pub fn gen_for_callback_incrementing<'ctx, 'a, G, BodyFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
init_val: IntValue<'ctx>,
|
||||
max_val: (IntValue<'ctx>, bool),
|
||||
body: BodyFn,
|
||||
incr_val: IntValue<'ctx>,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
BodyFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<(), String>,
|
||||
{
|
||||
let init_val_t = init_val.get_type();
|
||||
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|generator, ctx| {
|
||||
let i_addr = generator.gen_var_alloc(ctx, init_val_t.into(), None)?;
|
||||
ctx.builder.build_store(i_addr, init_val).unwrap();
|
||||
|
||||
Ok(i_addr)
|
||||
},
|
||||
|_, ctx, i_addr| {
|
||||
let cmp_op = if max_val.1 {
|
||||
IntPredicate::ULE
|
||||
} else {
|
||||
IntPredicate::ULT
|
||||
};
|
||||
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let max_val = ctx.builder
|
||||
.build_int_z_extend_or_bit_cast(max_val.0, init_val_t, "")
|
||||
.unwrap();
|
||||
|
||||
Ok(ctx.builder.build_int_compare(cmp_op, i, max_val, "").unwrap())
|
||||
},
|
||||
|generator, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
|
||||
body(generator, ctx, i)
|
||||
},
|
||||
|_, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let incr_val = ctx.builder
|
||||
.build_int_z_extend_or_bit_cast(incr_val, init_val_t, "")
|
||||
.unwrap();
|
||||
let i = ctx.builder.build_int_add(i, incr_val, "").unwrap();
|
||||
ctx.builder.build_store(i_addr, i).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// See [`CodeGenerator::gen_while`].
|
||||
pub fn gen_while<G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
|
@ -701,8 +782,8 @@ pub fn final_proxy<'ctx>(
|
|||
|
||||
/// Inserts the declaration of the builtin function with the specified `symbol` name, and returns
|
||||
/// the function.
|
||||
pub fn get_builtins<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
pub fn get_builtins<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
symbol: &str,
|
||||
) -> FunctionValue<'ctx> {
|
||||
|
@ -795,8 +876,8 @@ pub fn exn_constructor<'ctx>(
|
|||
///
|
||||
/// * `exception` - The exception thrown by the `raise` statement.
|
||||
/// * `loc` - The location where the exception is raised from.
|
||||
pub fn gen_raise<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
exception: Option<&BasicValueEnum<'ctx>>,
|
||||
loc: Location,
|
||||
|
|
|
@ -5,11 +5,14 @@ use crate::{
|
|||
expr::destructure_range,
|
||||
irrt::*,
|
||||
llvm_intrinsics::*,
|
||||
numpy::*,
|
||||
stmt::exn_constructor,
|
||||
},
|
||||
symbol_resolver::SymbolValue,
|
||||
toplevel::helper::PRIMITIVE_DEF_IDS,
|
||||
toplevel::numpy::*,
|
||||
toplevel::{
|
||||
helper::PRIMITIVE_DEF_IDS,
|
||||
numpy::make_ndarray_ty,
|
||||
},
|
||||
typecheck::typedef::VarMap,
|
||||
};
|
||||
use inkwell::{
|
||||
|
|
|
@ -1,24 +1,9 @@
|
|||
use inkwell::{IntPredicate, types::BasicType, values::{BasicValueEnum, PointerValue}};
|
||||
use inkwell::values::{AggregateValueEnum, ArrayValue, IntValue};
|
||||
use itertools::Itertools;
|
||||
use nac3parser::ast::StrRef;
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{ListValue, NDArrayValue},
|
||||
CodeGenContext,
|
||||
CodeGenerator,
|
||||
irrt::{
|
||||
call_ndarray_calc_nd_indices,
|
||||
call_ndarray_calc_size,
|
||||
},
|
||||
llvm_intrinsics::call_memcpy_generic,
|
||||
stmt::gen_for_callback
|
||||
},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{DefinitionId, helper::PRIMITIVE_DEF_IDS},
|
||||
toplevel::helper::PRIMITIVE_DEF_IDS,
|
||||
typecheck::{
|
||||
type_inferencer::PrimitiveStore,
|
||||
typedef::{FunSignature, Type, TypeEnum, Unifier, VarMap},
|
||||
typedef::{Type, TypeEnum, Unifier, VarMap},
|
||||
},
|
||||
};
|
||||
|
||||
|
@ -76,885 +61,3 @@ pub fn unpack_ndarray_tvars(
|
|||
.collect_tuple()
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
/// Creates an `NDArray` instance from a dynamic shape.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The shape of the `NDArray`.
|
||||
/// * `shape_len_fn` - A function that retrieves the number of dimensions from `shape`.
|
||||
/// * `shape_data_fn` - A function that retrieves the size of a dimension from `shape`.
|
||||
fn create_ndarray_dyn_shape<'ctx, 'a, V, LenFn, DataFn>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
elem_ty: Type,
|
||||
shape: &V,
|
||||
shape_len_fn: LenFn,
|
||||
shape_data_fn: DataFn,
|
||||
) -> Result<NDArrayValue<'ctx>, String>
|
||||
where
|
||||
LenFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, &V) -> Result<IntValue<'ctx>, String>,
|
||||
DataFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, &V, IntValue<'ctx>) -> Result<IntValue<'ctx>, String>,
|
||||
{
|
||||
let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
|
||||
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
|
||||
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
|
||||
assert!(llvm_ndarray_data_t.is_sized());
|
||||
|
||||
// Assert that all dimensions are non-negative
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|generator, ctx| {
|
||||
let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
|
||||
ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
|
||||
|
||||
Ok(i)
|
||||
},
|
||||
|generator, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let shape_len = shape_len_fn(generator, ctx, shape)?;
|
||||
debug_assert!(shape_len.get_type().get_bit_width() <= llvm_usize.get_bit_width());
|
||||
|
||||
Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, shape_len, "").unwrap())
|
||||
},
|
||||
|generator, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
|
||||
debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
|
||||
|
||||
let shape_dim_gez = ctx.builder
|
||||
.build_int_compare(IntPredicate::SGE, shape_dim, shape_dim.get_type().const_zero(), "")
|
||||
.unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
shape_dim_gez,
|
||||
"0:ValueError",
|
||||
"negative dimensions not supported",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|_, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
|
||||
ctx.builder.build_store(i_addr, i).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
)?;
|
||||
|
||||
let ndarray = generator.gen_var_alloc(
|
||||
ctx,
|
||||
llvm_ndarray_t.into(),
|
||||
None,
|
||||
)?;
|
||||
let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
|
||||
|
||||
let num_dims = shape_len_fn(generator, ctx, shape)?;
|
||||
ndarray.store_ndims(ctx, generator, num_dims);
|
||||
|
||||
let ndarray_num_dims = ndarray.load_ndims(ctx);
|
||||
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
|
||||
|
||||
// Copy the dimension sizes from shape to ndarray.dims
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|generator, ctx| {
|
||||
let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
|
||||
ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
|
||||
|
||||
Ok(i)
|
||||
},
|
||||
|generator, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let shape_len = shape_len_fn(generator, ctx, shape)?;
|
||||
debug_assert!(shape_len.get_type().get_bit_width() <= llvm_usize.get_bit_width());
|
||||
|
||||
Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, shape_len, "").unwrap())
|
||||
},
|
||||
|generator, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let shape_dim = shape_data_fn(generator, ctx, shape, i)?;
|
||||
debug_assert!(shape_dim.get_type().get_bit_width() <= llvm_usize.get_bit_width());
|
||||
let shape_dim = ctx.builder
|
||||
.build_int_z_extend(shape_dim, llvm_usize, "")
|
||||
.unwrap();
|
||||
|
||||
let ndarray_pdim = ndarray.dim_sizes().ptr_offset(ctx, generator, i, None);
|
||||
|
||||
ctx.builder.build_store(ndarray_pdim, shape_dim).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|_, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
|
||||
ctx.builder.build_store(i_addr, i).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
)?;
|
||||
|
||||
let ndarray_num_elems = call_ndarray_calc_size(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.load_ndims(ctx),
|
||||
ndarray.dim_sizes().as_ptr_value(ctx),
|
||||
);
|
||||
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// Creates an `NDArray` instance from a constant shape.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The shape of the `NDArray`, represented as an LLVM [`ArrayValue`].
|
||||
fn create_ndarray_const_shape<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: ArrayValue<'ctx>
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
|
||||
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
|
||||
let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
|
||||
let llvm_ndarray_data_t = ctx.get_llvm_type(generator, elem_ty).as_basic_type_enum();
|
||||
assert!(llvm_ndarray_data_t.is_sized());
|
||||
|
||||
for i in 0..shape.get_type().len() {
|
||||
let shape_dim = ctx.builder
|
||||
.build_extract_value(shape, i, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
|
||||
let shape_dim_gez = ctx.builder
|
||||
.build_int_compare(IntPredicate::SGE, shape_dim, llvm_usize.const_zero(), "")
|
||||
.unwrap();
|
||||
|
||||
ctx.make_assert(
|
||||
generator,
|
||||
shape_dim_gez,
|
||||
"0:ValueError",
|
||||
"negative dimensions not supported",
|
||||
[None, None, None],
|
||||
ctx.current_loc,
|
||||
);
|
||||
}
|
||||
|
||||
let ndarray = generator.gen_var_alloc(
|
||||
ctx,
|
||||
llvm_ndarray_t.into(),
|
||||
None,
|
||||
)?;
|
||||
let ndarray = NDArrayValue::from_ptr_val(ndarray, llvm_usize, None);
|
||||
|
||||
let num_dims = llvm_usize.const_int(shape.get_type().len() as u64, false);
|
||||
ndarray.store_ndims(ctx, generator, num_dims);
|
||||
|
||||
let ndarray_num_dims = ndarray.load_ndims(ctx);
|
||||
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
|
||||
|
||||
for i in 0..shape.get_type().len() {
|
||||
let ndarray_dim = ndarray
|
||||
.dim_sizes()
|
||||
.ptr_offset(ctx, generator, llvm_usize.const_int(i as u64, true), None);
|
||||
let shape_dim = ctx.builder.build_extract_value(shape, i, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
|
||||
ctx.builder.build_store(ndarray_dim, shape_dim).unwrap();
|
||||
}
|
||||
|
||||
let ndarray_dims = ndarray.dim_sizes().as_ptr_value(ctx);
|
||||
let ndarray_num_elems = call_ndarray_calc_size(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.load_ndims(ctx),
|
||||
ndarray_dims,
|
||||
);
|
||||
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
fn ndarray_zero_value<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
|
||||
ctx.ctx.i32_type().const_zero().into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
|
||||
ctx.ctx.i64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_zero().into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "")
|
||||
} else {
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
fn ndarray_one_value<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
) -> BasicValueEnum<'ctx> {
|
||||
if [ctx.primitives.int32, ctx.primitives.uint32].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
|
||||
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
|
||||
ctx.ctx.i32_type().const_int(1, is_signed).into()
|
||||
} else if [ctx.primitives.int64, ctx.primitives.uint64].iter().any(|ty| ctx.unifier.unioned(elem_ty, *ty)) {
|
||||
let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
|
||||
ctx.ctx.i64_type().const_int(1, is_signed).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
|
||||
ctx.ctx.f64_type().const_float(1.0).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
|
||||
ctx.ctx.bool_type().const_int(1, false).into()
|
||||
} else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
|
||||
ctx.gen_string(generator, "1")
|
||||
} else {
|
||||
unreachable!()
|
||||
}
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for constructing an `NDArray`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
|
||||
fn call_ndarray_empty_impl<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: ListValue<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
create_ndarray_dyn_shape(
|
||||
generator,
|
||||
ctx,
|
||||
elem_ty,
|
||||
&shape,
|
||||
|_, ctx, shape| {
|
||||
Ok(shape.load_size(ctx, None))
|
||||
},
|
||||
|generator, ctx, shape, idx| {
|
||||
Ok(shape.data().get(ctx, generator, idx, None).into_int_value())
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for populating the entire `NDArray` using a lambda with its flattened index as
|
||||
/// its input.
|
||||
fn ndarray_fill_flattened<'ctx, 'a, ValueFn>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, 'a>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
value_fn: ValueFn,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
ValueFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, 'a>, IntValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
let ndarray_num_elems = call_ndarray_calc_size(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.load_ndims(ctx),
|
||||
ndarray.dim_sizes().as_ptr_value(ctx),
|
||||
);
|
||||
|
||||
gen_for_callback(
|
||||
generator,
|
||||
ctx,
|
||||
|generator, ctx| {
|
||||
let i = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
|
||||
ctx.builder.build_store(i, llvm_usize.const_zero()).unwrap();
|
||||
|
||||
Ok(i)
|
||||
},
|
||||
|_, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
|
||||
Ok(ctx.builder.build_int_compare(IntPredicate::ULT, i, ndarray_num_elems, "").unwrap())
|
||||
},
|
||||
|generator, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let elem = unsafe {
|
||||
ndarray.data().ptr_to_data_flattened_unchecked(ctx, i, None)
|
||||
};
|
||||
|
||||
let value = value_fn(generator, ctx, i)?;
|
||||
ctx.builder.build_store(elem, value).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
|_, ctx, i_addr| {
|
||||
let i = ctx.builder
|
||||
.build_load(i_addr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.unwrap();
|
||||
let i = ctx.builder.build_int_add(i, llvm_usize.const_int(1, true), "").unwrap();
|
||||
ctx.builder.build_store(i_addr, i).unwrap();
|
||||
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for populating the entire `NDArray` using a lambda with the dimension-indices
|
||||
/// as its input.
|
||||
fn ndarray_fill_indexed<'ctx, ValueFn>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ndarray: NDArrayValue<'ctx>,
|
||||
value_fn: ValueFn,
|
||||
) -> Result<(), String>
|
||||
where
|
||||
ValueFn: Fn(&mut dyn CodeGenerator, &mut CodeGenContext<'ctx, '_>, PointerValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, idx| {
|
||||
let indices = call_ndarray_calc_nd_indices(
|
||||
generator,
|
||||
ctx,
|
||||
idx,
|
||||
ndarray,
|
||||
);
|
||||
|
||||
value_fn(generator, ctx, indices)
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
|
||||
fn call_ndarray_zeros_impl<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: ListValue<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let supported_types = [
|
||||
ctx.primitives.int32,
|
||||
ctx.primitives.int64,
|
||||
ctx.primitives.uint32,
|
||||
ctx.primitives.uint64,
|
||||
ctx.primitives.float,
|
||||
ctx.primitives.bool,
|
||||
ctx.primitives.str,
|
||||
];
|
||||
assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
|
||||
|
||||
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, _| {
|
||||
let value = ndarray_zero_value(generator, ctx, elem_ty);
|
||||
|
||||
Ok(value)
|
||||
}
|
||||
)?;
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.ones`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
|
||||
fn call_ndarray_ones_impl<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: ListValue<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let supported_types = [
|
||||
ctx.primitives.int32,
|
||||
ctx.primitives.int64,
|
||||
ctx.primitives.uint32,
|
||||
ctx.primitives.uint64,
|
||||
ctx.primitives.float,
|
||||
ctx.primitives.bool,
|
||||
ctx.primitives.str,
|
||||
];
|
||||
assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
|
||||
|
||||
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, _| {
|
||||
let value = ndarray_one_value(generator, ctx, elem_ty);
|
||||
|
||||
Ok(value)
|
||||
}
|
||||
)?;
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.full`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
|
||||
fn call_ndarray_full_impl<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: ListValue<'ctx>,
|
||||
fill_value: BasicValueEnum<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, _| {
|
||||
let value = if fill_value.is_pointer_value() {
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
|
||||
let copy = generator.gen_var_alloc(ctx, fill_value.get_type(), None)?;
|
||||
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
copy,
|
||||
fill_value.into_pointer_value(),
|
||||
fill_value.get_type().size_of().map(Into::into).unwrap(),
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
copy.into()
|
||||
} else if fill_value.is_int_value() || fill_value.is_float_value() {
|
||||
fill_value
|
||||
} else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
Ok(value)
|
||||
}
|
||||
)?;
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.eye`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
fn call_ndarray_eye_impl<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
nrows: IntValue<'ctx>,
|
||||
ncols: IntValue<'ctx>,
|
||||
offset: IntValue<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_usize_2 = llvm_usize.array_type(2);
|
||||
|
||||
let shape_addr = generator.gen_var_alloc(ctx, llvm_usize_2.into(), None)?;
|
||||
|
||||
let shape = ctx.builder.build_load(shape_addr, "")
|
||||
.map(BasicValueEnum::into_array_value)
|
||||
.unwrap();
|
||||
|
||||
let nrows = ctx.builder.build_int_z_extend_or_bit_cast(nrows, llvm_usize, "").unwrap();
|
||||
let shape = ctx.builder
|
||||
.build_insert_value(shape, nrows, 0, "")
|
||||
.map(AggregateValueEnum::into_array_value)
|
||||
.unwrap();
|
||||
|
||||
let ncols = ctx.builder.build_int_z_extend_or_bit_cast(ncols, llvm_usize, "").unwrap();
|
||||
let shape = ctx.builder
|
||||
.build_insert_value(shape, ncols, 1, "")
|
||||
.map(AggregateValueEnum::into_array_value)
|
||||
.unwrap();
|
||||
|
||||
let ndarray = create_ndarray_const_shape(generator, ctx, elem_ty, shape)?;
|
||||
|
||||
ndarray_fill_indexed(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, indices| {
|
||||
let row = ctx.build_gep_and_load(
|
||||
indices,
|
||||
&[llvm_usize.const_int(0, false)],
|
||||
None,
|
||||
).into_int_value();
|
||||
let col = ctx.build_gep_and_load(
|
||||
indices,
|
||||
&[llvm_usize.const_int(1, false)],
|
||||
None,
|
||||
).into_int_value();
|
||||
|
||||
let col_with_offset = ctx.builder
|
||||
.build_int_add(
|
||||
col,
|
||||
ctx.builder.build_int_s_extend_or_bit_cast(offset, llvm_usize, "").unwrap(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
let is_on_diag = ctx.builder
|
||||
.build_int_compare(IntPredicate::EQ, row, col_with_offset, "")
|
||||
.unwrap();
|
||||
|
||||
let zero = ndarray_zero_value(generator, ctx, elem_ty);
|
||||
let one = ndarray_one_value(generator, ctx, elem_ty);
|
||||
|
||||
let value = ctx.builder.build_select(is_on_diag, one, zero, "").unwrap();
|
||||
|
||||
Ok(value)
|
||||
},
|
||||
)?;
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.copy`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
fn ndarray_copy_impl<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
this: NDArrayValue<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
|
||||
let ndarray = create_ndarray_dyn_shape(
|
||||
generator,
|
||||
ctx,
|
||||
elem_ty,
|
||||
&this,
|
||||
|_, ctx, shape| {
|
||||
Ok(shape.load_ndims(ctx))
|
||||
},
|
||||
|generator, ctx, shape, idx| {
|
||||
Ok(shape.dim_sizes().get(ctx, generator, idx, None))
|
||||
},
|
||||
)?;
|
||||
|
||||
let len = call_ndarray_calc_size(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.load_ndims(ctx),
|
||||
ndarray.dim_sizes().as_ptr_value(ctx),
|
||||
);
|
||||
let sizeof_ty = ctx.get_llvm_type(generator, elem_ty);
|
||||
let len_bytes = ctx.builder
|
||||
.build_int_mul(
|
||||
len,
|
||||
sizeof_ty.size_of().unwrap(),
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
ndarray.data().as_ptr_value(ctx),
|
||||
this.data().as_ptr_value(ctx),
|
||||
len_bytes,
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.empty`.
|
||||
pub fn gen_ndarray_empty<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, shape_ty)?;
|
||||
|
||||
call_ndarray_empty_impl(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.zeros`.
|
||||
pub fn gen_ndarray_zeros<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, shape_ty)?;
|
||||
|
||||
call_ndarray_zeros_impl(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.ones`.
|
||||
pub fn gen_ndarray_ones<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, shape_ty)?;
|
||||
|
||||
call_ndarray_ones_impl(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.full`.
|
||||
pub fn gen_ndarray_full<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 2);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
let shape_ty = fun.0.args[0].ty;
|
||||
let shape_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, shape_ty)?;
|
||||
let fill_value_ty = fun.0.args[1].ty;
|
||||
let fill_value_arg = args[1].1.clone()
|
||||
.to_basic_value_enum(context, generator, fill_value_ty)?;
|
||||
|
||||
call_ndarray_full_impl(
|
||||
generator,
|
||||
context,
|
||||
fill_value_ty,
|
||||
ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
|
||||
fill_value_arg,
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.eye`.
|
||||
pub fn gen_ndarray_eye<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert!(matches!(args.len(), 1..=3));
|
||||
|
||||
let nrows_ty = fun.0.args[0].ty;
|
||||
let nrows_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, nrows_ty)?;
|
||||
|
||||
let ncols_ty = fun.0.args[1].ty;
|
||||
let ncols_arg = args.iter()
|
||||
.find(|arg| arg.0.is_some_and(|name| name == fun.0.args[1].name))
|
||||
.map(|arg| arg.1.clone().to_basic_value_enum(context, generator, ncols_ty))
|
||||
.unwrap_or_else(|| {
|
||||
args[0].1.clone().to_basic_value_enum(context, generator, nrows_ty)
|
||||
})?;
|
||||
|
||||
let offset_ty = fun.0.args[2].ty;
|
||||
let offset_arg = args.iter()
|
||||
.find(|arg| arg.0.is_some_and(|name| name == fun.0.args[2].name))
|
||||
.map(|arg| arg.1.clone().to_basic_value_enum(context, generator, offset_ty))
|
||||
.unwrap_or_else(|| {
|
||||
Ok(context.gen_symbol_val(
|
||||
generator,
|
||||
fun.0.args[2].default_value.as_ref().unwrap(),
|
||||
offset_ty
|
||||
))
|
||||
})?;
|
||||
|
||||
call_ndarray_eye_impl(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
nrows_arg.into_int_value(),
|
||||
ncols_arg.into_int_value(),
|
||||
offset_arg.into_int_value(),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.identity`.
|
||||
pub fn gen_ndarray_identity<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_none());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
|
||||
let n_ty = fun.0.args[0].ty;
|
||||
let n_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, n_ty)?;
|
||||
|
||||
call_ndarray_eye_impl(
|
||||
generator,
|
||||
context,
|
||||
context.primitives.float,
|
||||
n_arg.into_int_value(),
|
||||
n_arg.into_int_value(),
|
||||
llvm_usize.const_zero(),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.copy`.
|
||||
pub fn gen_ndarray_copy<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
_fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
assert!(obj.is_some());
|
||||
assert!(args.is_empty());
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
|
||||
let this_ty = obj.as_ref().unwrap().0;
|
||||
let (this_elem_ty, _) = unpack_ndarray_tvars(&mut context.unifier, this_ty);
|
||||
let this_arg = obj
|
||||
.as_ref()
|
||||
.unwrap()
|
||||
.1
|
||||
.clone()
|
||||
.to_basic_value_enum(context, generator, this_ty)?;
|
||||
|
||||
ndarray_copy_impl(
|
||||
generator,
|
||||
context,
|
||||
this_elem_ty,
|
||||
NDArrayValue::from_ptr_val(this_arg.into_pointer_value(), llvm_usize, None),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.fill`.
|
||||
pub fn gen_ndarray_fill<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<(), String> {
|
||||
assert!(obj.is_some());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
|
||||
let this_ty = obj.as_ref().unwrap().0;
|
||||
let this_arg = obj.as_ref().unwrap().1.clone()
|
||||
.to_basic_value_enum(context, generator, this_ty)?
|
||||
.into_pointer_value();
|
||||
let value_ty = fun.0.args[0].ty;
|
||||
let value_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, value_ty)?;
|
||||
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
context,
|
||||
NDArrayValue::from_ptr_val(this_arg, llvm_usize, None),
|
||||
|generator, ctx, _| {
|
||||
let value = if value_arg.is_pointer_value() {
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
|
||||
let copy = generator.gen_var_alloc(ctx, value_arg.get_type(), None)?;
|
||||
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
copy,
|
||||
value_arg.into_pointer_value(),
|
||||
value_arg.get_type().size_of().map(Into::into).unwrap(),
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
copy.into()
|
||||
} else if value_arg.is_int_value() || value_arg.is_float_value() {
|
||||
value_arg
|
||||
} else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
Ok(value)
|
||||
}
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
|
|
@ -699,7 +699,7 @@ impl Unifier {
|
|||
self.set_a_to_b(a, x);
|
||||
}
|
||||
(TVar { fields: Some(fields), range, is_const_generic: false, .. }, TTuple { ty }) => {
|
||||
let len = ty.len() as i32;
|
||||
let len = i32::try_from(ty.len()).unwrap();
|
||||
for (k, v) in fields {
|
||||
match *k {
|
||||
RecordKey::Int(i) => {
|
||||
|
|
|
@ -74,7 +74,8 @@ impl SymbolResolver for Resolver {
|
|||
if let Some(id) = str_store.get(s) {
|
||||
*id
|
||||
} else {
|
||||
let id = str_store.len() as i32;
|
||||
let id = i32::try_from(str_store.len())
|
||||
.expect("Symbol resolver string store size exceeds max capacity (i32::MAX)");
|
||||
str_store.insert(s.to_string(), id);
|
||||
id
|
||||
}
|
||||
|
|
|
@ -247,6 +247,8 @@ fn handle_assignment_pattern(
|
|||
}
|
||||
|
||||
fn main() {
|
||||
const SIZE_T: u32 = usize::BITS;
|
||||
|
||||
let cli = CommandLineArgs::parse();
|
||||
let CommandLineArgs {
|
||||
file_name,
|
||||
|
@ -287,7 +289,6 @@ fn main() {
|
|||
// The default behavior for -O<n> where n>3 defaults to O3 for both Clang and GCC
|
||||
_ => OptimizationLevel::Aggressive,
|
||||
};
|
||||
const SIZE_T: u32 = 64;
|
||||
|
||||
let program = match fs::read_to_string(file_name.clone()) {
|
||||
Ok(program) => program,
|
||||
|
|
Loading…
Reference in New Issue