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...
b2994ff90a
Author | SHA1 | Date |
---|---|---|
David Mak | b2994ff90a | |
David Mak | 2d1f243975 | |
David Mak | 4f236ea411 | |
David Mak | e3fe3f03fb | |
David Mak | deb325de4f | |
David Mak | aa84cc425f | |
David Mak | f7fbc629aa | |
David Mak | 724651d2bb | |
David Mak | 2665668e21 | |
David Mak | 9b1c559efb | |
David Mak | 5ecc2a905e | |
David Mak | 2a8a5bbfea | |
David Mak | 3ed8ce7215 |
|
@ -616,6 +616,7 @@ name = "nac3core"
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version = "0.1.0"
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dependencies = [
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"crossbeam",
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"indexmap 2.2.5",
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"indoc",
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"inkwell",
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"insta",
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|
|
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@ -5,7 +5,7 @@ use nac3core::{
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toplevel::{
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DefinitionId,
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helper::PRIMITIVE_DEF_IDS,
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numpy::{make_ndarray_ty, unpack_ndarray_tvars},
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numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
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TopLevelDef,
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},
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typecheck::{
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|
@ -654,7 +654,7 @@ impl InnerResolver {
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}
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}
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(TypeEnum::TObj { obj_id, .. }, false) if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
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let (ty, ndims) = unpack_ndarray_tvars(unifier, extracted_ty);
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let (ty, ndims) = unpack_ndarray_var_tys(unifier, extracted_ty);
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let len: usize = self.helper.len_fn.call1(py, (obj,))?.extract(py)?;
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if len == 0 {
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assert!(matches!(
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|
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@ -7,6 +7,7 @@ edition = "2021"
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[dependencies]
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itertools = "0.12"
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crossbeam = "0.8"
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indexmap = "2.2"
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parking_lot = "0.12"
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rayon = "1.8"
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nac3parser = { path = "../nac3parser" }
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|
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@ -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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@ -9,6 +9,7 @@ use crate::{
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use nac3parser::ast::StrRef;
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use std::collections::HashMap;
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use indexmap::IndexMap;
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pub struct ConcreteTypeStore {
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store: Vec<ConcreteTypeEnum>,
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@ -50,7 +51,7 @@ pub enum ConcreteTypeEnum {
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TObj {
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obj_id: DefinitionId,
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fields: HashMap<StrRef, (ConcreteType, bool)>,
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params: HashMap<u32, ConcreteType>,
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params: IndexMap<u32, ConcreteType>,
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},
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TVirtual {
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ty: ConcreteType,
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|
|
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@ -2,13 +2,21 @@ 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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get_llvm_abi_type,
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irrt::*,
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llvm_intrinsics::{call_expect, call_float_floor, call_float_pow, call_float_powi},
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numpy,
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stmt::{gen_raise, gen_var},
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CodeGenContext, CodeGenTask,
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},
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|
@ -16,7 +24,7 @@ use crate::{
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toplevel::{
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DefinitionId,
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helper::PRIMITIVE_DEF_IDS,
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numpy::make_ndarray_ty,
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numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
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TopLevelDef,
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},
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typecheck::{
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|
@ -52,7 +60,7 @@ pub fn get_subst_key(
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params.clone()
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})
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.unwrap_or_default();
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vars.extend(fun_vars.iter());
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vars.extend(fun_vars);
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let sorted = vars.keys().filter(|id| filter.map_or(true, |v| v.contains(id))).sorted();
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sorted
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.map(|id| {
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|
@ -103,9 +111,9 @@ 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 +182,9 @@ 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 +199,9 @@ 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 +217,9 @@ 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 +299,9 @@ 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 +500,21 @@ 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 +558,9 @@ 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 +571,9 @@ 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 +890,7 @@ 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 +990,7 @@ 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 +1023,7 @@ 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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|
@ -1078,34 +1090,22 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
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Ok(Some(list.as_ptr_value().into()))
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}
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/// Generates LLVM IR for a [binary operator expression][expr].
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///
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/// * `left` - The left-hand side of the binary operator.
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/// * `op` - The operator applied on the operands.
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/// * `right` - The right-hand side of the binary operator.
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/// * `loc` - The location of the full expression.
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/// * `is_aug_assign` - Whether the binary operator expression is also an assignment operator.
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pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
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/// Generates LLVM IR for a binary operator expression using the [`Type`] and
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/// [LLVM value][`BasicValueEnum`] of the operands.
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pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
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generator: &mut G,
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ctx: &mut CodeGenContext<'ctx, '_>,
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left: &Expr<Option<Type>>,
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left: (&Option<Type>, BasicValueEnum<'ctx>),
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op: &Operator,
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right: &Expr<Option<Type>>,
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right: (&Option<Type>, BasicValueEnum<'ctx>),
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loc: Location,
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is_aug_assign: bool,
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) -> Result<Option<ValueEnum<'ctx>>, String> {
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let ty1 = ctx.unifier.get_representative(left.custom.unwrap());
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let ty2 = ctx.unifier.get_representative(right.custom.unwrap());
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let left_val = if let Some(v) = generator.gen_expr(ctx, left)? {
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v.to_basic_value_enum(ctx, generator, left.custom.unwrap())?
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} else {
|
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return Ok(None)
|
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};
|
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let right_val = if let Some(v) = generator.gen_expr(ctx, right)? {
|
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v.to_basic_value_enum(ctx, generator, right.custom.unwrap())?
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} else {
|
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return Ok(None)
|
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};
|
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let (left_ty, left_val) = left;
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let (right_ty, right_val) = right;
|
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|
||||
let ty1 = ctx.unifier.get_representative(left_ty.unwrap());
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let ty2 = ctx.unifier.get_representative(right_ty.unwrap());
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|
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// we can directly compare the types, because we've got their representatives
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// which would be unchanged until further unification, which we would never do
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|
@ -1129,8 +1129,46 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
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Some("f_pow_i")
|
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);
|
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Ok(Some(res.into()))
|
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} else if matches!(&*ctx.unifier.get_ty(ty1), TypeEnum::TObj { obj_id, .. } if obj_id == &PRIMITIVE_DEF_IDS.ndarray) && matches!(&*ctx.unifier.get_ty(ty2), TypeEnum::TObj { obj_id, .. } if obj_id == &PRIMITIVE_DEF_IDS.ndarray) {
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let llvm_usize = generator.get_size_type(ctx.ctx);
|
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let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
|
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let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
|
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|
||||
assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
|
||||
|
||||
let left_val = NDArrayValue::from_ptr_val(
|
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left_val.into_pointer_value(),
|
||||
llvm_usize,
|
||||
None
|
||||
);
|
||||
let right_val = NDArrayValue::from_ptr_val(
|
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right_val.into_pointer_value(),
|
||||
llvm_usize,
|
||||
None
|
||||
);
|
||||
let res = numpy::ndarray_elementwise_binop_impl(
|
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generator,
|
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ctx,
|
||||
ndarray_dtype1,
|
||||
if is_aug_assign { Some(left_val) } else { None },
|
||||
left_val,
|
||||
right_val,
|
||||
|generator, ctx, elem_ty, (lhs, rhs)| {
|
||||
gen_binop_expr_with_values(
|
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generator,
|
||||
ctx,
|
||||
(&Some(elem_ty), lhs),
|
||||
op,
|
||||
(&Some(elem_ty), rhs),
|
||||
ctx.current_loc,
|
||||
is_aug_assign,
|
||||
)?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)
|
||||
},
|
||||
)?;
|
||||
|
||||
Ok(Some(res.as_ptr_value().into()))
|
||||
} else {
|
||||
let left_ty_enum = ctx.unifier.get_ty_immutable(left.custom.unwrap());
|
||||
let left_ty_enum = ctx.unifier.get_ty_immutable(left_ty.unwrap());
|
||||
let TypeEnum::TObj { fields, obj_id, .. } = left_ty_enum.as_ref() else {
|
||||
unreachable!("must be tobj")
|
||||
};
|
||||
|
@ -1150,7 +1188,7 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
|
|||
let signature = if let Some(call) = ctx.calls.get(&loc.into()) {
|
||||
ctx.unifier.get_call_signature(*call).unwrap()
|
||||
} else {
|
||||
let left_enum_ty = ctx.unifier.get_ty_immutable(left.custom.unwrap());
|
||||
let left_enum_ty = ctx.unifier.get_ty_immutable(left_ty.unwrap());
|
||||
let TypeEnum::TObj { fields, .. } = left_enum_ty.as_ref() else {
|
||||
unreachable!("must be tobj")
|
||||
};
|
||||
|
@ -1175,13 +1213,51 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
|
|||
generator
|
||||
.gen_call(
|
||||
ctx,
|
||||
Some((left.custom.unwrap(), left_val.into())),
|
||||
Some((left_ty.unwrap(), left_val.into())),
|
||||
(&signature, fun_id),
|
||||
vec![(None, right_val.into())],
|
||||
).map(|f| f.map(Into::into))
|
||||
}
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for a [binary operator expression][expr].
|
||||
///
|
||||
/// * `left` - The left-hand side of the binary operator.
|
||||
/// * `op` - The operator applied on the operands.
|
||||
/// * `right` - The right-hand side of the binary operator.
|
||||
/// * `loc` - The location of the full expression.
|
||||
/// * `is_aug_assign` - Whether the binary operator expression is also an assignment operator.
|
||||
pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
left: &Expr<Option<Type>>,
|
||||
op: &Operator,
|
||||
right: &Expr<Option<Type>>,
|
||||
loc: Location,
|
||||
is_aug_assign: bool,
|
||||
) -> Result<Option<ValueEnum<'ctx>>, String> {
|
||||
let left_val = if let Some(v) = generator.gen_expr(ctx, left)? {
|
||||
v.to_basic_value_enum(ctx, generator, left.custom.unwrap())?
|
||||
} else {
|
||||
return Ok(None)
|
||||
};
|
||||
let right_val = if let Some(v) = generator.gen_expr(ctx, right)? {
|
||||
v.to_basic_value_enum(ctx, generator, right.custom.unwrap())?
|
||||
} else {
|
||||
return Ok(None)
|
||||
};
|
||||
|
||||
gen_binop_expr_with_values(
|
||||
generator,
|
||||
ctx,
|
||||
(&left.custom, left_val),
|
||||
op,
|
||||
(&right.custom, right_val),
|
||||
loc,
|
||||
is_aug_assign,
|
||||
)
|
||||
}
|
||||
|
||||
/// Generates code for a subscript expression on an `ndarray`.
|
||||
///
|
||||
/// * `ty` - The `Type` of the `NDArray` elements.
|
||||
|
@ -1256,7 +1332,7 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
|
|||
};
|
||||
|
||||
Ok(Some(v.data()
|
||||
.get_const(
|
||||
.get(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.ctx.i32_type().const_array(&[index]),
|
||||
|
@ -1300,15 +1376,17 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
|
|||
ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
|
||||
|
||||
let ndarray_num_dims = ndarray.load_ndims(ctx);
|
||||
let v_dims_src_ptr = v.dim_sizes().ptr_offset(
|
||||
ctx,
|
||||
generator,
|
||||
llvm_usize.const_int(1, false),
|
||||
None,
|
||||
);
|
||||
let v_dims_src_ptr = unsafe {
|
||||
v.dim_sizes().ptr_offset_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
llvm_usize.const_int(1, false),
|
||||
None,
|
||||
)
|
||||
};
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
ndarray.dim_sizes().as_ptr_value(ctx),
|
||||
ndarray.dim_sizes().base_ptr(ctx, generator),
|
||||
v_dims_src_ptr,
|
||||
ctx.builder
|
||||
.build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
|
||||
|
@ -1320,12 +1398,11 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
|
|||
let ndarray_num_elems = call_ndarray_calc_size(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray.load_ndims(ctx),
|
||||
ndarray.dim_sizes().as_ptr_value(ctx),
|
||||
&ndarray.dim_sizes().as_slice_value(ctx, generator),
|
||||
);
|
||||
ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
|
||||
|
||||
let v_data_src_ptr = v.data().ptr_offset_const(
|
||||
let v_data_src_ptr = v.data().ptr_offset(
|
||||
ctx,
|
||||
generator,
|
||||
ctx.ctx.i32_type().const_array(&[index]),
|
||||
|
@ -1333,7 +1410,7 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
|
|||
);
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
ndarray.data().as_ptr_value(ctx),
|
||||
ndarray.data().base_ptr(ctx, generator),
|
||||
v_data_src_ptr,
|
||||
ctx.builder
|
||||
.build_int_mul(ndarray_num_elems, llvm_ndarray_data_t.size_of().unwrap(), "")
|
||||
|
@ -1971,7 +2048,6 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
|
|||
}
|
||||
TypeEnum::TObj { obj_id, params, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
|
||||
let (ty, ndims) = params.iter()
|
||||
.sorted_by_key(|(var_id, _)| *var_id)
|
||||
.map(|(_, ty)| ty)
|
||||
.collect_tuple()
|
||||
.unwrap();
|
||||
|
|
|
@ -1,5 +1,5 @@
|
|||
use crate::{
|
||||
codegen::{expr::*, stmt::*, bool_to_i1, bool_to_i8, CodeGenContext},
|
||||
codegen::{classes::ArraySliceValue, expr::*, stmt::*, bool_to_i1, bool_to_i8, CodeGenContext},
|
||||
symbol_resolver::ValueEnum,
|
||||
toplevel::{DefinitionId, TopLevelDef},
|
||||
typecheck::typedef::{FunSignature, Type},
|
||||
|
@ -99,8 +99,8 @@ pub trait CodeGenerator {
|
|||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ty: BasicTypeEnum<'ctx>,
|
||||
size: IntValue<'ctx>,
|
||||
name: Option<&str>,
|
||||
) -> Result<PointerValue<'ctx>, String> {
|
||||
name: Option<&'ctx str>,
|
||||
) -> Result<ArraySliceValue<'ctx>, String> {
|
||||
gen_array_var(ctx, ty, size, name)
|
||||
}
|
||||
|
||||
|
|
|
@ -8,6 +8,8 @@ typedef unsigned _BitInt(64) uint64_t;
|
|||
# define MAX(a, b) (a > b ? a : b)
|
||||
# define MIN(a, b) (a > b ? b : a)
|
||||
|
||||
# define NULL ((void *) 0)
|
||||
|
||||
// adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
|
||||
// need to make sure `exp >= 0` before calling this function
|
||||
#define DEF_INT_EXP(T) T __nac3_int_exp_##T( \
|
||||
|
@ -293,3 +295,87 @@ uint64_t __nac3_ndarray_flatten_index64(
|
|||
}
|
||||
return idx;
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast(
|
||||
const uint32_t *lhs_dims,
|
||||
uint32_t lhs_ndims,
|
||||
const uint32_t *rhs_dims,
|
||||
uint32_t rhs_ndims,
|
||||
uint32_t *out_dims
|
||||
) {
|
||||
uint32_t max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
|
||||
|
||||
for (uint32_t i = 0; i < max_ndims; ++i) {
|
||||
uint32_t *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : NULL;
|
||||
uint32_t *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : NULL;
|
||||
uint32_t *out_dim = &out_dims[max_ndims - i - 1];
|
||||
|
||||
if (lhs_dim_sz == NULL) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (rhs_dim_sz == NULL) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == 1) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (*rhs_dim_sz == 1) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == *rhs_dim_sz) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast64(
|
||||
const uint64_t *lhs_dims,
|
||||
uint64_t lhs_ndims,
|
||||
const uint64_t *rhs_dims,
|
||||
uint64_t rhs_ndims,
|
||||
uint64_t *out_dims
|
||||
) {
|
||||
uint64_t max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
|
||||
|
||||
for (uint64_t i = 0; i < max_ndims; ++i) {
|
||||
uint64_t *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : NULL;
|
||||
uint64_t *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : NULL;
|
||||
uint64_t *out_dim = &out_dims[max_ndims - i - 1];
|
||||
|
||||
if (lhs_dim_sz == NULL) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (rhs_dim_sz == NULL) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == 1) {
|
||||
*out_dim = *rhs_dim_sz;
|
||||
} else if (*rhs_dim_sz == 1) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else if (*lhs_dim_sz == *rhs_dim_sz) {
|
||||
*out_dim = *lhs_dim_sz;
|
||||
} else {
|
||||
__builtin_unreachable();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx(
|
||||
const uint32_t *src_dims,
|
||||
uint32_t src_ndims,
|
||||
const uint32_t *in_idx,
|
||||
uint32_t *out_idx
|
||||
) {
|
||||
for (uint32_t i = 0; i < src_ndims; ++i) {
|
||||
uint32_t src_i = src_ndims - i - 1;
|
||||
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
|
||||
}
|
||||
}
|
||||
|
||||
void __nac3_ndarray_calc_broadcast_idx64(
|
||||
const uint64_t *src_dims,
|
||||
uint64_t src_ndims,
|
||||
const uint64_t *in_idx,
|
||||
uint64_t *out_idx
|
||||
) {
|
||||
for (uint64_t i = 0; i < src_ndims; ++i) {
|
||||
uint64_t src_i = src_ndims - i - 1;
|
||||
out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
|
||||
}
|
||||
}
|
||||
|
|
|
@ -1,9 +1,18 @@
|
|||
use crate::typecheck::typedef::Type;
|
||||
|
||||
use super::{
|
||||
classes::{ListValue, NDArrayValue},
|
||||
classes::{
|
||||
ArrayLikeIndexer,
|
||||
ArraySliceValue,
|
||||
ArrayLikeValue,
|
||||
ListValue,
|
||||
NDArrayValue,
|
||||
UntypedArrayLikeAccessor,
|
||||
UntypedArrayLikeMutator,
|
||||
},
|
||||
CodeGenContext,
|
||||
CodeGenerator,
|
||||
llvm_intrinsics,
|
||||
};
|
||||
use inkwell::{
|
||||
attributes::{Attribute, AttributeLoc},
|
||||
|
@ -39,8 +48,8 @@ pub fn load_irrt(ctx: &Context) -> Module {
|
|||
|
||||
// repeated squaring method adapted from GNU Scientific Library:
|
||||
// https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
|
||||
pub fn integer_power<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
pub fn integer_power<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
base: IntValue<'ctx>,
|
||||
exp: IntValue<'ctx>,
|
||||
|
@ -81,8 +90,8 @@ pub fn integer_power<'ctx>(
|
|||
.unwrap()
|
||||
}
|
||||
|
||||
pub fn calculate_len_for_slice_range<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
start: IntValue<'ctx>,
|
||||
end: IntValue<'ctx>,
|
||||
|
@ -303,8 +312,8 @@ pub fn handle_slice_index_bound<'ctx, G: CodeGenerator>(
|
|||
/// This function handles 'end' **inclusively**.
|
||||
/// Order of tuples `assign_idx` and `value_idx` is ('start', 'end', 'step').
|
||||
/// Negative index should be handled before entering this function
|
||||
pub fn list_slice_assignment<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
ty: BasicTypeEnum<'ctx>,
|
||||
dest_arr: ListValue<'ctx>,
|
||||
|
@ -338,7 +347,7 @@ pub fn list_slice_assignment<'ctx>(
|
|||
|
||||
let zero = int32.const_zero();
|
||||
let one = int32.const_int(1, false);
|
||||
let dest_arr_ptr = dest_arr.data().as_ptr_value(ctx);
|
||||
let dest_arr_ptr = dest_arr.data().base_ptr(ctx, generator);
|
||||
let dest_arr_ptr = ctx.builder.build_pointer_cast(
|
||||
dest_arr_ptr,
|
||||
elem_ptr_type,
|
||||
|
@ -346,7 +355,7 @@ pub fn list_slice_assignment<'ctx>(
|
|||
).unwrap();
|
||||
let dest_len = dest_arr.load_size(ctx, Some("dest.len"));
|
||||
let dest_len = ctx.builder.build_int_truncate_or_bit_cast(dest_len, int32, "srclen32").unwrap();
|
||||
let src_arr_ptr = src_arr.data().as_ptr_value(ctx);
|
||||
let src_arr_ptr = src_arr.data().base_ptr(ctx, generator);
|
||||
let src_arr_ptr = ctx.builder.build_pointer_cast(
|
||||
src_arr_ptr,
|
||||
elem_ptr_type,
|
||||
|
@ -468,8 +477,8 @@ pub fn list_slice_assignment<'ctx>(
|
|||
}
|
||||
|
||||
/// Generates a call to `isinf` in IR. Returns an `i1` representing the result.
|
||||
pub fn call_isinf<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
pub fn call_isinf<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
v: FloatValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
|
@ -489,8 +498,8 @@ pub fn call_isinf<'ctx>(
|
|||
}
|
||||
|
||||
/// Generates a call to `isnan` in IR. Returns an `i1` representing the result.
|
||||
pub fn call_isnan<'ctx>(
|
||||
generator: &mut dyn CodeGenerator,
|
||||
pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &CodeGenContext<'ctx, '_>,
|
||||
v: FloatValue<'ctx>,
|
||||
) -> IntValue<'ctx> {
|
||||
|
@ -574,12 +583,14 @@ 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>(
|
||||
generator: &G,
|
||||
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 +617,8 @@ 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(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
|
@ -622,12 +633,12 @@ 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>,
|
||||
) -> PointerValue<'ctx> {
|
||||
) -> ArraySliceValue<'ctx> {
|
||||
let llvm_void = ctx.ctx.void_type();
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
|
||||
|
@ -666,7 +677,7 @@ 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(),
|
||||
],
|
||||
|
@ -674,16 +685,18 @@ pub fn call_ndarray_calc_nd_indices<'ctx>(
|
|||
)
|
||||
.unwrap();
|
||||
|
||||
indices
|
||||
ArraySliceValue::from_ptr_val(indices, ndarray_num_dims, None)
|
||||
}
|
||||
|
||||
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 +704,14 @@ 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 +742,10 @@ 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 +763,21 @@ 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 +786,8 @@ 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 +799,165 @@ 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,
|
||||
)
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_broadcast`. Returns a tuple containing the number of
|
||||
/// dimension and size of each dimension of the resultant `ndarray`.
|
||||
pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
lhs: NDArrayValue<'ctx>,
|
||||
rhs: NDArrayValue<'ctx>,
|
||||
) -> (IntValue<'ctx>, PointerValue<'ctx>) {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_broadcast",
|
||||
64 => "__nac3_ndarray_calc_broadcast64",
|
||||
bw => unreachable!("Unsupported size type bit width: {}", bw)
|
||||
};
|
||||
let ndarray_calc_broadcast_fn = ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
|
||||
let fn_type = llvm_usize.fn_type(
|
||||
&[
|
||||
llvm_pusize.into(),
|
||||
llvm_usize.into(),
|
||||
llvm_pusize.into(),
|
||||
llvm_usize.into(),
|
||||
llvm_pusize.into(),
|
||||
],
|
||||
false,
|
||||
);
|
||||
|
||||
ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
|
||||
});
|
||||
|
||||
let lhs_ndims = lhs.load_ndims(ctx);
|
||||
let rhs_ndims = rhs.load_ndims(ctx);
|
||||
let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
|
||||
|
||||
// TODO: Generate assertion checks for whether each dimension is compatible
|
||||
// gen_for_callback_incrementing(
|
||||
// generator,
|
||||
// ctx,
|
||||
// llvm_usize.const_zero(),
|
||||
// (max_ndims, false),
|
||||
// |generator, ctx, idx| {
|
||||
// let lhs_dim_sz =
|
||||
//
|
||||
// let lhs_elem = lhs.get_dims().get(ctx, generator, idx, None);
|
||||
// let rhs_elem = rhs.get_dims().get(ctx, generator, idx, None);
|
||||
//
|
||||
//
|
||||
// },
|
||||
// llvm_usize.const_int(1, false),
|
||||
// ).unwrap();
|
||||
|
||||
let lhs_dims = lhs.dim_sizes().base_ptr(ctx, generator);
|
||||
let lhs_ndims = lhs.load_ndims(ctx);
|
||||
let rhs_dims = rhs.dim_sizes().base_ptr(ctx, generator);
|
||||
let rhs_ndims = rhs.load_ndims(ctx);
|
||||
let out_dims = ctx.builder.build_array_alloca(llvm_usize, max_ndims, "").unwrap();
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
ndarray_calc_broadcast_fn,
|
||||
&[
|
||||
lhs_dims.into(),
|
||||
lhs_ndims.into(),
|
||||
rhs_dims.into(),
|
||||
rhs_ndims.into(),
|
||||
out_dims.into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
(max_ndims, out_dims)
|
||||
}
|
||||
|
||||
/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
|
||||
/// containing the indices used for accessing `array` corresponding to the `broadcast_idx`.
|
||||
pub fn call_ndarray_calc_broadcast_index<'ctx, G: CodeGenerator + ?Sized, BroadcastIdx: UntypedArrayLikeAccessor<'ctx>>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
array: NDArrayValue<'ctx>,
|
||||
broadcast_idx: &BroadcastIdx,
|
||||
) -> ArraySliceValue<'ctx> {
|
||||
let llvm_usize = generator.get_size_type(ctx.ctx);
|
||||
let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
|
||||
|
||||
let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
|
||||
32 => "__nac3_ndarray_calc_broadcast_idx",
|
||||
64 => "__nac3_ndarray_calc_broadcast_idx64",
|
||||
bw => unreachable!("Unsupported size type bit width: {}", bw)
|
||||
};
|
||||
let ndarray_calc_broadcast_fn = ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
|
||||
let fn_type = llvm_usize.fn_type(
|
||||
&[
|
||||
llvm_pusize.into(),
|
||||
llvm_usize.into(),
|
||||
llvm_pusize.into(),
|
||||
llvm_usize.into(),
|
||||
],
|
||||
false,
|
||||
);
|
||||
|
||||
ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
|
||||
});
|
||||
|
||||
// TODO: Assertions
|
||||
|
||||
let broadcast_size = broadcast_idx.size(ctx, generator);
|
||||
let out_idx = ctx.builder.build_array_alloca(llvm_usize, broadcast_size, "").unwrap();
|
||||
let out_idx = ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None);
|
||||
|
||||
let array_dims = array.dim_sizes().base_ptr(ctx, generator);
|
||||
let array_ndims = array.load_ndims(ctx);
|
||||
let broadcast_idx_ptr = unsafe {
|
||||
broadcast_idx.ptr_offset_unchecked(
|
||||
ctx,
|
||||
generator,
|
||||
llvm_usize.const_zero(),
|
||||
None
|
||||
)
|
||||
};
|
||||
|
||||
ctx.builder
|
||||
.build_call(
|
||||
ndarray_calc_broadcast_fn,
|
||||
&[
|
||||
array_dims.into(),
|
||||
array_ndims.into(),
|
||||
broadcast_idx_ptr.into(),
|
||||
out_idx.base_ptr(ctx, generator).into(),
|
||||
],
|
||||
"",
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
out_idx
|
||||
}
|
|
@ -2,7 +2,7 @@ use crate::{
|
|||
symbol_resolver::{StaticValue, SymbolResolver},
|
||||
toplevel::{
|
||||
helper::PRIMITIVE_DEF_IDS,
|
||||
numpy::unpack_ndarray_tvars,
|
||||
numpy::unpack_ndarray_var_tys,
|
||||
TopLevelContext,
|
||||
TopLevelDef,
|
||||
},
|
||||
|
@ -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>>,
|
||||
|
@ -450,7 +451,7 @@ fn get_llvm_type<'ctx>(
|
|||
|
||||
TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
|
||||
let llvm_usize = generator.get_size_type(ctx);
|
||||
let (dtype, _) = unpack_ndarray_tvars(unifier, ty);
|
||||
let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
|
||||
let element_type = get_llvm_type(
|
||||
ctx,
|
||||
module,
|
||||
|
@ -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 +1,928 @@
|
|||
use inkwell::{
|
||||
IntPredicate,
|
||||
types::BasicType,
|
||||
values::{AggregateValueEnum, ArrayValue, BasicValueEnum, IntValue, PointerValue}
|
||||
};
|
||||
use nac3parser::ast::StrRef;
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{
|
||||
ArrayLikeIndexer,
|
||||
ArraySliceValue,
|
||||
ArrayLikeValue,
|
||||
ListValue,
|
||||
NDArrayValue,
|
||||
TypedArrayLikeAccessor,
|
||||
UntypedArrayLikeAccessor,
|
||||
},
|
||||
CodeGenContext,
|
||||
CodeGenerator,
|
||||
irrt::{
|
||||
call_ndarray_calc_broadcast,
|
||||
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_var_tys},
|
||||
},
|
||||
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, '_>, ArraySliceValue<'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)
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
/// Generates the LLVM IR for populating the entire `NDArray` using a lambda with the same-indexed
|
||||
/// element from two other `NDArray` as its input.
|
||||
fn ndarray_broadcast_fill_flattened<'ctx, G, ValueFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
res: NDArrayValue<'ctx>,
|
||||
lhs: NDArrayValue<'ctx>,
|
||||
rhs: NDArrayValue<'ctx>,
|
||||
value_fn: ValueFn,
|
||||
) -> Result<NDArrayValue<'ctx>, String>
|
||||
where
|
||||
G: CodeGenerator + ?Sized,
|
||||
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, Type, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
ctx,
|
||||
res,
|
||||
|generator, ctx, idx| {
|
||||
let elem = unsafe {
|
||||
(
|
||||
lhs.data().get_unchecked(ctx, generator, idx, None),
|
||||
rhs.data().get_unchecked(ctx, generator, idx, None),
|
||||
)
|
||||
};
|
||||
|
||||
debug_assert_eq!(elem.0.get_type(), elem.1.get_type());
|
||||
|
||||
value_fn(generator, ctx, elem_ty, elem)
|
||||
},
|
||||
)?;
|
||||
|
||||
Ok(res)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
|
||||
fn call_ndarray_zeros_impl<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: ListValue<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let supported_types = [
|
||||
ctx.primitives.int32,
|
||||
ctx.primitives.int64,
|
||||
ctx.primitives.uint32,
|
||||
ctx.primitives.uint64,
|
||||
ctx.primitives.float,
|
||||
ctx.primitives.bool,
|
||||
ctx.primitives.str,
|
||||
];
|
||||
assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
|
||||
|
||||
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, _| {
|
||||
let value = ndarray_zero_value(generator, ctx, elem_ty);
|
||||
|
||||
Ok(value)
|
||||
}
|
||||
)?;
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.ones`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
|
||||
fn call_ndarray_ones_impl<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: ListValue<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let supported_types = [
|
||||
ctx.primitives.int32,
|
||||
ctx.primitives.int64,
|
||||
ctx.primitives.uint32,
|
||||
ctx.primitives.uint64,
|
||||
ctx.primitives.float,
|
||||
ctx.primitives.bool,
|
||||
ctx.primitives.str,
|
||||
];
|
||||
assert!(supported_types.iter().any(|supported_ty| ctx.unifier.unioned(*supported_ty, elem_ty)));
|
||||
|
||||
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, _| {
|
||||
let value = ndarray_one_value(generator, ctx, elem_ty);
|
||||
|
||||
Ok(value)
|
||||
}
|
||||
)?;
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.full`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `shape` - The `shape` parameter used to construct the `NDArray`.
|
||||
fn call_ndarray_full_impl<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
shape: ListValue<'ctx>,
|
||||
fill_value: BasicValueEnum<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
ctx,
|
||||
ndarray,
|
||||
|generator, ctx, _| {
|
||||
let value = if fill_value.is_pointer_value() {
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
|
||||
let copy = generator.gen_var_alloc(ctx, fill_value.get_type(), None)?;
|
||||
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
copy,
|
||||
fill_value.into_pointer_value(),
|
||||
fill_value.get_type().size_of().map(Into::into).unwrap(),
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
copy.into()
|
||||
} else if fill_value.is_int_value() || fill_value.is_float_value() {
|
||||
fill_value
|
||||
} else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
Ok(value)
|
||||
}
|
||||
)?;
|
||||
|
||||
Ok(ndarray)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for generating the implementation for `ndarray.eye`.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
fn call_ndarray_eye_impl<'ctx, G: CodeGenerator + ?Sized>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
nrows: IntValue<'ctx>,
|
||||
ncols: IntValue<'ctx>,
|
||||
offset: IntValue<'ctx>,
|
||||
) -> Result<NDArrayValue<'ctx>, String> {
|
||||
let llvm_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, col) = unsafe {
|
||||
(
|
||||
indices.get_unchecked(ctx, generator, llvm_usize.const_int(0, false), None).into_int_value(),
|
||||
indices.get_unchecked(ctx, generator, 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)
|
||||
}
|
||||
|
||||
/// LLVM-typed implementation for computing elementwise binary operations.
|
||||
///
|
||||
/// * `elem_ty` - The element type of the `NDArray`.
|
||||
/// * `res` - The `ndarray` instance to write results into, or [`None`] if the result should be
|
||||
/// written to a new `ndarray`.
|
||||
/// * `value_fn` - Function mapping the two input elements into the result.
|
||||
pub fn ndarray_elementwise_binop_impl<'ctx, G, ValueFn>(
|
||||
generator: &mut G,
|
||||
ctx: &mut CodeGenContext<'ctx, '_>,
|
||||
elem_ty: Type,
|
||||
res: Option<NDArrayValue<'ctx>>,
|
||||
this: NDArrayValue<'ctx>,
|
||||
other: NDArrayValue<'ctx>,
|
||||
value_fn: ValueFn,
|
||||
) -> Result<NDArrayValue<'ctx>, String>
|
||||
where
|
||||
G: CodeGenerator,
|
||||
ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, Type, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
|
||||
{
|
||||
let ndarray_dims = call_ndarray_calc_broadcast(generator, ctx, this, other);
|
||||
let ndarray = res.unwrap_or_else(|| {
|
||||
create_ndarray_dyn_shape(
|
||||
generator,
|
||||
ctx,
|
||||
elem_ty,
|
||||
&ndarray_dims,
|
||||
|_, _, v| {
|
||||
Ok(v.0)
|
||||
},
|
||||
|_, ctx, v, idx| {
|
||||
unsafe {
|
||||
let data_ptr = ctx.builder.build_in_bounds_gep(v.1, &[idx], "")
|
||||
.map_err(|e| e.to_string())?;
|
||||
|
||||
ctx.builder.build_load(data_ptr, "")
|
||||
.map(BasicValueEnum::into_int_value)
|
||||
.map_err(|e| e.to_string())
|
||||
}
|
||||
},
|
||||
).unwrap()
|
||||
});
|
||||
|
||||
ndarray_broadcast_fill_flattened(
|
||||
generator,
|
||||
ctx,
|
||||
elem_ty,
|
||||
ndarray,
|
||||
this,
|
||||
other,
|
||||
|generator, ctx, elem_ty, elems| {
|
||||
value_fn(generator, ctx, elem_ty, elems)
|
||||
},
|
||||
)?;
|
||||
|
||||
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_var_tys(&mut context.unifier, this_ty);
|
||||
let this_arg = obj
|
||||
.as_ref()
|
||||
.unwrap()
|
||||
.1
|
||||
.clone()
|
||||
.to_basic_value_enum(context, generator, this_ty)?;
|
||||
|
||||
ndarray_copy_impl(
|
||||
generator,
|
||||
context,
|
||||
this_elem_ty,
|
||||
NDArrayValue::from_ptr_val(this_arg.into_pointer_value(), llvm_usize, None),
|
||||
).map(NDArrayValue::into)
|
||||
}
|
||||
|
||||
/// Generates LLVM IR for `ndarray.fill`.
|
||||
pub fn gen_ndarray_fill<'ctx>(
|
||||
context: &mut CodeGenContext<'ctx, '_>,
|
||||
obj: &Option<(Type, ValueEnum<'ctx>)>,
|
||||
fun: (&FunSignature, DefinitionId),
|
||||
args: &[(Option<StrRef>, ValueEnum<'ctx>)],
|
||||
generator: &mut dyn CodeGenerator,
|
||||
) -> Result<(), String> {
|
||||
assert!(obj.is_some());
|
||||
assert_eq!(args.len(), 1);
|
||||
|
||||
let llvm_usize = generator.get_size_type(context.ctx);
|
||||
|
||||
let this_ty = obj.as_ref().unwrap().0;
|
||||
let this_arg = obj.as_ref().unwrap().1.clone()
|
||||
.to_basic_value_enum(context, generator, this_ty)?
|
||||
.into_pointer_value();
|
||||
let value_ty = fun.0.args[0].ty;
|
||||
let value_arg = args[0].1.clone()
|
||||
.to_basic_value_enum(context, generator, value_ty)?;
|
||||
|
||||
ndarray_fill_flattened(
|
||||
generator,
|
||||
context,
|
||||
NDArrayValue::from_ptr_val(this_arg, llvm_usize, None),
|
||||
|generator, ctx, _| {
|
||||
let value = if value_arg.is_pointer_value() {
|
||||
let llvm_i1 = ctx.ctx.bool_type();
|
||||
|
||||
let copy = generator.gen_var_alloc(ctx, value_arg.get_type(), None)?;
|
||||
|
||||
call_memcpy_generic(
|
||||
ctx,
|
||||
copy,
|
||||
value_arg.into_pointer_value(),
|
||||
value_arg.get_type().size_of().map(Into::into).unwrap(),
|
||||
llvm_i1.const_zero(),
|
||||
);
|
||||
|
||||
copy.into()
|
||||
} else if value_arg.is_int_value() || value_arg.is_float_value() {
|
||||
value_arg
|
||||
} else {
|
||||
unreachable!()
|
||||
};
|
||||
|
||||
Ok(value)
|
||||
}
|
||||
)?;
|
||||
|
||||
Ok(())
|
||||
}
|
|
@ -6,14 +6,14 @@ use super::{
|
|||
};
|
||||
use crate::{
|
||||
codegen::{
|
||||
classes::{ListValue, RangeValue},
|
||||
classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
|
||||
expr::gen_binop_expr,
|
||||
gen_in_range_check,
|
||||
},
|
||||
toplevel::{
|
||||
DefinitionId,
|
||||
helper::PRIMITIVE_DEF_IDS,
|
||||
numpy::unpack_ndarray_tvars,
|
||||
numpy::unpack_ndarray_var_tys,
|
||||
TopLevelDef,
|
||||
},
|
||||
typecheck::typedef::{FunSignature, Type, TypeEnum},
|
||||
|
@ -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);
|
||||
|
@ -250,7 +251,7 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
|
|||
let ty = match &*ctx.unifier.get_ty_immutable(target.custom.unwrap()) {
|
||||
TypeEnum::TList { ty } => *ty,
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
|
||||
unpack_ndarray_tvars(&mut ctx.unifier, target.custom.unwrap()).0
|
||||
unpack_ndarray_var_tys(&mut ctx.unifier, target.custom.unwrap()).0
|
||||
}
|
||||
_ => unreachable!(),
|
||||
};
|
||||
|
@ -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,
|
||||
|
|
|
@ -10,7 +10,7 @@ use crate::{
|
|||
},
|
||||
typecheck::{
|
||||
type_inferencer::{FunctionData, Inferencer, PrimitiveStore},
|
||||
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier},
|
||||
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
|
||||
},
|
||||
};
|
||||
use indoc::indoc;
|
||||
|
@ -25,7 +25,6 @@ use nac3parser::{
|
|||
use parking_lot::RwLock;
|
||||
use std::collections::{HashMap, HashSet};
|
||||
use std::sync::Arc;
|
||||
use crate::typecheck::typedef::VarMap;
|
||||
|
||||
struct Resolver {
|
||||
id_to_type: HashMap<StrRef, Type>,
|
||||
|
|
|
@ -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::{
|
||||
|
@ -296,6 +299,8 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
|
|||
Some("N".into()),
|
||||
None,
|
||||
);
|
||||
let size_t = primitives.0.usize();
|
||||
|
||||
let var_map: VarMap = vec![(num_ty.1, num_ty.0)].into_iter().collect();
|
||||
let exception_fields = vec![
|
||||
("__name__".into(), int32, true),
|
||||
|
@ -342,8 +347,27 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
|
|||
.nth(1)
|
||||
.map(|(var_id, ty)| (*ty, *var_id))
|
||||
.unwrap();
|
||||
let ndarray_usized_ndims_tvar = primitives.1.get_fresh_const_generic_var(
|
||||
size_t,
|
||||
Some("ndarray_ndims".into()),
|
||||
None,
|
||||
);
|
||||
let ndarray_copy_ty = *ndarray_fields.get(&"copy".into()).unwrap();
|
||||
let ndarray_fill_ty = *ndarray_fields.get(&"fill".into()).unwrap();
|
||||
let ndarray_add_ty = *ndarray_fields.get(&"__add__".into()).unwrap();
|
||||
let ndarray_sub_ty = *ndarray_fields.get(&"__sub__".into()).unwrap();
|
||||
let ndarray_mul_ty = *ndarray_fields.get(&"__mul__".into()).unwrap();
|
||||
let ndarray_truediv_ty = *ndarray_fields.get(&"__truediv__".into()).unwrap();
|
||||
let ndarray_floordiv_ty = *ndarray_fields.get(&"__floordiv__".into()).unwrap();
|
||||
let ndarray_mod_ty = *ndarray_fields.get(&"__mod__".into()).unwrap();
|
||||
let ndarray_pow_ty = *ndarray_fields.get(&"__pow__".into()).unwrap();
|
||||
let ndarray_iadd_ty = *ndarray_fields.get(&"__iadd__".into()).unwrap();
|
||||
let ndarray_isub_ty = *ndarray_fields.get(&"__isub__".into()).unwrap();
|
||||
let ndarray_imul_ty = *ndarray_fields.get(&"__imul__".into()).unwrap();
|
||||
let ndarray_itruediv_ty = *ndarray_fields.get(&"__itruediv__".into()).unwrap();
|
||||
let ndarray_ifloordiv_ty = *ndarray_fields.get(&"__ifloordiv__".into()).unwrap();
|
||||
let ndarray_imod_ty = *ndarray_fields.get(&"__imod__".into()).unwrap();
|
||||
let ndarray_ipow_ty = *ndarray_fields.get(&"__ipow__".into()).unwrap();
|
||||
|
||||
let top_level_def_list = vec![
|
||||
Arc::new(RwLock::new(TopLevelComposer::make_top_level_class_def(
|
||||
|
@ -521,6 +545,20 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
|
|||
methods: vec![
|
||||
("copy".into(), ndarray_copy_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 1)),
|
||||
("fill".into(), ndarray_fill_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 2)),
|
||||
("__add__".into(), ndarray_add_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 3)),
|
||||
("__sub__".into(), ndarray_sub_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 4)),
|
||||
("__mul__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 5)),
|
||||
("__truediv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 6)),
|
||||
("__floordiv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 7)),
|
||||
("__mod__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 8)),
|
||||
("__pow__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 9)),
|
||||
("__iadd__".into(), ndarray_iadd_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 10)),
|
||||
("__isub__".into(), ndarray_isub_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 11)),
|
||||
("__imul__".into(), ndarray_imul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 12)),
|
||||
("__itruediv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 13)),
|
||||
("__ifloordiv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 14)),
|
||||
("__imod__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 15)),
|
||||
("__ipow__".into(), ndarray_imul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 16)),
|
||||
],
|
||||
ancestors: Vec::default(),
|
||||
constructor: None,
|
||||
|
@ -559,6 +597,216 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
|
|||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__add__".into(),
|
||||
simple_name: "__add__".into(),
|
||||
signature: ndarray_add_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__sub__".into(),
|
||||
simple_name: "__sub__".into(),
|
||||
signature: ndarray_sub_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__mul__".into(),
|
||||
simple_name: "__mul__".into(),
|
||||
signature: ndarray_mul_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__truediv__".into(),
|
||||
simple_name: "__truediv__".into(),
|
||||
signature: ndarray_truediv_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__floordiv__".into(),
|
||||
simple_name: "__floordiv__".into(),
|
||||
signature: ndarray_floordiv_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__mod__".into(),
|
||||
simple_name: "__mod__".into(),
|
||||
signature: ndarray_mod_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__pow__".into(),
|
||||
simple_name: "__pow__".into(),
|
||||
signature: ndarray_pow_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__iadd__".into(),
|
||||
simple_name: "__iadd__".into(),
|
||||
signature: ndarray_iadd_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id, ndarray_usized_ndims_tvar.1],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__isub__".into(),
|
||||
simple_name: "__isub__".into(),
|
||||
signature: ndarray_isub_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__imul__".into(),
|
||||
simple_name: "__imul__".into(),
|
||||
signature: ndarray_imul_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__itruediv__".into(),
|
||||
simple_name: "__itruediv__".into(),
|
||||
signature: ndarray_itruediv_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__ifloordiv__".into(),
|
||||
simple_name: "__ifloordiv__".into(),
|
||||
signature: ndarray_ifloordiv_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__imod__".into(),
|
||||
simple_name: "__imod__".into(),
|
||||
signature: ndarray_imod_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "ndarray.__ipow__".into(),
|
||||
simple_name: "__ipow__".into(),
|
||||
signature: ndarray_ipow_ty.0,
|
||||
var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
|
||||
instance_to_symbol: HashMap::default(),
|
||||
instance_to_stmt: HashMap::default(),
|
||||
resolver: None,
|
||||
codegen_callback: Some(Arc::new(GenCall::new(Box::new(
|
||||
|_, _, _, _, _| {
|
||||
unreachable!("handled in gen_expr")
|
||||
},
|
||||
)))),
|
||||
loc: None,
|
||||
})),
|
||||
Arc::new(RwLock::new(TopLevelDef::Function {
|
||||
name: "int32".into(),
|
||||
simple_name: "int32".into(),
|
||||
|
|
|
@ -1926,9 +1926,8 @@ impl TopLevelComposer {
|
|||
ret_str,
|
||||
name,
|
||||
ast.as_ref().unwrap().location
|
||||
),
|
||||
]))
|
||||
}
|
||||
),]))
|
||||
}
|
||||
|
||||
instance_to_stmt.insert(
|
||||
get_subst_key(unifier, self_type, &subst, Some(&vars.keys().copied().collect())),
|
||||
|
|
|
@ -1,6 +1,7 @@
|
|||
use std::convert::TryInto;
|
||||
|
||||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::numpy::subst_ndarray_tvars;
|
||||
use crate::typecheck::typedef::{Mapping, VarMap};
|
||||
use nac3parser::ast::{Constant, Location};
|
||||
|
||||
|
@ -226,11 +227,57 @@ impl TopLevelComposer {
|
|||
(ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
|
||||
]),
|
||||
}));
|
||||
let ndarray_binop_fun_other_ty = unifier.get_fresh_var(None, None);
|
||||
let ndarray_binop_fun_ret_ty = unifier.get_fresh_var(None, None);
|
||||
let ndarray_binop_fun_ty = unifier.add_ty(TypeEnum::TFunc(FunSignature {
|
||||
args: vec![
|
||||
FuncArg {
|
||||
name: "other".into(),
|
||||
ty: ndarray_binop_fun_other_ty.0,
|
||||
default_value: None,
|
||||
},
|
||||
],
|
||||
ret: ndarray_binop_fun_ret_ty.0,
|
||||
vars: VarMap::from([
|
||||
(ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
|
||||
(ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
|
||||
]),
|
||||
}));
|
||||
let ndarray_truediv_fun_other_ty = unifier.get_fresh_var(None, None);
|
||||
let ndarray_truediv_fun_ret_ty = unifier.get_fresh_var(None, None);
|
||||
let ndarray_truediv_fun_ty = unifier.add_ty(TypeEnum::TFunc(FunSignature {
|
||||
args: vec![
|
||||
FuncArg {
|
||||
name: "other".into(),
|
||||
ty: ndarray_truediv_fun_other_ty.0,
|
||||
default_value: None,
|
||||
},
|
||||
],
|
||||
ret: ndarray_truediv_fun_ret_ty.0,
|
||||
vars: VarMap::from([
|
||||
(ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
|
||||
(ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
|
||||
]),
|
||||
}));
|
||||
let ndarray = unifier.add_ty(TypeEnum::TObj {
|
||||
obj_id: PRIMITIVE_DEF_IDS.ndarray,
|
||||
fields: Mapping::from([
|
||||
("copy".into(), (ndarray_copy_fun_ty, true)),
|
||||
("fill".into(), (ndarray_fill_fun_ty, true)),
|
||||
("__add__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__sub__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__mul__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__truediv__".into(), (ndarray_truediv_fun_ty, true)),
|
||||
("__floordiv__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__mod__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__pow__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__iadd__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__isub__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__imul__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__itruediv__".into(), (ndarray_truediv_fun_ty, true)),
|
||||
("__ifloordiv__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__imod__".into(), (ndarray_binop_fun_ty, true)),
|
||||
("__ipow__".into(), (ndarray_binop_fun_ty, true)),
|
||||
]),
|
||||
params: VarMap::from([
|
||||
(ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
|
||||
|
@ -238,7 +285,16 @@ impl TopLevelComposer {
|
|||
]),
|
||||
});
|
||||
|
||||
let ndarray_usized_ndims_tvar = unifier.get_fresh_const_generic_var(size_t_ty, Some("ndarray_ndims".into()), None);
|
||||
let ndarray_unsized = subst_ndarray_tvars(&mut unifier, ndarray, Some(ndarray_usized_ndims_tvar.0), None);
|
||||
|
||||
unifier.unify(ndarray_copy_fun_ret_ty.0, ndarray).unwrap();
|
||||
unifier.unify(ndarray_binop_fun_other_ty.0, ndarray_unsized).unwrap();
|
||||
unifier.unify(ndarray_binop_fun_ret_ty.0, ndarray).unwrap();
|
||||
|
||||
let ndarray_float = subst_ndarray_tvars(&mut unifier, ndarray, Some(float), None);
|
||||
unifier.unify(ndarray_truediv_fun_other_ty.0, ndarray).unwrap();
|
||||
unifier.unify(ndarray_truediv_fun_ret_ty.0, ndarray_float).unwrap();
|
||||
|
||||
let primitives = PrimitiveStore {
|
||||
int32,
|
||||
|
|
|
@ -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},
|
||||
},
|
||||
};
|
||||
|
||||
|
@ -34,16 +19,32 @@ pub fn make_ndarray_ty(
|
|||
dtype: Option<Type>,
|
||||
ndims: Option<Type>,
|
||||
) -> Type {
|
||||
let ndarray = primitives.ndarray;
|
||||
subst_ndarray_tvars(unifier, primitives.ndarray, dtype, ndims)
|
||||
}
|
||||
|
||||
/// Substitutes type variables in `ndarray`.
|
||||
///
|
||||
/// * `dtype` - The element type of the `ndarray`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
/// * `ndims` - The number of dimensions of the `ndarray`, or [`None`] if the type variable is not
|
||||
/// specialized.
|
||||
pub fn subst_ndarray_tvars(
|
||||
unifier: &mut Unifier,
|
||||
ndarray: Type,
|
||||
dtype: Option<Type>,
|
||||
ndims: Option<Type>,
|
||||
) -> Type {
|
||||
let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(ndarray) else {
|
||||
panic!("Expected `ndarray` to be TObj, but got {}", unifier.stringify(ndarray))
|
||||
};
|
||||
debug_assert_eq!(*obj_id, PRIMITIVE_DEF_IDS.ndarray);
|
||||
|
||||
if dtype.is_none() && ndims.is_none() {
|
||||
return ndarray
|
||||
}
|
||||
|
||||
let tvar_ids = params.iter()
|
||||
.map(|(obj_id, _)| *obj_id)
|
||||
.sorted()
|
||||
.collect_vec();
|
||||
debug_assert_eq!(tvar_ids.len(), 2);
|
||||
|
||||
|
@ -58,12 +59,10 @@ pub fn make_ndarray_ty(
|
|||
unifier.subst(ndarray, &tvar_subst).unwrap_or(ndarray)
|
||||
}
|
||||
|
||||
/// Unpacks the type variables of `ndarray` into a tuple. The elements of the tuple corresponds to
|
||||
/// `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray` respectively.
|
||||
pub fn unpack_ndarray_tvars(
|
||||
fn unpack_ndarray_tvars(
|
||||
unifier: &mut Unifier,
|
||||
ndarray: Type,
|
||||
) -> (Type, Type) {
|
||||
) -> Vec<(u32, Type)> {
|
||||
let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(ndarray) else {
|
||||
panic!("Expected `ndarray` to be TObj, but got {}", unifier.stringify(ndarray))
|
||||
};
|
||||
|
@ -72,889 +71,33 @@ pub fn unpack_ndarray_tvars(
|
|||
|
||||
params.iter()
|
||||
.sorted_by_key(|(obj_id, _)| *obj_id)
|
||||
.map(|(_, ty)| *ty)
|
||||
.map(|(var_id, ty)| (*var_id, *ty))
|
||||
.collect_vec()
|
||||
}
|
||||
|
||||
/// Unpacks the type variable IDs of `ndarray` into a tuple. The elements of the tuple corresponds
|
||||
/// to `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray`
|
||||
/// respectively.
|
||||
pub fn unpack_ndarray_var_ids(
|
||||
unifier: &mut Unifier,
|
||||
ndarray: Type,
|
||||
) -> (u32, u32) {
|
||||
unpack_ndarray_tvars(unifier, ndarray)
|
||||
.into_iter()
|
||||
.map(|v| v.0)
|
||||
.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()
|
||||
/// Unpacks the type variables of `ndarray` into a tuple. The elements of the tuple corresponds to
|
||||
/// `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray` respectively.
|
||||
pub fn unpack_ndarray_var_tys(
|
||||
unifier: &mut Unifier,
|
||||
ndarray: Type,
|
||||
) -> (Type, Type) {
|
||||
unpack_ndarray_tvars(unifier, ndarray)
|
||||
.into_iter()
|
||||
.map(|v| v.1)
|
||||
.collect_tuple()
|
||||
.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(())
|
||||
}
|
||||
|
|
|
@ -7,7 +7,7 @@ expression: res_vec
|
|||
"Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [32]\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [37]\n}\n",
|
||||
"Function {\nname: \"gfun\",\nsig: \"fn[[a:A[int32, list[float]]], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
]
|
||||
|
|
|
@ -3,11 +3,11 @@ source: nac3core/src/toplevel/test.rs
|
|||
expression: res_vec
|
||||
---
|
||||
[
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[typevar18, typevar19]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[bool, float], b:B], none]\"), (\"fun\", \"fn[[a:A[bool, float]], A[bool, int32]]\")],\ntype_vars: [\"typevar18\", \"typevar19\"]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[bool, float], b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[bool, float]], A[bool, int32]]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[bool, float]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[int32, list[B]]], tuple[A[bool, virtual[A[B, int32]]], B]]\")],\ntype_vars: []\n}\n",
|
||||
"Class {\nname: \"A\",\nancestors: [\"A[typevar18, typevar19]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar18\", \"typevar19\"]\n}\n",
|
||||
"Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
|
||||
"Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",
|
||||
"Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"B.foo\",\nsig: \"fn[[b:B], B]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"B.bar\",\nsig: \"fn[[a:A[int32, list[B]]], tuple[A[bool, virtual[A[B, int32]]], B]]\",\nvar_id: []\n}\n",
|
||||
"Function {\nname: \"B.bar\",\nsig: \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\",\nvar_id: []\n}\n",
|
||||
]
|
||||
|
|
|
@ -1,5 +1,6 @@
|
|||
use crate::symbol_resolver::SymbolValue;
|
||||
use crate::toplevel::helper::PRIMITIVE_DEF_IDS;
|
||||
use crate::typecheck::typedef::VarMap;
|
||||
use super::*;
|
||||
use nac3parser::ast::Constant;
|
||||
|
||||
|
|
|
@ -1,3 +1,4 @@
|
|||
use crate::toplevel::numpy::make_ndarray_ty;
|
||||
use crate::typecheck::{
|
||||
type_inferencer::*,
|
||||
typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
|
||||
|
@ -234,8 +235,14 @@ pub fn impl_bitwise_shift(unifier: &mut Unifier, store: &PrimitiveStore, ty: Typ
|
|||
}
|
||||
|
||||
/// `Div`
|
||||
pub fn impl_div(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type, other_ty: &[Type]) {
|
||||
impl_binop(unifier, store, ty, other_ty, store.float, &[Operator::Div]);
|
||||
pub fn impl_div(
|
||||
unifier: &mut Unifier,
|
||||
store: &PrimitiveStore,
|
||||
ty: Type,
|
||||
other_ty: &[Type],
|
||||
ret_ty: Type,
|
||||
) {
|
||||
impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::Div]);
|
||||
}
|
||||
|
||||
/// `FloorDiv`
|
||||
|
@ -299,8 +306,10 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
|
|||
bool: bool_t,
|
||||
uint32: uint32_t,
|
||||
uint64: uint64_t,
|
||||
ndarray: ndarray_t,
|
||||
..
|
||||
} = *store;
|
||||
let size_t = store.usize();
|
||||
|
||||
/* int ======== */
|
||||
for t in [int32_t, int64_t, uint32_t, uint64_t] {
|
||||
|
@ -308,7 +317,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
|
|||
impl_pow(unifier, store, t, &[t], t);
|
||||
impl_bitwise_arithmetic(unifier, store, t);
|
||||
impl_bitwise_shift(unifier, store, t);
|
||||
impl_div(unifier, store, t, &[t]);
|
||||
impl_div(unifier, store, t, &[t], float_t);
|
||||
impl_floordiv(unifier, store, t, &[t], t);
|
||||
impl_mod(unifier, store, t, &[t], t);
|
||||
impl_invert(unifier, store, t);
|
||||
|
@ -323,7 +332,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
|
|||
/* float ======== */
|
||||
impl_basic_arithmetic(unifier, store, float_t, &[float_t], float_t);
|
||||
impl_pow(unifier, store, float_t, &[int32_t, float_t], float_t);
|
||||
impl_div(unifier, store, float_t, &[float_t]);
|
||||
impl_div(unifier, store, float_t, &[float_t], float_t);
|
||||
impl_floordiv(unifier, store, float_t, &[float_t], float_t);
|
||||
impl_mod(unifier, store, float_t, &[float_t], float_t);
|
||||
impl_sign(unifier, store, float_t);
|
||||
|
@ -334,4 +343,14 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
|
|||
/* bool ======== */
|
||||
impl_not(unifier, store, bool_t);
|
||||
impl_eq(unifier, store, bool_t);
|
||||
|
||||
/* ndarray ===== */
|
||||
let ndarray_float_t = make_ndarray_ty(unifier, store, Some(float_t), None);
|
||||
let ndarray_usized_ndims_tvar = unifier.get_fresh_const_generic_var(size_t, Some("ndarray_ndims".into()), None);
|
||||
let ndarray_unsized_t = make_ndarray_ty(unifier, store, None, Some(ndarray_usized_ndims_tvar.0));
|
||||
impl_basic_arithmetic(unifier, store, ndarray_t, &[ndarray_unsized_t], ndarray_t);
|
||||
impl_pow(unifier, store, ndarray_t, &[ndarray_unsized_t], ndarray_t);
|
||||
impl_div(unifier, store, ndarray_t, &[ndarray_t], ndarray_float_t);
|
||||
impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t], ndarray_t);
|
||||
impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t], ndarray_t);
|
||||
}
|
||||
|
|
|
@ -9,7 +9,7 @@ use crate::{
|
|||
symbol_resolver::{SymbolResolver, SymbolValue},
|
||||
toplevel::{
|
||||
helper::PRIMITIVE_DEF_IDS,
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_tvars},
|
||||
numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
|
||||
TopLevelContext,
|
||||
},
|
||||
};
|
||||
|
@ -1334,7 +1334,7 @@ impl<'a> Inferencer<'a> {
|
|||
let list_like_ty = match &*self.unifier.get_ty(value.custom.unwrap()) {
|
||||
TypeEnum::TList { .. } => self.unifier.add_ty(TypeEnum::TList { ty }),
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
|
||||
let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
|
||||
let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
|
||||
|
||||
make_ndarray_ty(self.unifier, self.primitives, Some(ty), Some(ndims))
|
||||
}
|
||||
|
@ -1347,7 +1347,7 @@ impl<'a> Inferencer<'a> {
|
|||
ExprKind::Constant { value: ast::Constant::Int(val), .. } => {
|
||||
match &*self.unifier.get_ty(value.custom.unwrap()) {
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
|
||||
let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
|
||||
let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
|
||||
self.infer_subscript_ndarray(value, ty, ndims)
|
||||
}
|
||||
_ => {
|
||||
|
@ -1379,7 +1379,7 @@ impl<'a> Inferencer<'a> {
|
|||
Ok(ty)
|
||||
}
|
||||
TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
|
||||
let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
|
||||
let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
|
||||
|
||||
self.constrain(slice.custom.unwrap(), self.primitives.usize(), &slice.location)?;
|
||||
self.infer_subscript_ndarray(value, ty, ndims)
|
||||
|
|
|
@ -1,10 +1,11 @@
|
|||
use std::cell::RefCell;
|
||||
use std::collections::{BTreeMap, HashMap};
|
||||
use std::collections::HashMap;
|
||||
use std::fmt::Display;
|
||||
use std::rc::Rc;
|
||||
use std::sync::{Arc, Mutex};
|
||||
use std::{borrow::Cow, collections::HashSet};
|
||||
use std::iter::zip;
|
||||
use indexmap::IndexMap;
|
||||
use itertools::Itertools;
|
||||
|
||||
use nac3parser::ast::{Location, StrRef};
|
||||
|
@ -25,14 +26,10 @@ pub type Type = UnificationKey;
|
|||
pub struct CallId(pub(super) usize);
|
||||
|
||||
pub type Mapping<K, V = Type> = HashMap<K, V>;
|
||||
pub type IndexMapping<K, V = Type> = IndexMap<K, V>;
|
||||
|
||||
/// A [`Mapping`] sorted by its key.
|
||||
///
|
||||
/// This type is recommended for mappings that should be stored and/or iterated by its sorted key.
|
||||
pub type SortedMapping<K, V = Type> = BTreeMap<K, V>;
|
||||
|
||||
/// A [`BTreeMap`] storing the mapping between type variable ID and [unifier type][`Type`].
|
||||
pub type VarMap = SortedMapping<u32>;
|
||||
/// The mapping between type variable ID and [unifier type][`Type`].
|
||||
pub type VarMap = IndexMapping<u32>;
|
||||
|
||||
#[derive(Clone)]
|
||||
pub struct Call {
|
||||
|
@ -920,8 +917,8 @@ impl Unifier {
|
|||
// Sort the type arguments by its UnificationKey first, since `HashMap::iter` visits
|
||||
// all K-V pairs "in arbitrary order"
|
||||
let (tv1, tv2) = (
|
||||
params1.iter().sorted_by_key(|(k, _)| *k).map(|(_, v)| v).collect_vec(),
|
||||
params2.iter().sorted_by_key(|(k, _)| *k).map(|(_, v)| v).collect_vec(),
|
||||
params1.iter().map(|(_, v)| v).collect_vec(),
|
||||
params2.iter().map(|(_, v)| v).collect_vec(),
|
||||
);
|
||||
for (x, y) in zip(tv1, tv2) {
|
||||
if self.unify_impl(*x, *y, false).is_err() {
|
||||
|
@ -1097,11 +1094,9 @@ impl Unifier {
|
|||
if params.is_empty() {
|
||||
name
|
||||
} else {
|
||||
let params = params
|
||||
let mut params = params
|
||||
.iter()
|
||||
.map(|(_, v)| self.internal_stringify(*v, obj_to_name, var_to_name, notes));
|
||||
// sort to preserve order
|
||||
let mut params = params.sorted();
|
||||
format!("{}[{}]", name, params.join(", "))
|
||||
}
|
||||
}
|
||||
|
@ -1283,12 +1278,12 @@ impl Unifier {
|
|||
|
||||
fn subst_map<K>(
|
||||
&mut self,
|
||||
map: &SortedMapping<K>,
|
||||
map: &IndexMapping<K>,
|
||||
mapping: &VarMap,
|
||||
cache: &mut HashMap<Type, Option<Type>>,
|
||||
) -> Option<SortedMapping<K>>
|
||||
where
|
||||
K: Ord + Eq + Clone,
|
||||
) -> Option<IndexMapping<K>>
|
||||
where
|
||||
K: std::hash::Hash + Eq + Clone,
|
||||
{
|
||||
let mut map2 = None;
|
||||
for (k, v) in map {
|
||||
|
|
|
@ -45,9 +45,9 @@ impl Unifier {
|
|||
}
|
||||
}
|
||||
|
||||
fn map_eq<K>(&mut self, map1: &SortedMapping<K>, map2: &SortedMapping<K>) -> bool
|
||||
where
|
||||
K: Ord + Eq + Clone,
|
||||
fn map_eq<K>(&mut self, map1: &IndexMapping<K>, map2: &IndexMapping<K>) -> bool
|
||||
where
|
||||
K: std::hash::Hash + Eq + Clone
|
||||
{
|
||||
if map1.len() != map2.len() {
|
||||
return false;
|
||||
|
|
|
@ -67,6 +67,181 @@ def test_ndarray_copy():
|
|||
output_float64(y[1][0])
|
||||
output_float64(y[1][1])
|
||||
|
||||
def test_ndarray_add():
|
||||
x = np_identity(2)
|
||||
y = x + np_ones([2, 2])
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
output_float64(y[0][0])
|
||||
output_float64(y[0][1])
|
||||
output_float64(y[1][0])
|
||||
output_float64(y[1][1])
|
||||
|
||||
# def test_ndarray_add_broadcast():
|
||||
# x = np_identity(2)
|
||||
# y: ndarray[float, 2] = x + np_ones([2])
|
||||
#
|
||||
# output_float64(x[0][0])
|
||||
# output_float64(x[0][1])
|
||||
# output_float64(x[1][0])
|
||||
# output_float64(x[1][1])
|
||||
#
|
||||
# output_float64(y[0][0])
|
||||
# output_float64(y[0][1])
|
||||
# output_float64(y[1][0])
|
||||
# output_float64(y[1][1])
|
||||
|
||||
def test_ndarray_iadd():
|
||||
x = np_identity(2)
|
||||
x += np_ones([2, 2])
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
def test_ndarray_sub():
|
||||
x = np_ones([2, 2])
|
||||
y = x - np_identity(2)
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
output_float64(y[0][0])
|
||||
output_float64(y[0][1])
|
||||
output_float64(y[1][0])
|
||||
output_float64(y[1][1])
|
||||
|
||||
def test_ndarray_isub():
|
||||
x = np_ones([2, 2])
|
||||
x -= np_identity(2)
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
def test_ndarray_mul():
|
||||
x = np_ones([2, 2])
|
||||
y = x * np_identity(2)
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
output_float64(y[0][0])
|
||||
output_float64(y[0][1])
|
||||
output_float64(y[1][0])
|
||||
output_float64(y[1][1])
|
||||
|
||||
def test_ndarray_imul():
|
||||
x = np_ones([2, 2])
|
||||
x *= np_identity(2)
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
def test_ndarray_truediv():
|
||||
x = np_identity(2)
|
||||
y = x / np_ones([2, 2])
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
output_float64(y[0][0])
|
||||
output_float64(y[0][1])
|
||||
output_float64(y[1][0])
|
||||
output_float64(y[1][1])
|
||||
|
||||
def test_ndarray_itruediv():
|
||||
x = np_identity(2)
|
||||
x /= np_ones([2, 2])
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
def test_ndarray_floordiv():
|
||||
x = np_identity(2)
|
||||
y = x // np_ones([2, 2])
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
output_float64(y[0][0])
|
||||
output_float64(y[0][1])
|
||||
output_float64(y[1][0])
|
||||
output_float64(y[1][1])
|
||||
|
||||
def test_ndarray_ifloordiv():
|
||||
x = np_identity(2)
|
||||
x //= np_ones([2, 2])
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
def test_ndarray_mod():
|
||||
x = np_identity(2)
|
||||
y = x % np_full([2, 2], 2.0)
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
output_float64(y[0][0])
|
||||
output_float64(y[0][1])
|
||||
output_float64(y[1][0])
|
||||
output_float64(y[1][1])
|
||||
|
||||
def test_ndarray_imod():
|
||||
x = np_identity(2)
|
||||
x %= np_full([2, 2], 2.0)
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
def test_ndarray_pow():
|
||||
x = np_identity(2)
|
||||
y = x ** np_full([2, 2], 2.0)
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
output_float64(y[0][0])
|
||||
output_float64(y[0][1])
|
||||
output_float64(y[1][0])
|
||||
output_float64(y[1][1])
|
||||
|
||||
def test_ndarray_ipow():
|
||||
x = np_identity(2)
|
||||
x **= np_full([2, 2], 2.0)
|
||||
|
||||
output_float64(x[0][0])
|
||||
output_float64(x[0][1])
|
||||
output_float64(x[1][0])
|
||||
output_float64(x[1][1])
|
||||
|
||||
def run() -> int32:
|
||||
test_ndarray_ctor()
|
||||
test_ndarray_empty()
|
||||
|
@ -77,5 +252,17 @@ def run() -> int32:
|
|||
test_ndarray_identity()
|
||||
test_ndarray_fill()
|
||||
test_ndarray_copy()
|
||||
test_ndarray_add()
|
||||
test_ndarray_iadd()
|
||||
test_ndarray_sub()
|
||||
test_ndarray_isub()
|
||||
test_ndarray_mul()
|
||||
test_ndarray_imul()
|
||||
test_ndarray_truediv()
|
||||
test_ndarray_itruediv()
|
||||
test_ndarray_floordiv()
|
||||
test_ndarray_ifloordiv()
|
||||
test_ndarray_mod()
|
||||
test_ndarray_imod()
|
||||
|
||||
return 0
|
||||
|
|
Loading…
Reference in New Issue