copy constructor -> clone

DWARF reader never had to reverse. Readers are already copied to achieve this effect.
Plus the position that it reverses to might be questionable.

.clang-format

@@ -1,3 +1,32 @@
-BasedOnStyle: Google
+BasedOnStyle: LLVM
+
+Language: Cpp
+Standard: Cpp11
+
+AccessModifierOffset: -1
+AlignEscapedNewlines: Left
+AlwaysBreakAfterReturnType: None
+AlwaysBreakTemplateDeclarations: Yes
+AllowAllParametersOfDeclarationOnNextLine: false
+AllowShortFunctionsOnASingleLine: Inline
+BinPackParameters: false
+BreakBeforeBinaryOperators: NonAssignment
+BreakBeforeTernaryOperators: true
+BreakConstructorInitializers: AfterColon
+BreakInheritanceList: AfterColon
+ColumnLimit: 120
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ContinuationIndentWidth: 4
+DerivePointerAlignment: false
+IndentCaseLabels: true
+IndentPPDirectives: None
 IndentWidth: 4
-ReflowComments: false
+MaxEmptyLinesToKeep: 1
+PointerAlignment: Left
+ReflowComments: true
+SortIncludes: false
+SortUsingDeclarations: true
+SpaceAfterTemplateKeyword: false
+SpacesBeforeTrailingComments: 2
+TabWidth: 4
+UseTab: Never

.pre-commit-config.yaml

@@ -1,24 +1,24 @@
 # See https://pre-commit.com for more information
 # See https://pre-commit.com/hooks.html for more hooks
 
-default_stages: [commit]
+default_stages: [pre-commit]
 
 repos:
   - repo: local
     hooks:
       - id: nac3-cargo-fmt
         name: nac3 cargo format
-        entry: cargo
+        entry: nix
         language: system
         types: [file, rust]
         pass_filenames: false
         description: Runs cargo fmt on the codebase.
-        args: [fmt]
+        args: [develop, -c, cargo, fmt, --all]
       - id: nac3-cargo-clippy
         name: nac3 cargo clippy
-        entry: cargo
+        entry: nix
         language: system
         types: [file, rust]
         pass_filenames: false
         description: Runs cargo clippy on the codebase.
-        args: [clippy, --tests]
+        args: [develop, -c, cargo, clippy, --tests]

Cargo.toml

@@ -4,6 +4,7 @@ members = [
   "nac3ast",
   "nac3parser",
   "nac3core",
+  "nac3core/nac3core_derive",
   "nac3standalone",
   "nac3artiq",
   "runkernel",

flake.lock

@@ -2,11 +2,11 @@
   "nodes": {
     "nixpkgs": {
       "locked": {
-        "lastModified": 1721924956,
-        "narHash": "sha256-Sb1jlyRO+N8jBXEX9Pg9Z1Qb8Bw9QyOgLDNMEpmjZ2M=",
+        "lastModified": 1731319897,
+        "narHash": "sha256-PbABj4tnbWFMfBp6OcUK5iGy1QY+/Z96ZcLpooIbuEI=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "5ad6a14c6bf098e98800b091668718c336effc95",
+        "rev": "dc460ec76cbff0e66e269457d7b728432263166c",
         "type": "github"
       },
       "original": {

flake.nix

@@ -14,7 +14,6 @@
           ''
           mkdir -p $out/bin
           ln -s ${pkgs.llvmPackages_14.clang-unwrapped}/bin/clang $out/bin/clang-irrt
-          ln -s ${pkgs.llvmPackages_14.clang}/bin/clang $out/bin/clang-irrt-test
           ln -s ${pkgs.llvmPackages_14.llvm.out}/bin/llvm-as $out/bin/llvm-as-irrt
           '';
         demo-linalg-stub = pkgs.rustPlatform.buildRustPackage {
@@ -41,7 +40,6 @@
             cargoLock = {
               lockFile = ./Cargo.lock;
             };
-            cargoTestFlags = [ "--features" "test" ];
             passthru.cargoLock = cargoLock;
             nativeBuildInputs = [ pkgs.python3 (pkgs.wrapClangMulti pkgs.llvmPackages_14.clang) llvm-tools-irrt pkgs.llvmPackages_14.llvm.out llvm-nac3 ];
             buildInputs = [ pkgs.python3 llvm-nac3 ];
@@ -109,18 +107,18 @@
             (pkgs.fetchFromGitHub {
               owner = "m-labs";
               repo = "sipyco";
-              rev = "939f84f9b5eef7efbf7423c735d1834783b6140e";
-              sha256 = "sha256-15Nun4EY35j+6SPZkjzZtyH/ncxLS60KuGJjFh5kSTc=";
+              rev = "094a6cd63ffa980ef63698920170e50dc9ba77fd";
+              sha256 = "sha256-PPnAyDedUQ7Og/Cby9x5OT9wMkNGTP8GS53V6N/dk4w=";
             })
             (pkgs.fetchFromGitHub {
               owner = "m-labs";
               repo = "artiq";
-              rev = "923ca3377d42c815f979983134ec549dc39d3ca0";
-              sha256 = "sha256-oJoEeNEeNFSUyh6jXG8Tzp6qHVikeHS0CzfE+mODPgw=";
+              rev = "28c9de3e251daa89a8c9fd79d5ab64a3ec03bac6";
+              sha256 = "sha256-vAvpbHc5B+1wtG8zqN7j9dQE1ON+i22v+uqA+tw6Gak=";
             })
           ];
           buildInputs = [
-            (python3-mimalloc.withPackages(ps: [ ps.numpy ps.scipy ps.jsonschema ps.lmdb nac3artiq-instrumented ]))
+            (python3-mimalloc.withPackages(ps: [ ps.numpy ps.scipy ps.jsonschema ps.lmdb ps.platformdirs nac3artiq-instrumented ]))
             pkgs.llvmPackages_14.llvm.out
           ];
           phases = [ "buildPhase" "installPhase" ];

nac3artiq/Cargo.toml

@@ -12,15 +12,10 @@ crate-type = ["cdylib"]
 itertools = "0.13"
 pyo3 = { version = "0.21", features = ["extension-module", "gil-refs"] }
 parking_lot = "0.12"
-tempfile = "3.10"
-nac3parser = { path = "../nac3parser" }
+tempfile = "3.13"
 nac3core = { path = "../nac3core" }
 nac3ld = { path = "../nac3ld" }
 
-[dependencies.inkwell]
-version = "0.4"
-default-features = false
-features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
-
 [features]
 init-llvm-profile = []
+no-escape-analysis = ["nac3core/no-escape-analysis"]

nac3artiq/demo/min_artiq.py

@@ -112,10 +112,15 @@ def extern(function):
     register_function(function)
     return function
 
-def rpc(function):
-    """Decorates a function declaration defined by the core device runtime."""
-    register_function(function)
-    return function
+
+def rpc(arg=None, flags={}):
+    """Decorates a function or method to be executed on the host interpreter."""
+    if arg is None:
+        def inner_decorator(function):
+            return rpc(function, flags)
+        return inner_decorator
+    register_function(arg)
+    return arg
 
 def kernel(function_or_method):
     """Decorates a function or method to be executed on the core device."""
@@ -201,7 +206,7 @@ class Core:
         embedding = EmbeddingMap()
 
         if allow_registration:
-            compiler.analyze(registered_functions, registered_classes)
+            compiler.analyze(registered_functions, registered_classes, set())
             allow_registration = False
 
         if hasattr(method, "__self__"):

nac3artiq/demo/str_abi.py

@@ -0,0 +1,26 @@
+from min_artiq import *
+from numpy import ndarray, zeros as np_zeros
+
+
+@nac3
+class StrFail:
+    core: KernelInvariant[Core]
+
+    def __init__(self):
+        self.core = Core()
+
+    @kernel
+    def hello(self, arg: str):
+        pass
+
+    @kernel
+    def consume_ndarray(self, arg: ndarray[str, 1]):
+        pass
+
+    def run(self):
+        self.hello("world")
+        self.consume_ndarray(np_zeros([10], dtype=str))
+
+
+if __name__ == "__main__":
+    StrFail().run()

nac3artiq/src/codegen.rs

@@ -1,35 +1,3 @@
-use nac3core::{
-    codegen::{
-        classes::{ListValue, NDArrayValue, RangeValue, UntypedArrayLikeAccessor},
-        expr::{destructure_range, gen_call},
-        irrt::call_ndarray_calc_size,
-        llvm_intrinsics::{call_int_smax, call_stackrestore, call_stacksave},
-        stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
-        CodeGenContext, CodeGenerator,
-    },
-    symbol_resolver::ValueEnum,
-    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, GenCall},
-    typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
-};
-
-use nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef};
-
-use inkwell::{
-    context::Context,
-    module::Linkage,
-    types::IntType,
-    values::{BasicValueEnum, StructValue},
-    AddressSpace, IntPredicate,
-};
-
-use pyo3::{
-    types::{PyDict, PyList},
-    PyObject, PyResult, Python,
-};
-
-use crate::{symbol_resolver::InnerResolver, timeline::TimeFns};
-
-use itertools::Itertools;
 use std::{
     collections::{hash_map::DefaultHasher, HashMap},
     hash::{Hash, Hasher},
@@ -38,6 +6,40 @@ use std::{
     sync::Arc,
 };
 
+use itertools::Itertools;
+use pyo3::{
+    types::{PyDict, PyList},
+    PyObject, PyResult, Python,
+};
+
+use nac3core::{
+    codegen::{
+        expr::{destructure_range, gen_call},
+        irrt::call_ndarray_calc_size,
+        llvm_intrinsics::{call_int_smax, call_memcpy_generic, call_stackrestore, call_stacksave},
+        stmt::{gen_block, gen_for_callback_incrementing, gen_if_callback, gen_with},
+        types::{NDArrayType, ProxyType},
+        values::{
+            ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue, ProxyValue,
+            RangeValue, UntypedArrayLikeAccessor,
+        },
+        CodeGenContext, CodeGenerator,
+    },
+    inkwell::{
+        context::Context,
+        module::Linkage,
+        types::{BasicType, IntType},
+        values::{BasicValueEnum, IntValue, PointerValue, StructValue},
+        AddressSpace, IntPredicate, OptimizationLevel,
+    },
+    nac3parser::ast::{Expr, ExprKind, Located, Stmt, StmtKind, StrRef},
+    symbol_resolver::ValueEnum,
+    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, GenCall},
+    typecheck::typedef::{iter_type_vars, FunSignature, FuncArg, Type, TypeEnum, VarMap},
+};
+
+use super::{symbol_resolver::InnerResolver, timeline::TimeFns};
+
 /// The parallelism mode within a block.
 #[derive(Copy, Clone, Eq, PartialEq)]
 enum ParallelMode {
@@ -422,7 +424,10 @@ fn gen_rpc_tag(
                 } else {
                     unreachable!()
                 };
-                assert!((0u64..=u64::from(u8::MAX)).contains(&ndarray_ndims));
+                assert!(
+                    (0u64..=u64::from(u8::MAX)).contains(&ndarray_ndims),
+                    "Only NDArrays of sizes between 0 and 255 can be RPCed"
+                );
 
                 buffer.push(b'a');
                 buffer.push((ndarray_ndims & 0xFF) as u8);
@@ -434,17 +439,395 @@ fn gen_rpc_tag(
     Ok(())
 }
 
+/// Formats an RPC argument to conform to the expected format required by `send_value`.
+///
+/// See `artiq/firmware/libproto_artiq/rpc_proto.rs` for the expected format.
+fn format_rpc_arg<'ctx>(
+    generator: &mut dyn CodeGenerator,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    (arg, arg_ty, arg_idx): (BasicValueEnum<'ctx>, Type, usize),
+) -> PointerValue<'ctx> {
+    let llvm_i8 = ctx.ctx.i8_type();
+    let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
+
+    let arg_slot = match &*ctx.unifier.get_ty_immutable(arg_ty) {
+        TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
+            // NAC3: NDArray = { usize, usize*, T* }
+            // libproto_artiq: NDArray = [data[..], dim_sz[..]]
+
+            let llvm_i1 = ctx.ctx.bool_type();
+            let llvm_usize = generator.get_size_type(ctx.ctx);
+
+            let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
+            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
+            let llvm_arg_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
+            let llvm_arg = llvm_arg_ty.map_value(arg.into_pointer_value(), None);
+
+            let llvm_usize_sizeof = ctx
+                .builder
+                .build_int_truncate_or_bit_cast(llvm_arg_ty.size_type().size_of(), llvm_usize, "")
+                .unwrap();
+            let llvm_pdata_sizeof = ctx
+                .builder
+                .build_int_truncate_or_bit_cast(
+                    llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
+                    llvm_usize,
+                    "",
+                )
+                .unwrap();
+
+            let dims_buf_sz =
+                ctx.builder.build_int_mul(llvm_arg.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
+
+            let buffer_size =
+                ctx.builder.build_int_add(dims_buf_sz, llvm_pdata_sizeof, "").unwrap();
+
+            let buffer = ctx.builder.build_array_alloca(llvm_i8, buffer_size, "rpc.arg").unwrap();
+            let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, Some("rpc.arg"));
+
+            call_memcpy_generic(
+                ctx,
+                buffer.base_ptr(ctx, generator),
+                llvm_arg.ptr_to_data(ctx),
+                llvm_pdata_sizeof,
+                llvm_i1.const_zero(),
+            );
+
+            let pbuffer_dims_begin =
+                unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
+            call_memcpy_generic(
+                ctx,
+                pbuffer_dims_begin,
+                llvm_arg.shape().base_ptr(ctx, generator),
+                dims_buf_sz,
+                llvm_i1.const_zero(),
+            );
+
+            buffer.base_ptr(ctx, generator)
+        }
+
+        _ => {
+            let arg_slot = generator
+                .gen_var_alloc(ctx, arg.get_type(), Some(&format!("rpc.arg{arg_idx}")))
+                .unwrap();
+            ctx.builder.build_store(arg_slot, arg).unwrap();
+
+            ctx.builder
+                .build_bit_cast(arg_slot, llvm_pi8, "rpc.arg")
+                .map(BasicValueEnum::into_pointer_value)
+                .unwrap()
+        }
+    };
+
+    debug_assert_eq!(arg_slot.get_type(), llvm_pi8);
+
+    arg_slot
+}
+
+/// Formats an RPC return value to conform to the expected format required by NAC3.
+fn format_rpc_ret<'ctx>(
+    generator: &mut dyn CodeGenerator,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ret_ty: Type,
+) -> Option<BasicValueEnum<'ctx>> {
+    // -- receive value:
+    // T result = {
+    //   void *ret_ptr = alloca(sizeof(T));
+    //   void *ptr = ret_ptr;
+    //   loop: int size = rpc_recv(ptr);
+    //   // Non-zero: Provide `size` bytes of extra storage for variable-length data.
+    //   if(size) { ptr = alloca(size); goto loop; }
+    //   else *(T*)ret_ptr
+    // }
+
+    let llvm_i8 = ctx.ctx.i8_type();
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_i8_8 = ctx.ctx.struct_type(&[llvm_i8.array_type(8).into()], false);
+    let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
+
+    let rpc_recv = ctx.module.get_function("rpc_recv").unwrap_or_else(|| {
+        ctx.module.add_function("rpc_recv", llvm_i32.fn_type(&[llvm_pi8.into()], false), None)
+    });
+
+    if ctx.unifier.unioned(ret_ty, ctx.primitives.none) {
+        ctx.build_call_or_invoke(rpc_recv, &[llvm_pi8.const_null().into()], "rpc_recv");
+        return None;
+    }
+
+    let prehead_bb = ctx.builder.get_insert_block().unwrap();
+    let current_function = prehead_bb.get_parent().unwrap();
+    let head_bb = ctx.ctx.append_basic_block(current_function, "rpc.head");
+    let alloc_bb = ctx.ctx.append_basic_block(current_function, "rpc.continue");
+    let tail_bb = ctx.ctx.append_basic_block(current_function, "rpc.tail");
+
+    let llvm_ret_ty = ctx.get_llvm_abi_type(generator, ret_ty);
+
+    let result = match &*ctx.unifier.get_ty_immutable(ret_ty) {
+        TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
+            let llvm_i1 = ctx.ctx.bool_type();
+            let llvm_usize = generator.get_size_type(ctx.ctx);
+
+            // Round `val` up to its modulo `power_of_two`
+            let round_up = |ctx: &mut CodeGenContext<'ctx, '_>,
+                            val: IntValue<'ctx>,
+                            power_of_two: IntValue<'ctx>| {
+                debug_assert_eq!(
+                    val.get_type().get_bit_width(),
+                    power_of_two.get_type().get_bit_width()
+                );
+
+                let llvm_val_t = val.get_type();
+
+                let max_rem = ctx
+                    .builder
+                    .build_int_sub(power_of_two, llvm_val_t.const_int(1, false), "")
+                    .unwrap();
+                ctx.builder
+                    .build_and(
+                        ctx.builder.build_int_add(val, max_rem, "").unwrap(),
+                        ctx.builder.build_not(max_rem, "").unwrap(),
+                        "",
+                    )
+                    .unwrap()
+            };
+
+            // Setup types
+            let (elem_ty, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
+            let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
+            let llvm_ret_ty = NDArrayType::new(generator, ctx.ctx, llvm_elem_ty);
+
+            // Allocate the resulting ndarray
+            // A condition after format_rpc_ret ensures this will not be popped this off.
+            let ndarray = llvm_ret_ty.new_value(generator, ctx, Some("rpc.result"));
+
+            // Setup ndims
+            let ndims =
+                if let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) {
+                    assert_eq!(values.len(), 1);
+
+                    u64::try_from(values[0].clone()).unwrap()
+                } else {
+                    unreachable!();
+                };
+            // Set `ndarray.ndims`
+            ndarray.store_ndims(ctx, generator, llvm_usize.const_int(ndims, false));
+            // Allocate `ndarray.shape` [size_t; ndims]
+            ndarray.create_shape(ctx, llvm_usize, ndarray.load_ndims(ctx));
+
+            /*
+                ndarray now:
+                  - .ndims: initialized
+                  - .shape: allocated but uninitialized .shape
+                  - .data: uninitialized
+            */
+
+            let llvm_usize_sizeof = ctx
+                .builder
+                .build_int_truncate_or_bit_cast(llvm_usize.size_of(), llvm_usize, "")
+                .unwrap();
+            let llvm_pdata_sizeof = ctx
+                .builder
+                .build_int_truncate_or_bit_cast(
+                    llvm_elem_ty.ptr_type(AddressSpace::default()).size_of(),
+                    llvm_usize,
+                    "",
+                )
+                .unwrap();
+            let llvm_elem_sizeof = ctx
+                .builder
+                .build_int_truncate_or_bit_cast(llvm_elem_ty.size_of().unwrap(), llvm_usize, "")
+                .unwrap();
+
+            // Allocates a buffer for the initial RPC'ed object, which is guaranteed to be
+            // (4 + 4 * ndims) bytes with 8-byte alignment
+            let sizeof_dims =
+                ctx.builder.build_int_mul(ndarray.load_ndims(ctx), llvm_usize_sizeof, "").unwrap();
+            let unaligned_buffer_size =
+                ctx.builder.build_int_add(sizeof_dims, llvm_pdata_sizeof, "").unwrap();
+            let buffer_size = round_up(ctx, unaligned_buffer_size, llvm_usize.const_int(8, false));
+
+            let stackptr = call_stacksave(ctx, None);
+            // Just to be absolutely sure, alloca in [i8 x 8] slices to force 8-byte alignment
+            let buffer = ctx
+                .builder
+                .build_array_alloca(
+                    llvm_i8_8,
+                    ctx.builder
+                        .build_int_unsigned_div(buffer_size, llvm_usize.const_int(8, false), "")
+                        .unwrap(),
+                    "rpc.buffer",
+                )
+                .unwrap();
+            let buffer = ctx
+                .builder
+                .build_bit_cast(buffer, llvm_pi8, "")
+                .map(BasicValueEnum::into_pointer_value)
+                .unwrap();
+            let buffer = ArraySliceValue::from_ptr_val(buffer, buffer_size, None);
+
+            // The first call to `rpc_recv` reads the top-level ndarray object: [pdata, shape]
+            //
+            // The returned value is the number of bytes for `ndarray.data`.
+            let ndarray_nbytes = ctx
+                .build_call_or_invoke(
+                    rpc_recv,
+                    &[buffer.base_ptr(ctx, generator).into()], // Reads [usize; ndims]. NOTE: We are allocated [size_t; ndims].
+                    "rpc.size.next",
+                )
+                .map(BasicValueEnum::into_int_value)
+                .unwrap();
+
+            // debug_assert(ndarray_nbytes > 0)
+            if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
+                ctx.make_assert(
+                    generator,
+                    ctx.builder
+                        .build_int_compare(
+                            IntPredicate::UGT,
+                            ndarray_nbytes,
+                            ndarray_nbytes.get_type().const_zero(),
+                            "",
+                        )
+                        .unwrap(),
+                    "0:AssertionError",
+                    "Unexpected RPC termination for ndarray - Expected data buffer next",
+                    [None, None, None],
+                    ctx.current_loc,
+                );
+            }
+
+            // Copy shape from the buffer to `ndarray.shape`.
+            let pbuffer_dims =
+                unsafe { buffer.ptr_offset_unchecked(ctx, generator, &llvm_pdata_sizeof, None) };
+
+            call_memcpy_generic(
+                ctx,
+                ndarray.shape().base_ptr(ctx, generator),
+                pbuffer_dims,
+                sizeof_dims,
+                llvm_i1.const_zero(),
+            );
+            // Restore stack from before allocation of buffer
+            call_stackrestore(ctx, stackptr);
+
+            // Allocate `ndarray.data`.
+            // `ndarray.shape` must be initialized beforehand in this implementation
+            //   (for ndarray.create_data() to know how many elements to allocate)
+            let num_elements =
+                call_ndarray_calc_size(generator, ctx, &ndarray.shape(), (None, None));
+
+            // debug_assert(nelems * sizeof(T) >= ndarray_nbytes)
+            if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
+                let sizeof_data =
+                    ctx.builder.build_int_mul(num_elements, llvm_elem_sizeof, "").unwrap();
+
+                ctx.make_assert(
+                    generator,
+                    ctx.builder.build_int_compare(IntPredicate::UGE,
+                        sizeof_data,
+                        ndarray_nbytes,
+                        "",
+                    ).unwrap(),
+                    "0:AssertionError",
+                    "Unexpected allocation size request for ndarray data - Expected up to {0} bytes, got {1} bytes",
+                    [Some(sizeof_data), Some(ndarray_nbytes), None],
+                    ctx.current_loc,
+                );
+            }
+
+            ndarray.create_data(ctx, llvm_elem_ty, num_elements);
+
+            let ndarray_data = ndarray.data().base_ptr(ctx, generator);
+            let ndarray_data_i8 =
+                ctx.builder.build_pointer_cast(ndarray_data, llvm_pi8, "").unwrap();
+
+            // NOTE: Currently on `prehead_bb`
+            ctx.builder.build_unconditional_branch(head_bb).unwrap();
+
+            // Inserting into `head_bb`. Do `rpc_recv` for `data` recursively.
+            ctx.builder.position_at_end(head_bb);
+
+            let phi = ctx.builder.build_phi(llvm_pi8, "rpc.ptr").unwrap();
+            phi.add_incoming(&[(&ndarray_data_i8, prehead_bb)]);
+
+            let alloc_size = ctx
+                .build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
+                .map(BasicValueEnum::into_int_value)
+                .unwrap();
+
+            let is_done = ctx
+                .builder
+                .build_int_compare(IntPredicate::EQ, llvm_i32.const_zero(), alloc_size, "rpc.done")
+                .unwrap();
+            ctx.builder.build_conditional_branch(is_done, tail_bb, alloc_bb).unwrap();
+
+            ctx.builder.position_at_end(alloc_bb);
+            // Align the allocation to sizeof(T)
+            let alloc_size = round_up(ctx, alloc_size, llvm_elem_sizeof);
+            let alloc_ptr = ctx
+                .builder
+                .build_array_alloca(
+                    llvm_elem_ty,
+                    ctx.builder.build_int_unsigned_div(alloc_size, llvm_elem_sizeof, "").unwrap(),
+                    "rpc.alloc",
+                )
+                .unwrap();
+            let alloc_ptr =
+                ctx.builder.build_pointer_cast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
+            phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
+            ctx.builder.build_unconditional_branch(head_bb).unwrap();
+
+            ctx.builder.position_at_end(tail_bb);
+            ndarray.as_base_value().into()
+        }
+
+        _ => {
+            let slot = ctx.builder.build_alloca(llvm_ret_ty, "rpc.ret.slot").unwrap();
+            let slotgen = ctx.builder.build_bit_cast(slot, llvm_pi8, "rpc.ret.ptr").unwrap();
+            ctx.builder.build_unconditional_branch(head_bb).unwrap();
+            ctx.builder.position_at_end(head_bb);
+
+            let phi = ctx.builder.build_phi(llvm_pi8, "rpc.ptr").unwrap();
+            phi.add_incoming(&[(&slotgen, prehead_bb)]);
+            let alloc_size = ctx
+                .build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
+                .unwrap()
+                .into_int_value();
+            let is_done = ctx
+                .builder
+                .build_int_compare(IntPredicate::EQ, llvm_i32.const_zero(), alloc_size, "rpc.done")
+                .unwrap();
+
+            ctx.builder.build_conditional_branch(is_done, tail_bb, alloc_bb).unwrap();
+            ctx.builder.position_at_end(alloc_bb);
+
+            let alloc_ptr =
+                ctx.builder.build_array_alloca(llvm_pi8, alloc_size, "rpc.alloc").unwrap();
+            let alloc_ptr =
+                ctx.builder.build_bit_cast(alloc_ptr, llvm_pi8, "rpc.alloc.ptr").unwrap();
+            phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
+            ctx.builder.build_unconditional_branch(head_bb).unwrap();
+
+            ctx.builder.position_at_end(tail_bb);
+            ctx.builder.build_load(slot, "rpc.result").unwrap()
+        }
+    };
+
+    Some(result)
+}
+
 fn rpc_codegen_callback_fn<'ctx>(
     ctx: &mut CodeGenContext<'ctx, '_>,
     obj: Option<(Type, ValueEnum<'ctx>)>,
     fun: (&FunSignature, DefinitionId),
     args: Vec<(Option<StrRef>, ValueEnum<'ctx>)>,
     generator: &mut dyn CodeGenerator,
+    is_async: bool,
 ) -> Result<Option<BasicValueEnum<'ctx>>, String> {
-    let ptr_type = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
-    let size_type = generator.get_size_type(ctx.ctx);
     let int8 = ctx.ctx.i8_type();
     let int32 = ctx.ctx.i32_type();
+    let size_type = generator.get_size_type(ctx.ctx);
+    let ptr_type = int8.ptr_type(AddressSpace::default());
     let tag_ptr_type = ctx.ctx.struct_type(&[ptr_type.into(), size_type.into()], false);
 
     let service_id = int32.const_int(fun.1 .0 as u64, false);
@@ -517,22 +900,25 @@ fn rpc_codegen_callback_fn<'ctx>(
         .0
         .args
         .iter()
-        .map(|arg| mapping.remove(&arg.name).unwrap().to_basic_value_enum(ctx, generator, arg.ty))
-        .collect::<Result<Vec<_>, _>>()?;
+        .map(|arg| {
+            mapping
+                .remove(&arg.name)
+                .unwrap()
+                .to_basic_value_enum(ctx, generator, arg.ty)
+                .map(|llvm_val| (llvm_val, arg.ty))
+        })
+        .collect::<Result<Vec<(_, _)>, _>>()?;
     if let Some(obj) = obj {
-        if let ValueEnum::Static(obj) = obj.1 {
-            real_params.insert(0, obj.get_const_obj(ctx, generator));
+        if let ValueEnum::Static(obj_val) = obj.1 {
+            real_params.insert(0, (obj_val.get_const_obj(ctx, generator), obj.0));
         } else {
             // should be an error here...
             panic!("only host object is allowed");
         }
     }
 
-    for (i, arg) in real_params.iter().enumerate() {
-        let arg_slot =
-            generator.gen_var_alloc(ctx, arg.get_type(), Some(&format!("rpc.arg{i}"))).unwrap();
-        ctx.builder.build_store(arg_slot, *arg).unwrap();
-        let arg_slot = ctx.builder.build_bitcast(arg_slot, ptr_type, "rpc.arg").unwrap();
+    for (i, (arg, arg_ty)) in real_params.iter().enumerate() {
+        let arg_slot = format_rpc_arg(generator, ctx, (*arg, *arg_ty, i));
         let arg_ptr = unsafe {
             ctx.builder.build_gep(
                 args_ptr,
@@ -545,6 +931,29 @@ fn rpc_codegen_callback_fn<'ctx>(
     }
 
     // call
+    if is_async {
+        let rpc_send_async = ctx.module.get_function("rpc_send_async").unwrap_or_else(|| {
+            ctx.module.add_function(
+                "rpc_send_async",
+                ctx.ctx.void_type().fn_type(
+                    &[
+                        int32.into(),
+                        tag_ptr_type.ptr_type(AddressSpace::default()).into(),
+                        ptr_type.ptr_type(AddressSpace::default()).into(),
+                    ],
+                    false,
+                ),
+                None,
+            )
+        });
+        ctx.builder
+            .build_call(
+                rpc_send_async,
+                &[service_id.into(), tag_ptr.into(), args_ptr.into()],
+                "rpc.send",
+            )
+            .unwrap();
+    } else {
         let rpc_send = ctx.module.get_function("rpc_send").unwrap_or_else(|| {
             ctx.module.add_function(
                 "rpc_send",
@@ -562,74 +971,32 @@ fn rpc_codegen_callback_fn<'ctx>(
         ctx.builder
             .build_call(rpc_send, &[service_id.into(), tag_ptr.into(), args_ptr.into()], "rpc.send")
             .unwrap();
+    }
 
     // reclaim stack space used by arguments
     call_stackrestore(ctx, stackptr);
 
-    // -- receive value:
-    // T result = {
-    //   void *ret_ptr = alloca(sizeof(T));
-    //   void *ptr = ret_ptr;
-    //   loop: int size = rpc_recv(ptr);
-    //   // Non-zero: Provide `size` bytes of extra storage for variable-length data.
-    //   if(size) { ptr = alloca(size); goto loop; }
-    //   else *(T*)ret_ptr
-    // }
-    let rpc_recv = ctx.module.get_function("rpc_recv").unwrap_or_else(|| {
-        ctx.module.add_function("rpc_recv", int32.fn_type(&[ptr_type.into()], false), None)
-    });
+    if is_async {
+        // async RPCs do not return any values
+        Ok(None)
+    } else {
+        let result = format_rpc_ret(generator, ctx, fun.0.ret);
 
-    if ctx.unifier.unioned(fun.0.ret, ctx.primitives.none) {
-        ctx.build_call_or_invoke(rpc_recv, &[ptr_type.const_null().into()], "rpc_recv");
-        return Ok(None);
-    }
-
-    let prehead_bb = ctx.builder.get_insert_block().unwrap();
-    let current_function = prehead_bb.get_parent().unwrap();
-    let head_bb = ctx.ctx.append_basic_block(current_function, "rpc.head");
-    let alloc_bb = ctx.ctx.append_basic_block(current_function, "rpc.continue");
-    let tail_bb = ctx.ctx.append_basic_block(current_function, "rpc.tail");
-
-    let ret_ty = ctx.get_llvm_abi_type(generator, fun.0.ret);
-    let need_load = !ret_ty.is_pointer_type();
-    let slot = ctx.builder.build_alloca(ret_ty, "rpc.ret.slot").unwrap();
-    let slotgen = ctx.builder.build_bitcast(slot, ptr_type, "rpc.ret.ptr").unwrap();
-    ctx.builder.build_unconditional_branch(head_bb).unwrap();
-    ctx.builder.position_at_end(head_bb);
-
-    let phi = ctx.builder.build_phi(ptr_type, "rpc.ptr").unwrap();
-    phi.add_incoming(&[(&slotgen, prehead_bb)]);
-    let alloc_size = ctx
-        .build_call_or_invoke(rpc_recv, &[phi.as_basic_value()], "rpc.size.next")
-        .unwrap()
-        .into_int_value();
-    let is_done = ctx
-        .builder
-        .build_int_compare(inkwell::IntPredicate::EQ, int32.const_zero(), alloc_size, "rpc.done")
-        .unwrap();
-
-    ctx.builder.build_conditional_branch(is_done, tail_bb, alloc_bb).unwrap();
-    ctx.builder.position_at_end(alloc_bb);
-
-    let alloc_ptr = ctx.builder.build_array_alloca(ptr_type, alloc_size, "rpc.alloc").unwrap();
-    let alloc_ptr = ctx.builder.build_bitcast(alloc_ptr, ptr_type, "rpc.alloc.ptr").unwrap();
-    phi.add_incoming(&[(&alloc_ptr, alloc_bb)]);
-    ctx.builder.build_unconditional_branch(head_bb).unwrap();
-
-    ctx.builder.position_at_end(tail_bb);
-
-    let result = ctx.builder.build_load(slot, "rpc.result").unwrap();
-    if need_load {
+        if !result.is_some_and(|res| res.get_type().is_pointer_type()) {
+            // An RPC returning an NDArray would not touch here.
             call_stackrestore(ctx, stackptr);
         }
-    Ok(Some(result))
+
+        Ok(result)
+    }
 }
 
-pub fn attributes_writeback(
-    ctx: &mut CodeGenContext<'_, '_>,
+pub fn attributes_writeback<'ctx>(
+    ctx: &mut CodeGenContext<'ctx, '_>,
     generator: &mut dyn CodeGenerator,
     inner_resolver: &InnerResolver,
     host_attributes: &PyObject,
+    return_obj: Option<(Type, ValueEnum<'ctx>)>,
 ) -> Result<(), String> {
     Python::with_gil(|py| -> PyResult<Result<(), String>> {
         let host_attributes: &PyList = host_attributes.downcast(py)?;
@@ -639,6 +1006,11 @@ pub fn attributes_writeback(
         let zero = int32.const_zero();
         let mut values = Vec::new();
         let mut scratch_buffer = Vec::new();
+
+        if let Some((ty, obj)) = return_obj {
+            values.push((ty, obj.to_basic_value_enum(ctx, generator, ty).unwrap()));
+        }
+
         for val in (*globals).values() {
             let val = val.as_ref(py);
             let ty = inner_resolver.get_obj_type(
@@ -717,7 +1089,7 @@ pub fn attributes_writeback(
         let args: Vec<_> =
             values.into_iter().map(|(_, val)| (None, ValueEnum::Dynamic(val))).collect();
         if let Err(e) =
-            rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator)
+            rpc_codegen_callback_fn(ctx, None, (&fun, PrimDef::Int32.id()), args, generator, true)
         {
             return Ok(Err(e));
         }
@@ -727,9 +1099,9 @@ pub fn attributes_writeback(
     Ok(())
 }
 
-pub fn rpc_codegen_callback() -> Arc<GenCall> {
-    Arc::new(GenCall::new(Box::new(|ctx, obj, fun, args, generator| {
-        rpc_codegen_callback_fn(ctx, obj, fun, args, generator)
+pub fn rpc_codegen_callback(is_async: bool) -> Arc<GenCall> {
+    Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
+        rpc_codegen_callback_fn(ctx, obj, fun, args, generator, is_async)
     })))
 }
 
@@ -798,7 +1170,8 @@ fn polymorphic_print<'ctx>(
             ctx.module.add_function(fn_name, fn_t, None)
         });
 
-        let fmt = ctx.gen_string(generator, &fmt).get_field(generator, ctx.ctx, |f| f.base).value;
+        let fmt = ctx.gen_string(generator, fmt);
+        let fmt = unsafe { fmt.get_field_at_index_unchecked(0) }.into_pointer_value();
 
         ctx.builder
             .build_call(
@@ -878,24 +1251,20 @@ fn polymorphic_print<'ctx>(
                 fmt.push_str("%.*s");
 
                 let true_str = ctx.gen_string(generator, "True");
-
-                let true_data = true_str.get_field(generator, ctx.ctx, |f| f.base);
-                let true_len = true_str.get_field(generator, ctx.ctx, |f| f.len);
-
+                let true_data =
+                    unsafe { true_str.get_field_at_index_unchecked(0) }.into_pointer_value();
+                let true_len = unsafe { true_str.get_field_at_index_unchecked(1) }.into_int_value();
                 let false_str = ctx.gen_string(generator, "False");
-
-                let false_data = false_str.get_field(generator, ctx.ctx, |f| f.base);
-                let false_len = false_str.get_field(generator, ctx.ctx, |f| f.len);
+                let false_data =
+                    unsafe { false_str.get_field_at_index_unchecked(0) }.into_pointer_value();
+                let false_len =
+                    unsafe { false_str.get_field_at_index_unchecked(1) }.into_int_value();
 
                 let bool_val = generator.bool_to_i1(ctx, value.into_int_value());
 
                 args.extend([
-                    ctx.builder
-                        .build_select(bool_val, true_len.value, false_len.value, "")
-                        .unwrap(),
-                    ctx.builder
-                        .build_select(bool_val, true_data.value, false_data.value, "")
-                        .unwrap(),
+                    ctx.builder.build_select(bool_val, true_len, false_len, "").unwrap(),
+                    ctx.builder.build_select(bool_val, true_data, false_data, "").unwrap(),
                 ]);
             }
 
@@ -946,7 +1315,8 @@ fn polymorphic_print<'ctx>(
                 fmt.push('[');
                 flush(ctx, generator, &mut fmt, &mut args);
 
-                let val = ListValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
+                let val =
+                    ListValue::from_pointer_value(value.into_pointer_value(), llvm_usize, None);
                 let len = val.load_size(ctx, None);
                 let last =
                     ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
@@ -998,12 +1368,18 @@ fn polymorphic_print<'ctx>(
 
             TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
                 let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
+                let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
                 fmt.push_str("array([");
                 flush(ctx, generator, &mut fmt, &mut args);
 
-                let val = NDArrayValue::from_ptr_val(value.into_pointer_value(), llvm_usize, None);
-                let len = call_ndarray_calc_size(generator, ctx, &val.dim_sizes(), (None, None));
+                let val = NDArrayValue::from_pointer_value(
+                    value.into_pointer_value(),
+                    llvm_elem_ty,
+                    llvm_usize,
+                    None,
+                );
+                let len = call_ndarray_calc_size(generator, ctx, &val.shape(), (None, None));
                 let last =
                     ctx.builder.build_int_sub(len, llvm_usize.const_int(1, false), "").unwrap();
 
@@ -1056,7 +1432,7 @@ fn polymorphic_print<'ctx>(
                 fmt.push_str("range(");
                 flush(ctx, generator, &mut fmt, &mut args);
 
-                let val = RangeValue::from_ptr_val(value.into_pointer_value(), None);
+                let val = RangeValue::from_pointer_value(value.into_pointer_value(), None);
 
                 let (start, stop, step) = destructure_range(ctx, val);
 

nac3artiq/src/lib.rs

@@ -1,10 +1,4 @@
-#![deny(
-    future_incompatible,
-    let_underscore,
-    nonstandard_style,
-    rust_2024_compatibility,
-    clippy::all
-)]
+#![deny(future_incompatible, let_underscore, nonstandard_style, clippy::all)]
 #![warn(clippy::pedantic)]
 #![allow(
     unsafe_op_in_unsafe_fn,
@@ -16,65 +10,65 @@
     clippy::wildcard_imports
 )]
 
-use std::collections::{HashMap, HashSet};
-use std::fs;
-use std::io::Write;
-use std::process::Command;
-use std::rc::Rc;
-use std::sync::Arc;
+use std::{
+    collections::{HashMap, HashSet},
+    fs,
+    io::Write,
+    process::Command,
+    rc::Rc,
+    sync::Arc,
+};
 
-use inkwell::{
+use itertools::Itertools;
+use parking_lot::{Mutex, RwLock};
+use pyo3::{
+    create_exception, exceptions,
+    prelude::*,
+    types::{PyBytes, PyDict, PyNone, PySet},
+};
+use tempfile::{self, TempDir};
+
+use nac3core::{
+    codegen::{
+        concrete_type::ConcreteTypeStore, gen_func_impl, irrt::load_irrt, CodeGenLLVMOptions,
+        CodeGenTargetMachineOptions, CodeGenTask, CodeGenerator, WithCall, WorkerRegistry,
+    },
+    inkwell::{
         context::Context,
         memory_buffer::MemoryBuffer,
-    module::{Linkage, Module},
+        module::{FlagBehavior, Linkage, Module},
         passes::PassBuilderOptions,
         support::is_multithreaded,
         targets::*,
         OptimizationLevel,
-};
-use itertools::Itertools;
-use nac3core::codegen::irrt::setup_irrt_exceptions;
-use nac3core::codegen::{gen_func_impl, CodeGenLLVMOptions, CodeGenTargetMachineOptions};
-use nac3core::toplevel::builtins::get_exn_constructor;
-use nac3core::typecheck::typedef::{into_var_map, TypeEnum, Unifier, VarMap};
-use nac3parser::{
-    ast::{ExprKind, Stmt, StmtKind, StrRef},
+    },
+    nac3parser::{
+        ast::{Constant, ExprKind, Located, Stmt, StmtKind, StrRef},
         parser::parse_program,
-};
-use pyo3::create_exception;
-use pyo3::prelude::*;
-use pyo3::{exceptions, types::PyBytes, types::PyDict, types::PySet};
-
-use parking_lot::{Mutex, RwLock};
-
-use nac3core::{
-    codegen::irrt::load_irrt,
-    codegen::{concrete_type::ConcreteTypeStore, CodeGenTask, WithCall, WorkerRegistry},
+    },
     symbol_resolver::SymbolResolver,
     toplevel::{
+        builtins::get_exn_constructor,
         composer::{BuiltinFuncCreator, BuiltinFuncSpec, ComposerConfig, TopLevelComposer},
         DefinitionId, GenCall, TopLevelDef,
     },
-    typecheck::typedef::{FunSignature, FuncArg},
-    typecheck::{type_inferencer::PrimitiveStore, typedef::Type},
+    typecheck::{
+        type_inferencer::PrimitiveStore,
+        typedef::{into_var_map, FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
+    },
 };
-
 use nac3ld::Linker;
 
-use crate::{
-    codegen::{
+use codegen::{
     attributes_writeback, gen_core_log, gen_rtio_log, rpc_codegen_callback, ArtiqCodeGenerator,
-    },
-    symbol_resolver::{DeferredEvaluationStore, InnerResolver, PythonHelper, Resolver},
 };
-use tempfile::{self, TempDir};
+use symbol_resolver::{DeferredEvaluationStore, InnerResolver, PythonHelper, Resolver};
+use timeline::TimeFns;
 
 mod codegen;
 mod symbol_resolver;
 mod timeline;
 
-use timeline::TimeFns;
-
 #[derive(PartialEq, Clone, Copy)]
 enum Isa {
     Host,
@@ -148,14 +142,32 @@ impl Nac3 {
         module: &PyObject,
         registered_class_ids: &HashSet<u64>,
     ) -> PyResult<()> {
-        let (module_name, source_file) = Python::with_gil(|py| -> PyResult<(String, String)> {
+        let (module_name, source_file, source) =
+            Python::with_gil(|py| -> PyResult<(String, String, String)> {
                 let module: &PyAny = module.extract(py)?;
-            Ok((module.getattr("__name__")?.extract()?, module.getattr("__file__")?.extract()?))
+                let source_file = module.getattr("__file__");
+                let (source_file, source) = if let Ok(source_file) = source_file {
+                    let source_file = source_file.extract()?;
+                    (
+                        source_file,
+                        fs::read_to_string(source_file).map_err(|e| {
+                            exceptions::PyIOError::new_err(format!(
+                                "failed to read input file: {e}"
+                            ))
+                        })?,
+                    )
+                } else {
+                    // kernels submitted by content have no file
+                    // but still can provide source by StringLoader
+                    let get_src_fn = module
+                        .getattr("__loader__")?
+                        .extract::<PyObject>()?
+                        .getattr(py, "get_source")?;
+                    ("<expcontent>", get_src_fn.call1(py, (PyNone::get(py),))?.extract(py)?)
+                };
+                Ok((module.getattr("__name__")?.extract()?, source_file.to_string(), source))
             })?;
 
-        let source = fs::read_to_string(&source_file).map_err(|e| {
-            exceptions::PyIOError::new_err(format!("failed to read input file: {e}"))
-        })?;
         let parser_result = parse_program(&source, source_file.into())
             .map_err(|e| exceptions::PySyntaxError::new_err(format!("parse error: {e}")))?;
 
@@ -195,10 +207,8 @@ impl Nac3 {
                     body.retain(|stmt| {
                         if let StmtKind::FunctionDef { ref decorator_list, .. } = stmt.node {
                             decorator_list.iter().any(|decorator| {
-                                if let ExprKind::Name { id, .. } = decorator.node {
-                                    id.to_string() == "kernel"
-                                        || id.to_string() == "portable"
-                                        || id.to_string() == "rpc"
+                                if let Some(id) = decorator_id_string(decorator) {
+                                    id == "kernel" || id == "portable" || id == "rpc"
                                 } else {
                                     false
                                 }
@@ -211,9 +221,8 @@ impl Nac3 {
                 }
                 StmtKind::FunctionDef { ref decorator_list, .. } => {
                     decorator_list.iter().any(|decorator| {
-                        if let ExprKind::Name { id, .. } = decorator.node {
-                            let id = id.to_string();
-                            id == "extern" || id == "portable" || id == "kernel" || id == "rpc"
+                        if let Some(id) = decorator_id_string(decorator) {
+                            id == "extern" || id == "kernel" || id == "portable" || id == "rpc"
                         } else {
                             false
                         }
@@ -449,7 +458,6 @@ impl Nac3 {
                         pyid_to_type: pyid_to_type.clone(),
                         primitive_ids: self.primitive_ids.clone(),
                         global_value_ids: global_value_ids.clone(),
-                        class_names: Mutex::default(),
                         name_to_pyid: name_to_pyid.clone(),
                         module: module.clone(),
                         id_to_pyval: RwLock::default(),
@@ -480,9 +488,25 @@ impl Nac3 {
 
             match &stmt.node {
                 StmtKind::FunctionDef { decorator_list, .. } => {
-                    if decorator_list.iter().any(|decorator| matches!(decorator.node, ExprKind::Name { id, .. } if id == "rpc".into())) {
-                        store_fun.call1(py, (def_id.0.into_py(py), module.getattr(py, name.to_string().as_str()).unwrap())).unwrap();
-                        rpc_ids.push((None, def_id));
+                    if decorator_list
+                        .iter()
+                        .any(|decorator| decorator_id_string(decorator) == Some("rpc".to_string()))
+                    {
+                        store_fun
+                            .call1(
+                                py,
+                                (
+                                    def_id.0.into_py(py),
+                                    module.getattr(py, name.to_string().as_str()).unwrap(),
+                                ),
+                            )
+                            .unwrap();
+                        let is_async = decorator_list.iter().any(|decorator| {
+                            decorator_get_flags(decorator)
+                                .iter()
+                                .any(|constant| *constant == Constant::Str("async".into()))
+                        });
+                        rpc_ids.push((None, def_id, is_async));
                     }
                 }
                 StmtKind::ClassDef { name, body, .. } => {
@@ -490,19 +514,26 @@ impl Nac3 {
                     let class_obj = module.getattr(py, class_name.as_str()).unwrap();
                     for stmt in body {
                         if let StmtKind::FunctionDef { name, decorator_list, .. } = &stmt.node {
-                            if decorator_list.iter().any(|decorator| matches!(decorator.node, ExprKind::Name { id, .. } if id == "rpc".into())) {
+                            if decorator_list.iter().any(|decorator| {
+                                decorator_id_string(decorator) == Some("rpc".to_string())
+                            }) {
+                                let is_async = decorator_list.iter().any(|decorator| {
+                                    decorator_get_flags(decorator)
+                                        .iter()
+                                        .any(|constant| *constant == Constant::Str("async".into()))
+                                });
                                 if name == &"__init__".into() {
                                     return Err(CompileError::new_err(format!(
                                         "compilation failed\n----------\nThe constructor of class {} should not be decorated with rpc decorator (at {})",
                                         class_name, stmt.location
                                     )));
                                 }
-                                rpc_ids.push((Some((class_obj.clone(), *name)), def_id));
+                                rpc_ids.push((Some((class_obj.clone(), *name)), def_id, is_async));
                             }
                         }
                     }
                 }
-                _ => ()
+                _ => (),
             }
 
             let id = *name_to_pyid.get(&name).unwrap();
@@ -541,7 +572,6 @@ impl Nac3 {
             pyid_to_type: pyid_to_type.clone(),
             primitive_ids: self.primitive_ids.clone(),
             global_value_ids: global_value_ids.clone(),
-            class_names: Mutex::default(),
             id_to_pyval: RwLock::default(),
             id_to_primitive: RwLock::default(),
             field_to_val: RwLock::default(),
@@ -559,9 +589,8 @@ impl Nac3 {
             .unwrap();
 
         // Process IRRT
-        let context = inkwell::context::Context::create();
-        let irrt = load_irrt(&context);
-        setup_irrt_exceptions(&context, &irrt, resolver.as_ref());
+        let context = Context::create();
+        let irrt = load_irrt(&context, resolver.as_ref());
 
         let fun_signature =
             FunSignature { args: vec![], ret: self.primitive.none, vars: VarMap::new() };
@@ -600,13 +629,12 @@ impl Nac3 {
         let top_level = Arc::new(composer.make_top_level_context());
 
         {
-            let rpc_codegen = rpc_codegen_callback();
             let defs = top_level.definitions.read();
-            for (class_data, id) in &rpc_ids {
+            for (class_data, id, is_async) in &rpc_ids {
                 let mut def = defs[id.0].write();
                 match &mut *def {
                     TopLevelDef::Function { codegen_callback, .. } => {
-                        *codegen_callback = Some(rpc_codegen.clone());
+                        *codegen_callback = Some(rpc_codegen_callback(*is_async));
                     }
                     TopLevelDef::Class { methods, .. } => {
                         let (class_def, method_name) = class_data.as_ref().unwrap();
@@ -617,7 +645,7 @@ impl Nac3 {
                             if let TopLevelDef::Function { codegen_callback, .. } =
                                 &mut *defs[id.0].write()
                             {
-                                *codegen_callback = Some(rpc_codegen.clone());
+                                *codegen_callback = Some(rpc_codegen_callback(*is_async));
                                 store_fun
                                     .call1(
                                         py,
@@ -632,6 +660,11 @@ impl Nac3 {
                             }
                         }
                     }
+                    TopLevelDef::Variable { .. } => {
+                        return Err(CompileError::new_err(String::from(
+                            "Unsupported @rpc annotation on global variable",
+                        )))
+                    }
                 }
             }
         }
@@ -652,33 +685,12 @@ impl Nac3 {
         let task = CodeGenTask {
             subst: Vec::default(),
             symbol_name: "__modinit__".to_string(),
-            body: instance.body,
-            signature,
-            resolver: resolver.clone(),
-            store,
-            unifier_index: instance.unifier_id,
-            calls: instance.calls,
-            id: 0,
-        };
-
-        let mut store = ConcreteTypeStore::new();
-        let mut cache = HashMap::new();
-        let signature = store.from_signature(
-            &mut composer.unifier,
-            &self.primitive,
-            &fun_signature,
-            &mut cache,
-        );
-        let signature = store.add_cty(signature);
-        let attributes_writeback_task = CodeGenTask {
-            subst: Vec::default(),
-            symbol_name: "attributes_writeback".to_string(),
             body: Arc::new(Vec::default()),
             signature,
             resolver,
             store,
             unifier_index: instance.unifier_id,
-            calls: Arc::new(HashMap::default()),
+            calls: instance.calls,
             id: 0,
         };
 
@@ -691,7 +703,7 @@ impl Nac3 {
             let buffer = buffer.as_slice().into();
             membuffer.lock().push(buffer);
         })));
-        let size_t = Context::create()
+        let size_t = context
             .ptr_sized_int_type(&self.get_llvm_target_machine().get_target_data(), None)
             .get_bit_width();
         let num_threads = if is_multithreaded() { 4 } else { 1 };
@@ -702,19 +714,27 @@ impl Nac3 {
             .collect();
 
         let membuffer = membuffers.clone();
+        let mut has_return = false;
         py.allow_threads(|| {
             let (registry, handles) =
                 WorkerRegistry::create_workers(threads, top_level.clone(), &self.llvm_options, &f);
-            registry.add_task(task);
-            registry.wait_tasks_complete(handles);
 
-            let mut generator =
-                ArtiqCodeGenerator::new("attributes_writeback".to_string(), size_t, self.time_fns);
-            let context = inkwell::context::Context::create();
-            let module = context.create_module("attributes_writeback");
+            let mut generator = ArtiqCodeGenerator::new("main".to_string(), size_t, self.time_fns);
+            let context = Context::create();
+            let module = context.create_module("main");
             let target_machine = self.llvm_options.create_target_machine().unwrap();
             module.set_data_layout(&target_machine.get_target_data().get_data_layout());
             module.set_triple(&target_machine.get_triple());
+            module.add_basic_value_flag(
+                "Debug Info Version",
+                FlagBehavior::Warning,
+                context.i32_type().const_int(3, false),
+            );
+            module.add_basic_value_flag(
+                "Dwarf Version",
+                FlagBehavior::Warning,
+                context.i32_type().const_int(4, false),
+            );
             let builder = context.create_builder();
             let (_, module, _) = gen_func_impl(
                 &context,
@@ -722,9 +742,27 @@ impl Nac3 {
                 &registry,
                 builder,
                 module,
-                attributes_writeback_task,
+                task,
                 |generator, ctx| {
-                    attributes_writeback(ctx, generator, inner_resolver.as_ref(), &host_attributes)
+                    assert_eq!(instance.body.len(), 1, "toplevel module should have 1 statement");
+                    let StmtKind::Expr { value: ref expr, .. } = instance.body[0].node else {
+                        unreachable!("toplevel statement must be an expression")
+                    };
+                    let ExprKind::Call { .. } = expr.node else {
+                        unreachable!("toplevel expression must be a function call")
+                    };
+
+                    let return_obj =
+                        generator.gen_expr(ctx, expr)?.map(|value| (expr.custom.unwrap(), value));
+                    has_return = return_obj.is_some();
+                    registry.wait_tasks_complete(handles);
+                    attributes_writeback(
+                        ctx,
+                        generator,
+                        inner_resolver.as_ref(),
+                        &host_attributes,
+                        return_obj,
+                    )
                 },
             )
             .unwrap();
@@ -733,35 +771,23 @@ impl Nac3 {
             membuffer.lock().push(buffer);
         });
 
+        embedding_map.setattr("expects_return", has_return).unwrap();
+
         // Link all modules into `main`.
         let buffers = membuffers.lock();
         let main = context
-            .create_module_from_ir(MemoryBuffer::create_from_memory_range(&buffers[0], "main"))
+            .create_module_from_ir(MemoryBuffer::create_from_memory_range(
+                buffers.last().unwrap(),
+                "main",
+            ))
             .unwrap();
-        for buffer in buffers.iter().skip(1) {
+        for buffer in buffers.iter().rev().skip(1) {
             let other = context
                 .create_module_from_ir(MemoryBuffer::create_from_memory_range(buffer, "main"))
                 .unwrap();
 
             main.link_in_module(other).map_err(|err| CompileError::new_err(err.to_string()))?;
         }
-        let builder = context.create_builder();
-        let modinit_return = main
-            .get_function("__modinit__")
-            .unwrap()
-            .get_last_basic_block()
-            .unwrap()
-            .get_terminator()
-            .unwrap();
-        builder.position_before(&modinit_return);
-        builder
-            .build_call(
-                main.get_function("attributes_writeback").unwrap(),
-                &[],
-                "attributes_writeback",
-            )
-            .unwrap();
-
         main.link_in_module(irrt).map_err(|err| CompileError::new_err(err.to_string()))?;
 
         let mut function_iter = main.get_first_function();
@@ -848,6 +874,41 @@ impl Nac3 {
     }
 }
 
+/// Retrieves the Name.id from a decorator, supports decorators with arguments.
+fn decorator_id_string(decorator: &Located<ExprKind>) -> Option<String> {
+    if let ExprKind::Name { id, .. } = decorator.node {
+        // Bare decorator
+        return Some(id.to_string());
+    } else if let ExprKind::Call { func, .. } = &decorator.node {
+        // Decorators that are calls (e.g. "@rpc()") have Call for the node,
+        // need to extract the id from within.
+        if let ExprKind::Name { id, .. } = func.node {
+            return Some(id.to_string());
+        }
+    }
+    None
+}
+
+/// Retrieves flags from a decorator, if any.
+fn decorator_get_flags(decorator: &Located<ExprKind>) -> Vec<Constant> {
+    let mut flags = vec![];
+    if let ExprKind::Call { keywords, .. } = &decorator.node {
+        for keyword in keywords {
+            if keyword.node.arg != Some("flags".into()) {
+                continue;
+            }
+            if let ExprKind::Set { elts } = &keyword.node.value.node {
+                for elt in elts {
+                    if let ExprKind::Constant { value, .. } = &elt.node {
+                        flags.push(value.clone());
+                    }
+                }
+            }
+        }
+    }
+    flags
+}
+
 fn link_with_lld(elf_filename: String, obj_filename: String) -> PyResult<()> {
     let linker_args = vec![
         "-shared".to_string(),
@@ -1029,7 +1090,12 @@ impl Nac3 {
         })
     }
 
-    fn analyze(&mut self, functions: &PySet, classes: &PySet) -> PyResult<()> {
+    fn analyze(
+        &mut self,
+        functions: &PySet,
+        classes: &PySet,
+        content_modules: &PySet,
+    ) -> PyResult<()> {
         let (modules, class_ids) =
             Python::with_gil(|py| -> PyResult<(HashMap<u64, PyObject>, HashSet<u64>)> {
                 let mut modules: HashMap<u64, PyObject> = HashMap::new();
@@ -1039,14 +1105,22 @@ impl Nac3 {
                 let getmodule_fn = PyModule::import(py, "inspect")?.getattr("getmodule")?;
 
                 for function in functions {
-                    let module = getmodule_fn.call1((function,))?.extract()?;
+                    let module: PyObject = getmodule_fn.call1((function,))?.extract()?;
+                    if !module.is_none(py) {
                         modules.insert(id_fn.call1((&module,))?.extract()?, module);
                     }
+                }
                 for class in classes {
-                    let module = getmodule_fn.call1((class,))?.extract()?;
+                    let module: PyObject = getmodule_fn.call1((class,))?.extract()?;
+                    if !module.is_none(py) {
                         modules.insert(id_fn.call1((&module,))?.extract()?, module);
+                    }
                     class_ids.insert(id_fn.call1((class,))?.extract()?);
                 }
+                for module in content_modules {
+                    let module: PyObject = module.extract()?;
+                    modules.insert(id_fn.call1((&module,))?.extract()?, module);
+                }
                 Ok((modules, class_ids))
             })?;
 

nac3artiq/src/symbol_resolver.rs

@@ -1,14 +1,30 @@
-use inkwell::{
+use std::{
+    collections::{HashMap, HashSet},
+    sync::{
+        atomic::{AtomicBool, Ordering::Relaxed},
+        Arc,
+    },
+};
+
+use itertools::Itertools;
+use parking_lot::RwLock;
+use pyo3::{
+    types::{PyDict, PyTuple},
+    PyAny, PyObject, PyResult, Python,
+};
+
+use nac3core::{
+    codegen::{
+        types::{NDArrayType, ProxyType},
+        CodeGenContext, CodeGenerator,
+    },
+    inkwell::{
+        module::Linkage,
         types::{BasicType, BasicTypeEnum},
         values::BasicValueEnum,
         AddressSpace,
-};
-use itertools::Itertools;
-use nac3core::{
-    codegen::{
-        classes::{NDArrayType, ProxyType},
-        CodeGenContext, CodeGenerator,
     },
+    nac3parser::ast::{self, StrRef},
     symbol_resolver::{StaticValue, SymbolResolver, SymbolValue, ValueEnum},
     toplevel::{
         helper::PrimDef,
@@ -20,21 +36,8 @@ use nac3core::{
         typedef::{into_var_map, iter_type_vars, Type, TypeEnum, TypeVar, Unifier, VarMap},
     },
 };
-use nac3parser::ast::{self, StrRef};
-use parking_lot::{Mutex, RwLock};
-use pyo3::{
-    types::{PyDict, PyTuple},
-    PyAny, PyObject, PyResult, Python,
-};
-use std::{
-    collections::{HashMap, HashSet},
-    sync::{
-        atomic::{AtomicBool, Ordering::Relaxed},
-        Arc,
-    },
-};
 
-use crate::PrimitivePythonId;
+use super::PrimitivePythonId;
 
 pub enum PrimitiveValue {
     I32(i32),
@@ -79,7 +82,6 @@ pub struct InnerResolver {
     pub id_to_primitive: RwLock<HashMap<u64, PrimitiveValue>>,
     pub field_to_val: RwLock<HashMap<ResolverField, Option<PyFieldHandle>>>,
     pub global_value_ids: Arc<RwLock<HashMap<u64, PyObject>>>,
-    pub class_names: Mutex<HashMap<StrRef, Type>>,
     pub pyid_to_def: Arc<RwLock<HashMap<u64, DefinitionId>>>,
     pub pyid_to_type: Arc<RwLock<HashMap<u64, Type>>>,
     pub primitive_ids: PrimitivePythonId,
@@ -133,6 +135,8 @@ impl StaticValue for PythonValue {
                         format!("{}_const", self.id).as_str(),
                     );
                     global.set_constant(true);
+                    // Set linkage of global to private to avoid name collisions
+                    global.set_linkage(Linkage::Private);
                     global.set_initializer(&ctx.ctx.const_struct(
                         &[ctx.ctx.i32_type().const_int(u64::from(id), false).into()],
                         false,
@@ -163,7 +167,7 @@ impl StaticValue for PythonValue {
                 PrimitiveValue::Bool(val) => {
                     ctx.ctx.i8_type().const_int(u64::from(*val), false).into()
                 }
-                PrimitiveValue::Str(val) => ctx.ctx.const_string(val.as_bytes(), true).into(),
+                PrimitiveValue::Str(val) => ctx.gen_string(generator, val).into(),
             });
         }
         if let Some(global) = ctx.module.get_global(&self.id.to_string()) {
@@ -977,7 +981,7 @@ impl InnerResolver {
         } else if ty_id == self.primitive_ids.string || ty_id == self.primitive_ids.np_str_ {
             let val: String = obj.extract().unwrap();
             self.id_to_primitive.write().insert(id, PrimitiveValue::Str(val.clone()));
-            Ok(Some(ctx.ctx.const_string(val.as_bytes(), true).into()))
+            Ok(Some(ctx.gen_string(generator, val).into()))
         } else if ty_id == self.primitive_ids.float || ty_id == self.primitive_ids.float64 {
             let val: f64 = obj.extract().unwrap();
             self.id_to_primitive.write().insert(id, PrimitiveValue::F64(val));
@@ -1092,7 +1096,7 @@ impl InnerResolver {
                 if self.global_value_ids.read().contains_key(&id) {
                     let global = ctx.module.get_global(&id_str).unwrap_or_else(|| {
                         ctx.module.add_global(
-                            ndarray_llvm_ty.as_underlying_type(),
+                            ndarray_llvm_ty.as_base_type().get_element_type().into_struct_type(),
                             Some(AddressSpace::default()),
                             &id_str,
                         )
@@ -1186,7 +1190,11 @@ impl InnerResolver {
             data_global.set_initializer(&data);
 
             // create a global for the ndarray object and initialize it
-            let value = ndarray_llvm_ty.as_underlying_type().const_named_struct(&[
+            let value = ndarray_llvm_ty
+                .as_base_type()
+                .get_element_type()
+                .into_struct_type()
+                .const_named_struct(&[
                     llvm_usize.const_int(ndarray_ndims, false).into(),
                     shape_global
                         .as_pointer_value()
@@ -1199,7 +1207,7 @@ impl InnerResolver {
                 ]);
 
             let ndarray = ctx.module.add_global(
-                ndarray_llvm_ty.as_underlying_type(),
+                ndarray_llvm_ty.as_base_type().get_element_type().into_struct_type(),
                 Some(AddressSpace::default()),
                 &id_str,
             );
@@ -1466,6 +1474,7 @@ impl SymbolResolver for Resolver {
         &self,
         id: StrRef,
         _: &mut CodeGenContext<'ctx, '_>,
+        _: &mut dyn CodeGenerator,
     ) -> Option<ValueEnum<'ctx>> {
         let sym_value = {
             let id_to_val = self.0.id_to_pyval.read();

nac3artiq/src/timeline.rs

@@ -1,9 +1,12 @@
-use inkwell::{
+use itertools::Either;
+
+use nac3core::{
+    codegen::CodeGenContext,
+    inkwell::{
         values::{BasicValueEnum, CallSiteValue},
         AddressSpace, AtomicOrdering,
+    },
 };
-use itertools::Either;
-use nac3core::codegen::CodeGenContext;
 
 /// Functions for manipulating the timeline.
 pub trait TimeFns {
@@ -31,7 +34,7 @@ impl TimeFns for NowPinningTimeFns64 {
             .unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
         let now_hiptr = ctx
             .builder
-            .build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
+            .build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap();
 
@@ -80,7 +83,7 @@ impl TimeFns for NowPinningTimeFns64 {
             .unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
         let now_hiptr = ctx
             .builder
-            .build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
+            .build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap();
 
@@ -109,7 +112,7 @@ impl TimeFns for NowPinningTimeFns64 {
             .unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
         let now_hiptr = ctx
             .builder
-            .build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
+            .build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap();
 
@@ -207,7 +210,7 @@ impl TimeFns for NowPinningTimeFns {
             .unwrap_or_else(|| ctx.module.add_global(i64_type, None, "now"));
         let now_hiptr = ctx
             .builder
-            .build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
+            .build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap();
 
@@ -258,7 +261,7 @@ impl TimeFns for NowPinningTimeFns {
         let time_lo = ctx.builder.build_int_truncate(time, i32_type, "time.lo").unwrap();
         let now_hiptr = ctx
             .builder
-            .build_bitcast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
+            .build_bit_cast(now, i32_type.ptr_type(AddressSpace::default()), "now.hi.addr")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap();
 

nac3ast/Cargo.toml

@@ -10,7 +10,6 @@ constant-optimization = ["fold"]
 fold = []
 
 [dependencies]
-lazy_static = "1.5"
 parking_lot = "0.12"
 string-interner = "0.17"
 fxhash = "0.2"

nac3ast/src/ast_gen.rs

@@ -5,14 +5,12 @@ pub use crate::location::Location;
 
 use fxhash::FxBuildHasher;
 use parking_lot::{Mutex, MutexGuard};
-use std::{cell::RefCell, collections::HashMap, fmt};
+use std::{cell::RefCell, collections::HashMap, fmt, sync::LazyLock};
 use string_interner::{symbol::SymbolU32, DefaultBackend, StringInterner};
 
 pub type Interner = StringInterner<DefaultBackend, FxBuildHasher>;
-lazy_static! {
-    static ref INTERNER: Mutex<Interner> =
-        Mutex::new(StringInterner::with_hasher(FxBuildHasher::default()));
-}
+static INTERNER: LazyLock<Mutex<Interner>> =
+    LazyLock::new(|| Mutex::new(StringInterner::with_hasher(FxBuildHasher::default())));
 
 thread_local! {
     static LOCAL_INTERNER: RefCell<HashMap<String, StrRef>> = RefCell::default();

nac3ast/src/lib.rs

@@ -1,10 +1,4 @@
-#![deny(
-    future_incompatible,
-    let_underscore,
-    nonstandard_style,
-    rust_2024_compatibility,
-    clippy::all
-)]
+#![deny(future_incompatible, let_underscore, nonstandard_style, clippy::all)]
 #![warn(clippy::pedantic)]
 #![allow(
     clippy::missing_errors_doc,
@@ -14,9 +8,6 @@
     clippy::wildcard_imports
 )]
 
-#[macro_use]
-extern crate lazy_static;
-
 mod ast_gen;
 mod constant;
 #[cfg(feature = "fold")]

nac3core/Cargo.toml

@@ -1,26 +1,29 @@
-[features]
-test = []
-
 [package]
 name = "nac3core"
 version = "0.1.0"
 authors = ["M-Labs"]
 edition = "2021"
 
+[features]
+default = ["derive"]
+derive = ["dep:nac3core_derive"]
+no-escape-analysis = []
+
 [dependencies]
 itertools = "0.13"
 crossbeam = "0.8"
-indexmap = "2.2"
+indexmap = "2.6"
 parking_lot = "0.12"
-rayon = "1.8"
+rayon = "1.10"
+nac3core_derive = { path = "nac3core_derive", optional = true }
 nac3parser = { path = "../nac3parser" }
-strum = "0.26.2"
-strum_macros = "0.26.4"
+strum = "0.26"
+strum_macros = "0.26"
 
 [dependencies.inkwell]
-version = "0.4"
+version = "0.5"
 default-features = false
-features = ["llvm14-0", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
+features = ["llvm14-0-prefer-dynamic", "target-x86", "target-arm", "target-riscv", "no-libffi-linking"]
 
 [dev-dependencies]
 test-case = "1.2.0"

nac3core/build.rs

@@ -1,67 +1,63 @@
-use regex::Regex;
 use std::{
     env,
     fs::File,
     io::Write,
-    path::{Path, PathBuf},
+    path::Path,
     process::{Command, Stdio},
 };
 
-const CMD_IRRT_CLANG: &str = "clang-irrt";
-const CMD_IRRT_CLANG_TEST: &str = "clang-irrt-test";
-const CMD_IRRT_LLVM_AS: &str = "llvm-as-irrt";
+use regex::Regex;
 
-fn get_out_dir() -> PathBuf {
-    PathBuf::from(env::var("OUT_DIR").unwrap())
-}
+fn main() {
+    let out_dir = env::var("OUT_DIR").unwrap();
+    let out_dir = Path::new(&out_dir);
+    let irrt_dir = Path::new("irrt");
 
-fn get_irrt_dir() -> &'static Path {
-    Path::new("irrt")
-}
-
-/// Compile `irrt.cpp` for use in `src/codegen`
-fn compile_irrt_cpp() {
-    let out_dir = get_out_dir();
-    let irrt_dir = get_irrt_dir();
+    let irrt_cpp_path = irrt_dir.join("irrt.cpp");
 
     /*
      * HACK: Sadly, clang doesn't let us emit generic LLVM bitcode.
      * Compiling for WASM32 and filtering the output with regex is the closest we can get.
      */
-    let irrt_cpp_path = irrt_dir.join("irrt.cpp");
-
-    let mut flags = vec![];
-    flags.push("--target=wasm32");
-    flags.extend(&["-x", "c++"]);
-    flags.extend(&["-fno-discard-value-names", "-fno-exceptions", "-fno-rtti"]);
-    flags.push("-emit-llvm");
-    flags.push("-S");
-    flags.extend(&["-Wall", "-Wextra"]);
-    flags.extend(&["-o", "-"]);
-    flags.extend(&["-I", irrt_dir.to_str().unwrap()]);
-    flags.push(irrt_cpp_path.to_str().unwrap());
+    let mut flags: Vec<&str> = vec![
+        "--target=wasm32",
+        "-x",
+        "c++",
+        "-std=c++20",
+        "-fno-discard-value-names",
+        "-fno-exceptions",
+        "-fno-rtti",
+        "-emit-llvm",
+        "-S",
+        "-Wall",
+        "-Wextra",
+        "-o",
+        "-",
+        "-I",
+        irrt_dir.to_str().unwrap(),
+        irrt_cpp_path.to_str().unwrap(),
+    ];
 
     match env::var("PROFILE").as_deref() {
         Ok("debug") => {
             flags.push("-O0");
-            flags.push("-DIRRT_DEBUG");
+            flags.push("-DIRRT_DEBUG_ASSERT");
         }
         Ok("release") => {
             flags.push("-O3");
         }
         flavor => panic!("Unknown or missing build flavor {flavor:?}"),
-    };
+    }
 
     // Tell Cargo to rerun if any file under `irrt_dir` (recursive) changes
     println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
 
     // Compile IRRT and capture the LLVM IR output
-    let output = Command::new(CMD_IRRT_CLANG)
+    let output = Command::new("clang-irrt")
         .args(flags)
         .output()
-        .map(|o| {
+        .inspect(|o| {
             assert!(o.status.success(), "{}", std::str::from_utf8(&o.stderr).unwrap());
-            o
         })
         .unwrap();
 
@@ -102,9 +98,7 @@ fn compile_irrt_cpp() {
         file.write_all(filtered_output.as_bytes()).unwrap();
     }
 
-    // Assemble the emitted and filtered IR to .bc
-    // That .bc will be integrated into nac3core's codegen
-    let mut llvm_as = Command::new(CMD_IRRT_LLVM_AS)
+    let mut llvm_as = Command::new("llvm-as-irrt")
         .stdin(Stdio::piped())
         .arg("-o")
         .arg(out_dir.join("irrt.bc"))
@@ -113,48 +107,3 @@ fn compile_irrt_cpp() {
     llvm_as.stdin.as_mut().unwrap().write_all(filtered_output.as_bytes()).unwrap();
     assert!(llvm_as.wait().unwrap().success());
 }
-
-/// Compile `irrt_test.cpp` for testing
-fn compile_irrt_test_cpp() {
-    let out_dir = get_out_dir();
-    let irrt_dir = get_irrt_dir();
-
-    let exe_path = out_dir.join("irrt_test.out"); // Output path of the compiled test executable
-    let irrt_test_cpp_path = irrt_dir.join("irrt_test.cpp");
-    let flags: &[&str] = &[
-        irrt_test_cpp_path.to_str().unwrap(),
-        "-x",
-        "c++",
-        "-I",
-        irrt_dir.to_str().unwrap(),
-        "-g",
-        "-fno-discard-value-names",
-        "-O0",
-        "-Wall",
-        "-Wextra",
-        "-Werror=return-type",
-        "-lm", // for `tgamma()`, `lgamma()`
-        "-o",
-        exe_path.to_str().unwrap(),
-    ];
-
-    Command::new(CMD_IRRT_CLANG_TEST)
-        .args(flags)
-        .output()
-        .map(|o| {
-            assert!(o.status.success(), "{}", std::str::from_utf8(&o.stderr).unwrap());
-            o
-        })
-        .unwrap();
-    println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
-}
-
-fn main() {
-    compile_irrt_cpp();
-
-    // https://github.com/rust-lang/cargo/issues/2549
-    // `cargo test -F test` to also build `irrt_test.cpp
-    if cfg!(feature = "test") {
-        compile_irrt_test_cpp();
-    }
-}

nac3core/irrt/irrt.cpp

@@ -1,10 +1,5 @@
-#define IRRT_DEFINE_TYPEDEF_INTS
-#include <irrt_everything.hpp>
-
-/*
- * All IRRT implementations.
- *
- * We don't have pre-compiled objects, so we are writing all implementations in
- * headers and concatenate them with `#include` into one massive source file that
- * contains all the IRRT stuff.
- */
+#include "irrt/exception.hpp"
+#include "irrt/list.hpp"
+#include "irrt/math.hpp"
+#include "irrt/ndarray.hpp"
+#include "irrt/slice.hpp"

nac3core/irrt/irrt/core.hpp

@@ -1,349 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-#include <irrt/util.hpp>
-
-// NDArray indices are always `uint32_t`.
-using NDIndexInt = uint32_t;
-// The type of an index or a value describing the length of a
-// range/slice is always `int32_t`.
-using SliceIndex = int32_t;
-
-namespace {
-// 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
-template <typename T>
-T __nac3_int_exp_impl(T base, T exp) {
-    T res = 1;
-    /* repeated squaring method */
-    do {
-        if (exp & 1) {
-            res *= base; /* for n odd */
-        }
-        exp >>= 1;
-        base *= base;
-    } while (exp);
-    return res;
-}
-
-template <typename SizeT>
-SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len,
-                                    SizeT begin_idx, SizeT end_idx) {
-    __builtin_assume(end_idx <= list_len);
-
-    SizeT num_elems = 1;
-    for (SizeT i = begin_idx; i < end_idx; ++i) {
-        SizeT val = list_data[i];
-        __builtin_assume(val > 0);
-        num_elems *= val;
-    }
-    return num_elems;
-}
-
-template <typename SizeT>
-void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
-                                         SizeT num_dims, NDIndexInt* idxs) {
-    SizeT stride = 1;
-    for (SizeT dim = 0; dim < num_dims; dim++) {
-        SizeT i = num_dims - dim - 1;
-        __builtin_assume(dims[i] > 0);
-        idxs[i] = (index / stride) % dims[i];
-        stride *= dims[i];
-    }
-}
-
-template <typename SizeT>
-SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims,
-                                        const NDIndexInt* indices,
-                                        SizeT num_indices) {
-    SizeT idx = 0;
-    SizeT stride = 1;
-    for (SizeT i = 0; i < num_dims; ++i) {
-        SizeT ri = num_dims - i - 1;
-        if (ri < num_indices) {
-            idx += stride * indices[ri];
-        }
-
-        __builtin_assume(dims[i] > 0);
-        stride *= dims[ri];
-    }
-    return idx;
-}
-
-template <typename SizeT>
-void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims, SizeT lhs_ndims,
-                                        const SizeT* rhs_dims, SizeT rhs_ndims,
-                                        SizeT* out_dims) {
-    SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
-
-    for (SizeT i = 0; i < max_ndims; ++i) {
-        const SizeT* lhs_dim_sz =
-            i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
-        const SizeT* rhs_dim_sz =
-            i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
-
-        SizeT* out_dim = &out_dims[max_ndims - i - 1];
-
-        if (lhs_dim_sz == nullptr) {
-            *out_dim = *rhs_dim_sz;
-        } else if (rhs_dim_sz == nullptr) {
-            *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();
-        }
-    }
-}
-
-template <typename SizeT>
-void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
-                                            SizeT src_ndims,
-                                            const NDIndexInt* in_idx,
-                                            NDIndexInt* out_idx) {
-    for (SizeT i = 0; i < src_ndims; ++i) {
-        SizeT src_i = src_ndims - i - 1;
-        out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
-    }
-}
-}  // namespace
-
-extern "C" {
-#define DEF_nac3_int_exp_(T)                   \
-    T __nac3_int_exp_##T(T base, T exp) {      \
-        return __nac3_int_exp_impl(base, exp); \
-    }
-
-DEF_nac3_int_exp_(int32_t);
-DEF_nac3_int_exp_(int64_t);
-DEF_nac3_int_exp_(uint32_t);
-DEF_nac3_int_exp_(uint64_t);
-
-SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
-    if (i < 0) {
-        i = len + i;
-    }
-    if (i < 0) {
-        return 0;
-    } else if (i > len) {
-        return len;
-    }
-    return i;
-}
-
-SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end,
-                                  const SliceIndex step) {
-    SliceIndex diff = end - start;
-    if (diff > 0 && step > 0) {
-        return ((diff - 1) / step) + 1;
-    } else if (diff < 0 && step < 0) {
-        return ((diff + 1) / step) + 1;
-    } else {
-        return 0;
-    }
-}
-
-// Handle list assignment and dropping part of the list when
-// both dest_step and src_step are +1.
-// - All the index must *not* be out-of-bound or negative,
-// - The end index is *inclusive*,
-// - The length of src and dest slice size should already
-// be checked: if dest.step == 1 then len(src) <= len(dest) else
-// len(src) == len(dest)
-SliceIndex __nac3_list_slice_assign_var_size(
-    SliceIndex dest_start, SliceIndex dest_end, SliceIndex dest_step,
-    uint8_t* dest_arr, SliceIndex dest_arr_len, SliceIndex src_start,
-    SliceIndex src_end, SliceIndex src_step, uint8_t* src_arr,
-    SliceIndex src_arr_len, const SliceIndex size) {
-    /* if dest_arr_len == 0, do nothing since we do not support
-     * extending list
-     */
-    if (dest_arr_len == 0) return dest_arr_len;
-    /* if both step is 1, memmove directly, handle the dropping of
-     * the list, and shrink size */
-    if (src_step == dest_step && dest_step == 1) {
-        const SliceIndex src_len =
-            (src_end >= src_start) ? (src_end - src_start + 1) : 0;
-        const SliceIndex dest_len =
-            (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
-        if (src_len > 0) {
-            __builtin_memmove(dest_arr + dest_start * size,
-                              src_arr + src_start * size, src_len * size);
-        }
-        if (dest_len > 0) {
-            /* dropping */
-            __builtin_memmove(dest_arr + (dest_start + src_len) * size,
-                              dest_arr + (dest_end + 1) * size,
-                              (dest_arr_len - dest_end - 1) * size);
-        }
-        /* shrink size */
-        return dest_arr_len - (dest_len - src_len);
-    }
-    /* if two range overlaps, need alloca */
-    uint8_t need_alloca =
-        (dest_arr == src_arr) &&
-        !(max(dest_start, dest_end) < min(src_start, src_end) ||
-          max(src_start, src_end) < min(dest_start, dest_end));
-    if (need_alloca) {
-        uint8_t* tmp =
-            reinterpret_cast<uint8_t*>(__builtin_alloca(src_arr_len * size));
-        __builtin_memcpy(tmp, src_arr, src_arr_len * size);
-        src_arr = tmp;
-    }
-    SliceIndex src_ind = src_start;
-    SliceIndex dest_ind = dest_start;
-    for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
-         src_ind += src_step, dest_ind += dest_step) {
-        /* for constant optimization */
-        if (size == 1) {
-            __builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
-        } else if (size == 4) {
-            __builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
-        } else if (size == 8) {
-            __builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
-        } else {
-            /* memcpy for var size, cannot overlap after previous
-             * alloca */
-            __builtin_memcpy(dest_arr + dest_ind * size,
-                             src_arr + src_ind * size, size);
-        }
-    }
-    /* only dest_step == 1 can we shrink the dest list. */
-    /* size should be ensured prior to calling this function */
-    if (dest_step == 1 && dest_end >= dest_start) {
-        __builtin_memmove(
-            dest_arr + dest_ind * size, dest_arr + (dest_end + 1) * size,
-            (dest_arr_len - dest_end - 1) * size + size + size + size);
-        return dest_arr_len - (dest_end - dest_ind) - 1;
-    }
-    return dest_arr_len;
-}
-
-int32_t __nac3_isinf(double x) { return __builtin_isinf(x); }
-
-int32_t __nac3_isnan(double x) { return __builtin_isnan(x); }
-
-double tgamma(double arg);
-
-double __nac3_gamma(double z) {
-    // Handling for denormals
-    //     | x                 | Python gamma(x) | C tgamma(x) |
-    // --- | ----------------- | --------------- | ----------- |
-    // (1) | nan               | nan             | nan         |
-    // (2) | -inf              | -inf            | inf         |
-    // (3) | inf               | inf             | inf         |
-    // (4) | 0.0               | inf             | inf         |
-    // (5) | {-1.0, -2.0, ...} | inf             | nan         |
-
-    // (1)-(3)
-    if (__builtin_isinf(z) || __builtin_isnan(z)) {
-        return z;
-    }
-
-    double v = tgamma(z);
-
-    // (4)-(5)
-    return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
-}
-
-double lgamma(double arg);
-
-double __nac3_gammaln(double x) {
-    // libm's handling of value overflows differs from scipy:
-    // - scipy: gammaln(-inf) -> -inf
-    // - libm : lgamma(-inf) -> inf
-
-    if (__builtin_isinf(x)) {
-        return x;
-    }
-
-    return lgamma(x);
-}
-
-double j0(double x);
-
-double __nac3_j0(double x) {
-    // libm's handling of value overflows differs from scipy:
-    // - scipy: j0(inf) -> nan
-    // - libm : j0(inf) -> 0.0
-
-    if (__builtin_isinf(x)) {
-        return __builtin_nan("");
-    }
-
-    return j0(x);
-}
-
-uint32_t __nac3_ndarray_calc_size(const uint32_t* list_data, uint32_t list_len,
-                                  uint32_t begin_idx, uint32_t end_idx) {
-    return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx,
-                                         end_idx);
-}
-
-uint64_t __nac3_ndarray_calc_size64(const uint64_t* list_data,
-                                    uint64_t list_len, uint64_t begin_idx,
-                                    uint64_t end_idx) {
-    return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx,
-                                         end_idx);
-}
-
-void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims,
-                                    uint32_t num_dims, NDIndexInt* idxs) {
-    __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
-}
-
-void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims,
-                                      uint64_t num_dims, NDIndexInt* idxs) {
-    __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
-}
-
-uint32_t __nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims,
-                                      const NDIndexInt* indices,
-                                      uint32_t num_indices) {
-    return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
-                                             num_indices);
-}
-
-uint64_t __nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims,
-                                        const NDIndexInt* indices,
-                                        uint64_t num_indices) {
-    return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
-                                             num_indices);
-}
-
-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) {
-    return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims,
-                                              rhs_ndims, out_dims);
-}
-
-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) {
-    return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims,
-                                              rhs_ndims, out_dims);
-}
-
-void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
-                                       uint32_t src_ndims,
-                                       const NDIndexInt* in_idx,
-                                       NDIndexInt* out_idx) {
-    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
-                                           out_idx);
-}
-
-void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
-                                         uint64_t src_ndims,
-                                         const NDIndexInt* in_idx,
-                                         NDIndexInt* out_idx) {
-    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
-                                           out_idx);
-}
-}  // extern "C"

nac3core/irrt/irrt/cslice.hpp

@@ -1,9 +1,9 @@
 #pragma once
 
-#include <irrt/int_defs.hpp>
+#include "irrt/int_types.hpp"
 
-template <typename SizeT>
+template<typename SizeT>
 struct CSlice {
-    uint8_t* base;
+    void* base;
     SizeT len;
 };

nac3core/irrt/irrt/debug.hpp

@@ -1,15 +1,25 @@
 #pragma once
 
+// Set in nac3core/build.rs
+#ifdef IRRT_DEBUG_ASSERT
+#define IRRT_DEBUG_ASSERT_BOOL true
+#else
+#define IRRT_DEBUG_ASSERT_BOOL false
+#endif
+
 #define raise_debug_assert(SizeT, msg, param1, param2, param3) \
-    raise_exception(SizeT, EXN_ASSERTION_ERROR,                \
-                    "IRRT debug assert failed: " msg, param1, param2, param3);
+    raise_exception(SizeT, EXN_ASSERTION_ERROR, "IRRT debug assert failed: " msg, param1, param2, param3)
 
 #define debug_assert_eq(SizeT, lhs, rhs)                                           \
-    if (IRRT_DEBUG_ASSERT_BOOL && (lhs) != (rhs)) {                            \
+    if constexpr (IRRT_DEBUG_ASSERT_BOOL) {                                        \
+        if ((lhs) != (rhs)) {                                                      \
             raise_debug_assert(SizeT, "LHS = {0}. RHS = {1}", lhs, rhs, NO_PARAM); \
+        }                                                                          \
     }
 
 #define debug_assert(SizeT, expr)                                                  \
-    if (IRRT_DEBUG_ASSERT_BOOL && !(expr)) {                                   \
+    if constexpr (IRRT_DEBUG_ASSERT_BOOL) {                                        \
+        if (!(expr)) {                                                             \
             raise_debug_assert(SizeT, "Got false.", NO_PARAM, NO_PARAM, NO_PARAM); \
+        }                                                                          \
     }

nac3core/irrt/irrt/exception.hpp

@@ -1,43 +1,37 @@
 #pragma once
 
-#include <irrt/cslice.hpp>
-#include <irrt/int_defs.hpp>
-#include <irrt/util.hpp>
+#include "irrt/cslice.hpp"
+#include "irrt/int_types.hpp"
 
 /**
  * @brief The int type of ARTIQ exception IDs.
- *
- * It is always `int32_t`
  */
-typedef int32_t ExceptionId;
+using ExceptionId = int32_t;
 
 /*
- * A set of exceptions IRRT can use.
+ * Set of exceptions C++ IRRT can use.
  * Must be synchronized with `setup_irrt_exceptions` in `nac3core/src/codegen/irrt/mod.rs`.
- * All exception IDs are initialized by `setup_irrt_exceptions`.
  */
-#ifdef IRRT_TESTING
-// If we are doing IRRT tests (i.e., running `cargo test -F test`), define them with a fake set of IDs.
-ExceptionId EXN_INDEX_ERROR = 0;
-ExceptionId EXN_VALUE_ERROR = 1;
-ExceptionId EXN_ASSERTION_ERROR = 2;
-ExceptionId EXN_RUNTIME_ERROR = 3;
-ExceptionId EXN_TYPE_ERROR = 4;
-#else
 extern "C" {
 ExceptionId EXN_INDEX_ERROR;
 ExceptionId EXN_VALUE_ERROR;
 ExceptionId EXN_ASSERTION_ERROR;
-ExceptionId EXN_RUNTIME_ERROR;
 ExceptionId EXN_TYPE_ERROR;
 }
-#endif
+
+/**
+ * @brief Extern function to `__nac3_raise`
+ *
+ * The parameter `err` could be `Exception<int32_t>` or `Exception<int64_t>`. The caller
+ * must make sure to pass `Exception`s with the correct `SizeT` depending on the `size_t` of the runtime.
+ */
+extern "C" void __nac3_raise(void* err);
 
 namespace {
 /**
  * @brief NAC3's Exception struct
  */
-template <typename SizeT>
+template<typename SizeT>
 struct Exception {
     ExceptionId id;
     CSlice<SizeT> filename;
@@ -47,51 +41,36 @@ struct Exception {
     CSlice<SizeT> msg;
     int64_t params[3];
 };
-}  // namespace
 
-// Declare/Define `__nac3_raise`
-#ifdef IRRT_TESTING
-#include <cstdio>
-void __nac3_raise(void* err) {
-    // TODO: Print the error content?
-    printf("__nac3_raise called. Exiting...\n");
-    exit(1);
-}
-#else
-/**
- * @brief Extern function to `__nac3_raise`
- *
- * The parameter `err` could be `Exception<int32_t>` or `Exception<int64_t>`. The caller
- * must make sure to pass `Exception`s with the correct `SizeT` depending on the `size_t` of the runtime.
- */
-extern "C" void __nac3_raise(void* err);
-#endif
+constexpr int64_t NO_PARAM = 0;
 
-namespace {
-const int64_t NO_PARAM = 0;
-
-// Helper function to raise an exception with `__nac3_raise`
-// Do not use this function directly. See `raise_exception`.
-template <typename SizeT>
-void _raise_exception_helper(ExceptionId id, const char* filename, int32_t line,
-                             const char* function, const char* msg,
-                             int64_t param0, int64_t param1, int64_t param2) {
+template<typename SizeT>
+void _raise_exception_helper(ExceptionId id,
+                             const char* filename,
+                             int32_t line,
+                             const char* function,
+                             const char* msg,
+                             int64_t param0,
+                             int64_t param1,
+                             int64_t param2) {
     Exception<SizeT> e = {
         .id = id,
-        .filename = {.base = (uint8_t*)filename,
-                     .len = (int32_t)cstr_utils::length(filename)},
+        .filename = {.base = reinterpret_cast<void*>(const_cast<char*>(filename)),
+                     .len = static_cast<SizeT>(__builtin_strlen(filename))},
         .line = line,
         .column = 0,
-        .function = {.base = (uint8_t*)function,
-                     .len = (int32_t)cstr_utils::length(function)},
-        .msg = {.base = (uint8_t*)msg, .len = (int32_t)cstr_utils::length(msg)},
+        .function = {.base = reinterpret_cast<void*>(const_cast<char*>(function)),
+                     .len = static_cast<SizeT>(__builtin_strlen(function))},
+        .msg = {.base = reinterpret_cast<void*>(const_cast<char*>(msg)),
+                .len = static_cast<SizeT>(__builtin_strlen(msg))},
     };
     e.params[0] = param0;
     e.params[1] = param1;
     e.params[2] = param2;
-    __nac3_raise((void*)&e);
+    __nac3_raise(reinterpret_cast<void*>(&e));
     __builtin_unreachable();
 }
+}  // namespace
 
 /**
  * @brief Raise an exception with location details (location in the IRRT source files).
@@ -99,25 +78,8 @@ void _raise_exception_helper(ExceptionId id, const char* filename, int32_t line,
  * @param id The ID of the exception to raise.
  * @param msg A global constant C-string of the error message.
  *
- * `param0` and `param2` are optional format arguments of `msg`. They should be set to
+ * `param0` to `param2` are optional format arguments of `msg`. They should be set to
  * `NO_PARAM` to indicate they are unused.
  */
 #define raise_exception(SizeT, id, msg, param0, param1, param2) \
-    _raise_exception_helper<SizeT>(id, __FILE__, __LINE__, __FUNCTION__, msg, \
-                                   param0, param1, param2)
-
-/**
- * @brief Throw a dummy error for testing.
- */
-template <typename SizeT>
-void throw_dummy_error() {
-    raise_exception(SizeT, EXN_RUNTIME_ERROR, "dummy error", NO_PARAM, NO_PARAM,
-                    NO_PARAM);
-}
-}  // namespace
-
-extern "C" {
-void __nac3_throw_dummy_error() { throw_dummy_error<int32_t>(); }
-
-void __nac3_throw_dummy_error64() { throw_dummy_error<int64_t>(); }
-}
+    _raise_exception_helper<SizeT>(id, __FILE__, __LINE__, __FUNCTION__, msg, param0, param1, param2)

nac3core/irrt/irrt/int_defs.hpp

@@ -1,12 +0,0 @@
-#pragma once
-
-// This is made toggleable since `irrt_test.cpp` itself would include
-// headers that define these typedefs
-#ifdef IRRT_DEFINE_TYPEDEF_INTS
-using int8_t = _BitInt(8);
-using uint8_t = unsigned _BitInt(8);
-using int32_t = _BitInt(32);
-using uint32_t = unsigned _BitInt(32);
-using int64_t = _BitInt(64);
-using uint64_t = unsigned _BitInt(64);
-#endif

nac3core/irrt/irrt/int_types.hpp

@@ -0,0 +1,27 @@
+#pragma once
+
+#if __STDC_VERSION__ >= 202000
+using int8_t = _BitInt(8);
+using uint8_t = unsigned _BitInt(8);
+using int32_t = _BitInt(32);
+using uint32_t = unsigned _BitInt(32);
+using int64_t = _BitInt(64);
+using uint64_t = unsigned _BitInt(64);
+#else
+
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wdeprecated-type"
+using int8_t = _ExtInt(8);
+using uint8_t = unsigned _ExtInt(8);
+using int32_t = _ExtInt(32);
+using uint32_t = unsigned _ExtInt(32);
+using int64_t = _ExtInt(64);
+using uint64_t = unsigned _ExtInt(64);
+#pragma clang diagnostic pop
+
+#endif
+
+// NDArray indices are always `uint32_t`.
+using NDIndex = uint32_t;
+// The type of an index or a value describing the length of a range/slice is always `int32_t`.
+using SliceIndex = int32_t;

nac3core/irrt/irrt/list.hpp

@@ -1,56 +1,81 @@
 #pragma once
 
-#include <irrt/int_defs.hpp>
-#include <irrt/slice.hpp>
-
-namespace {
-/**
- * @brief A list in NAC3.
- *
- * The `items` field is opaque. You must rely on external contexts to
- * know how to interpret it.
- */
-template <typename SizeT>
-struct List {
-    uint8_t* items;
-    SizeT len;
-};
-
-namespace list {
-template <typename SizeT>
-void slice_assign(List<SizeT>* dst, List<SizeT>* src, SizeT itemsize,
-                  UserSlice* user_slice) {
-    Slice slice = user_slice->indices_checked<SizeT>(dst->len);
-
-    // NOTE: Python does not have this restriction.
-    if (slice.len() != src->len) {
-        raise_exception(SizeT, EXN_VALUE_ERROR,
-                        "List destination has {} item(s), but source has {} "
-                        "item(s). The lengths must match.",
-                        slice.len(), src->len, NO_PARAM);
-    }
-
-    // TODO: Look into how the original implementation was implemented and optimized.
-    SizeT dst_i = slice.start;
-    SizeT src_i = 0;
-    while (src_i < slice.len()) {
-        __builtin_memcpy(dst->items + dst_i, src->items + src_i, itemsize);
-
-        src_i += 1;
-        dst_i += slice.step;
-    }
-}
-}  // namespace list
-}  // namespace
+#include "irrt/int_types.hpp"
+#include "irrt/math_util.hpp"
 
 extern "C" {
-void __nac3_list_slice_assign(List<int32_t>* dst, List<int32_t>* src,
-                              int32_t itemsize, UserSlice* user_slice) {
-    list::slice_assign(dst, src, itemsize, user_slice);
-}
-
-void __nac3_list_slice_assign64(List<int64_t>* dst, List<int64_t>* src,
-                                int64_t itemsize, UserSlice* user_slice) {
-    list::slice_assign(dst, src, itemsize, user_slice);
-}
+// Handle list assignment and dropping part of the list when
+// both dest_step and src_step are +1.
+// - All the index must *not* be out-of-bound or negative,
+// - The end index is *inclusive*,
+// - The length of src and dest slice size should already
+// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
+SliceIndex __nac3_list_slice_assign_var_size(SliceIndex dest_start,
+                                             SliceIndex dest_end,
+                                             SliceIndex dest_step,
+                                             void* dest_arr,
+                                             SliceIndex dest_arr_len,
+                                             SliceIndex src_start,
+                                             SliceIndex src_end,
+                                             SliceIndex src_step,
+                                             void* src_arr,
+                                             SliceIndex src_arr_len,
+                                             const SliceIndex size) {
+    /* if dest_arr_len == 0, do nothing since we do not support extending list */
+    if (dest_arr_len == 0)
+        return dest_arr_len;
+    /* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
+    if (src_step == dest_step && dest_step == 1) {
+        const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
+        const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
+        if (src_len > 0) {
+            __builtin_memmove(static_cast<uint8_t*>(dest_arr) + dest_start * size,
+                              static_cast<uint8_t*>(src_arr) + src_start * size, src_len * size);
+        }
+        if (dest_len > 0) {
+            /* dropping */
+            __builtin_memmove(static_cast<uint8_t*>(dest_arr) + (dest_start + src_len) * size,
+                              static_cast<uint8_t*>(dest_arr) + (dest_end + 1) * size,
+                              (dest_arr_len - dest_end - 1) * size);
+        }
+        /* shrink size */
+        return dest_arr_len - (dest_len - src_len);
+    }
+    /* if two range overlaps, need alloca */
+    uint8_t need_alloca = (dest_arr == src_arr)
+                          && !(max(dest_start, dest_end) < min(src_start, src_end)
+                               || max(src_start, src_end) < min(dest_start, dest_end));
+    if (need_alloca) {
+        void* tmp = __builtin_alloca(src_arr_len * size);
+        __builtin_memcpy(tmp, src_arr, src_arr_len * size);
+        src_arr = tmp;
+    }
+    SliceIndex src_ind = src_start;
+    SliceIndex dest_ind = dest_start;
+    for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end); src_ind += src_step, dest_ind += dest_step) {
+        /* for constant optimization */
+        if (size == 1) {
+            __builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind, static_cast<uint8_t*>(src_arr) + src_ind, 1);
+        } else if (size == 4) {
+            __builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind * 4,
+                             static_cast<uint8_t*>(src_arr) + src_ind * 4, 4);
+        } else if (size == 8) {
+            __builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind * 8,
+                             static_cast<uint8_t*>(src_arr) + src_ind * 8, 8);
+        } else {
+            /* memcpy for var size, cannot overlap after previous alloca */
+            __builtin_memcpy(static_cast<uint8_t*>(dest_arr) + dest_ind * size,
+                             static_cast<uint8_t*>(src_arr) + src_ind * size, size);
+        }
+    }
+    /* only dest_step == 1 can we shrink the dest list. */
+    /* size should be ensured prior to calling this function */
+    if (dest_step == 1 && dest_end >= dest_start) {
+        __builtin_memmove(static_cast<uint8_t*>(dest_arr) + dest_ind * size,
+                          static_cast<uint8_t*>(dest_arr) + (dest_end + 1) * size,
+                          (dest_arr_len - dest_end - 1) * size);
+        return dest_arr_len - (dest_end - dest_ind) - 1;
+    }
+    return dest_arr_len;
 }
+}  // extern "C"

nac3core/irrt/irrt/math.hpp

@@ -0,0 +1,93 @@
+#pragma once
+
+namespace {
+// 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
+template<typename T>
+T __nac3_int_exp_impl(T base, T exp) {
+    T res = 1;
+    /* repeated squaring method */
+    do {
+        if (exp & 1) {
+            res *= base; /* for n odd */
+        }
+        exp >>= 1;
+        base *= base;
+    } while (exp);
+    return res;
+}
+}  // namespace
+
+#define DEF_nac3_int_exp_(T)                   \
+    T __nac3_int_exp_##T(T base, T exp) {      \
+        return __nac3_int_exp_impl(base, exp); \
+    }
+
+extern "C" {
+
+// Putting semicolons here to make clang-format not reformat this into
+// a stair shape.
+DEF_nac3_int_exp_(int32_t);
+DEF_nac3_int_exp_(int64_t);
+DEF_nac3_int_exp_(uint32_t);
+DEF_nac3_int_exp_(uint64_t);
+
+int32_t __nac3_isinf(double x) {
+    return __builtin_isinf(x);
+}
+
+int32_t __nac3_isnan(double x) {
+    return __builtin_isnan(x);
+}
+
+double tgamma(double arg);
+
+double __nac3_gamma(double z) {
+    // Handling for denormals
+    //     | x                 | Python gamma(x) | C tgamma(x) |
+    // --- | ----------------- | --------------- | ----------- |
+    // (1) | nan               | nan             | nan         |
+    // (2) | -inf              | -inf            | inf         |
+    // (3) | inf               | inf             | inf         |
+    // (4) | 0.0               | inf             | inf         |
+    // (5) | {-1.0, -2.0, ...} | inf             | nan         |
+
+    // (1)-(3)
+    if (__builtin_isinf(z) || __builtin_isnan(z)) {
+        return z;
+    }
+
+    double v = tgamma(z);
+
+    // (4)-(5)
+    return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
+}
+
+double lgamma(double arg);
+
+double __nac3_gammaln(double x) {
+    // libm's handling of value overflows differs from scipy:
+    // - scipy: gammaln(-inf) -> -inf
+    // - libm : lgamma(-inf) -> inf
+
+    if (__builtin_isinf(x)) {
+        return x;
+    }
+
+    return lgamma(x);
+}
+
+double j0(double x);
+
+double __nac3_j0(double x) {
+    // libm's handling of value overflows differs from scipy:
+    // - scipy: j0(inf) -> nan
+    // - libm : j0(inf) -> 0.0
+
+    if (__builtin_isinf(x)) {
+        return __builtin_nan("");
+    }
+
+    return j0(x);
+}
+}  // namespace

nac3core/irrt/irrt/math_util.hpp

@@ -0,0 +1,13 @@
+#pragma once
+
+namespace {
+template<typename T>
+const T& max(const T& a, const T& b) {
+    return a > b ? a : b;
+}
+
+template<typename T>
+const T& min(const T& a, const T& b) {
+    return a > b ? b : a;
+}
+}  // namespace

nac3core/irrt/irrt/ndarray.hpp

@@ -0,0 +1,144 @@
+#pragma once
+
+#include "irrt/int_types.hpp"
+
+namespace {
+template<typename SizeT>
+SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len, SizeT begin_idx, SizeT end_idx) {
+    __builtin_assume(end_idx <= list_len);
+
+    SizeT num_elems = 1;
+    for (SizeT i = begin_idx; i < end_idx; ++i) {
+        SizeT val = list_data[i];
+        __builtin_assume(val > 0);
+        num_elems *= val;
+    }
+    return num_elems;
+}
+
+template<typename SizeT>
+void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT num_dims, NDIndex* idxs) {
+    SizeT stride = 1;
+    for (SizeT dim = 0; dim < num_dims; dim++) {
+        SizeT i = num_dims - dim - 1;
+        __builtin_assume(dims[i] > 0);
+        idxs[i] = (index / stride) % dims[i];
+        stride *= dims[i];
+    }
+}
+
+template<typename SizeT>
+SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims, const NDIndex* indices, SizeT num_indices) {
+    SizeT idx = 0;
+    SizeT stride = 1;
+    for (SizeT i = 0; i < num_dims; ++i) {
+        SizeT ri = num_dims - i - 1;
+        if (ri < num_indices) {
+            idx += stride * indices[ri];
+        }
+
+        __builtin_assume(dims[i] > 0);
+        stride *= dims[ri];
+    }
+    return idx;
+}
+
+template<typename SizeT>
+void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims,
+                                        SizeT lhs_ndims,
+                                        const SizeT* rhs_dims,
+                                        SizeT rhs_ndims,
+                                        SizeT* out_dims) {
+    SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
+
+    for (SizeT i = 0; i < max_ndims; ++i) {
+        const SizeT* lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
+        const SizeT* rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
+        SizeT* out_dim = &out_dims[max_ndims - i - 1];
+
+        if (lhs_dim_sz == nullptr) {
+            *out_dim = *rhs_dim_sz;
+        } else if (rhs_dim_sz == nullptr) {
+            *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();
+        }
+    }
+}
+
+template<typename SizeT>
+void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
+                                            SizeT src_ndims,
+                                            const NDIndex* in_idx,
+                                            NDIndex* out_idx) {
+    for (SizeT i = 0; i < src_ndims; ++i) {
+        SizeT src_i = src_ndims - i - 1;
+        out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
+    }
+}
+}  // namespace
+
+extern "C" {
+uint32_t __nac3_ndarray_calc_size(const uint32_t* list_data, uint32_t list_len, uint32_t begin_idx, uint32_t end_idx) {
+    return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
+}
+
+uint64_t
+__nac3_ndarray_calc_size64(const uint64_t* list_data, uint64_t list_len, uint64_t begin_idx, uint64_t end_idx) {
+    return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
+}
+
+void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims, uint32_t num_dims, NDIndex* idxs) {
+    __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
+}
+
+void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims, uint64_t num_dims, NDIndex* idxs) {
+    __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
+}
+
+uint32_t
+__nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims, const NDIndex* indices, uint32_t num_indices) {
+    return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
+}
+
+uint64_t
+__nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims, const NDIndex* indices, uint64_t num_indices) {
+    return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
+}
+
+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) {
+    return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
+}
+
+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) {
+    return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
+}
+
+void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
+                                       uint32_t src_ndims,
+                                       const NDIndex* in_idx,
+                                       NDIndex* out_idx) {
+    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
+}
+
+void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
+                                         uint64_t src_ndims,
+                                         const NDIndex* in_idx,
+                                         NDIndex* out_idx) {
+    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
+}
+}  // namespace

nac3core/irrt/irrt/ndarray/array.hpp

@@ -1,119 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-#include <irrt/list.hpp>
-#include <irrt/ndarray/basic.hpp>
-#include <irrt/ndarray/def.hpp>
-
-namespace {
-namespace ndarray {
-namespace array {
-// TODO: Document me
-template <typename SizeT>
-void set_and_validate_list_shape_helper(SizeT axis, List<SizeT>* list,
-                                        SizeT ndims, SizeT* shape) {
-    if (shape[axis] == -1) {
-        // Dimension is unspecified. Set it.
-        shape[axis] = list->len;
-    } else {
-        // Dimension is specified. Check.
-        if (shape[axis] != list->len) {
-            // Mismatch, throw an error.
-            // NOTE: NumPy's error message is more complex and needs more PARAMS to display.
-            raise_exception(SizeT, EXN_VALUE_ERROR,
-                            "The requested array has an inhomogenous shape "
-                            "after {0} dimension(s).",
-                            axis, shape[axis], list->len);
-        }
-    }
-
-    if (axis + 1 == ndims) {
-        // `list` has type `list[ItemType]`
-        // Do nothing
-    } else {
-        // `list` has type `list[list[...]]`
-        List<SizeT>** lists = (List<SizeT>**)(list->items);
-        for (SizeT i = 0; i < list->len; i++) {
-            set_and_validate_list_shape_helper<SizeT>(axis + 1, lists[i], ndims,
-                                                      shape);
-        }
-    }
-}
-
-// TODO: Document me
-template <typename SizeT>
-void set_and_validate_list_shape(List<SizeT>* list, SizeT ndims, SizeT* shape) {
-    for (SizeT axis = 0; axis < ndims; axis++) {
-        shape[axis] = -1;  // Sentinel to say this dimension is unspecified.
-    }
-    set_and_validate_list_shape_helper<SizeT>(0, list, ndims, shape);
-}
-
-// TODO: Document me
-template <typename SizeT>
-void write_list_to_array_helper(SizeT axis, SizeT* index, List<SizeT>* list,
-                                NDArray<SizeT>* ndarray) {
-    debug_assert_eq(SizeT, list->len, ndarray->shape[axis]);
-    if (IRRT_DEBUG_ASSERT_BOOL) {
-        if (!ndarray::basic::is_c_contiguous(ndarray)) {
-            raise_debug_assert(SizeT, "ndarray is not C-contiguous", ndarray->strides[0],
-                               ndarray->strides[1], NO_PARAM);
-        }
-    }
-
-    if (axis + 1 == ndarray->ndims) {
-        // `list` has type `list[ItemType]`
-        // `ndarray` is contiguous, so we can do this, and this is fast.
-        uint8_t* dst = ndarray->data + (ndarray->itemsize * (*index));
-        __builtin_memcpy(dst, list->items, ndarray->itemsize * list->len);
-        *index += list->len;
-    } else {
-        // `list` has type `list[list[...]]`
-        List<SizeT>** lists = (List<SizeT>**)(list->items);
-
-        for (SizeT i = 0; i < list->len; i++) {
-            write_list_to_array_helper<SizeT>(axis + 1, index, lists[i],
-                                              ndarray);
-        }
-    }
-}
-
-// TODO: Document me
-template <typename SizeT>
-void write_list_to_array(List<SizeT>* list, NDArray<SizeT>* ndarray) {
-    // done after set_and_validate(list, ndims, shape), list is well-formed
-    // ndarray->data is allocated and owned
-    // ndarray->itemsize is set
-    // ndarray->ndims is set
-    // ndarray->shape is set
-    // ndarray->strides is ???
-    SizeT index = 0;
-    write_list_to_array_helper<SizeT>((SizeT)0, &index, list, ndarray);
-}
-}  // namespace array
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-using namespace ndarray::array;
-
-void __nac3_array_set_and_validate_list_shape(List<int32_t>* list,
-                                              int32_t ndims, int32_t* shape) {
-    set_and_validate_list_shape(list, ndims, shape);
-}
-
-void __nac3_array_set_and_validate_list_shape64(List<int64_t>* list,
-                                                int64_t ndims, int64_t* shape) {
-    set_and_validate_list_shape(list, ndims, shape);
-}
-
-void __nac3_array_write_list_to_array(List<int32_t>* list,
-                                      NDArray<int32_t>* ndarray) {
-    write_list_to_array(list, ndarray);
-}
-
-void __nac3_array_write_list_to_array64(List<int64_t>* list,
-                                        NDArray<int64_t>* ndarray) {
-    write_list_to_array(list, ndarray);
-}
-}

nac3core/irrt/irrt/ndarray/basic.hpp

@@ -1,345 +0,0 @@
-#pragma once
-
-#include <irrt/exception.hpp>
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/def.hpp>
-
-namespace {
-namespace ndarray {
-namespace basic {
-/**
- * @brief Asserts that `shape` does not contain negative dimensions.
- *
- * @param ndims Number of dimensions in `shape`
- * @param shape The shape to check on
- */
-template <typename SizeT>
-void assert_shape_no_negative(SizeT ndims, const SizeT* shape) {
-    for (SizeT axis = 0; axis < ndims; axis++) {
-        if (shape[axis] < 0) {
-            raise_exception(SizeT, EXN_VALUE_ERROR,
-                            "negative dimensions are not allowed; axis {0} "
-                            "has dimension {1}",
-                            axis, shape[axis], NO_PARAM);
-        }
-    }
-}
-
-/**
- * @brief Check two shapes are the same in the context of writing outputting to an ndarray.
- *
- * This function throws error messages for output shape mismatches.
- */
-template <typename SizeT>
-void assert_output_shape_same(SizeT ndarray_ndims, const SizeT* ndarray_shape,
-                              SizeT output_ndims, const SizeT* output_shape) {
-    if (ndarray_ndims != output_ndims) {
-        // There is no corresponding NumPy error message like this.
-        raise_exception(
-            SizeT, EXN_VALUE_ERROR,
-            "Cannot write output of ndims {0} to an ndarray with ndims {1}",
-            output_ndims, ndarray_ndims, NO_PARAM);
-    }
-
-    for (SizeT axis = 0; axis < ndarray_ndims; axis++) {
-        if (ndarray_shape[axis] != output_shape[axis]) {
-            // There is no corresponding NumPy error message like this.
-            raise_exception(
-                SizeT, EXN_VALUE_ERROR,
-                "Mismatched dimensions on axis {0}, output has "
-                "dimension {1}, but destination ndarray has dimension {2}.",
-                axis, output_shape[axis], ndarray_shape[axis]);
-        }
-    }
-}
-
-/**
- * @brief Returns the number of elements of an ndarray given its shape.
- *
- * @param ndims Number of dimensions in `shape`
- * @param shape The shape of the ndarray
- */
-template <typename SizeT>
-SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
-    SizeT size = 1;
-    for (SizeT axis = 0; axis < ndims; axis++) size *= shape[axis];
-    return size;
-}
-
-/**
- * @brief Compute the array indices of the `nth` (0-based) element of an ndarray given only its shape.
- *
- * @param ndims Number of elements in `shape` and `indices`
- * @param shape The shape of the ndarray
- * @param indices The returned indices indexing the ndarray with shape `shape`.
- * @param nth The index of the element of interest.
- */
-template <typename SizeT>
-void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices,
-                        SizeT nth) {
-    for (SizeT i = 0; i < ndims; i++) {
-        SizeT axis = ndims - i - 1;
-        SizeT dim = shape[axis];
-
-        indices[axis] = nth % dim;
-        nth /= dim;
-    }
-}
-
-/**
- * @brief Return the number of elements of an `ndarray`
- *
- * This function corresponds to `<an_ndarray>.size`
- */
-template <typename SizeT>
-SizeT size(const NDArray<SizeT>* ndarray) {
-    return calc_size_from_shape(ndarray->ndims, ndarray->shape);
-}
-
-/**
- * @brief Return of the number of its content of an `ndarray`.
- *
- * This function corresponds to `<an_ndarray>.nbytes`.
- */
-template <typename SizeT>
-SizeT nbytes(const NDArray<SizeT>* ndarray) {
-    return size(ndarray) * ndarray->itemsize;
-}
-
-/**
- * @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
- *
- * This function corresponds to `<an_ndarray>.__len__`.
- *
- * @param dst_length The returned result
- */
-template <typename SizeT>
-SizeT len(const NDArray<SizeT>* ndarray) {
-    // numpy prohibits `__len__` on unsized objects
-    if (ndarray->ndims == 0) {
-        raise_exception(SizeT, EXN_TYPE_ERROR, "len() of unsized object",
-                        NO_PARAM, NO_PARAM, NO_PARAM);
-    } else {
-        return ndarray->shape[0];
-    }
-}
-
-/**
- * @brief Return a boolean indicating if `ndarray` is (C-)contiguous.
- *
- * You may want to see: ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
- */
-template <typename SizeT>
-bool is_c_contiguous(const NDArray<SizeT>* ndarray) {
-    // Other references:
-    // - tinynumpy's implementation: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L102
-    // - ndarray's flags["C_CONTIGUOUS"]: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags
-    // - ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
-
-    // From https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45:
-    //
-    // The traditional rule is that for an array to be flagged as C contiguous,
-    // the following must hold:
-    //
-    // strides[-1] == itemsize
-    // strides[i] == shape[i+1] * strides[i + 1]
-    // [...]
-    // According to these rules, a 0- or 1-dimensional array is either both
-    // C- and F-contiguous, or neither; and an array with 2+ dimensions
-    // can be C- or F- contiguous, or neither, but not both. Though there
-    // there are exceptions for arrays with zero or one item, in the first
-    // case the check is relaxed up to and including the first dimension
-    // with shape[i] == 0. In the second case `strides == itemsize` will
-    // can be true for all dimensions and both flags are set.
-
-    if (ndarray->ndims == 0) {
-        return true;
-    }
-
-    if (ndarray->strides[ndarray->ndims - 1] != ndarray->itemsize) {
-        return false;
-    }
-
-    for (SizeT i = 1; i < ndarray->ndims; i++) {
-        SizeT axis_i = ndarray->ndims - i - 1;
-        if (ndarray->strides[axis_i] !=
-            ndarray->shape[axis_i + 1] * ndarray->strides[axis_i + 1]) {
-            return false;
-        }
-    }
-
-    return true;
-}
-
-/**
- * @brief Return the pointer to the element indexed by `indices`.
- */
-template <typename SizeT>
-uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray,
-                                 const SizeT* indices) {
-    uint8_t* element = ndarray->data;
-    for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
-        element += indices[dim_i] * ndarray->strides[dim_i];
-    return element;
-}
-
-/**
- * @brief Convenience function. Like `get_pelement_by_indices` but
- * reinterprets the element pointer.
- */
-template <typename SizeT, typename T>
-T* get_ptr(const NDArray<SizeT>* ndarray, const SizeT* indices) {
-    return (T*)get_pelement_by_indices(ndarray, indices);
-}
-
-/**
- * @brief Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
- *
- * This function does no bound check.
- */
-template <typename SizeT>
-uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
-    uint8_t* element = ndarray->data;
-    for (SizeT i = 0; i < ndarray->ndims; i++) {
-        SizeT axis = ndarray->ndims - i - 1;
-        SizeT dim = ndarray->shape[axis];
-        element += ndarray->strides[axis] * (nth % dim);
-        nth /= dim;
-    }
-    return element;
-}
-
-/**
- * @brief Update the strides of an ndarray given an ndarray `shape`
- * and assuming that the ndarray is fully c-contagious.
- *
- * You might want to read https://ajcr.net/stride-guide-part-1/.
- */
-template <typename SizeT>
-void set_strides_by_shape(NDArray<SizeT>* ndarray) {
-    SizeT stride_product = 1;
-    for (SizeT i = 0; i < ndarray->ndims; i++) {
-        SizeT axis = ndarray->ndims - i - 1;
-        ndarray->strides[axis] = stride_product * ndarray->itemsize;
-        stride_product *= ndarray->shape[axis];
-    }
-}
-
-/**
- * @brief Set an element in `ndarray`.
- *
- * @param pelement Pointer to the element in `ndarray` to be set.
- * @param pvalue Pointer to the value `pelement` will be set to.
- */
-template <typename SizeT>
-void set_pelement_value(NDArray<SizeT>* ndarray, uint8_t* pelement,
-                        const uint8_t* pvalue) {
-    __builtin_memcpy(pelement, pvalue, ndarray->itemsize);
-}
-
-/**
- * @brief Copy data from one ndarray to another of the exact same size and itemsize.
- *
- * Both ndarrays will be viewed in their flatten views when copying the elements.
- */
-template <typename SizeT>
-void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
-    // TODO: Make this faster with memcpy
-
-    debug_assert_eq(SizeT, src_ndarray->itemsize, dst_ndarray->itemsize);
-
-    for (SizeT i = 0; i < size(src_ndarray); i++) {
-        auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
-        auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
-        ndarray::basic::set_pelement_value(dst_ndarray, dst_element,
-                                           src_element);
-    }
-}
-}  // namespace basic
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-using namespace ndarray::basic;
-
-void __nac3_ndarray_util_assert_shape_no_negative(int32_t ndims,
-                                                  int32_t* shape) {
-    assert_shape_no_negative(ndims, shape);
-}
-
-void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims,
-                                                    int64_t* shape) {
-    assert_shape_no_negative(ndims, shape);
-}
-
-void __nac3_ndarray_util_assert_output_shape_same(int32_t ndarray_ndims,
-                                                  const int32_t* ndarray_shape,
-                                                  int32_t output_ndims,
-                                                  const int32_t* output_shape) {
-    assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims,
-                             output_shape);
-}
-
-void __nac3_ndarray_util_assert_output_shape_same64(
-    int64_t ndarray_ndims, const int64_t* ndarray_shape, int64_t output_ndims,
-    const int64_t* output_shape) {
-    assert_output_shape_same(ndarray_ndims, ndarray_shape, output_ndims,
-                             output_shape);
-}
-
-uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
-    return size(ndarray);
-}
-
-uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
-    return size(ndarray);
-}
-
-uint32_t __nac3_ndarray_nbytes(NDArray<int32_t>* ndarray) {
-    return nbytes(ndarray);
-}
-
-uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t>* ndarray) {
-    return nbytes(ndarray);
-}
-
-int32_t __nac3_ndarray_len(NDArray<int32_t>* ndarray) { return len(ndarray); }
-
-int64_t __nac3_ndarray_len64(NDArray<int64_t>* ndarray) { return len(ndarray); }
-
-bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t>* ndarray) {
-    return is_c_contiguous(ndarray);
-}
-
-bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
-    return is_c_contiguous(ndarray);
-}
-
-uint8_t* __nac3_ndarray_get_nth_pelement(const NDArray<int32_t>* ndarray,
-                                         int32_t nth) {
-    return get_nth_pelement(ndarray, nth);
-}
-
-uint8_t* __nac3_ndarray_get_nth_pelement64(const NDArray<int64_t>* ndarray,
-                                           int64_t nth) {
-    return get_nth_pelement(ndarray, nth);
-}
-
-void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t>* ndarray) {
-    set_strides_by_shape(ndarray);
-}
-
-void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
-    set_strides_by_shape(ndarray);
-}
-
-void __nac3_ndarray_copy_data(NDArray<int32_t>* src_ndarray,
-                              NDArray<int32_t>* dst_ndarray) {
-    copy_data(src_ndarray, dst_ndarray);
-}
-
-void __nac3_ndarray_copy_data64(NDArray<int64_t>* src_ndarray,
-                                NDArray<int64_t>* dst_ndarray) {
-    copy_data(src_ndarray, dst_ndarray);
-}
-}

nac3core/irrt/irrt/ndarray/broadcast.hpp

@@ -1,171 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/def.hpp>
-#include <irrt/slice.hpp>
-
-namespace {
-template <typename SizeT>
-struct ShapeEntry {
-    SizeT ndims;
-    SizeT* shape;
-};
-}  // namespace
-
-namespace {
-namespace ndarray {
-namespace broadcast {
-/**
- * @brief Return true if `src_shape` can broadcast to `dst_shape`.
- *
- * See https://numpy.org/doc/stable/user/basics.broadcasting.html
- */
-template <typename SizeT>
-bool can_broadcast_shape_to(SizeT target_ndims, const SizeT* target_shape,
-                            SizeT src_ndims, const SizeT* src_shape) {
-    if (src_ndims > target_ndims) {
-        return false;
-    }
-
-    for (SizeT i = 0; i < src_ndims; i++) {
-        SizeT target_dim = target_shape[target_ndims - i - 1];
-        SizeT src_dim = src_shape[src_ndims - i - 1];
-        if (!(src_dim == 1 || target_dim == src_dim)) {
-            return false;
-        }
-    }
-    return true;
-}
-
-/**
- * @brief Performs `np.broadcast_shapes(<shapes>)`
- *
- * @param num_shapes Number of entries in `shapes`
- * @param shapes The list of shape to do `np.broadcast_shapes` on.
- * @param dst_ndims The length of `dst_shape`.
- *   `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it.
- *   for this function since they should already know in order to allocate `dst_shape` in the first place.
- * @param dst_shape The resulting shape. Must be pre-allocated by the caller. This function calculate the result
- *   of `np.broadcast_shapes` and write it here.
- */
-template <typename SizeT>
-void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT>* shapes,
-                      SizeT dst_ndims, SizeT* dst_shape) {
-    for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
-        dst_shape[dst_axis] = 1;
-    }
-
-#ifdef IRRT_DEBUG_ASSERT
-    SizeT max_ndims_found = 0;
-#endif
-
-    for (SizeT i = 0; i < num_shapes; i++) {
-        ShapeEntry<SizeT> entry = shapes[i];
-
-        // Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
-        debug_assert(SizeT, entry.ndims <= dst_ndims);
-
-#ifdef IRRT_DEBUG_ASSERT
-        max_ndims_found = max(max_ndims_found, entry.ndims);
-#endif
-
-        for (SizeT j = 0; j < entry.ndims; j++) {
-            SizeT entry_axis = entry.ndims - j - 1;
-            SizeT dst_axis = dst_ndims - j - 1;
-
-            SizeT entry_dim = entry.shape[entry_axis];
-            SizeT dst_dim = dst_shape[dst_axis];
-
-            if (dst_dim == 1) {
-                dst_shape[dst_axis] = entry_dim;
-            } else if (entry_dim == 1 || entry_dim == dst_dim) {
-                // Do nothing
-            } else {
-                raise_exception(SizeT, EXN_VALUE_ERROR,
-                                "shape mismatch: objects cannot be broadcast "
-                                "to a single shape.",
-                                NO_PARAM, NO_PARAM, NO_PARAM);
-            }
-        }
-    }
-
-    // Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
-    debug_assert_eq(SizeT, max_ndims_found, dst_ndims);
-}
-
-/**
- * @brief Perform `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
- *
- * This function attempts to broadcast `src_ndarray` to a new shape defined by `dst_ndarray.shape`,
- * and return the result by modifying `dst_ndarray`.
- *
- * # Notes on `dst_ndarray`
- * The caller is responsible for allocating space for the resulting ndarray.
- * Here is what this function expects from `dst_ndarray` when called:
- *   - `dst_ndarray->data` does not have to be initialized.
- *   - `dst_ndarray->itemsize` does not have to be initialized.
- *   - `dst_ndarray->ndims` must be initialized, determining the length of `dst_ndarray->shape`
- *   - `dst_ndarray->shape` must be allocated, and must contain the desired target broadcast shape.
- *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
- * When this function call ends:
- *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
- *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
- *   - `dst_ndarray->ndims` is unchanged.
- *   - `dst_ndarray->shape` is unchanged.
- *   - `dst_ndarray->strides` is updated accordingly by how ndarray broadcast_to works.
- */
-template <typename SizeT>
-void broadcast_to(const NDArray<SizeT>* src_ndarray,
-                  NDArray<SizeT>* dst_ndarray) {
-    if (!ndarray::broadcast::can_broadcast_shape_to(
-            dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
-            src_ndarray->shape)) {
-        raise_exception(SizeT, EXN_VALUE_ERROR,
-                        "operands could not be broadcast together", NO_PARAM,
-                        NO_PARAM, NO_PARAM);
-    }
-
-    dst_ndarray->data = src_ndarray->data;
-    dst_ndarray->itemsize = src_ndarray->itemsize;
-
-    for (SizeT i = 0; i < dst_ndarray->ndims; i++) {
-        SizeT src_axis = src_ndarray->ndims - i - 1;
-        SizeT dst_axis = dst_ndarray->ndims - i - 1;
-        if (src_axis < 0 || (src_ndarray->shape[src_axis] == 1 &&
-                             dst_ndarray->shape[dst_axis] != 1)) {
-            // Freeze the steps in-place
-            dst_ndarray->strides[dst_axis] = 0;
-        } else {
-            dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
-        }
-    }
-}
-}  // namespace broadcast
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-using namespace ndarray::broadcast;
-
-void __nac3_ndarray_broadcast_to(NDArray<int32_t>* src_ndarray,
-                                 NDArray<int32_t>* dst_ndarray) {
-    broadcast_to(src_ndarray, dst_ndarray);
-}
-
-void __nac3_ndarray_broadcast_to64(NDArray<int64_t>* src_ndarray,
-                                   NDArray<int64_t>* dst_ndarray) {
-    broadcast_to(src_ndarray, dst_ndarray);
-}
-
-void __nac3_ndarray_broadcast_shapes(int32_t num_shapes,
-                                     const ShapeEntry<int32_t>* shapes,
-                                     int32_t dst_ndims, int32_t* dst_shape) {
-    broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
-}
-
-void __nac3_ndarray_broadcast_shapes64(int64_t num_shapes,
-                                       const ShapeEntry<int64_t>* shapes,
-                                       int64_t dst_ndims, int64_t* dst_shape) {
-    broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
-}
-}

nac3core/irrt/irrt/ndarray/def.hpp

@@ -1,44 +0,0 @@
-#pragma once
-
-namespace {
-/**
- * @brief The NDArray object
- *
- * The official numpy implementations: https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst
- */
-template <typename SizeT>
-struct NDArray {
-    /**
-     * @brief The underlying data this `ndarray` is pointing to.
-     *
-     * Must be set to `nullptr` to indicate that this NDArray's `data` is uninitialized.
-     */
-    uint8_t* data;
-
-    /**
-     * @brief The number of bytes of a single element in `data`.
-     */
-    SizeT itemsize;
-
-    /**
-     * @brief The number of dimensions of this shape.
-     */
-    SizeT ndims;
-
-    /**
-     * @brief The NDArray shape, with length equal to `ndims`.
-     *
-     * Note that it may contain 0.
-     */
-    SizeT* shape;
-
-    /**
-     * @brief Array strides, with length equal to `ndims`
-     *
-     * The stride values are in units of bytes, not number of elements.
-     *
-     * Note that `strides` can have negative values.
-     */
-    SizeT* strides;
-};
-}  // namespace

nac3core/irrt/irrt/ndarray/indexing.hpp

@@ -1,221 +0,0 @@
-#pragma once
-
-#include <irrt/exception.hpp>
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/basic.hpp>
-#include <irrt/ndarray/def.hpp>
-#include <irrt/slice.hpp>
-
-namespace {
-typedef uint8_t NDIndexType;
-
-/**
- * @brief A single element index
- *
- * `data` points to a `SliceIndex`.
- */
-
-const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
-/**
- * @brief A slice index
- *
- * `data` points to a `UserRange`.
- */
-const NDIndexType ND_INDEX_TYPE_SLICE = 1;
-
-/**
- * @brief `np.newaxis` / `None`
- *
- * `data` is unused.
- */
-const NDIndexType ND_INDEX_TYPE_NEWAXIS = 2;
-
-/**
- * @brief `Ellipsis` / `...`
- *
- * `data` is unused.
- */
-const NDIndexType ND_INDEX_TYPE_ELLIPSIS = 3;
-
-/**
- * @brief An index used in ndarray indexing
- */
-struct NDIndex {
-    /**
-     * @brief Enum tag to specify the type of index.
-     *
-     * Please see comments of each enum constant.
-     */
-    NDIndexType type;
-
-    /**
-     * @brief The accompanying data associated with `type`.
-     *
-     * Please see comments of each enum constant.
-     */
-    uint8_t* data;
-};
-}  // namespace
-
-namespace {
-namespace ndarray {
-namespace indexing {
-/**
- * @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
- *
- * This is function very similar to performing `dst_ndarray = src_ndarray[indexes]` in Python (where the variables
- * can all be found in the parameter of this function).
- *
- * In other words, this function takes in an ndarray (`src_ndarray`), index it with `indexes`, and return the
- * indexed array (by writing the result to `dst_ndarray`).
- *
- * This function also does proper assertions on `indexes`.
- *
- * # Notes on `dst_ndarray`
- * The caller is responsible for allocating space for the resulting ndarray.
- * Here is what this function expects from `dst_ndarray` when called:
- *   - `dst_ndarray->data` does not have to be initialized.
- *   - `dst_ndarray->itemsize` does not have to be initialized.
- *   - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
- *       indexing `src_ndarray` with `indexes`.
- *   - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
- *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
- * When this function call ends:
- *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
- *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
- *   - `dst_ndarray->ndims` is unchanged.
- *   - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
- *   - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
- *
- * @param indexes Indexes to index `src_ndarray`, ordered in the same way you would write them in Python.
- * @param src_ndarray The NDArray to be indexed.
- * @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
- */
-template <typename SizeT>
-void index(SizeT num_indexes, const NDIndex* indexes,
-           const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
-    // First, validate `indexes`.
-
-    // Expected value of `dst_ndarray->ndims`.
-    SizeT expected_dst_ndims = src_ndarray->ndims;
-    // To check for "too many indices for array: array is ?-dimensional, but ? were indexed"
-    SizeT num_indexed = 0;
-    // There may be ellipsis `...` in `indexes`. There can only be 0 or 1 ellipsis.
-    SizeT num_ellipsis = 0;
-
-    for (SizeT i = 0; i < num_indexes; i++) {
-        if (indexes[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
-            expected_dst_ndims--;
-            num_indexed++;
-        } else if (indexes[i].type == ND_INDEX_TYPE_SLICE) {
-            num_indexed++;
-        } else if (indexes[i].type == ND_INDEX_TYPE_NEWAXIS) {
-            expected_dst_ndims++;
-        } else if (indexes[i].type == ND_INDEX_TYPE_ELLIPSIS) {
-            num_ellipsis++;
-            if (num_ellipsis > 1) {
-                raise_exception(
-                    SizeT, EXN_INDEX_ERROR,
-                    "an index can only have a single ellipsis ('...')",
-                    NO_PARAM, NO_PARAM, NO_PARAM);
-            }
-        } else {
-            __builtin_unreachable();
-        }
-    }
-
-    debug_assert_eq(SizeT, expected_dst_ndims, dst_ndarray->ndims);
-
-    if (src_ndarray->ndims - num_indexed < 0) {
-        raise_exception(SizeT, EXN_INDEX_ERROR,
-                        "too many indices for array: array is {0}-dimensional, "
-                        "but {1} were indexed",
-                        src_ndarray->ndims, num_indexes, NO_PARAM);
-    }
-
-    dst_ndarray->data = src_ndarray->data;
-    dst_ndarray->itemsize = src_ndarray->itemsize;
-
-    // Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
-    SizeT src_axis = 0;
-    SizeT dst_axis = 0;
-
-    for (SliceIndex i = 0; i < num_indexes; i++) {
-        const NDIndex* index = &indexes[i];
-        if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
-            SliceIndex input = *((SliceIndex*)index->data);
-            SliceIndex k = slice::resolve_index_in_length(
-                src_ndarray->shape[src_axis], input);
-
-            if (k == slice::OUT_OF_BOUNDS) {
-                raise_exception(SizeT, EXN_INDEX_ERROR,
-                                "index {0} is out of bounds for axis {1} "
-                                "with size {2}",
-                                input, src_axis, src_ndarray->shape[src_axis]);
-            }
-
-            dst_ndarray->data += k * src_ndarray->strides[src_axis];
-
-            src_axis++;
-        } else if (index->type == ND_INDEX_TYPE_SLICE) {
-            UserSlice* input = (UserSlice*)index->data;
-
-            Slice slice =
-                input->indices_checked<SizeT>(src_ndarray->shape[src_axis]);
-
-            dst_ndarray->data +=
-                (SizeT)slice.start * src_ndarray->strides[src_axis];
-            dst_ndarray->strides[dst_axis] =
-                ((SizeT)slice.step) * src_ndarray->strides[src_axis];
-            dst_ndarray->shape[dst_axis] = (SizeT)slice.len();
-
-            dst_axis++;
-            src_axis++;
-        } else if (index->type == ND_INDEX_TYPE_NEWAXIS) {
-            dst_ndarray->strides[dst_axis] = 0;
-            dst_ndarray->shape[dst_axis] = 1;
-
-            dst_axis++;
-        } else if (index->type == ND_INDEX_TYPE_ELLIPSIS) {
-            // The number of ':' entries this '...' implies.
-            SizeT ellipsis_size = src_ndarray->ndims - num_indexed;
-
-            for (SizeT j = 0; j < ellipsis_size; j++) {
-                dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
-                dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
-
-                dst_axis++;
-                src_axis++;
-            }
-        } else {
-            __builtin_unreachable();
-        }
-    }
-
-    for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
-        dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
-        dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
-    }
-
-    debug_assert_eq(SizeT, src_ndarray->ndims, src_axis);
-    debug_assert_eq(SizeT, dst_ndarray->ndims, dst_axis);
-}
-}  // namespace indexing
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-using namespace ndarray::indexing;
-
-void __nac3_ndarray_index(int32_t num_indexes, NDIndex* indexes,
-                          NDArray<int32_t>* src_ndarray,
-                          NDArray<int32_t>* dst_ndarray) {
-    index(num_indexes, indexes, src_ndarray, dst_ndarray);
-}
-
-void __nac3_ndarray_index64(int64_t num_indexes, NDIndex* indexes,
-                            NDArray<int64_t>* src_ndarray,
-                            NDArray<int64_t>* dst_ndarray) {
-    index(num_indexes, indexes, src_ndarray, dst_ndarray);
-}
-}

nac3core/irrt/irrt/ndarray/iter.hpp

@@ -1,118 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/def.hpp>
-
-namespace {
-/**
- * @brief Helper struct to enumerate through all indices under a shape.
- *
- * i.e., If `shape` is `[3, 2]`, by repeating `next()`, then you get:
- * - `[0, 0]`
- * - `[0, 1]`
- * - `[1, 0]`
- * - `[1, 1]`
- * - `[2, 0]`
- * - `[2, 1]`
- * - end.
- *
- * Interesting cases:
- * - If ndims == 0, there is one enumeration.
- * - If shape contains zeroes, there are no enumerations.
- */
-template <typename SizeT>
-struct NDIter {
-    SizeT ndims;
-    SizeT* shape;
-    SizeT* strides;
-
-    /**
-     * @brief The current indices.
-     *
-     * Must be allocated by the caller.
-     */
-    SizeT* indices;
-
-    /**
-     * @brief The nth (0-based) index of the current indices.
-     */
-    SizeT nth;
-
-    /**
-     * @brief Pointer to the current element.
-     */
-    uint8_t* element;
-
-    /**
-     * @brief The product of shape.
-     */
-    SizeT size;
-
-    // TODO:: There is something called backstrides to speedup iteration.
-    // See https://ajcr.net/stride-guide-part-1/, and https://docs.scipy.org/doc/numpy-1.13.0/reference/c-api.types-and-structures.html#c.PyArrayIterObject.PyArrayIterObject.backstrides.
-    // Maybe LLVM is clever and knows how to optimize.
-
-    void initialize(SizeT ndims, SizeT* shape, SizeT* strides, uint8_t* element,
-                    SizeT* indices) {
-        this->ndims = ndims;
-        this->shape = shape;
-        this->strides = strides;
-
-        this->indices = indices;
-        this->element = element;
-
-        // Compute size and backstrides
-        this->size = 1;
-        for (SizeT i = 0; i < ndims; i++) {
-            this->size *= shape[i];
-        }
-
-        for (SizeT axis = 0; axis < ndims; axis++) indices[axis] = 0;
-        nth = 0;
-    }
-
-    void initialize_by_ndarray(NDArray<SizeT>* ndarray, SizeT* indices) {
-        this->initialize(ndarray->ndims, ndarray->shape, ndarray->strides,
-                         ndarray->data, indices);
-    }
-
-    bool has_next() { return nth < size; }
-
-    void next() {
-        for (SizeT i = 0; i < ndims; i++) {
-            SizeT axis = ndims - i - 1;
-            indices[axis]++;
-            if (indices[axis] >= shape[axis]) {
-                indices[axis] = 0;
-
-                // TODO: Can be optimized with backstrides.
-                element -= strides[axis] * (shape[axis] - 1);
-            } else {
-                element += strides[axis];
-                break;
-            }
-        }
-        nth++;
-    }
-};
-}  // namespace
-
-extern "C" {
-void __nac3_nditer_initialize(NDIter<int32_t>* iter, NDArray<int32_t>* ndarray,
-                              int32_t* indices) {
-    iter->initialize_by_ndarray(ndarray, indices);
-}
-
-void __nac3_nditer_initialize64(NDIter<int64_t>* iter,
-                                NDArray<int64_t>* ndarray, int64_t* indices) {
-    iter->initialize_by_ndarray(ndarray, indices);
-}
-
-bool __nac3_nditer_has_next(NDIter<int32_t>* iter) { return iter->has_next(); }
-
-bool __nac3_nditer_has_next64(NDIter<int64_t>* iter) { return iter->has_next(); }
-
-void __nac3_nditer_next(NDIter<int32_t>* iter) { iter->next(); }
-
-void __nac3_nditer_next64(NDIter<int64_t>* iter) { iter->next(); }
-}

nac3core/irrt/irrt/ndarray/product.hpp

@@ -1,194 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/basic.hpp>
-#include <irrt/ndarray/broadcast.hpp>
-#include <irrt/ndarray/iter.hpp>
-
-// NOTE: Everything would be much easier and elegant if einsum is implemented.
-
-namespace {
-namespace ndarray {
-namespace matmul {
-
-/*
- * In einsum notation, the output is the broadcasts performed by `np.einsum("...ij,...jk->...ik", a, b)`.
- *
- * Example:
- *   Suppose      `a_shape == [99,  1, 97, 4, 2]`
- *       and      `b_shape == [ 1, 98,  1, 2, 5]`,
- *
- *   ...then  `new_a_shape == [99, 98, 97, 4, 2]`,
- *            `new_b_shape == [99, 98, 97, 2, 5]`,
- *      and     `dst_shape == [99, 98, 97, 4, 5]`.
- *                             ^^^^^^^^^^  ^^^^
- *                         (by broadcast)  (4x2 @ 2x5 => 4x5)
- */
-template <typename SizeT>
-void calculate_shapes(SizeT a_ndims, SizeT* a_shape, SizeT b_ndims,
-                      SizeT* b_shape, SizeT final_ndims, SizeT* new_a_shape,
-                      SizeT* new_b_shape, SizeT* dst_shape) {
-    debug_assert(SizeT, a_ndims >= 2);
-    debug_assert(SizeT, b_ndims >= 2);
-    debug_assert_eq(SizeT, max(a_ndims, b_ndims), final_ndims);
-
-    const SizeT num_entries = 2;
-    ShapeEntry<SizeT> entries[num_entries] = {
-        {.ndims = a_ndims - 2, .shape = a_shape},
-        {.ndims = b_ndims - 2, .shape = b_shape}};
-
-    ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries,
-                                                final_ndims - 2, new_a_shape);
-    ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries,
-                                                final_ndims - 2, new_b_shape);
-    ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries,
-                                                final_ndims - 2, dst_shape);
-
-    new_a_shape[final_ndims - 2] = a_shape[a_ndims - 2];
-    new_a_shape[final_ndims - 1] = a_shape[a_ndims - 1];
-    new_b_shape[final_ndims - 2] = b_shape[b_ndims - 2];
-    new_b_shape[final_ndims - 1] = b_shape[b_ndims - 1];
-    dst_shape[final_ndims - 2] = a_shape[a_ndims - 2];
-    dst_shape[final_ndims - 1] = b_shape[b_ndims - 1];
-}
-
-/**
- * @brief Perform `np.matmul(a, b)` but the inputs are both rank >=2 matrices and `a.shape[:-2] == b.shape[:-2]`.
- *
- * The compatibility of `a` and `b` (for their `.shape[-2:]`) are asserted.
- *
- * Also see https://numpy.org/doc/stable/reference/generated/numpy.matmul.html#numpy-matmul.
- *
- * This function expects `dst_ndarray` to contain the following content when called:
- * - `dst_ndarray->data` is allocated. Can be uninitialized.
- * - `dst_ndarray->itemsize` is set to `sizeof(T)`.
- * - `dst_ndarray->ndims` is set appropriately.
- * - `dst_ndarray->shape` is set appropriately.
- * - `dst_ndarray->strides` is ignored.
- *
- * Moreover, the shapes of `a_ndarray`, `b_ndarray`, and `dst_ndarray` **must be the same**. This implies
- */
-template <typename SizeT, typename T>
-void matmul_at_least_2d(NDArray<SizeT>* a_ndarray, NDArray<SizeT>* b_ndarray,
-                        NDArray<SizeT>* dst_ndarray) {
-    // All inputs' ndims should be >= 2 and be the same.
-    debug_assert_eq(SizeT, a_ndarray->ndims, b_ndarray->ndims);
-    debug_assert_eq(SizeT, a_ndarray->ndims, dst_ndarray->ndims);
-    debug_assert(SizeT, a_ndarray->ndims >= 2);
-
-    debug_assert_eq(SizeT, a_ndarray->itemsize, sizeof(T));
-    debug_assert_eq(SizeT, b_ndarray->itemsize, sizeof(T));
-    debug_assert_eq(SizeT, dst_ndarray->itemsize, sizeof(T));
-
-    if (IRRT_DEBUG_ASSERT_BOOL) {
-        // Check that the shapes are the same.
-        for (SizeT i = 0; i < a_ndarray->ndims - 2; i++) {
-            if (dst_ndarray->shape[0] != a_ndarray->shape[0]) {
-                raise_debug_assert(
-                    SizeT, "Bad shape. At axis {0}, a has {1}, dst has {2}", i,
-                    a_ndarray->shape[i], dst_ndarray->shape[i]);
-            }
-            if (dst_ndarray->shape[0] != b_ndarray->shape[0]) {
-                raise_debug_assert(
-                    SizeT, "Bad shape. At axis {0}, b has {1}, dst has {2}", i,
-                    b_ndarray->shape[i], dst_ndarray->shape[i]);
-            }
-        }
-    }
-
-    // Number of dimensions dedicated to stacking
-    // e.g., [4, 6, 1, 2, 3]
-    //        ^^^^^^^ count these
-    const SizeT u = a_ndarray->ndims - 2;  // Alias
-
-    SizeT* a_mat_shape = a_ndarray->shape + u;
-    SizeT* b_mat_shape = b_ndarray->shape + u;
-    SizeT* dst_mat_shape = dst_ndarray->shape + u;
-
-    // Assert that dst_ndarray has the correct shape
-    debug_assert_eq(SizeT, dst_mat_shape[0], a_mat_shape[0]);
-    debug_assert_eq(SizeT, dst_mat_shape[1], b_mat_shape[1]);
-
-    // Check that a and b are compatible for matmul
-    if (a_mat_shape[1] != b_mat_shape[0]) {
-        // This is a custom error message. Different from NumPy.
-        raise_exception(
-            SizeT, EXN_VALUE_ERROR,
-            "Cannot multiply LHS (shape ?x{0}) with RHS (shape {1}x?})",
-            a_mat_shape[1], b_mat_shape[0], NO_PARAM);
-    }
-
-    // Iterate through shape[:-2]. i.e,
-    // Given a = [5, 4, 3, m, p] and b = [5, 4, 3, p, n]. We iterate through [5, 4, 3].
-    SizeT* indices =
-        (SizeT*)__builtin_alloca(sizeof(SizeT) * dst_ndarray->ndims);
-    SizeT* mat_indices = indices + u;
-    NDIter<SizeT> iter;
-    iter.initialize(u, dst_ndarray->shape, dst_ndarray->strides,
-                    dst_ndarray->data, indices);
-
-    for (; iter.has_next(); iter.next()) {
-        for (SizeT i = 0; i < dst_mat_shape[0]; i++) {
-            for (SizeT j = 0; j < dst_mat_shape[1]; j++) {
-                // `indices` is being reused to index into different ndarrays.
-                mat_indices[0] = i;
-                mat_indices[1] = j;
-                T* d = ndarray::basic::get_ptr<SizeT, T>(dst_ndarray, indices);
-                *d = 0;
-
-                for (SizeT k = 0; k < a_ndarray->shape[1]; k++) {
-                    mat_indices[0] = i;
-                    mat_indices[1] = k;
-                    T* a =
-                        ndarray::basic::get_ptr<SizeT, T>(a_ndarray, indices);
-
-                    mat_indices[0] = k;
-                    mat_indices[1] = j;
-                    T* b =
-                        ndarray::basic::get_ptr<SizeT, T>(b_ndarray, indices);
-
-                    *d += (*a) * (*b);
-                }
-            }
-        }
-    }
-}
-}  // namespace matmul
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-using namespace ndarray::matmul;
-
-void __nac3_ndarray_matmul_calculate_shapes(int32_t a_ndims, int32_t* a_shape,
-                                            int32_t b_ndims, int32_t* b_shape,
-                                            int32_t final_ndims,
-                                            int32_t* new_a_shape,
-                                            int32_t* new_b_shape,
-                                            int32_t* dst_shape) {
-    calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims,
-                     new_a_shape, new_b_shape, dst_shape);
-}
-
-void __nac3_ndarray_matmul_calculate_shapes64(int64_t a_ndims, int64_t* a_shape,
-                                              int64_t b_ndims, int64_t* b_shape,
-                                              int64_t final_ndims,
-                                              int64_t* new_a_shape,
-                                              int64_t* new_b_shape,
-                                              int64_t* dst_shape) {
-    calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims,
-                     new_a_shape, new_b_shape, dst_shape);
-}
-
-void __nac3_ndarray_float64_matmul_at_least_2d(NDArray<int32_t>* a_ndarray,
-                                               NDArray<int32_t>* b_ndarray,
-                                               NDArray<int32_t>* dst_ndarray) {
-    matmul_at_least_2d<int32_t, double>(a_ndarray, b_ndarray, dst_ndarray);
-}
-
-void __nac3_ndarray_float64_matmul_at_least_2d64(
-    NDArray<int64_t>* a_ndarray, NDArray<int64_t>* b_ndarray,
-    NDArray<int64_t>* dst_ndarray) {
-    matmul_at_least_2d<int64_t, double>(a_ndarray, b_ndarray, dst_ndarray);
-}
-}

nac3core/irrt/irrt/ndarray/reshape.hpp

@@ -1,106 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/def.hpp>
-
-namespace {
-namespace ndarray {
-namespace reshape {
-/**
- * @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
- *
- * If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
- * modified to contain the resolved dimension.
- *
- * To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
- * `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
- *
- * @param size The `.size` of `<ndarray>`
- * @param new_ndims Number of elements in `new_shape`
- * @param new_shape Target shape to reshape to
- */
-template <typename SizeT>
-void resolve_and_check_new_shape(SizeT size, SizeT new_ndims,
-                                 SizeT* new_shape) {
-    // Is there a -1 in `new_shape`?
-    bool neg1_exists = false;
-    // Location of -1, only initialized if `neg1_exists` is true
-    SizeT neg1_axis_i;
-    // The computed ndarray size of `new_shape`
-    SizeT new_size = 1;
-
-    for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
-        SizeT dim = new_shape[axis_i];
-        if (dim < 0) {
-            if (dim == -1) {
-                if (neg1_exists) {
-                    // Multiple `-1` found. Throw an error.
-                    raise_exception(SizeT, EXN_VALUE_ERROR,
-                                    "can only specify one unknown dimension",
-                                    NO_PARAM, NO_PARAM, NO_PARAM);
-                } else {
-                    neg1_exists = true;
-                    neg1_axis_i = axis_i;
-                }
-            } else {
-                // TODO: What? In `np.reshape` any negative dimensions is
-                // treated like its `-1`.
-                //
-                // Try running `np.zeros((3, 4)).reshape((-999, 2))`
-                //
-                // It is not documented by numpy.
-                // Throw an error for now...
-
-                raise_exception(
-                    SizeT, EXN_VALUE_ERROR,
-                    "Found non -1 negative dimension {0} on axis {1}", dim,
-                    axis_i, NO_PARAM);
-            }
-        } else {
-            new_size *= dim;
-        }
-    }
-
-    bool can_reshape;
-    if (neg1_exists) {
-        // Let `x` be the unknown dimension
-        // Solve `x * <new_size> = <size>`
-        if (new_size == 0 && size == 0) {
-            // `x` has infinitely many solutions
-            can_reshape = false;
-        } else if (new_size == 0 && size != 0) {
-            // `x` has no solutions
-            can_reshape = false;
-        } else if (size % new_size != 0) {
-            // `x` has no integer solutions
-            can_reshape = false;
-        } else {
-            can_reshape = true;
-            new_shape[neg1_axis_i] = size / new_size;  // Resolve dimension
-        }
-    } else {
-        can_reshape = (new_size == size);
-    }
-
-    if (!can_reshape) {
-        raise_exception(SizeT, EXN_VALUE_ERROR,
-                        "cannot reshape array of size {0} into given shape",
-                        size, NO_PARAM, NO_PARAM);
-    }
-}
-}  // namespace reshape
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-void __nac3_ndarray_resolve_and_check_new_shape(int32_t size, int32_t new_ndims,
-                                                int32_t* new_shape) {
-    ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
-}
-
-void __nac3_ndarray_resolve_and_check_new_shape64(int64_t size,
-                                                  int64_t new_ndims,
-                                                  int64_t* new_shape) {
-    ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
-}
-}

nac3core/irrt/irrt/ndarray/transpose.hpp

@@ -1,145 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/def.hpp>
-#include <irrt/slice.hpp>
-
-/*
- * Notes on `np.transpose(<array>, <axes>)`
- *
- * TODO: `axes`, if specified, can actually contain negative indices,
- * but it is not documented in numpy.
- *
- * Supporting it for now.
- */
-
-namespace {
-namespace ndarray {
-namespace transpose {
-/**
- * @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
- *
- * Note that `np.transpose`'s `<axe>` argument is optional. If the argument
- * is specified but the user, use this function to do assertions on it.
- *
- * @param ndims The number of dimensions of `<array>`
- * @param num_axes Number of elements in `<axes>` as specified by the user.
- *   This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
- * @param axes The user specified `<axes>`.
- */
-template <typename SizeT>
-void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
-    if (ndims != num_axes) {
-        raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array",
-                        NO_PARAM, NO_PARAM, NO_PARAM);
-    }
-
-    // TODO: Optimize this
-    bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
-    for (SizeT i = 0; i < ndims; i++) axe_specified[i] = false;
-
-    for (SizeT i = 0; i < ndims; i++) {
-        SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
-        if (axis == slice::OUT_OF_BOUNDS) {
-            // TODO: numpy actually throws a `numpy.exceptions.AxisError`
-            raise_exception(
-                SizeT, EXN_VALUE_ERROR,
-                "axis {0} is out of bounds for array of dimension {1}", axis,
-                ndims, NO_PARAM);
-        }
-
-        if (axe_specified[axis]) {
-            raise_exception(SizeT, EXN_VALUE_ERROR,
-                            "repeated axis in transpose", NO_PARAM, NO_PARAM,
-                            NO_PARAM);
-        }
-
-        axe_specified[axis] = true;
-    }
-}
-
-/**
- * @brief Create a transpose view of `src_ndarray` and perform proper assertions.
- *
- * This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
- * If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
- *
- * The transpose view created is returned by modifying `dst_ndarray`.
- *
- * The caller is responsible for setting up `dst_ndarray` before calling this function.
- * Here is what this function expects from `dst_ndarray` when called:
- *   - `dst_ndarray->data` does not have to be initialized.
- *   - `dst_ndarray->itemsize` does not have to be initialized.
- *   - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
- *   - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
- *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
- * When this function call ends:
- *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
- *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
- *   - `dst_ndarray->ndims` is unchanged
- *   - `dst_ndarray->shape` is updated according to how `np.transpose` works
- *   - `dst_ndarray->strides` is updated according to how `np.transpose` works
- *
- * @param src_ndarray The NDArray to build a transpose view on
- * @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
- * @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
- * @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
- */
-template <typename SizeT>
-void transpose(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray,
-               SizeT num_axes, const SizeT* axes) {
-    debug_assert_eq(SizeT, src_ndarray->ndims, dst_ndarray->ndims);
-    const auto ndims = src_ndarray->ndims;
-
-    if (axes != nullptr) assert_transpose_axes(ndims, num_axes, axes);
-
-    dst_ndarray->data = src_ndarray->data;
-    dst_ndarray->itemsize = src_ndarray->itemsize;
-
-    // Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
-    if (axes == nullptr) {
-        // `np.transpose(<array>, axes=None)`
-
-        /*
-         * Minor note: `np.transpose(<array>, axes=None)` is equivalent to
-         * `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
-         * is reversing the order of strides and shape.
-         *
-         * This is a fast implementation to handle this special (but very common) case.
-         */
-
-        for (SizeT axis = 0; axis < ndims; axis++) {
-            dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
-            dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
-        }
-    } else {
-        // `np.transpose(<array>, <axes>)`
-
-        // Permute strides and shape according to `axes`, while resolving negative indices in `axes`
-        for (SizeT axis = 0; axis < ndims; axis++) {
-            // `i` cannot be OUT_OF_BOUNDS because of assertions
-            SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
-
-            dst_ndarray->shape[axis] = src_ndarray->shape[i];
-            dst_ndarray->strides[axis] = src_ndarray->strides[i];
-        }
-    }
-}
-}  // namespace transpose
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-using namespace ndarray::transpose;
-void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
-                              NDArray<int32_t>* dst_ndarray, int32_t num_axes,
-                              const int32_t* axes) {
-    transpose(src_ndarray, dst_ndarray, num_axes, axes);
-}
-
-void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
-                                NDArray<int64_t>* dst_ndarray, int64_t num_axes,
-                                const int64_t* axes) {
-    transpose(src_ndarray, dst_ndarray, num_axes, axes);
-}
-}

nac3core/irrt/irrt/slice.hpp

@@ -1,35 +1,22 @@
 #pragma once
 
-#include <irrt/int_defs.hpp>
-#include <irrt/slice.hpp>
-#include <irrt/util.hpp>
+#include "irrt/int_types.hpp"
 
-#include "exception.hpp"
+extern "C" {
+SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
+    if (i < 0) {
+        i = len + i;
+    }
+    if (i < 0) {
+        return 0;
+    } else if (i > len) {
+        return len;
+    }
+    return i;
+}
 
-// The type of an index or a value describing the length of a
-// range/slice is always `int32_t`.
-using SliceIndex = int32_t;
-
-namespace {
-
-/**
- * @brief A Python-like slice with resolved indices.
- *
- * "Resolved indices" means that `start` and `stop` must be positive and are
- * bound to a known length.
- */
-struct Slice {
-    SliceIndex start;
-    SliceIndex stop;
-    SliceIndex step;
-
-    /**
-     * @brief Calculate and return the length / the number of the slice.
-     * 
-     * If this were a Python range, this function would be `len(range(start, stop, step))`.
-     */
-    SliceIndex len() {
-        SliceIndex diff = stop - start;
+SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end, const SliceIndex step) {
+    SliceIndex diff = end - start;
     if (diff > 0 && step > 0) {
         return ((diff - 1) / step) + 1;
     } else if (diff < 0 && step < 0) {
@@ -37,131 +24,5 @@ struct Slice {
     } else {
         return 0;
     }
-    }
-};
-
-namespace slice {
-/**
- * @brief Resolve a slice index under a given length like Python indexing.
- *
- * In Python, if you have a `list` of length 100, `list[-1]` resolves to
- * `list[99]`, so `resolve_index_in_length_clamped(100, -1)` returns `99`.
- *
- * If `length` is 0, 0 is returned for any value of `index`.
- *
- * If `index` is out of bounds, clamps the returned value between `0` and
- * `length - 1` (inclusive).
- *
- */
-SliceIndex resolve_index_in_length_clamped(SliceIndex length,
-                                           SliceIndex index) {
-    if (index < 0) {
-        return max<SliceIndex>(length + index, 0);
-    } else {
-        return min<SliceIndex>(length, index);
-    }
 }
-
-const SliceIndex OUT_OF_BOUNDS = -1;
-
-/**
- * @brief Like `resolve_index_in_length_clamped`, but returns `OUT_OF_BOUNDS`
- * if `index` is out of bounds.
- */
-SliceIndex resolve_index_in_length(SliceIndex length, SliceIndex index) {
-    SliceIndex resolved = index < 0 ? length + index : index;
-    if (0 <= resolved && resolved < length) {
-        return resolved;
-    } else {
-        return OUT_OF_BOUNDS;
-    }
-}
-}  // namespace slice
-
-/**
- * @brief A Python-like slice with **unresolved** indices.
- */
-struct UserSlice {
-    bool start_defined;
-    SliceIndex start;
-
-    bool stop_defined;
-    SliceIndex stop;
-
-    bool step_defined;
-    SliceIndex step;
-
-    UserSlice() { this->reset(); }
-
-    void reset() {
-        this->start_defined = false;
-        this->stop_defined = false;
-        this->step_defined = false;
-    }
-
-    void set_start(SliceIndex start) {
-        this->start_defined = true;
-        this->start = start;
-    }
-
-    void set_stop(SliceIndex stop) {
-        this->stop_defined = true;
-        this->stop = stop;
-    }
-
-    void set_step(SliceIndex step) {
-        this->step_defined = true;
-        this->step = step;
-    }
-
-    /**
-     * @brief Resolve this slice.
-     *
-     * In Python, this would be `slice(start, stop, step).indices(length)`.
-     *
-     * @return A `Slice` with the resolved indices.
-     */
-    Slice indices(SliceIndex length) {
-        Slice result;
-
-        result.step = step_defined ? step : 1;
-        bool step_is_negative = result.step < 0;
-
-        if (start_defined) {
-            result.start =
-                slice::resolve_index_in_length_clamped(length, start);
-        } else {
-            result.start = step_is_negative ? length - 1 : 0;
-        }
-
-        if (stop_defined) {
-            result.stop = slice::resolve_index_in_length_clamped(length, stop);
-        } else {
-            result.stop = step_is_negative ? -1 : length;
-        }
-
-        return result;
-    }
-
-    /**
-     * @brief Like `.indices()` but with assertions.
-     */
-    template <typename SizeT>
-    Slice indices_checked(SliceIndex length) {
-        // TODO: Switch to `SizeT length`
-
-        if (length < 0) {
-            raise_exception(SizeT, EXN_VALUE_ERROR,
-                            "length should not be negative, got {0}", length,
-                            NO_PARAM, NO_PARAM);
-        }
-
-        if (this->step_defined && this->step == 0) {
-            raise_exception(SizeT, EXN_VALUE_ERROR, "slice step cannot be zero",
-                            NO_PARAM, NO_PARAM, NO_PARAM);
-        }
-
-        return this->indices(length);
-    }
-};
 }  // namespace

nac3core/irrt/irrt/util.hpp

@@ -1,101 +0,0 @@
-#pragma once
-
-namespace {
-template <typename T>
-const T& max(const T& a, const T& b) {
-    return a > b ? a : b;
-}
-
-template <typename T>
-const T& min(const T& a, const T& b) {
-    return a > b ? b : a;
-}
-
-template <typename T>
-bool arrays_match(int len, T* as, T* bs) {
-    for (int i = 0; i < len; i++) {
-        if (as[i] != bs[i]) return false;
-    }
-    return true;
-}
-
-namespace cstr_utils {
-/**
- * @brief Return true if `str` is empty.
- */
-bool is_empty(const char* str) { return str[0] == '\0'; }
-
-/**
- * @brief Implementation of `strcmp()`
- */
-int8_t compare(const char* a, const char* b) {
-    uint32_t i = 0;
-    while (true) {
-        if (a[i] < b[i]) {
-            return -1;
-        } else if (a[i] > b[i]) {
-            return 1;
-        } else {
-            if (a[i] == '\0') {
-                return 0;
-            } else {
-                i++;
-            }
-        }
-    }
-}
-
-/**
- * @brief Return true two strings have the same content.
- */
-int8_t equal(const char* a, const char* b) { return compare(a, b) == 0; }
-
-/**
- * @brief Implementation of `strlen()`.
- */
-uint32_t length(const char* str) {
-    uint32_t length = 0;
-    while (*str != '\0') {
-        length++;
-        str++;
-    }
-    return length;
-}
-
-/**
- * @brief Copy a null-terminated string to a buffer with limited size and guaranteed null-termination.
- *
- * `dst_max_size` must be greater than 0, otherwise this function has undefined behavior.
- *
- * This function attempts to copy everything from `src` from `dst`, and *always* null-terminates `dst`.
- *
- * If the size of `dst` is too small, the final byte (`dst[dst_max_size - 1]`) of `dst` will be set to
- * the null terminator.
- *
- * @param src String to copy from.
- * @param dst Buffer to copy string to.
- * @param dst_max_size
- *   Number of bytes of this buffer, including the space needed for the null terminator.
- *   Must be greater than 0.
- * @return If `dst` is too small to contain everything in `src`.
- */
-bool copy(const char* src, char* dst, uint32_t dst_max_size) {
-    for (uint32_t i = 0; i < dst_max_size; i++) {
-        bool is_last = i + 1 == dst_max_size;
-        if (is_last && src[i] != '\0') {
-            dst[i] = '\0';
-            return false;
-        }
-
-        if (src[i] == '\0') {
-            dst[i] = '\0';
-            return true;
-        }
-
-        dst[i] = src[i];
-    }
-
-    __builtin_unreachable();
-}
-}  // namespace cstr_utils
-}  // namespace

nac3core/irrt/irrt_everything.hpp

@@ -1,24 +0,0 @@
-#pragma once
-
-#ifdef IRRT_DEBUG
-#define IRRT_DEBUG_ASSERT
-#define IRRT_DEBUG_ASSERT_BOOL true
-#else
-#define IRRT_DEBUG_ASSERT_BOOL false
-#endif
-
-#include <irrt/core.hpp>
-#include <irrt/debug.hpp>
-#include <irrt/exception.hpp>
-#include <irrt/int_defs.hpp>
-#include <irrt/list.hpp>
-#include <irrt/ndarray/array.hpp>
-#include <irrt/ndarray/basic.hpp>
-#include <irrt/ndarray/broadcast.hpp>
-#include <irrt/ndarray/def.hpp>
-#include <irrt/ndarray/indexing.hpp>
-#include <irrt/ndarray/iter.hpp>
-#include <irrt/ndarray/product.hpp>
-#include <irrt/ndarray/reshape.hpp>
-#include <irrt/ndarray/transpose.hpp>
-#include <irrt/util.hpp>

nac3core/irrt/irrt_test.cpp

@@ -1,25 +0,0 @@
-// This file will be compiled like a real C++ program,
-// and we do have the luxury to use the standard libraries.
-// That is if the nix flakes do not have issues... especially on msys2...
-
-#include <cstdint>
-#include <cstdio>
-#include <cstdlib>
-
-// Special macro to inform `#include <irrt/*>` that we are testing.
-#define IRRT_TESTING
-
-// Note that failure unit tests are not supported.
-
-#include <test/test_core.hpp>
-#include <test/test_ndarray_basic.hpp>
-#include <test/test_ndarray_broadcast.hpp>
-#include <test/test_ndarray_indexing.hpp>
-
-int main() {
-    test::core::run();
-    test::ndarray_basic::run();
-    test::ndarray_indexing::run();
-    test::ndarray_broadcast::run();
-    return 0;
-}

nac3core/irrt/test/includes.hpp

@@ -1,11 +0,0 @@
-#pragma once
-
-#include <cstdint>
-#include <cstdio>
-#include <cstdlib>
-#include <irrt_everything.hpp>
-#include <test/util.hpp>
-
-/*
-    Include this header for every test_*.cpp
-*/

nac3core/irrt/test/test_core.hpp

@@ -1,16 +0,0 @@
-#pragma once
-
-#include <test/includes.hpp>
-
-namespace test {
-namespace core {
-void test_int_exp() {
-    BEGIN_TEST();
-
-    assert_values_match(125L, __nac3_int_exp_impl<int64_t>(5, 3));
-    assert_values_match(3125L, __nac3_int_exp_impl<int64_t>(5, 5));
-}
-
-void run() { test_int_exp(); }
-}  // namespace core
-}  // namespace test

nac3core/irrt/test/test_ndarray_basic.hpp

@@ -1,30 +0,0 @@
-#pragma once
-
-#include <test/includes.hpp>
-
-namespace test {
-namespace ndarray_basic {
-void test_calc_size_from_shape_normal() {
-    // Test shapes with normal values
-    BEGIN_TEST();
-
-    int64_t shape[4] = {2, 3, 5, 7};
-    assert_values_match(
-        210L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
-}
-
-void test_calc_size_from_shape_has_zero() {
-    // Test shapes with 0 in them
-    BEGIN_TEST();
-
-    int64_t shape[4] = {2, 0, 5, 7};
-    assert_values_match(
-        0L, ndarray::basic::util::calc_size_from_shape<int64_t>(4, shape));
-}
-
-void run() {
-    test_calc_size_from_shape_normal();
-    test_calc_size_from_shape_has_zero();
-}
-}  // namespace ndarray_basic
-}  // namespace test

nac3core/irrt/test/test_ndarray_broadcast.hpp

@@ -1,127 +0,0 @@
-#pragma once
-
-#include <test/includes.hpp>
-
-namespace test {
-namespace ndarray_broadcast {
-void test_can_broadcast_shape() {
-    BEGIN_TEST();
-
-    assert_values_match(true,
-                        ndarray::broadcast::util::can_broadcast_shape_to(
-                            1, (int32_t[]){3}, 5, (int32_t[]){1, 1, 1, 1, 3}));
-    assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
-                                   1, (int32_t[]){3}, 2, (int32_t[]){3, 1}));
-    assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
-                                  1, (int32_t[]){3}, 1, (int32_t[]){3}));
-    assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
-                                   1, (int32_t[]){1}, 1, (int32_t[]){3}));
-    assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
-                                  1, (int32_t[]){1}, 1, (int32_t[]){1}));
-    assert_values_match(
-        true, ndarray::broadcast::util::can_broadcast_shape_to(
-                  3, (int32_t[]){256, 256, 3}, 3, (int32_t[]){256, 1, 3}));
-    assert_values_match(true,
-                        ndarray::broadcast::util::can_broadcast_shape_to(
-                            3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){3}));
-    assert_values_match(false,
-                        ndarray::broadcast::util::can_broadcast_shape_to(
-                            3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){2}));
-    assert_values_match(true,
-                        ndarray::broadcast::util::can_broadcast_shape_to(
-                            3, (int32_t[]){256, 256, 3}, 1, (int32_t[]){1}));
-
-    // In cases when the shapes contain zero(es)
-    assert_values_match(true, ndarray::broadcast::util::can_broadcast_shape_to(
-                                  1, (int32_t[]){0}, 1, (int32_t[]){1}));
-    assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
-                                   1, (int32_t[]){0}, 1, (int32_t[]){2}));
-    assert_values_match(true,
-                        ndarray::broadcast::util::can_broadcast_shape_to(
-                            4, (int32_t[]){0, 4, 0, 0}, 1, (int32_t[]){1}));
-    assert_values_match(
-        true, ndarray::broadcast::util::can_broadcast_shape_to(
-                  4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 1, 1, 1}));
-    assert_values_match(
-        true, ndarray::broadcast::util::can_broadcast_shape_to(
-                  4, (int32_t[]){0, 4, 0, 0}, 4, (int32_t[]){1, 4, 1, 1}));
-    assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
-                                   2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 3}));
-    assert_values_match(false, ndarray::broadcast::util::can_broadcast_shape_to(
-                                   2, (int32_t[]){4, 3}, 2, (int32_t[]){0, 0}));
-}
-
-void test_ndarray_broadcast() {
-    /*
-    # array = np.array([[19.9, 29.9, 39.9, 49.9]], dtype=np.float64)
-    # >>> [[19.9 29.9 39.9 49.9]]
-    #
-    # array = np.broadcast_to(array, (2, 3, 4))
-    # >>> [[[19.9 29.9 39.9 49.9]
-    # >>>   [19.9 29.9 39.9 49.9]
-    # >>>   [19.9 29.9 39.9 49.9]]
-    # >>>  [[19.9 29.9 39.9 49.9]
-    # >>>   [19.9 29.9 39.9 49.9]
-    # >>>   [19.9 29.9 39.9 49.9]]]
-    #
-    # assery array.strides == (0, 0, 8)
-
-    */
-    BEGIN_TEST();
-
-    double in_data[4] = {19.9, 29.9, 39.9, 49.9};
-    const int32_t in_ndims = 2;
-    int32_t in_shape[in_ndims] = {1, 4};
-    int32_t in_strides[in_ndims] = {};
-    NDArray<int32_t> ndarray = {.data = (uint8_t*)in_data,
-                                .itemsize = sizeof(double),
-                                .ndims = in_ndims,
-                                .shape = in_shape,
-                                .strides = in_strides};
-    ndarray::basic::set_strides_by_shape(&ndarray);
-
-    const int32_t dst_ndims = 3;
-    int32_t dst_shape[dst_ndims] = {2, 3, 4};
-    int32_t dst_strides[dst_ndims] = {};
-    NDArray<int32_t> dst_ndarray = {
-        .ndims = dst_ndims, .shape = dst_shape, .strides = dst_strides};
-
-    ndarray::broadcast::broadcast_to(&ndarray, &dst_ndarray);
-
-    assert_arrays_match(dst_ndims, ((int32_t[]){0, 0, 8}), dst_ndarray.strides);
-
-    assert_values_match(19.9,
-                        *((double*)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, ((int32_t[]){0, 0, 0}))));
-    assert_values_match(29.9,
-                        *((double*)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, ((int32_t[]){0, 0, 1}))));
-    assert_values_match(39.9,
-                        *((double*)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, ((int32_t[]){0, 0, 2}))));
-    assert_values_match(49.9,
-                        *((double*)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, ((int32_t[]){0, 0, 3}))));
-    assert_values_match(19.9,
-                        *((double*)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, ((int32_t[]){0, 1, 0}))));
-    assert_values_match(29.9,
-                        *((double*)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, ((int32_t[]){0, 1, 1}))));
-    assert_values_match(39.9,
-                        *((double*)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, ((int32_t[]){0, 1, 2}))));
-    assert_values_match(49.9,
-                        *((double*)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, ((int32_t[]){0, 1, 3}))));
-    assert_values_match(49.9,
-                        *((double*)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, ((int32_t[]){1, 2, 3}))));
-}
-
-void run() {
-    test_can_broadcast_shape();
-    test_ndarray_broadcast();
-}
-}  // namespace ndarray_broadcast
-}  // namespace test

nac3core/irrt/test/test_ndarray_indexing.hpp

@@ -1,165 +0,0 @@
-#pragma once
-
-#include <test/includes.hpp>
-
-namespace test {
-namespace ndarray_indexing {
-void test_normal_1() {
-    /*
-        Reference Python code:
-        ```python
-        ndarray = np.arange(12, dtype=np.float64).reshape((3, 4));
-        # array([[ 0.,  1.,  2.,  3.],
-        #        [ 4.,  5.,  6.,  7.],
-        #        [ 8.,  9., 10., 11.]])
-
-        dst_ndarray = ndarray[-2:, 1::2]
-        # array([[ 5.,  7.],
-        #        [ 9., 11.]])
-
-        assert dst_ndarray.shape == (2, 2)
-        assert dst_ndarray.strides == (32, 16)
-        assert dst_ndarray[0, 0] == 5.0
-        assert dst_ndarray[0, 1] == 7.0
-        assert dst_ndarray[1, 0] == 9.0
-        assert dst_ndarray[1, 1] == 11.0
-        ```
-    */
-    BEGIN_TEST();
-
-    // Prepare src_ndarray
-    double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0,  5.0,
-                           6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
-    int64_t src_itemsize = sizeof(double);
-    const int64_t src_ndims = 2;
-    int64_t src_shape[src_ndims] = {3, 4};
-    int64_t src_strides[src_ndims] = {};
-    NDArray<int64_t> src_ndarray = {.data = (uint8_t *)src_data,
-                                    .itemsize = src_itemsize,
-                                    .ndims = src_ndims,
-                                    .shape = src_shape,
-                                    .strides = src_strides};
-    ndarray::basic::set_strides_by_shape(&src_ndarray);
-
-    // Prepare dst_ndarray
-    const int64_t dst_ndims = 2;
-    int64_t dst_shape[dst_ndims] = {999, 999};    // Empty values
-    int64_t dst_strides[dst_ndims] = {999, 999};  // Empty values
-    NDArray<int64_t> dst_ndarray = {.data = nullptr,
-                                    .ndims = dst_ndims,
-                                    .shape = dst_shape,
-                                    .strides = dst_strides};
-
-    // Create the subscripts in `ndarray[-2::, 1::2]`
-    UserSlice subscript_1;
-    subscript_1.set_start(-2);
-
-    UserSlice subscript_2;
-    subscript_2.set_start(1);
-    subscript_2.set_step(2);
-
-    const int64_t num_indexes = 2;
-    NDIndex indexes[num_indexes] = {
-        {.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_1},
-        {.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
-
-    ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
-
-    int64_t expected_shape[dst_ndims] = {2, 2};
-    int64_t expected_strides[dst_ndims] = {32, 16};
-
-    assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
-    assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
-
-    // dst_ndarray[0, 0]
-    assert_values_match(5.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int64_t[dst_ndims]){0, 0})));
-    // dst_ndarray[0, 1]
-    assert_values_match(7.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int64_t[dst_ndims]){0, 1})));
-    // dst_ndarray[1, 0]
-    assert_values_match(9.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int64_t[dst_ndims]){1, 0})));
-    // dst_ndarray[1, 1]
-    assert_values_match(11.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int64_t[dst_ndims]){1, 1})));
-}
-
-void test_normal_2() {
-    /*
-        ```python
-        ndarray = np.arange(12, dtype=np.float64).reshape((3, 4))
-        # array([[ 0.,  1.,  2.,  3.],
-        #        [ 4.,  5.,  6.,  7.],
-        #        [ 8.,  9., 10., 11.]])
-
-        dst_ndarray = ndarray[2, ::-2]
-        # array([11.,  9.])
-
-        assert dst_ndarray.shape == (2,)
-        assert dst_ndarray.strides == (-16,)
-        assert dst_ndarray[0] == 11.0
-        assert dst_ndarray[1] == 9.0
-        ```
-    */
-    BEGIN_TEST();
-
-    // Prepare src_ndarray
-    double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0,  5.0,
-                           6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
-    int64_t src_itemsize = sizeof(double);
-    const int64_t src_ndims = 2;
-    int64_t src_shape[src_ndims] = {3, 4};
-    int64_t src_strides[src_ndims] = {};
-    NDArray<int64_t> src_ndarray = {.data = (uint8_t *)src_data,
-                                    .itemsize = src_itemsize,
-                                    .ndims = src_ndims,
-                                    .shape = src_shape,
-                                    .strides = src_strides};
-    ndarray::basic::set_strides_by_shape(&src_ndarray);
-
-    // Prepare dst_ndarray
-    const int64_t dst_ndims = 1;
-    int64_t dst_shape[dst_ndims] = {999};    // Empty values
-    int64_t dst_strides[dst_ndims] = {999};  // Empty values
-    NDArray<int64_t> dst_ndarray = {.data = nullptr,
-                                    .ndims = dst_ndims,
-                                    .shape = dst_shape,
-                                    .strides = dst_strides};
-
-    // Create the subscripts in `ndarray[2, ::-2]`
-    int64_t subscript_1 = 2;
-
-    UserSlice subscript_2;
-    subscript_2.set_step(-2);
-
-    const int64_t num_indexes = 2;
-    NDIndex indexes[num_indexes] = {
-        {.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
-        {.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
-
-    ndarray::indexing::index(num_indexes, indexes, &src_ndarray, &dst_ndarray);
-
-    int64_t expected_shape[dst_ndims] = {2};
-    int64_t expected_strides[dst_ndims] = {-16};
-    assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
-    assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
-
-    assert_values_match(11.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int64_t[dst_ndims]){0})));
-    assert_values_match(9.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int64_t[dst_ndims]){1})));
-}
-
-void run() {
-    test_normal_1();
-    test_normal_2();
-}
-}  // namespace ndarray_indexing
-}  // namespace test

nac3core/irrt/test/util.hpp

@@ -1,131 +0,0 @@
-#pragma once
-
-#include <cstdio>
-#include <cstdlib>
-
-template <class T>
-void print_value(const T& value);
-
-template <>
-void print_value(const bool& value) {
-    printf("%s", value ? "true" : "false");
-}
-
-template <>
-void print_value(const int8_t& value) {
-    printf("%d", value);
-}
-
-template <>
-void print_value(const int32_t& value) {
-    printf("%d", value);
-}
-
-template <>
-void print_value(const int64_t& value) {
-    printf("%d", value);
-}
-
-template <>
-void print_value(const uint8_t& value) {
-    printf("%u", value);
-}
-
-template <>
-void print_value(const uint32_t& value) {
-    printf("%u", value);
-}
-
-template <>
-void print_value(const uint64_t& value) {
-    printf("%d", value);
-}
-
-template <>
-void print_value(const float& value) {
-    printf("%f", value);
-}
-
-template <>
-void print_value(const double& value) {
-    printf("%f", value);
-}
-
-void __begin_test(const char* function_name, const char* file, int line) {
-    printf("######### Running %s @ %s:%d\n", function_name, file, line);
-}
-
-#define BEGIN_TEST() __begin_test(__FUNCTION__, __FILE__, __LINE__)
-
-void test_fail() {
-    printf("[!] Test failed. Exiting with status code 1.\n");
-    exit(1);
-}
-
-template <typename T>
-void debug_print_array(int len, const T* as) {
-    printf("[");
-    for (int i = 0; i < len; i++) {
-        if (i != 0) printf(", ");
-        print_value(as[i]);
-    }
-    printf("]");
-}
-
-void print_assertion_passed(const char* file, int line) {
-    printf("[*] Assertion passed on %s:%d\n", file, line);
-}
-
-void print_assertion_failed(const char* file, int line) {
-    printf("[!] Assertion failed on %s:%d\n", file, line);
-}
-
-void __assert_true(const char* file, int line, bool cond) {
-    if (cond) {
-        print_assertion_passed(file, line);
-    } else {
-        print_assertion_failed(file, line);
-        test_fail();
-    }
-}
-
-#define assert_true(cond) __assert_true(__FILE__, __LINE__, cond)
-
-template <typename T>
-void __assert_arrays_match(const char* file, int line, int len,
-                           const T* expected, const T* got) {
-    if (arrays_match(len, expected, got)) {
-        print_assertion_passed(file, line);
-    } else {
-        print_assertion_failed(file, line);
-        printf("Expect = ");
-        debug_print_array(len, expected);
-        printf("\n");
-        printf("   Got = ");
-        debug_print_array(len, got);
-        printf("\n");
-        test_fail();
-    }
-}
-
-#define assert_arrays_match(len, expected, got) \
-    __assert_arrays_match(__FILE__, __LINE__, len, expected, got)
-
-template <typename T>
-void __assert_values_match(const char* file, int line, T expected, T got) {
-    if (expected == got) {
-        print_assertion_passed(file, line);
-    } else {
-        print_assertion_failed(file, line);
-        printf("Expect = ");
-        print_value(expected);
-        printf("\n");
-        printf("   Got = ");
-        print_value(got);
-        printf("\n");
-        test_fail();
-    }
-}
-
-#define assert_values_match(expected, got) \
-    __assert_values_match(__FILE__, __LINE__, expected, got)

nac3core/nac3core_derive/Cargo.toml

@@ -0,0 +1,21 @@
+[package]
+name = "nac3core_derive"
+version = "0.1.0"
+edition = "2021"
+
+[lib]
+proc-macro = true
+
+[[test]]
+name = "structfields_tests"
+path = "tests/structfields_test.rs"
+
+[dev-dependencies]
+nac3core = { path = ".." }
+trybuild = { version = "1.0", features = ["diff"] }
+
+[dependencies]
+proc-macro2 = "1.0"
+proc-macro-error = "1.0"
+syn = "2.0"
+quote = "1.0"

nac3core/nac3core_derive/src/lib.rs

@@ -0,0 +1,320 @@
+use proc_macro::TokenStream;
+use proc_macro_error::{abort, proc_macro_error};
+use quote::quote;
+use syn::{
+    parse_macro_input, spanned::Spanned, Data, DataStruct, Expr, ExprField, ExprMethodCall,
+    ExprPath, GenericArgument, Ident, LitStr, Path, PathArguments, Type, TypePath,
+};
+
+/// Extracts all generic arguments of a [`Type`] into a [`Vec`].
+///
+/// Returns [`Some`] of a possibly-empty [`Vec`] if the path of `ty` matches with
+/// `expected_ty_name`, otherwise returns [`None`].
+fn extract_generic_args(expected_ty_name: &'static str, ty: &Type) -> Option<Vec<GenericArgument>> {
+    let Type::Path(TypePath { qself: None, path, .. }) = ty else {
+        return None;
+    };
+
+    let segments = &path.segments;
+    if segments.len() != 1 {
+        return None;
+    };
+
+    let segment = segments.iter().next().unwrap();
+    if segment.ident != expected_ty_name {
+        return None;
+    }
+
+    let PathArguments::AngleBracketed(path_args) = &segment.arguments else {
+        return Some(Vec::new());
+    };
+    let args = &path_args.args;
+
+    Some(args.iter().cloned().collect::<Vec<_>>())
+}
+
+/// Maps a `path` matching one of the `target_idents` into the `replacement` [`Ident`].
+fn map_path_to_ident(path: &Path, target_idents: &[&str], replacement: &str) -> Option<Ident> {
+    path.require_ident()
+        .ok()
+        .filter(|ident| target_idents.iter().any(|target| ident == target))
+        .map(|ident| Ident::new(replacement, ident.span()))
+}
+
+/// Extracts the left-hand side of a dot-expression.
+fn extract_dot_operand(expr: &Expr) -> Option<&Expr> {
+    match expr {
+        Expr::MethodCall(ExprMethodCall { receiver: operand, .. })
+        | Expr::Field(ExprField { base: operand, .. }) => Some(operand),
+        _ => None,
+    }
+}
+
+/// Replaces the top-level receiver of a dot-expression with an [`Ident`], returning `Some(&mut expr)` if the
+/// replacement is performed.
+///
+/// The top-level receiver is the left-most receiver expression, e.g. the top-level receiver of `a.b.c.foo()` is `a`.
+fn replace_top_level_receiver(expr: &mut Expr, ident: Ident) -> Option<&mut Expr> {
+    if let Expr::MethodCall(ExprMethodCall { receiver: operand, .. })
+    | Expr::Field(ExprField { base: operand, .. }) = expr
+    {
+        return if extract_dot_operand(operand).is_some() {
+            if replace_top_level_receiver(operand, ident).is_some() {
+                Some(expr)
+            } else {
+                None
+            }
+        } else {
+            *operand = Box::new(Expr::Path(ExprPath {
+                attrs: Vec::default(),
+                qself: None,
+                path: ident.into(),
+            }));
+
+            Some(expr)
+        };
+    }
+
+    None
+}
+
+/// Iterates all operands to the left-hand side of the `.` of an [expression][`Expr`], i.e. the container operand of all
+/// [`Expr::Field`] and the receiver operand of all [`Expr::MethodCall`].
+///
+/// The iterator will return the operand expressions in reverse order of appearance. For example, `a.b.c.func()` will
+/// return `vec![c, b, a]`.
+fn iter_dot_operands(expr: &Expr) -> impl Iterator<Item = &Expr> {
+    let mut o = extract_dot_operand(expr);
+
+    std::iter::from_fn(move || {
+        let this = o;
+        o = o.as_ref().and_then(|o| extract_dot_operand(o));
+
+        this
+    })
+}
+
+/// Normalizes a value expression for use when creating an instance of this structure, returning a
+/// [`proc_macro2::TokenStream`] of tokens representing the normalized expression.
+fn normalize_value_expr(expr: &Expr) -> proc_macro2::TokenStream {
+    match &expr {
+        Expr::Path(ExprPath { qself: None, path, .. }) => {
+            if let Some(ident) = map_path_to_ident(path, &["usize", "size_t"], "llvm_usize") {
+                quote! { #ident }
+            } else {
+                abort!(
+                    path,
+                    format!(
+                        "Expected one of `size_t`, `usize`, or an implicit call expression in #[value_type(...)], found {}", 
+                        quote!(#expr).to_string(),
+                    )
+                )
+            }
+        }
+
+        Expr::Call(_) => {
+            quote! { ctx.#expr }
+        }
+
+        Expr::MethodCall(_) => {
+            let base_receiver = iter_dot_operands(expr).last();
+
+            match base_receiver {
+                // `usize.{...}`, `size_t.{...}` -> Rewrite the identifiers to `llvm_usize`
+                Some(Expr::Path(ExprPath { qself: None, path, .. }))
+                    if map_path_to_ident(path, &["usize", "size_t"], "llvm_usize").is_some() =>
+                {
+                    let ident =
+                        map_path_to_ident(path, &["usize", "size_t"], "llvm_usize").unwrap();
+
+                    let mut expr = expr.clone();
+                    let expr = replace_top_level_receiver(&mut expr, ident).unwrap();
+
+                    quote!(#expr)
+                }
+
+                // `ctx.{...}`, `context.{...}` -> Rewrite the identifiers to `ctx`
+                Some(Expr::Path(ExprPath { qself: None, path, .. }))
+                    if map_path_to_ident(path, &["ctx", "context"], "ctx").is_some() =>
+                {
+                    let ident = map_path_to_ident(path, &["ctx", "context"], "ctx").unwrap();
+
+                    let mut expr = expr.clone();
+                    let expr = replace_top_level_receiver(&mut expr, ident).unwrap();
+
+                    quote!(#expr)
+                }
+
+                // No reserved identifier prefix -> Prepend `ctx.` to the entire expression
+                _ => quote! { ctx.#expr },
+            }
+        }
+
+        _ => {
+            abort!(
+                expr,
+                format!(
+                    "Expected one of `size_t`, `usize`, or an implicit call expression in #[value_type(...)], found {}", 
+                    quote!(#expr).to_string(),
+                )
+            )
+        }
+    }
+}
+
+/// Derives an implementation of `codegen::types::structure::StructFields`.
+///
+/// The benefit of using `#[derive(StructFields)]` is that all index- or order-dependent logic required by
+/// `impl StructFields` is automatically generated by this implementation, including the field index as required by
+/// `StructField::new` and the fields as returned by `StructFields::to_vec`.
+///
+/// # Prerequisites
+///
+/// In order to derive from [`StructFields`], you must implement (or derive) [`Eq`] and [`Copy`] as required by
+/// `StructFields`.
+///
+/// Moreover, `#[derive(StructFields)]` can only be used for `struct`s with named fields, and may only contain fields
+/// with either `StructField` or [`PhantomData`] types.
+///
+/// # Attributes for [`StructFields`]
+///
+/// Each `StructField` field must be declared with the `#[value_type(...)]` attribute. The argument of `value_type`
+/// accepts one of the following:
+///
+/// - An expression returning an instance of `inkwell::types::BasicType` (with or without the receiver `ctx`/`context`).
+/// For example, `context.i8_type()`, `ctx.i8_type()`, and `i8_type()` all refer to `i8`.
+/// - The reserved identifiers `usize` and `size_t` referring to an `inkwell::types::IntType` of the platform-dependent
+/// integer size. `usize` and `size_t` can also be used as the receiver to other method calls, e.g.
+/// `usize.array_type(3)`.
+///
+/// # Example
+///
+/// The following is an example of an LLVM slice implemented using `#[derive(StructFields)]`.
+///
+/// ```rust,ignore
+/// use nac3core::{
+///     codegen::types::structure::StructField,
+///     inkwell::{
+///         values::{IntValue, PointerValue},
+///         AddressSpace,
+///     },
+/// };
+/// use nac3core_derive::StructFields;
+///
+/// // All classes that implement StructFields must also implement Eq and Copy
+/// #[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+/// pub struct SliceValue<'ctx> {
+///     // Declares ptr have a value type of i8*
+///     //
+///     // Can also be written as `ctx.i8_type().ptr_type(...)` or `context.i8_type().ptr_type(...)`
+///     #[value_type(i8_type().ptr_type(AddressSpace::default()))]
+///     ptr: StructField<'ctx, PointerValue<'ctx>>,
+///
+///     // Declares len have a value type of usize, depending on the target compilation platform
+///     #[value_type(usize)]
+///     len: StructField<'ctx, IntValue<'ctx>>,
+/// }
+/// ```
+#[proc_macro_derive(StructFields, attributes(value_type))]
+#[proc_macro_error]
+pub fn derive(input: TokenStream) -> TokenStream {
+    let input = parse_macro_input!(input as syn::DeriveInput);
+    let ident = &input.ident;
+
+    let Data::Struct(DataStruct { fields, .. }) = &input.data else {
+        abort!(input, "Only structs with named fields are supported");
+    };
+    if let Err(err_span) =
+        fields
+            .iter()
+            .try_for_each(|field| if field.ident.is_some() { Ok(()) } else { Err(field.span()) })
+    {
+        abort!(err_span, "Only structs with named fields are supported");
+    };
+
+    // Check if struct<'ctx>
+    if input.generics.params.len() != 1 {
+        abort!(input.generics, "Expected exactly 1 generic parameter")
+    }
+
+    let phantom_info = fields
+        .iter()
+        .filter(|field| extract_generic_args("PhantomData", &field.ty).is_some())
+        .map(|field| field.ident.as_ref().unwrap())
+        .cloned()
+        .collect::<Vec<_>>();
+
+    let field_info = fields
+        .iter()
+        .filter(|field| extract_generic_args("PhantomData", &field.ty).is_none())
+        .map(|field| {
+            let ident = field.ident.as_ref().unwrap();
+            let ty = &field.ty;
+
+            let Some(_) = extract_generic_args("StructField", ty) else {
+                abort!(field, "Only StructField and PhantomData are allowed")
+            };
+
+            let attrs = &field.attrs;
+            let Some(value_type_attr) =
+                attrs.iter().find(|attr| attr.path().is_ident("value_type"))
+            else {
+                abort!(field, "Expected #[value_type(...)] attribute for field");
+            };
+
+            let Ok(value_type_expr) = value_type_attr.parse_args::<Expr>() else {
+                abort!(value_type_attr, "Expected expression in #[value_type(...)]");
+            };
+
+            let value_expr_toks = normalize_value_expr(&value_type_expr);
+
+            (ident.clone(), value_expr_toks)
+        })
+        .collect::<Vec<_>>();
+
+    // `<*>::new` impl of `StructField` and `PhantomData` for `StructFields::new`
+    let phantoms_create = phantom_info
+        .iter()
+        .map(|id| quote! { #id: ::std::marker::PhantomData })
+        .collect::<Vec<_>>();
+    let fields_create = field_info
+        .iter()
+        .map(|(id, ty)| {
+            let id_lit = LitStr::new(&id.to_string(), id.span());
+            quote! {
+                #id: ::nac3core::codegen::types::structure::StructField::create(
+                    &mut counter,
+                    #id_lit,
+                    #ty,
+                )
+            }
+        })
+        .collect::<Vec<_>>();
+
+    // `.into()` impl of `StructField` for `StructFields::to_vec`
+    let fields_into =
+        field_info.iter().map(|(id, _)| quote! { self.#id.into() }).collect::<Vec<_>>();
+
+    let impl_block = quote! {
+        impl<'ctx> ::nac3core::codegen::types::structure::StructFields<'ctx> for #ident<'ctx> {
+            fn new(ctx: impl ::nac3core::inkwell::context::AsContextRef<'ctx>, llvm_usize: ::nac3core::inkwell::types::IntType<'ctx>) -> Self {
+                let ctx = unsafe { ::nac3core::inkwell::context::ContextRef::new(ctx.as_ctx_ref()) };
+
+                let mut counter = ::nac3core::codegen::types::structure::FieldIndexCounter::default();
+
+                #ident {
+                    #(#fields_create),*
+                    #(#phantoms_create),*
+                }
+            }
+
+            fn to_vec(&self) -> ::std::vec::Vec<(&'static str, ::nac3core::inkwell::types::BasicTypeEnum<'ctx>)> {
+                vec![
+                    #(#fields_into),*
+                ]
+            }
+        }
+    };
+
+    impl_block.into()
+}

nac3core/nac3core_derive/tests/structfields_empty.rs

@@ -0,0 +1,9 @@
+use nac3core_derive::StructFields;
+use std::marker::PhantomData;
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct EmptyValue<'ctx> {
+    _phantom: PhantomData<&'ctx ()>,
+}
+
+fn main() {}

nac3core/nac3core_derive/tests/structfields_ndarray.rs

@@ -0,0 +1,20 @@
+use nac3core::{
+    codegen::types::structure::StructField,
+    inkwell::{
+        values::{IntValue, PointerValue},
+        AddressSpace,
+    },
+};
+use nac3core_derive::StructFields;
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct NDArrayValue<'ctx> {
+    #[value_type(usize)]
+    ndims: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(usize.ptr_type(AddressSpace::default()))]
+    shape: StructField<'ctx, PointerValue<'ctx>>,
+    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
+    data: StructField<'ctx, PointerValue<'ctx>>,
+}
+
+fn main() {}

nac3core/nac3core_derive/tests/structfields_slice.rs

@@ -0,0 +1,18 @@
+use nac3core::{
+    codegen::types::structure::StructField,
+    inkwell::{
+        values::{IntValue, PointerValue},
+        AddressSpace,
+    },
+};
+use nac3core_derive::StructFields;
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct SliceValue<'ctx> {
+    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
+    ptr: StructField<'ctx, PointerValue<'ctx>>,
+    #[value_type(usize)]
+    len: StructField<'ctx, IntValue<'ctx>>,
+}
+
+fn main() {}

nac3core/nac3core_derive/tests/structfields_slice_context.rs

@@ -0,0 +1,18 @@
+use nac3core::{
+    codegen::types::structure::StructField,
+    inkwell::{
+        values::{IntValue, PointerValue},
+        AddressSpace,
+    },
+};
+use nac3core_derive::StructFields;
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct SliceValue<'ctx> {
+    #[value_type(context.i8_type().ptr_type(AddressSpace::default()))]
+    ptr: StructField<'ctx, PointerValue<'ctx>>,
+    #[value_type(usize)]
+    len: StructField<'ctx, IntValue<'ctx>>,
+}
+
+fn main() {}

nac3core/nac3core_derive/tests/structfields_slice_ctx.rs

@@ -0,0 +1,18 @@
+use nac3core::{
+    codegen::types::structure::StructField,
+    inkwell::{
+        values::{IntValue, PointerValue},
+        AddressSpace,
+    },
+};
+use nac3core_derive::StructFields;
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct SliceValue<'ctx> {
+    #[value_type(ctx.i8_type().ptr_type(AddressSpace::default()))]
+    ptr: StructField<'ctx, PointerValue<'ctx>>,
+    #[value_type(usize)]
+    len: StructField<'ctx, IntValue<'ctx>>,
+}
+
+fn main() {}

nac3core/nac3core_derive/tests/structfields_slice_sizet.rs

@@ -0,0 +1,18 @@
+use nac3core::{
+    codegen::types::structure::StructField,
+    inkwell::{
+        values::{IntValue, PointerValue},
+        AddressSpace,
+    },
+};
+use nac3core_derive::StructFields;
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct SliceValue<'ctx> {
+    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
+    ptr: StructField<'ctx, PointerValue<'ctx>>,
+    #[value_type(size_t)]
+    len: StructField<'ctx, IntValue<'ctx>>,
+}
+
+fn main() {}

nac3core/nac3core_derive/tests/structfields_test.rs

@@ -0,0 +1,10 @@
+#[test]
+fn test_parse_empty() {
+    let t = trybuild::TestCases::new();
+    t.pass("tests/structfields_empty.rs");
+    t.pass("tests/structfields_slice.rs");
+    t.pass("tests/structfields_slice_ctx.rs");
+    t.pass("tests/structfields_slice_context.rs");
+    t.pass("tests/structfields_slice_sizet.rs");
+    t.pass("tests/structfields_ndarray.rs");
+}

nac3core/src/codegen/concrete_type.rs

@@ -1,3 +1,9 @@
+use std::collections::HashMap;
+
+use indexmap::IndexMap;
+
+use nac3parser::ast::StrRef;
+
 use crate::{
     symbol_resolver::SymbolValue,
     toplevel::DefinitionId,
@@ -9,10 +15,6 @@ use crate::{
     },
 };
 
-use indexmap::IndexMap;
-use nac3parser::ast::StrRef;
-use std::collections::HashMap;
-
 pub struct ConcreteTypeStore {
     store: Vec<ConcreteTypeEnum>,
 }

nac3core/src/codegen/extern_fns.rs

@@ -1,8 +1,10 @@
-use inkwell::attributes::{Attribute, AttributeLoc};
-use inkwell::values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue};
+use inkwell::{
+    attributes::{Attribute, AttributeLoc},
+    values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
+};
 use itertools::Either;
 
-use crate::codegen::CodeGenContext;
+use super::CodeGenContext;
 
 /// Macro to generate extern function
 /// Both function return type and function parameter type are `FloatValue`
@@ -13,8 +15,8 @@ use crate::codegen::CodeGenContext;
 /// * `$extern_fn:literal`: Name of underlying extern function
 ///
 /// Optional Arguments:
-/// * `$(,$attributes:literal)*)`: Attributes linked with the extern function
-/// The default attributes are "mustprogress", "nofree", "nounwind", "willreturn", and "writeonly"
+/// * `$(,$attributes:literal)*)`: Attributes linked with the extern function.
+///   The default attributes are "mustprogress", "nofree", "nounwind", "willreturn", and "writeonly".
 ///   These will be used unless other attributes are specified
 /// * `$(,$args:ident)*`: Operands of the extern function
 ///   The data type of these operands will be set to `FloatValue`

nac3core/src/codegen/generator.rs

@@ -1,16 +1,18 @@
-use crate::{
-    codegen::{bool_to_i1, bool_to_i8, classes::ArraySliceValue, expr::*, stmt::*, CodeGenContext},
-    symbol_resolver::ValueEnum,
-    toplevel::{DefinitionId, TopLevelDef},
-    typecheck::typedef::{FunSignature, Type},
-};
 use inkwell::{
     context::Context,
     types::{BasicTypeEnum, IntType},
     values::{BasicValueEnum, IntValue, PointerValue},
 };
+
 use nac3parser::ast::{Expr, Stmt, StrRef};
 
+use super::{bool_to_i1, bool_to_i8, expr::*, stmt::*, values::ArraySliceValue, CodeGenContext};
+use crate::{
+    symbol_resolver::ValueEnum,
+    toplevel::{DefinitionId, TopLevelDef},
+    typecheck::typedef::{FunSignature, Type},
+};
+
 pub trait CodeGenerator {
     /// Return the module name for the code generator.
     fn get_name(&self) -> &str;
@@ -57,6 +59,7 @@ pub trait CodeGenerator {
     /// - fun: Function signature, definition ID and the substitution key.
     /// - params: Function parameters. Note that this does not include the object even if the
     ///   function is a class method.
+    ///
     /// Note that this function should check if the function is generated in another thread (due to
     /// possible race condition), see the default implementation for an example.
     fn gen_func_instance<'ctx>(

nac3core/src/codegen/irrt/list.rs

@@ -0,0 +1,162 @@
+use inkwell::{
+    types::BasicTypeEnum,
+    values::{BasicValueEnum, CallSiteValue, IntValue},
+    AddressSpace, IntPredicate,
+};
+use itertools::Either;
+
+use super::calculate_len_for_slice_range;
+use crate::codegen::{
+    macros::codegen_unreachable,
+    values::{ArrayLikeValue, ListValue},
+    CodeGenContext, 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, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ty: BasicTypeEnum<'ctx>,
+    dest_arr: ListValue<'ctx>,
+    dest_idx: (IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>),
+    src_arr: ListValue<'ctx>,
+    src_idx: (IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>),
+) {
+    let size_ty = generator.get_size_type(ctx.ctx);
+    let int8_ptr = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
+    let int32 = ctx.ctx.i32_type();
+    let (fun_symbol, elem_ptr_type) = ("__nac3_list_slice_assign_var_size", int8_ptr);
+    let slice_assign_fun = {
+        let ty_vec = vec![
+            int32.into(),         // dest start idx
+            int32.into(),         // dest end idx
+            int32.into(),         // dest step
+            elem_ptr_type.into(), // dest arr ptr
+            int32.into(),         // dest arr len
+            int32.into(),         // src start idx
+            int32.into(),         // src end idx
+            int32.into(),         // src step
+            elem_ptr_type.into(), // src arr ptr
+            int32.into(),         // src arr len
+            int32.into(),         // size
+        ];
+        ctx.module.get_function(fun_symbol).unwrap_or_else(|| {
+            let fn_t = int32.fn_type(ty_vec.as_slice(), false);
+            ctx.module.add_function(fun_symbol, fn_t, None)
+        })
+    };
+
+    let zero = int32.const_zero();
+    let one = int32.const_int(1, false);
+    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, "dest_arr_ptr_cast").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().base_ptr(ctx, generator);
+    let src_arr_ptr =
+        ctx.builder.build_pointer_cast(src_arr_ptr, elem_ptr_type, "src_arr_ptr_cast").unwrap();
+    let src_len = src_arr.load_size(ctx, Some("src.len"));
+    let src_len = ctx.builder.build_int_truncate_or_bit_cast(src_len, int32, "srclen32").unwrap();
+
+    // index in bound and positive should be done
+    // assert if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest), and
+    // throw exception if not satisfied
+    let src_end = ctx
+        .builder
+        .build_select(
+            ctx.builder.build_int_compare(IntPredicate::SLT, src_idx.2, zero, "is_neg").unwrap(),
+            ctx.builder.build_int_sub(src_idx.1, one, "e_min_one").unwrap(),
+            ctx.builder.build_int_add(src_idx.1, one, "e_add_one").unwrap(),
+            "final_e",
+        )
+        .map(BasicValueEnum::into_int_value)
+        .unwrap();
+    let dest_end = ctx
+        .builder
+        .build_select(
+            ctx.builder.build_int_compare(IntPredicate::SLT, dest_idx.2, zero, "is_neg").unwrap(),
+            ctx.builder.build_int_sub(dest_idx.1, one, "e_min_one").unwrap(),
+            ctx.builder.build_int_add(dest_idx.1, one, "e_add_one").unwrap(),
+            "final_e",
+        )
+        .map(BasicValueEnum::into_int_value)
+        .unwrap();
+    let src_slice_len =
+        calculate_len_for_slice_range(generator, ctx, src_idx.0, src_end, src_idx.2);
+    let dest_slice_len =
+        calculate_len_for_slice_range(generator, ctx, dest_idx.0, dest_end, dest_idx.2);
+    let src_eq_dest = ctx
+        .builder
+        .build_int_compare(IntPredicate::EQ, src_slice_len, dest_slice_len, "slice_src_eq_dest")
+        .unwrap();
+    let src_slt_dest = ctx
+        .builder
+        .build_int_compare(IntPredicate::SLT, src_slice_len, dest_slice_len, "slice_src_slt_dest")
+        .unwrap();
+    let dest_step_eq_one = ctx
+        .builder
+        .build_int_compare(
+            IntPredicate::EQ,
+            dest_idx.2,
+            dest_idx.2.get_type().const_int(1, false),
+            "slice_dest_step_eq_one",
+        )
+        .unwrap();
+    let cond_1 = ctx.builder.build_and(dest_step_eq_one, src_slt_dest, "slice_cond_1").unwrap();
+    let cond = ctx.builder.build_or(src_eq_dest, cond_1, "slice_cond").unwrap();
+    ctx.make_assert(
+        generator,
+        cond,
+        "0:ValueError",
+        "attempt to assign sequence of size {0} to slice of size {1} with step size {2}",
+        [Some(src_slice_len), Some(dest_slice_len), Some(dest_idx.2)],
+        ctx.current_loc,
+    );
+
+    let new_len = {
+        let args = vec![
+            dest_idx.0.into(),   // dest start idx
+            dest_idx.1.into(),   // dest end idx
+            dest_idx.2.into(),   // dest step
+            dest_arr_ptr.into(), // dest arr ptr
+            dest_len.into(),     // dest arr len
+            src_idx.0.into(),    // src start idx
+            src_idx.1.into(),    // src end idx
+            src_idx.2.into(),    // src step
+            src_arr_ptr.into(),  // src arr ptr
+            src_len.into(),      // src arr len
+            {
+                let s = match ty {
+                    BasicTypeEnum::FloatType(t) => t.size_of(),
+                    BasicTypeEnum::IntType(t) => t.size_of(),
+                    BasicTypeEnum::PointerType(t) => t.size_of(),
+                    BasicTypeEnum::StructType(t) => t.size_of().unwrap(),
+                    _ => codegen_unreachable!(ctx),
+                };
+                ctx.builder.build_int_truncate_or_bit_cast(s, int32, "size").unwrap()
+            }
+            .into(),
+        ];
+        ctx.builder
+            .build_call(slice_assign_fun, args.as_slice(), "slice_assign")
+            .map(CallSiteValue::try_as_basic_value)
+            .map(|v| v.map_left(BasicValueEnum::into_int_value))
+            .map(Either::unwrap_left)
+            .unwrap()
+    };
+    // update length
+    let need_update =
+        ctx.builder.build_int_compare(IntPredicate::NE, new_len, dest_len, "need_update").unwrap();
+    let current = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
+    let update_bb = ctx.ctx.append_basic_block(current, "update");
+    let cont_bb = ctx.ctx.append_basic_block(current, "cont");
+    ctx.builder.build_conditional_branch(need_update, update_bb, cont_bb).unwrap();
+    ctx.builder.position_at_end(update_bb);
+    let new_len = ctx.builder.build_int_z_extend_or_bit_cast(new_len, size_ty, "new_len").unwrap();
+    dest_arr.store_size(ctx, generator, new_len);
+    ctx.builder.build_unconditional_branch(cont_bb).unwrap();
+    ctx.builder.position_at_end(cont_bb);
+}

nac3core/src/codegen/irrt/math.rs

@@ -0,0 +1,152 @@
+use inkwell::{
+    values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
+    IntPredicate,
+};
+use itertools::Either;
+
+use crate::codegen::{
+    macros::codegen_unreachable,
+    {CodeGenContext, CodeGenerator},
+};
+
+// 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, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    base: IntValue<'ctx>,
+    exp: IntValue<'ctx>,
+    signed: bool,
+) -> IntValue<'ctx> {
+    let symbol = match (base.get_type().get_bit_width(), exp.get_type().get_bit_width(), signed) {
+        (32, 32, true) => "__nac3_int_exp_int32_t",
+        (64, 64, true) => "__nac3_int_exp_int64_t",
+        (32, 32, false) => "__nac3_int_exp_uint32_t",
+        (64, 64, false) => "__nac3_int_exp_uint64_t",
+        _ => codegen_unreachable!(ctx),
+    };
+    let base_type = base.get_type();
+    let pow_fun = ctx.module.get_function(symbol).unwrap_or_else(|| {
+        let fn_type = base_type.fn_type(&[base_type.into(), base_type.into()], false);
+        ctx.module.add_function(symbol, fn_type, None)
+    });
+    // throw exception when exp < 0
+    let ge_zero = ctx
+        .builder
+        .build_int_compare(
+            IntPredicate::SGE,
+            exp,
+            exp.get_type().const_zero(),
+            "assert_int_pow_ge_0",
+        )
+        .unwrap();
+    ctx.make_assert(
+        generator,
+        ge_zero,
+        "0:ValueError",
+        "integer power must be positive or zero",
+        [None, None, None],
+        ctx.current_loc,
+    );
+    ctx.builder
+        .build_call(pow_fun, &[base.into(), exp.into()], "call_int_pow")
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_int_value))
+        .map(Either::unwrap_left)
+        .unwrap()
+}
+
+/// Generates a call to `isinf` in IR. Returns an `i1` representing the result.
+pub fn call_isinf<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    v: FloatValue<'ctx>,
+) -> IntValue<'ctx> {
+    let intrinsic_fn = ctx.module.get_function("__nac3_isinf").unwrap_or_else(|| {
+        let fn_type = ctx.ctx.i32_type().fn_type(&[ctx.ctx.f64_type().into()], false);
+        ctx.module.add_function("__nac3_isinf", fn_type, None)
+    });
+
+    let ret = ctx
+        .builder
+        .build_call(intrinsic_fn, &[v.into()], "isinf")
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_int_value))
+        .map(Either::unwrap_left)
+        .unwrap();
+
+    generator.bool_to_i1(ctx, ret)
+}
+
+/// Generates a call to `isnan` in IR. Returns an `i1` representing the result.
+pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    v: FloatValue<'ctx>,
+) -> IntValue<'ctx> {
+    let intrinsic_fn = ctx.module.get_function("__nac3_isnan").unwrap_or_else(|| {
+        let fn_type = ctx.ctx.i32_type().fn_type(&[ctx.ctx.f64_type().into()], false);
+        ctx.module.add_function("__nac3_isnan", fn_type, None)
+    });
+
+    let ret = ctx
+        .builder
+        .build_call(intrinsic_fn, &[v.into()], "isnan")
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_int_value))
+        .map(Either::unwrap_left)
+        .unwrap();
+
+    generator.bool_to_i1(ctx, ret)
+}
+
+/// Generates a call to `gamma` in IR. Returns an `f64` representing the result.
+pub fn call_gamma<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
+    let llvm_f64 = ctx.ctx.f64_type();
+
+    let intrinsic_fn = ctx.module.get_function("__nac3_gamma").unwrap_or_else(|| {
+        let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
+        ctx.module.add_function("__nac3_gamma", fn_type, None)
+    });
+
+    ctx.builder
+        .build_call(intrinsic_fn, &[v.into()], "gamma")
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_float_value))
+        .map(Either::unwrap_left)
+        .unwrap()
+}
+
+/// Generates a call to `gammaln` in IR. Returns an `f64` representing the result.
+pub fn call_gammaln<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
+    let llvm_f64 = ctx.ctx.f64_type();
+
+    let intrinsic_fn = ctx.module.get_function("__nac3_gammaln").unwrap_or_else(|| {
+        let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
+        ctx.module.add_function("__nac3_gammaln", fn_type, None)
+    });
+
+    ctx.builder
+        .build_call(intrinsic_fn, &[v.into()], "gammaln")
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_float_value))
+        .map(Either::unwrap_left)
+        .unwrap()
+}
+
+/// Generates a call to `j0` in IR. Returns an `f64` representing the result.
+pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
+    let llvm_f64 = ctx.ctx.f64_type();
+
+    let intrinsic_fn = ctx.module.get_function("__nac3_j0").unwrap_or_else(|| {
+        let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
+        ctx.module.add_function("__nac3_j0", fn_type, None)
+    });
+
+    ctx.builder
+        .build_call(intrinsic_fn, &[v.into()], "j0")
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_float_value))
+        .map(Either::unwrap_left)
+        .unwrap()
+}

nac3core/src/codegen/irrt/ndarray.rs

@@ -0,0 +1,384 @@
+use inkwell::{
+    types::IntType,
+    values::{BasicValueEnum, CallSiteValue, IntValue},
+    AddressSpace, IntPredicate,
+};
+use itertools::Either;
+
+use crate::codegen::{
+    llvm_intrinsics,
+    macros::codegen_unreachable,
+    stmt::gen_for_callback_incrementing,
+    values::{
+        ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, NDArrayValue, TypedArrayLikeAccessor,
+        TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_calc_size`. Returns an [`IntValue`] representing the
+/// calculated total size.
+///
+/// * `dims` - An [`ArrayLikeIndexer`] containing the size of each dimension.
+/// * `range` - The dimension index to begin and end (exclusively) calculating the dimensions for,
+///   or [`None`] if starting from the first dimension and ending at the last dimension
+///   respectively.
+pub fn call_ndarray_calc_size<'ctx, G, Dims>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    dims: &Dims,
+    (begin, end): (Option<IntValue<'ctx>>, Option<IntValue<'ctx>>),
+) -> IntValue<'ctx>
+where
+    G: CodeGenerator + ?Sized,
+    Dims: ArrayLikeIndexer<'ctx>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
+
+    let ndarray_calc_size_fn_name = match llvm_usize.get_bit_width() {
+        32 => "__nac3_ndarray_calc_size",
+        64 => "__nac3_ndarray_calc_size64",
+        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
+    };
+    let ndarray_calc_size_fn_t = llvm_usize.fn_type(
+        &[llvm_pusize.into(), llvm_usize.into(), llvm_usize.into(), llvm_usize.into()],
+        false,
+    );
+    let ndarray_calc_size_fn =
+        ctx.module.get_function(ndarray_calc_size_fn_name).unwrap_or_else(|| {
+            ctx.module.add_function(ndarray_calc_size_fn_name, ndarray_calc_size_fn_t, None)
+        });
+
+    let begin = begin.unwrap_or_else(|| llvm_usize.const_zero());
+    let end = end.unwrap_or_else(|| dims.size(ctx, generator));
+    ctx.builder
+        .build_call(
+            ndarray_calc_size_fn,
+            &[
+                dims.base_ptr(ctx, generator).into(),
+                dims.size(ctx, generator).into(),
+                begin.into(),
+                end.into(),
+            ],
+            "",
+        )
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_int_value))
+        .map(Either::unwrap_left)
+        .unwrap()
+}
+
+/// Generates a call to `__nac3_ndarray_calc_nd_indices`. Returns a [`TypeArrayLikeAdpater`]
+/// containing `i32` indices of the flattened index.
+///
+/// * `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, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    index: IntValue<'ctx>,
+    ndarray: NDArrayValue<'ctx>,
+) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
+    let llvm_void = ctx.ctx.void_type();
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
+
+    let ndarray_calc_nd_indices_fn_name = match llvm_usize.get_bit_width() {
+        32 => "__nac3_ndarray_calc_nd_indices",
+        64 => "__nac3_ndarray_calc_nd_indices64",
+        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
+    };
+    let ndarray_calc_nd_indices_fn =
+        ctx.module.get_function(ndarray_calc_nd_indices_fn_name).unwrap_or_else(|| {
+            let fn_type = llvm_void.fn_type(
+                &[llvm_usize.into(), llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into()],
+                false,
+            );
+
+            ctx.module.add_function(ndarray_calc_nd_indices_fn_name, fn_type, None)
+        });
+
+    let ndarray_num_dims = ndarray.load_ndims(ctx);
+    let ndarray_dims = ndarray.shape();
+
+    let indices = ctx.builder.build_array_alloca(llvm_i32, ndarray_num_dims, "").unwrap();
+
+    ctx.builder
+        .build_call(
+            ndarray_calc_nd_indices_fn,
+            &[
+                index.into(),
+                ndarray_dims.base_ptr(ctx, generator).into(),
+                ndarray_num_dims.into(),
+                indices.into(),
+            ],
+            "",
+        )
+        .unwrap();
+
+    TypedArrayLikeAdapter::from(
+        ArraySliceValue::from_ptr_val(indices, ndarray_num_dims, None),
+        Box::new(|_, v| v.into_int_value()),
+        Box::new(|_, v| v.into()),
+    )
+}
+
+fn call_ndarray_flatten_index_impl<'ctx, G, Indices>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    ndarray: NDArrayValue<'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);
+
+    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
+
+    debug_assert_eq!(
+        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(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`"
+    );
+
+    let ndarray_flatten_index_fn_name = match llvm_usize.get_bit_width() {
+        32 => "__nac3_ndarray_flatten_index",
+        64 => "__nac3_ndarray_flatten_index64",
+        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
+    };
+    let ndarray_flatten_index_fn =
+        ctx.module.get_function(ndarray_flatten_index_fn_name).unwrap_or_else(|| {
+            let fn_type = llvm_usize.fn_type(
+                &[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_usize.into()],
+                false,
+            );
+
+            ctx.module.add_function(ndarray_flatten_index_fn_name, fn_type, None)
+        });
+
+    let ndarray_num_dims = ndarray.load_ndims(ctx);
+    let ndarray_dims = ndarray.shape();
+
+    let index = ctx
+        .builder
+        .build_call(
+            ndarray_flatten_index_fn,
+            &[
+                ndarray_dims.base_ptr(ctx, generator).into(),
+                ndarray_num_dims.into(),
+                indices.base_ptr(ctx, generator).into(),
+                indices.size(ctx, generator).into(),
+            ],
+            "",
+        )
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_int_value))
+        .map(Either::unwrap_left)
+        .unwrap();
+
+    index
+}
+
+/// Generates a call to `__nac3_ndarray_flatten_index`. Returns the flattened index for the
+/// multidimensional index.
+///
+/// * `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, G, Index>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray: NDArrayValue<'ctx>,
+    indices: &Index,
+) -> IntValue<'ctx>
+where
+    G: CodeGenerator + ?Sized,
+    Index: ArrayLikeIndexer<'ctx>,
+{
+    call_ndarray_flatten_index_impl(generator, ctx, ndarray, indices)
+}
+
+/// 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>,
+) -> TypedArrayLikeAdapter<'ctx, IntValue<'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 => codegen_unreachable!(ctx, "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 min_ndims = llvm_intrinsics::call_int_umin(ctx, lhs_ndims, rhs_ndims, None);
+
+    gen_for_callback_incrementing(
+        generator,
+        ctx,
+        None,
+        llvm_usize.const_zero(),
+        (min_ndims, false),
+        |generator, ctx, _, idx| {
+            let idx = ctx.builder.build_int_sub(min_ndims, idx, "").unwrap();
+            let (lhs_dim_sz, rhs_dim_sz) = unsafe {
+                (
+                    lhs.shape().get_typed_unchecked(ctx, generator, &idx, None),
+                    rhs.shape().get_typed_unchecked(ctx, generator, &idx, None),
+                )
+            };
+
+            let llvm_usize_const_one = llvm_usize.const_int(1, false);
+            let lhs_eqz = ctx
+                .builder
+                .build_int_compare(IntPredicate::EQ, lhs_dim_sz, llvm_usize_const_one, "")
+                .unwrap();
+            let rhs_eqz = ctx
+                .builder
+                .build_int_compare(IntPredicate::EQ, rhs_dim_sz, llvm_usize_const_one, "")
+                .unwrap();
+            let lhs_or_rhs_eqz = ctx.builder.build_or(lhs_eqz, rhs_eqz, "").unwrap();
+
+            let lhs_eq_rhs = ctx
+                .builder
+                .build_int_compare(IntPredicate::EQ, lhs_dim_sz, rhs_dim_sz, "")
+                .unwrap();
+
+            let is_compatible = ctx.builder.build_or(lhs_or_rhs_eqz, lhs_eq_rhs, "").unwrap();
+
+            ctx.make_assert(
+                generator,
+                is_compatible,
+                "0:ValueError",
+                "operands could not be broadcast together",
+                [None, None, None],
+                ctx.current_loc,
+            );
+
+            Ok(())
+        },
+        llvm_usize.const_int(1, false),
+    )
+    .unwrap();
+
+    let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
+    let lhs_dims = lhs.shape().base_ptr(ctx, generator);
+    let lhs_ndims = lhs.load_ndims(ctx);
+    let rhs_dims = rhs.shape().base_ptr(ctx, generator);
+    let rhs_ndims = rhs.load_ndims(ctx);
+    let out_dims = ctx.builder.build_array_alloca(llvm_usize, max_ndims, "").unwrap();
+    let out_dims = ArraySliceValue::from_ptr_val(out_dims, max_ndims, None);
+
+    ctx.builder
+        .build_call(
+            ndarray_calc_broadcast_fn,
+            &[
+                lhs_dims.into(),
+                lhs_ndims.into(),
+                rhs_dims.into(),
+                rhs_ndims.into(),
+                out_dims.base_ptr(ctx, generator).into(),
+            ],
+            "",
+        )
+        .unwrap();
+
+    TypedArrayLikeAdapter::from(
+        out_dims,
+        Box::new(|_, v| v.into_int_value()),
+        Box::new(|_, v| v.into()),
+    )
+}
+
+/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
+/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
+/// array `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,
+) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
+    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 => codegen_unreachable!(ctx, "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_pi32.into(), llvm_pi32.into()],
+                false,
+            );
+
+            ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
+        });
+
+    let broadcast_size = broadcast_idx.size(ctx, generator);
+    let out_idx = ctx.builder.build_array_alloca(llvm_i32, broadcast_size, "").unwrap();
+
+    let array_dims = array.shape().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.into()],
+            "",
+        )
+        .unwrap();
+
+    TypedArrayLikeAdapter::from(
+        ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None),
+        Box::new(|_, v| v.into_int_value()),
+        Box::new(|_, v| v.into()),
+    )
+}

nac3core/src/codegen/irrt/slice.rs

@@ -0,0 +1,76 @@
+use inkwell::{
+    values::{BasicValueEnum, CallSiteValue, IntValue},
+    IntPredicate,
+};
+use itertools::Either;
+use nac3parser::ast::Expr;
+
+use crate::{
+    codegen::{CodeGenContext, CodeGenerator},
+    typecheck::typedef::Type,
+};
+
+/// this function allows index out of range, since python
+/// allows index out of range in slice (`a = [1,2,3]; a[1:10] == [2,3]`).
+pub fn handle_slice_index_bound<'ctx, G: CodeGenerator>(
+    i: &Expr<Option<Type>>,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    generator: &mut G,
+    length: IntValue<'ctx>,
+) -> Result<Option<IntValue<'ctx>>, String> {
+    const SYMBOL: &str = "__nac3_slice_index_bound";
+    let func = ctx.module.get_function(SYMBOL).unwrap_or_else(|| {
+        let i32_t = ctx.ctx.i32_type();
+        let fn_t = i32_t.fn_type(&[i32_t.into(), i32_t.into()], false);
+        ctx.module.add_function(SYMBOL, fn_t, None)
+    });
+
+    let i = if let Some(v) = generator.gen_expr(ctx, i)? {
+        v.to_basic_value_enum(ctx, generator, i.custom.unwrap())?
+    } else {
+        return Ok(None);
+    };
+    Ok(Some(
+        ctx.builder
+            .build_call(func, &[i.into(), length.into()], "bounded_ind")
+            .map(CallSiteValue::try_as_basic_value)
+            .map(|v| v.map_left(BasicValueEnum::into_int_value))
+            .map(Either::unwrap_left)
+            .unwrap(),
+    ))
+}
+
+pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    start: IntValue<'ctx>,
+    end: IntValue<'ctx>,
+    step: IntValue<'ctx>,
+) -> IntValue<'ctx> {
+    const SYMBOL: &str = "__nac3_range_slice_len";
+    let len_func = ctx.module.get_function(SYMBOL).unwrap_or_else(|| {
+        let i32_t = ctx.ctx.i32_type();
+        let fn_t = i32_t.fn_type(&[i32_t.into(), i32_t.into(), i32_t.into()], false);
+        ctx.module.add_function(SYMBOL, fn_t, None)
+    });
+
+    // assert step != 0, throw exception if not
+    let not_zero = ctx
+        .builder
+        .build_int_compare(IntPredicate::NE, step, step.get_type().const_zero(), "range_step_ne")
+        .unwrap();
+    ctx.make_assert(
+        generator,
+        not_zero,
+        "0:ValueError",
+        "step must not be zero",
+        [None, None, None],
+        ctx.current_loc,
+    );
+    ctx.builder
+        .build_call(len_func, &[start.into(), end.into(), step.into()], "calc_len")
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_int_value))
+        .map(Either::unwrap_left)
+        .unwrap()
+}

nac3core/src/codegen/irrt/test.rs

@@ -1,26 +0,0 @@
-#[cfg(test)]
-mod tests {
-    use std::{path::Path, process::Command};
-
-    #[test]
-    fn run_irrt_test() {
-        assert!(
-            cfg!(feature = "test"),
-            "Please do `cargo test -F test` to compile `irrt_test.out` and run test"
-        );
-
-        let irrt_test_out_path = Path::new(concat!(env!("OUT_DIR"), "/irrt_test.out"));
-        let output = Command::new(irrt_test_out_path.to_str().unwrap()).output().unwrap();
-
-        if !output.status.success() {
-            eprintln!("irrt_test failed with status {}:", output.status);
-            eprintln!("====== stdout ======");
-            eprintln!("{}", String::from_utf8(output.stdout).unwrap());
-            eprintln!("====== stderr ======");
-            eprintln!("{}", String::from_utf8(output.stderr).unwrap());
-            eprintln!("====================");
-
-            panic!("irrt_test failed");
-        }
-    }
-}

nac3core/src/codegen/llvm_intrinsics.rs

@@ -1,12 +1,14 @@
-use crate::codegen::CodeGenContext;
-use inkwell::context::Context;
-use inkwell::intrinsics::Intrinsic;
-use inkwell::types::AnyTypeEnum::IntType;
-use inkwell::types::FloatType;
-use inkwell::values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue};
-use inkwell::AddressSpace;
+use inkwell::{
+    context::Context,
+    intrinsics::Intrinsic,
+    types::{AnyTypeEnum::IntType, FloatType},
+    values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
+    AddressSpace,
+};
 use itertools::Either;
 
+use super::CodeGenContext;
+
 /// Returns the string representation for the floating-point type `ft` when used in intrinsic
 /// functions.
 fn get_float_intrinsic_repr(ctx: &Context, ft: FloatType) -> &'static str {
@@ -183,7 +185,7 @@ pub fn call_memcpy_generic<'ctx>(
         dest
     } else {
         ctx.builder
-            .build_bitcast(dest, llvm_p0i8, "")
+            .build_bit_cast(dest, llvm_p0i8, "")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap()
     };
@@ -191,7 +193,7 @@ pub fn call_memcpy_generic<'ctx>(
         src
     } else {
         ctx.builder
-            .build_bitcast(src, llvm_p0i8, "")
+            .build_bit_cast(src, llvm_p0i8, "")
             .map(BasicValueEnum::into_pointer_value)
             .unwrap()
     };
@@ -205,8 +207,9 @@ pub fn call_memcpy_generic<'ctx>(
 /// * `$ctx:ident`: Reference to the current Code Generation Context
 /// * `$name:ident`: Optional name to be assigned to the llvm build call (Option<&str>)
 /// * `$llvm_name:literal`: Name of underlying llvm intrinsic function
-/// * `$map_fn:ident`: Mapping function to be applied on `BasicValue` (`BasicValue` -> Function Return Type)
-/// Use `BasicValueEnum::into_int_value` for Integer return type and `BasicValueEnum::into_float_value` for Float return type
+/// * `$map_fn:ident`: Mapping function to be applied on `BasicValue` (`BasicValue` -> Function Return Type).
+///   Use `BasicValueEnum::into_int_value` for Integer return type and
+///   `BasicValueEnum::into_float_value` for Float return type
 /// * `$llvm_ty:ident`: Type of first operand
 /// * `,($val:ident)*`: Comma separated list of operands
 macro_rules! generate_llvm_intrinsic_fn_body {
@@ -222,7 +225,7 @@ macro_rules! generate_llvm_intrinsic_fn_body {
 /// Arguments:
 /// * `float/int`: Indicates the return and argument type of the function
 /// * `$fn_name:ident`: The identifier of the rust function to be generated
-/// * `$llvm_name:literal`: Name of underlying llvm intrinsic function
+/// * `$llvm_name:literal`: Name of underlying llvm intrinsic function.
 ///   Omit "llvm." prefix from the function name i.e. use "ceil" instead of "llvm.ceil"
 /// * `$val:ident`: The operand for unary operations
 /// * `$val1:ident`, `$val2:ident`: The operands for binary operations

nac3core/src/codegen/mod.rs

@@ -1,12 +1,12 @@
-use crate::{
-    codegen::classes::{ListType, ProxyType, RangeType},
-    symbol_resolver::{StaticValue, SymbolResolver},
-    toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
-    typecheck::{
-        type_inferencer::{CodeLocation, PrimitiveStore},
-        typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
+use std::{
+    collections::{HashMap, HashSet},
+    sync::{
+        atomic::{AtomicBool, Ordering},
+        Arc,
     },
+    thread,
 };
+
 use crossbeam::channel::{unbounded, Receiver, Sender};
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
@@ -24,36 +24,52 @@ use inkwell::{
     AddressSpace, IntPredicate, OptimizationLevel,
 };
 use itertools::Itertools;
-use model::*;
-use nac3parser::ast::{Location, Stmt, StrRef};
 use parking_lot::{Condvar, Mutex};
-use std::collections::{HashMap, HashSet};
-use std::sync::{
-    atomic::{AtomicBool, Ordering},
-    Arc,
-};
-use std::thread;
-use structure::{CSlice, Exception, NDArray};
 
-pub mod classes;
+use nac3parser::ast::{Location, Stmt, StrRef};
+
+use crate::{
+    symbol_resolver::{StaticValue, SymbolResolver},
+    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
+    typecheck::{
+        type_inferencer::{CodeLocation, PrimitiveStore},
+        typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
+    },
+};
+use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
+pub use generator::{CodeGenerator, DefaultCodeGenerator};
+use types::{ListType, NDArrayType, ProxyType, RangeType};
+
+pub mod builtin_fns;
 pub mod concrete_type;
 pub mod expr;
 pub mod extern_fns;
 mod generator;
 pub mod irrt;
 pub mod llvm_intrinsics;
-pub mod model;
 pub mod numpy;
-pub mod numpy_new;
-pub mod object;
 pub mod stmt;
-pub mod structure;
+pub mod types;
+pub mod values;
 
 #[cfg(test)]
 mod test;
 
-use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
-pub use generator::{CodeGenerator, DefaultCodeGenerator};
+mod macros {
+    /// Codegen-variant of [`std::unreachable`] which accepts an instance of [`CodeGenContext`] as
+    /// its first argument to provide Python source information to indicate the codegen location
+    /// causing the assertion.
+    macro_rules! codegen_unreachable {
+        ($ctx:expr $(,)?) => {
+            std::unreachable!("unreachable code while processing {}", &$ctx.current_loc)
+        };
+        ($ctx:expr, $($arg:tt)*) => {
+            std::unreachable!("unreachable code while processing {}: {}", &$ctx.current_loc, std::format!("{}", std::format_args!($($arg)+)))
+        };
+    }
+
+    pub(crate) use codegen_unreachable;
+}
 
 #[derive(Default)]
 pub struct StaticValueStore {
@@ -173,11 +189,11 @@ pub struct CodeGenContext<'ctx, 'a> {
     pub registry: &'a WorkerRegistry,
 
     /// Cache for constant strings.
-    pub const_strings: HashMap<String, Struct<'ctx, CSlice>>,
+    pub const_strings: HashMap<String, BasicValueEnum<'ctx>>,
 
     /// [`BasicBlock`] containing all `alloca` statements for the current function.
     pub init_bb: BasicBlock<'ctx>,
-    pub exception_val: Option<Ptr<'ctx, StructModel<Exception>>>,
+    pub exception_val: Option<PointerValue<'ctx>>,
 
     /// The header and exit basic blocks of a loop in this context. See
     /// <https://llvm.org/docs/LoopTerminology.html> for explanation of these terminology.
@@ -494,8 +510,12 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                         }
 
                         TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                            let pndarray_model = PtrModel(StructModel(NDArray));
-                            pndarray_model.get_type(generator, ctx).as_basic_type_enum()
+                            let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
+                            let element_type = get_llvm_type(
+                                ctx, module, generator, unifier, top_level, type_cache, dtype,
+                            );
+
+                            NDArrayType::new(generator, ctx, element_type).as_base_type().into()
                         }
 
                         _ => unreachable!(
@@ -581,11 +601,11 @@ fn get_llvm_abi_type<'ctx, G: CodeGenerator + ?Sized>(
 ) -> BasicTypeEnum<'ctx> {
     // If the type is used in the definition of a function, return `i1` instead of `i8` for ABI
     // consistency.
-    return if unifier.unioned(ty, primitives.bool) {
+    if unifier.unioned(ty, primitives.bool) {
         ctx.bool_type().into()
     } else {
         get_llvm_type(ctx, module, generator, unifier, top_level, type_cache, ty)
-    };
+    }
 }
 
 /// Whether `sret` is needed for a return value with type `ty`.
@@ -701,19 +721,43 @@ pub fn gen_func_impl<
         ..primitives
     };
 
-    let cslice_model = StructModel(CSlice);
-    let pexn_model = PtrModel(StructModel(Exception));
-
-    let mut type_cache: HashMap<_, BasicTypeEnum<'ctx>> = [
+    let mut type_cache: HashMap<_, _> = [
         (primitives.int32, context.i32_type().into()),
         (primitives.int64, context.i64_type().into()),
         (primitives.uint32, context.i32_type().into()),
         (primitives.uint64, context.i64_type().into()),
         (primitives.float, context.f64_type().into()),
         (primitives.bool, context.i8_type().into()),
-        (primitives.str, cslice_model.get_type(generator, context).into()),
+        (primitives.str, {
+            let name = "str";
+            match module.get_struct_type(name) {
+                None => {
+                    let str_type = context.opaque_struct_type("str");
+                    let fields = [
+                        context.i8_type().ptr_type(AddressSpace::default()).into(),
+                        generator.get_size_type(context).into(),
+                    ];
+                    str_type.set_body(&fields, false);
+                    str_type.into()
+                }
+                Some(t) => t.as_basic_type_enum(),
+            }
+        }),
         (primitives.range, RangeType::new(context).as_base_type().into()),
-        (primitives.exception, pexn_model.get_type(generator, context).into()),
+        (primitives.exception, {
+            let name = "Exception";
+            if let Some(t) = module.get_struct_type(name) {
+                t.ptr_type(AddressSpace::default()).as_basic_type_enum()
+            } else {
+                let exception = context.opaque_struct_type("Exception");
+                let int32 = context.i32_type().into();
+                let int64 = context.i64_type().into();
+                let str_ty = module.get_struct_type("str").unwrap().as_basic_type_enum();
+                let fields = [int32, str_ty, int32, int32, str_ty, str_ty, int64, int64, int64];
+                exception.set_body(&fields, false);
+                exception.ptr_type(AddressSpace::default()).as_basic_type_enum()
+            }
+        }),
     ]
     .iter()
     .copied()
@@ -809,10 +853,9 @@ pub fn gen_func_impl<
     builder.position_at_end(init_bb);
     let body_bb = context.append_basic_block(fn_val, "body");
 
+    // Store non-vararg argument values into local variables
     let mut var_assignment = HashMap::new();
     let offset = u32::from(has_sret);
-
-    // Store non-vararg argument values into local variables
     for (n, arg) in args.iter().enumerate().filter(|(_, arg)| !arg.is_vararg) {
         let param = fn_val.get_nth_param((n as u32) + offset).unwrap();
         let local_type = get_llvm_type(

nac3core/src/codegen/model/any.rs

@@ -1,40 +0,0 @@
-use inkwell::{
-    context::Context,
-    types::{BasicType, BasicTypeEnum},
-    values::BasicValueEnum,
-};
-
-use crate::codegen::CodeGenerator;
-
-use super::*;
-
-#[derive(Debug, Clone, Copy)]
-pub struct AnyModel<'ctx>(pub BasicTypeEnum<'ctx>);
-pub type Anything<'ctx> = Instance<'ctx, AnyModel<'ctx>>;
-
-impl<'ctx> Model<'ctx> for AnyModel<'ctx> {
-    type Value = BasicValueEnum<'ctx>;
-    type Type = BasicTypeEnum<'ctx>;
-
-    fn get_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _generator: &G,
-        _ctx: &'ctx Context,
-    ) -> Self::Type {
-        self.0
-    }
-
-    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        _generator: &mut G,
-        _ctx: &'ctx Context,
-        ty: T,
-    ) -> Result<(), ModelError> {
-        let ty = ty.as_basic_type_enum();
-        if ty == self.0 {
-            Ok(())
-        } else {
-            Err(ModelError(format!("Expecting {}, but got {}", self.0, ty)))
-        }
-    }
-}

nac3core/src/codegen/model/array.rs

@@ -1,122 +0,0 @@
-use inkwell::{
-    context::Context,
-    types::{ArrayType, BasicType, BasicTypeEnum},
-    values::ArrayValue,
-};
-
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-use super::*;
-
-/// A Model for an [`ArrayType`].
-#[derive(Debug, Clone, Copy)]
-pub struct ArrayModel<Element> {
-    pub len: u32,
-    pub element: Element,
-}
-pub type Array<'ctx, Element> = Instance<'ctx, ArrayModel<Element>>;
-
-impl<'ctx, Element: Model<'ctx>> Model<'ctx> for ArrayModel<Element> {
-    type Value = ArrayValue<'ctx>;
-    type Type = ArrayType<'ctx>;
-
-    fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
-        self.element.get_type(generator, ctx).array_type(self.len)
-    }
-
-    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        ty: T,
-    ) -> Result<(), ModelError> {
-        let ty = ty.as_basic_type_enum();
-        let BasicTypeEnum::ArrayType(ty) = ty else {
-            return Err(ModelError(format!("Expecting ArrayType, but got {ty:?}")));
-        };
-
-        if ty.len() != self.len {
-            return Err(ModelError(format!(
-                "Expecting ArrayType with size {}, but got an ArrayType with size {}",
-                ty.len(),
-                self.len
-            )));
-        }
-
-        self.element
-            .check_type(generator, ctx, ty.get_element_type())
-            .map_err(|err| err.under_context("an ArrayType"))?;
-
-        Ok(())
-    }
-}
-
-impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, ArrayModel<Element>> {
-    /// Get the pointer to the `i`-th (0-based) array element.
-    pub fn at<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        i: u32,
-        name: &str,
-    ) -> Ptr<'ctx, Element> {
-        assert!(i < self.model.0.len);
-
-        let zero = ctx.ctx.i32_type().const_zero();
-        let i = ctx.ctx.i32_type().const_int(u64::from(i), false);
-        let ptr = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[zero, i], name).unwrap() };
-
-        PtrModel(self.model.0.element).check_value(generator, ctx.ctx, ptr).unwrap()
-    }
-}
-
-/// Like [`ArrayModel`] but length is strongly-typed.
-#[derive(Debug, Clone, Copy, Default)]
-pub struct NArrayModel<const LEN: u32, Element>(pub Element);
-pub type NArray<'ctx, const LEN: u32, Element> = Instance<'ctx, NArrayModel<LEN, Element>>;
-
-impl<'ctx, const LEN: u32, Element: Model<'ctx>> NArrayModel<LEN, Element> {
-    /// Forget the `LEN` constant generic and get an [`ArrayModel`] with the same length.
-    pub fn forget_len(&self) -> ArrayModel<Element> {
-        ArrayModel { element: self.0, len: LEN }
-    }
-}
-
-impl<'ctx, const LEN: u32, Element: Model<'ctx>> Model<'ctx> for NArrayModel<LEN, Element> {
-    type Value = ArrayValue<'ctx>;
-    type Type = ArrayType<'ctx>;
-
-    fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
-        // Convenient implementation
-        self.forget_len().get_type(generator, ctx)
-    }
-
-    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        ty: T,
-    ) -> Result<(), ModelError> {
-        // Convenient implementation
-        self.forget_len().check_type(generator, ctx, ty)
-    }
-}
-
-impl<'ctx, const LEN: u32, Element: Model<'ctx>> Ptr<'ctx, NArrayModel<LEN, Element>> {
-    /// Get the pointer to the `i`-th (0-based) array element.
-    pub fn at_const<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        i: u32,
-        name: &str,
-    ) -> Ptr<'ctx, Element> {
-        assert!(i < LEN);
-
-        let zero = ctx.ctx.i32_type().const_zero();
-        let i = ctx.ctx.i32_type().const_int(u64::from(i), false);
-        let ptr = unsafe { ctx.builder.build_in_bounds_gep(self.value, &[zero, i], name).unwrap() };
-
-        PtrModel(self.model.0 .0).check_value(generator, ctx.ctx, ptr).unwrap()
-    }
-}

nac3core/src/codegen/model/core.rs

@@ -1,123 +0,0 @@
-use std::fmt;
-
-use inkwell::{context::Context, types::*, values::*};
-
-use super::*;
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-#[derive(Debug, Clone)]
-pub struct ModelError(pub String);
-
-impl ModelError {
-    pub(super) fn under_context(mut self, context: &str) -> Self {
-        self.0.push_str(" ... in ");
-        self.0.push_str(context);
-        self
-    }
-}
-
-pub trait Model<'ctx>: fmt::Debug + Clone + Copy {
-    type Value: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>>;
-    type Type: BasicType<'ctx>;
-
-    /// Return the [`BasicType`] of this model.
-    #[must_use]
-    fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type;
-
-    /// Check if a [`BasicType`] is the same type of this model.
-    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        ty: T,
-    ) -> Result<(), ModelError>;
-
-    /// Create an instance from a value with [`Instance::model`] being this model.
-    ///
-    /// Caller must make sure the type of `value` and the type of this `model` are equivalent.
-    #[must_use]
-    fn believe_value(&self, value: Self::Value) -> Instance<'ctx, Self> {
-        Instance { model: *self, value }
-    }
-
-    /// Check if a [`BasicValue`]'s type is equivalent to the type of this model.
-    /// Wrap it into an [`Instance`] if it is.
-    fn check_value<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        value: V,
-    ) -> Result<Instance<'ctx, Self>, ModelError> {
-        let value = value.as_basic_value_enum();
-        self.check_type(generator, ctx, value.get_type())
-            .map_err(|err| err.under_context(format!("the value {value:?}").as_str()))?;
-
-        let Ok(value) = Self::Value::try_from(value) else {
-            unreachable!("check_type() has bad implementation")
-        };
-        Ok(self.believe_value(value))
-    }
-
-    // Allocate a value on the stack and return its pointer.
-    fn alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        name: &str,
-    ) -> Ptr<'ctx, Self> {
-        let pmodel = PtrModel(*self);
-        let p = ctx.builder.build_alloca(self.get_type(generator, ctx.ctx), name).unwrap();
-        pmodel.believe_value(p)
-    }
-
-    // Allocate an array on the stack and return its pointer.
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        len: IntValue<'ctx>,
-        name: &str,
-    ) -> Ptr<'ctx, Self> {
-        let pmodel = PtrModel(*self);
-        let p =
-            ctx.builder.build_array_alloca(self.get_type(generator, ctx.ctx), len, name).unwrap();
-        pmodel.believe_value(p)
-    }
-
-    fn var_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&str>,
-    ) -> Result<Ptr<'ctx, Self>, String> {
-        let pmodel = PtrModel(*self);
-        let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
-        let p = generator.gen_var_alloc(ctx, ty, name)?;
-        Ok(pmodel.believe_value(p))
-    }
-
-    fn array_var_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        len: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> Result<Ptr<'ctx, Self>, String> {
-        // TODO: Remove ArraySliceValue
-        let pmodel = PtrModel(*self);
-        let ty = self.get_type(generator, ctx.ctx).as_basic_type_enum();
-        let p = generator.gen_array_var_alloc(ctx, ty, len, name)?;
-        Ok(pmodel.believe_value(PointerValue::from(p)))
-    }
-}
-
-#[derive(Debug, Clone, Copy)]
-pub struct Instance<'ctx, M: Model<'ctx>> {
-    /// The model of this instance.
-    pub model: M,
-    /// The value of this instance.
-    ///
-    /// Caller must make sure the type of `value` and the type of this `model` are equivalent,
-    /// down to having the same [`IntType::get_bit_width`] in case of [`IntType`] for example.
-    pub value: M::Value,
-}

nac3core/src/codegen/model/float.rs

@@ -1,88 +0,0 @@
-use std::fmt;
-
-use inkwell::{context::Context, types::FloatType, values::FloatValue};
-
-use crate::codegen::CodeGenerator;
-
-use super::*;
-
-pub trait FloatKind<'ctx>: fmt::Debug + Clone + Copy {
-    fn get_float_type<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &G,
-        ctx: &'ctx Context,
-    ) -> FloatType<'ctx>;
-}
-
-#[derive(Debug, Clone, Copy, Default)]
-pub struct Float32;
-#[derive(Debug, Clone, Copy, Default)]
-pub struct Float64;
-
-impl<'ctx> FloatKind<'ctx> for Float32 {
-    fn get_float_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _generator: &G,
-        ctx: &'ctx Context,
-    ) -> FloatType<'ctx> {
-        ctx.f32_type()
-    }
-}
-
-impl<'ctx> FloatKind<'ctx> for Float64 {
-    fn get_float_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _generator: &G,
-        ctx: &'ctx Context,
-    ) -> FloatType<'ctx> {
-        ctx.f64_type()
-    }
-}
-
-#[derive(Debug, Clone, Copy)]
-pub struct AnyFloat<'ctx>(FloatType<'ctx>);
-
-impl<'ctx> FloatKind<'ctx> for AnyFloat<'ctx> {
-    fn get_float_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _generator: &G,
-        _ctx: &'ctx Context,
-    ) -> FloatType<'ctx> {
-        self.0
-    }
-}
-
-#[derive(Debug, Clone, Copy, Default)]
-pub struct FloatModel<N>(pub N);
-pub type Float<'ctx, N> = Instance<'ctx, FloatModel<N>>;
-
-impl<'ctx, N: FloatKind<'ctx>> Model<'ctx> for FloatModel<N> {
-    type Value = FloatValue<'ctx>;
-    type Type = FloatType<'ctx>;
-
-    fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
-        self.0.get_float_type(generator, ctx)
-    }
-
-    fn check_type<T: inkwell::types::BasicType<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        ty: T,
-    ) -> Result<(), ModelError> {
-        let ty = ty.as_basic_type_enum();
-        let Ok(ty) = FloatType::try_from(ty) else {
-            return Err(ModelError(format!("Expecting FloatType, but got {ty:?}")));
-        };
-
-        let exp_ty = self.0.get_float_type(generator, ctx);
-
-        // TODO: Inkwell does not have get_bit_width for FloatType?
-        // TODO: Quick hack for now, but does this actually work?
-        if ty != exp_ty {
-            return Err(ModelError(format!("Expecting {exp_ty:?}, but got {ty:?}")));
-        }
-
-        Ok(())
-    }
-}

nac3core/src/codegen/model/function.rs

@@ -1,125 +0,0 @@
-use inkwell::{
-    attributes::{Attribute, AttributeLoc},
-    types::{BasicMetadataTypeEnum, BasicType, FunctionType},
-    values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
-};
-use itertools::Itertools;
-
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-use super::*;
-
-// When [`TypeContext::size_type`] is 32-bits, the function name is "{fn_name}".
-// When [`TypeContext::size_type`] is 64-bits, the function name is "{fn_name}64".
-#[must_use]
-pub fn get_sizet_dependent_function_name<G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &CodeGenContext<'_, '_>,
-    name: &str,
-) -> String {
-    let mut name = name.to_owned();
-    match generator.get_size_type(ctx.ctx).get_bit_width() {
-        32 => {}
-        64 => name.push_str("64"),
-        bit_width => {
-            panic!("Unsupported int type bit width {bit_width}, must be either 32-bits or 64-bits")
-        }
-    }
-    name
-}
-
-#[derive(Debug, Clone, Copy)]
-struct Arg<'ctx> {
-    ty: BasicMetadataTypeEnum<'ctx>,
-    val: BasicMetadataValueEnum<'ctx>,
-}
-
-/// A structure to construct & call an LLVM function.
-///
-/// This is a helper to reduce IRRT Inkwell function call boilerplate
-// TODO: Remove the lifetimes somehow? There is 4 of them.
-pub struct CallFunction<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> {
-    generator: &'d mut G,
-    ctx: &'b CodeGenContext<'ctx, 'a>,
-    /// Function name
-    name: &'c str,
-    /// Call arguments
-    args: Vec<Arg<'ctx>>,
-    /// LLVM function Attributes
-    attrs: Vec<&'static str>,
-}
-
-impl<'ctx, 'a, 'b, 'c, 'd, G: CodeGenerator + ?Sized> CallFunction<'ctx, 'a, 'b, 'c, 'd, G> {
-    pub fn begin(generator: &'d mut G, ctx: &'b CodeGenContext<'ctx, 'a>, name: &'c str) -> Self {
-        CallFunction { generator, ctx, name, args: Vec::new(), attrs: Vec::new() }
-    }
-
-    /// Push a list of LLVM function attributes to the function declaration.
-    #[must_use]
-    pub fn attrs(mut self, attrs: Vec<&'static str>) -> Self {
-        self.attrs = attrs;
-        self
-    }
-
-    /// Push a call argument to the function call.
-    #[allow(clippy::needless_pass_by_value)]
-    #[must_use]
-    pub fn arg<M: Model<'ctx>>(mut self, arg: Instance<'ctx, M>) -> Self {
-        let arg = Arg {
-            ty: arg.model.get_type(self.generator, self.ctx.ctx).as_basic_type_enum().into(),
-            val: arg.value.as_basic_value_enum().into(),
-        };
-        self.args.push(arg);
-        self
-    }
-
-    /// Call the function and expect the function to return a value of type of `return_model`.
-    #[must_use]
-    pub fn returning<M: Model<'ctx>>(self, name: &str, return_model: M) -> Instance<'ctx, M> {
-        let ret_ty = return_model.get_type(self.generator, self.ctx.ctx);
-
-        let ret = self.get_function(|tys| ret_ty.fn_type(tys, false), name);
-        let ret = BasicValueEnum::try_from(ret.as_any_value_enum()).unwrap(); // Must work
-        let ret = return_model.check_value(self.generator, self.ctx.ctx, ret).unwrap(); // Must work
-        ret
-    }
-
-    /// Like [`CallFunction::returning_`] but `return_model` is automatically inferred.
-    #[must_use]
-    pub fn returning_auto<M: Model<'ctx> + Default>(self, name: &str) -> Instance<'ctx, M> {
-        self.returning(name, M::default())
-    }
-
-    /// Call the function and expect the function to return a void-type.
-    pub fn returning_void(self) {
-        let ret_ty = self.ctx.ctx.void_type();
-
-        let _ = self.get_function(|tys| ret_ty.fn_type(tys, false), "");
-    }
-
-    fn get_function<F>(&self, make_fn_type: F, return_value_name: &str) -> CallSiteValue<'ctx>
-    where
-        F: FnOnce(&[BasicMetadataTypeEnum<'ctx>]) -> FunctionType<'ctx>,
-    {
-        // Get the LLVM function.
-        let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
-            // Declare the function if it doesn't exist.
-            let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
-
-            let func_type = make_fn_type(&tys);
-            let func = self.ctx.module.add_function(self.name, func_type, None);
-
-            for attr in &self.attrs {
-                func.add_attribute(
-                    AttributeLoc::Function,
-                    self.ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
-                );
-            }
-
-            func
-        });
-
-        let vals = self.args.iter().map(|arg| arg.val).collect_vec();
-        self.ctx.builder.build_call(func, &vals, return_value_name).unwrap()
-    }
-}

nac3core/src/codegen/model/int.rs

@@ -1,275 +0,0 @@
-use std::fmt;
-
-use inkwell::{context::Context, types::IntType, values::IntValue, IntPredicate};
-
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-use super::*;
-
-pub trait IntKind<'ctx>: fmt::Debug + Clone + Copy {
-    fn get_int_type<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &G,
-        ctx: &'ctx Context,
-    ) -> IntType<'ctx>;
-}
-
-#[derive(Debug, Clone, Copy, Default)]
-pub struct Bool;
-#[derive(Debug, Clone, Copy, Default)]
-pub struct Byte;
-#[derive(Debug, Clone, Copy, Default)]
-pub struct Int32;
-#[derive(Debug, Clone, Copy, Default)]
-pub struct Int64;
-#[derive(Debug, Clone, Copy, Default)]
-pub struct SizeT;
-
-impl<'ctx> IntKind<'ctx> for Bool {
-    fn get_int_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _generator: &G,
-        ctx: &'ctx Context,
-    ) -> IntType<'ctx> {
-        ctx.bool_type()
-    }
-}
-
-impl<'ctx> IntKind<'ctx> for Byte {
-    fn get_int_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _generator: &G,
-        ctx: &'ctx Context,
-    ) -> IntType<'ctx> {
-        ctx.i8_type()
-    }
-}
-
-impl<'ctx> IntKind<'ctx> for Int32 {
-    fn get_int_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _generator: &G,
-        ctx: &'ctx Context,
-    ) -> IntType<'ctx> {
-        ctx.i32_type()
-    }
-}
-
-impl<'ctx> IntKind<'ctx> for Int64 {
-    fn get_int_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _generator: &G,
-        ctx: &'ctx Context,
-    ) -> IntType<'ctx> {
-        ctx.i64_type()
-    }
-}
-
-impl<'ctx> IntKind<'ctx> for SizeT {
-    fn get_int_type<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &G,
-        ctx: &'ctx Context,
-    ) -> IntType<'ctx> {
-        generator.get_size_type(ctx)
-    }
-}
-
-#[derive(Debug, Clone, Copy)]
-pub struct AnyInt<'ctx>(pub IntType<'ctx>);
-
-impl<'ctx> IntKind<'ctx> for AnyInt<'ctx> {
-    fn get_int_type<G: CodeGenerator + ?Sized>(
-        &self,
-        _generator: &G,
-        _ctx: &'ctx Context,
-    ) -> IntType<'ctx> {
-        self.0
-    }
-}
-
-#[derive(Debug, Clone, Copy, Default)]
-pub struct IntModel<N>(pub N);
-pub type Int<'ctx, N> = Instance<'ctx, IntModel<N>>;
-
-impl<'ctx, N: IntKind<'ctx>> Model<'ctx> for IntModel<N> {
-    type Value = IntValue<'ctx>;
-    type Type = IntType<'ctx>;
-
-    fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
-        self.0.get_int_type(generator, ctx)
-    }
-
-    fn check_type<T: inkwell::types::BasicType<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        ty: T,
-    ) -> Result<(), ModelError> {
-        let ty = ty.as_basic_type_enum();
-        let Ok(ty) = IntType::try_from(ty) else {
-            return Err(ModelError(format!("Expecting IntType, but got {ty:?}")));
-        };
-
-        let exp_ty = self.0.get_int_type(generator, ctx);
-        if ty.get_bit_width() != exp_ty.get_bit_width() {
-            return Err(ModelError(format!(
-                "Expecting IntType to have {} bit(s), but got {} bit(s)",
-                exp_ty.get_bit_width(),
-                ty.get_bit_width()
-            )));
-        }
-
-        Ok(())
-    }
-}
-
-impl<'ctx, N: IntKind<'ctx>> IntModel<N> {
-    pub fn constant<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        value: u64,
-    ) -> Int<'ctx, N> {
-        let value = self.get_type(generator, ctx).const_int(value, false);
-        self.believe_value(value)
-    }
-
-    pub fn const_0<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-    ) -> Int<'ctx, N> {
-        self.constant(generator, ctx, 0)
-    }
-
-    pub fn const_1<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-    ) -> Int<'ctx, N> {
-        self.constant(generator, ctx, 1)
-    }
-
-    pub fn const_all_1s<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-    ) -> Int<'ctx, N> {
-        let value = self.get_type(generator, ctx).const_all_ones();
-        self.believe_value(value)
-    }
-
-    pub fn s_extend_or_bit_cast<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        value: IntValue<'ctx>,
-        name: &str,
-    ) -> Int<'ctx, N> {
-        let value = ctx
-            .builder
-            .build_int_s_extend_or_bit_cast(value, self.get_type(generator, ctx.ctx), name)
-            .unwrap();
-        self.believe_value(value)
-    }
-
-    pub fn truncate<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        value: IntValue<'ctx>,
-        name: &str,
-    ) -> Int<'ctx, N> {
-        let value =
-            ctx.builder.build_int_truncate(value, self.get_type(generator, ctx.ctx), name).unwrap();
-        self.believe_value(value)
-    }
-}
-
-impl IntModel<Bool> {
-    #[must_use]
-    pub fn const_false<'ctx, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-    ) -> Int<'ctx, Bool> {
-        self.constant(generator, ctx, 0)
-    }
-
-    #[must_use]
-    pub fn const_true<'ctx, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-    ) -> Int<'ctx, Bool> {
-        self.constant(generator, ctx, 1)
-    }
-}
-
-impl<'ctx, N: IntKind<'ctx>> Int<'ctx, N> {
-    pub fn s_extend_or_bit_cast<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        to_int_kind: NewN,
-        name: &str,
-    ) -> Int<'ctx, NewN> {
-        IntModel(to_int_kind).s_extend_or_bit_cast(generator, ctx, self.value, name)
-    }
-
-    pub fn truncate<NewN: IntKind<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        to_int_kind: NewN,
-        name: &str,
-    ) -> Int<'ctx, NewN> {
-        IntModel(to_int_kind).truncate(generator, ctx, self.value, name)
-    }
-
-    #[must_use]
-    pub fn add(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        other: Int<'ctx, N>,
-        name: &str,
-    ) -> Int<'ctx, N> {
-        let value = ctx.builder.build_int_add(self.value, other.value, name).unwrap();
-        self.model.believe_value(value)
-    }
-
-    #[must_use]
-    pub fn sub(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        other: Int<'ctx, N>,
-        name: &str,
-    ) -> Int<'ctx, N> {
-        let value = ctx.builder.build_int_sub(self.value, other.value, name).unwrap();
-        self.model.believe_value(value)
-    }
-
-    #[must_use]
-    pub fn mul(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        other: Int<'ctx, N>,
-        name: &str,
-    ) -> Int<'ctx, N> {
-        let value = ctx.builder.build_int_mul(self.value, other.value, name).unwrap();
-        self.model.believe_value(value)
-    }
-
-    pub fn compare(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        op: IntPredicate,
-        other: Int<'ctx, N>,
-        name: &str,
-    ) -> Int<'ctx, Bool> {
-        let bool_model = IntModel(Bool);
-        let value = ctx.builder.build_int_compare(op, self.value, other.value, name).unwrap();
-        bool_model.believe_value(value)
-    }
-}

nac3core/src/codegen/model/mod.rs

@@ -1,17 +0,0 @@
-mod any;
-mod array;
-mod core;
-mod float;
-pub mod function;
-mod int;
-mod ptr;
-mod structure;
-pub mod util;
-
-pub use any::*;
-pub use array::*;
-pub use core::*;
-pub use float::*;
-pub use int::*;
-pub use ptr::*;
-pub use structure::*;

nac3core/src/codegen/model/ptr.rs

@@ -1,145 +0,0 @@
-use inkwell::{
-    context::Context,
-    types::{BasicType, BasicTypeEnum, PointerType},
-    values::{IntValue, PointerValue},
-    AddressSpace,
-};
-
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-use super::*;
-
-#[derive(Debug, Clone, Copy, Default)]
-pub struct PtrModel<Element>(pub Element);
-pub type Ptr<'ctx, Element> = Instance<'ctx, PtrModel<Element>>;
-
-impl<'ctx, Element: Model<'ctx>> Model<'ctx> for PtrModel<Element> {
-    type Value = PointerValue<'ctx>;
-    type Type = PointerType<'ctx>;
-
-    fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
-        self.0.get_type(generator, ctx).ptr_type(AddressSpace::default())
-    }
-
-    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        ty: T,
-    ) -> Result<(), ModelError> {
-        let ty = ty.as_basic_type_enum();
-        let Ok(ty) = PointerType::try_from(ty) else {
-            return Err(ModelError(format!("Expecting PointerType, but got {ty:?}")));
-        };
-
-        let elem_ty = ty.get_element_type();
-        let Ok(elem_ty) = BasicTypeEnum::try_from(elem_ty) else {
-            return Err(ModelError(format!(
-                "Expecting pointer element type to be a BasicTypeEnum, but got {elem_ty:?}"
-            )));
-        };
-
-        // TODO: inkwell `get_element_type()` will be deprecated.
-        // Remove the check for `get_element_type()` when the time comes.
-        self.0
-            .check_type(generator, ctx, elem_ty)
-            .map_err(|err| err.under_context("a PointerType"))?;
-
-        Ok(())
-    }
-}
-
-impl<'ctx, Element: Model<'ctx>> PtrModel<Element> {
-    /// Return a ***constant*** nullptr.
-    pub fn nullptr<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-    ) -> Ptr<'ctx, Element> {
-        let ptr = self.get_type(generator, ctx).const_null();
-        self.believe_value(ptr)
-    }
-
-    /// Cast a pointer into this model with [`inkwell::builder::Builder::build_pointer_cast`]
-    pub fn pointer_cast<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        ptr: PointerValue<'ctx>,
-        name: &str,
-    ) -> Ptr<'ctx, Element> {
-        let ptr =
-            ctx.builder.build_pointer_cast(ptr, self.get_type(generator, ctx.ctx), name).unwrap();
-        self.believe_value(ptr)
-    }
-}
-
-impl<'ctx, Element: Model<'ctx>> Ptr<'ctx, Element> {
-    /// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`].
-    #[must_use]
-    pub fn offset<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        offset: IntValue<'ctx>,
-        name: &str,
-    ) -> Ptr<'ctx, Element> {
-        let new_ptr =
-            unsafe { ctx.builder.build_in_bounds_gep(self.value, &[offset], name).unwrap() };
-        self.model.check_value(generator, ctx.ctx, new_ptr).unwrap()
-    }
-
-    /// Offset the pointer by [`inkwell::builder::Builder::build_in_bounds_gep`] by a constant offset.
-    #[must_use]
-    pub fn offset_const<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        offset: u64,
-        name: &str,
-    ) -> Ptr<'ctx, Element> {
-        let offset = ctx.ctx.i32_type().const_int(offset, false);
-        self.offset(generator, ctx, offset, name)
-    }
-
-    /// Load the value with [`inkwell::builder::Builder::build_load`].
-    pub fn load<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        name: &str,
-    ) -> Instance<'ctx, Element> {
-        let value = ctx.builder.build_load(self.value, name).unwrap();
-        self.model.0.check_value(generator, ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error.
-    }
-
-    /// Store a value with [`inkwell::builder::Builder::build_store`].
-    pub fn store(&self, ctx: &CodeGenContext<'ctx, '_>, value: Instance<'ctx, Element>) {
-        ctx.builder.build_store(self.value, value.value).unwrap();
-    }
-
-    /// Return a casted pointer of element type `NewElement` with [`inkwell::builder::Builder::build_pointer_cast`].
-    pub fn pointer_cast<NewElement: Model<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        new_model: NewElement,
-        name: &str,
-    ) -> Ptr<'ctx, NewElement> {
-        PtrModel(new_model).pointer_cast(generator, ctx, self.value, name)
-    }
-
-    /// Check if the pointer is null with [`inkwell::builder::Builder::build_is_null`].
-    pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> Int<'ctx, Bool> {
-        let bool_model = IntModel(Bool);
-        let value = ctx.builder.build_is_null(self.value, name).unwrap();
-        bool_model.believe_value(value)
-    }
-
-    /// Check if the pointer is not null with [`inkwell::builder::Builder::build_is_not_null`].
-    pub fn is_not_null(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> Int<'ctx, Bool> {
-        let bool_model = IntModel(Bool);
-        let value = ctx.builder.build_is_not_null(self.value, name).unwrap();
-        bool_model.believe_value(value)
-    }
-}

nac3core/src/codegen/model/structure.rs

@@ -1,222 +0,0 @@
-use std::fmt;
-
-use inkwell::{
-    context::Context,
-    types::{BasicType, BasicTypeEnum, StructType},
-    values::StructValue,
-};
-
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-use super::*;
-
-#[derive(Debug, Clone, Copy)]
-pub struct GepField<M> {
-    pub gep_index: u64,
-    pub name: &'static str,
-    pub model: M,
-}
-
-pub trait FieldTraversal<'ctx> {
-    type Out<M>;
-
-    fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M>;
-
-    /// Like [`FieldTraversal::visit`] but [`Model`] is automatically inferred from [`Default`] trait.
-    fn add_auto<M: Model<'ctx> + Default>(&mut self, name: &'static str) -> Self::Out<M> {
-        self.add(name, M::default())
-    }
-}
-
-pub struct GepFieldTraversal {
-    gep_index_counter: u64,
-}
-
-impl<'ctx> FieldTraversal<'ctx> for GepFieldTraversal {
-    type Out<M> = GepField<M>;
-
-    fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
-        let gep_index = self.gep_index_counter;
-        self.gep_index_counter += 1;
-        Self::Out { gep_index, name, model }
-    }
-}
-
-struct TypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
-    generator: &'a G,
-    ctx: &'ctx Context,
-    field_types: Vec<BasicTypeEnum<'ctx>>,
-}
-
-impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx> for TypeFieldTraversal<'ctx, 'a, G> {
-    type Out<M> = ();
-
-    fn add<M: Model<'ctx>>(&mut self, _name: &'static str, model: M) -> Self::Out<M> {
-        let t = model.get_type(self.generator, self.ctx).as_basic_type_enum();
-        self.field_types.push(t);
-    }
-}
-
-struct CheckTypeFieldTraversal<'ctx, 'a, G: CodeGenerator + ?Sized> {
-    generator: &'a mut G,
-    ctx: &'ctx Context,
-    index: u32,
-    scrutinee: StructType<'ctx>,
-    errors: Vec<ModelError>,
-}
-
-impl<'ctx, 'a, G: CodeGenerator + ?Sized> FieldTraversal<'ctx>
-    for CheckTypeFieldTraversal<'ctx, 'a, G>
-{
-    type Out<M> = ();
-
-    fn add<M: Model<'ctx>>(&mut self, name: &'static str, model: M) -> Self::Out<M> {
-        let i = self.index;
-        self.index += 1;
-
-        if let Some(t) = self.scrutinee.get_field_type_at_index(i) {
-            if let Err(err) = model.check_type(self.generator, self.ctx, t) {
-                self.errors.push(err.under_context(format!("field #{i} '{name}'").as_str()));
-            }
-        } // Otherwise, it will be caught
-    }
-}
-
-pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy {
-    type Fields<F: FieldTraversal<'ctx>>;
-
-    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F>;
-
-    fn fields(&self) -> Self::Fields<GepFieldTraversal> {
-        self.traverse_fields(&mut GepFieldTraversal { gep_index_counter: 0 })
-    }
-
-    fn get_struct_type<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &G,
-        ctx: &'ctx Context,
-    ) -> StructType<'ctx> {
-        let mut traversal = TypeFieldTraversal { generator, ctx, field_types: Vec::new() };
-        self.traverse_fields(&mut traversal);
-
-        ctx.struct_type(&traversal.field_types, false)
-    }
-}
-
-#[derive(Debug, Clone, Copy, Default)]
-pub struct StructModel<S>(pub S);
-pub type Struct<'ctx, S> = Instance<'ctx, StructModel<S>>;
-
-impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for StructModel<S> {
-    type Value = StructValue<'ctx>;
-    type Type = StructType<'ctx>;
-
-    fn get_type<G: CodeGenerator + ?Sized>(&self, generator: &G, ctx: &'ctx Context) -> Self::Type {
-        self.0.get_struct_type(generator, ctx)
-    }
-
-    fn check_type<T: BasicType<'ctx>, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        ty: T,
-    ) -> Result<(), ModelError> {
-        let ty = ty.as_basic_type_enum();
-        let Ok(ty) = StructType::try_from(ty) else {
-            return Err(ModelError(format!("Expecting StructType, but got {ty:?}")));
-        };
-
-        let mut traversal =
-            CheckTypeFieldTraversal { generator, ctx, index: 0, errors: Vec::new(), scrutinee: ty };
-        self.0.traverse_fields(&mut traversal);
-
-        let exp_num_fields = traversal.index;
-        let got_num_fields = u32::try_from(ty.get_field_types().len()).unwrap();
-        if exp_num_fields != got_num_fields {
-            return Err(ModelError(format!(
-                "Expecting StructType with {exp_num_fields} field(s), but got {got_num_fields}"
-            )));
-        }
-
-        if !traversal.errors.is_empty() {
-            return Err(traversal.errors[0].clone()); // TODO: Return other errors as well
-        }
-
-        Ok(())
-    }
-}
-
-impl<'ctx, S: StructKind<'ctx>> Struct<'ctx, S> {
-    pub fn get_field<G: CodeGenerator + ?Sized, M, GetField>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        get_field: GetField,
-    ) -> Instance<'ctx, M>
-    where
-        M: Model<'ctx>,
-        GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
-    {
-        let field = get_field(self.model.0.fields());
-        let val = self.value.get_field_at_index(field.gep_index as u32).unwrap();
-        field.model.check_value(generator, ctx, val).unwrap()
-    }
-}
-
-impl<'ctx, S: StructKind<'ctx>> Ptr<'ctx, StructModel<S>> {
-    pub fn gep<M, GetField>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        get_field: GetField,
-    ) -> Ptr<'ctx, M>
-    where
-        M: Model<'ctx>,
-        GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
-    {
-        let field = get_field(self.model.0 .0.fields());
-        let llvm_i32 = ctx.ctx.i32_type(); // i64 would segfault
-
-        let ptr = unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.value,
-                    &[llvm_i32.const_zero(), llvm_i32.const_int(field.gep_index, false)],
-                    field.name,
-                )
-                .unwrap()
-        };
-
-        let ptr_model = PtrModel(field.model);
-        ptr_model.believe_value(ptr)
-    }
-
-    /// Convenience function equivalent to `.gep(...).load(...)`.
-    pub fn get<M, GetField, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        get_field: GetField,
-        name: &str,
-    ) -> Instance<'ctx, M>
-    where
-        M: Model<'ctx>,
-        GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
-    {
-        self.gep(ctx, get_field).load(generator, ctx, name)
-    }
-
-    /// Convenience function equivalent to `.gep(...).store(...)`.
-    pub fn set<M, GetField>(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        get_field: GetField,
-        value: Instance<'ctx, M>,
-    ) where
-        M: Model<'ctx>,
-        GetField: FnOnce(S::Fields<GepFieldTraversal>) -> GepField<M>,
-    {
-        self.gep(ctx, get_field).store(ctx, value);
-    }
-}
-
-// TODO: Add an opaque struct type?

nac3core/src/codegen/model/util.rs

@@ -1,62 +0,0 @@
-use inkwell::{types::BasicType, values::IntValue};
-
-/// `llvm.memcpy` but under the [`Model`] abstraction
-use crate::codegen::{
-    llvm_intrinsics::call_memcpy_generic,
-    stmt::{gen_for_callback_incrementing, BreakContinueHooks},
-    CodeGenContext, CodeGenerator,
-};
-
-use super::*;
-
-/// Convenience function.
-///
-/// Like [`call_memcpy_generic`] but with model abstractions and `is_volatile` set to `false`.
-pub fn call_memcpy_model<'ctx, Item: Model<'ctx> + Default, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &CodeGenContext<'ctx, '_>,
-    dst_array: Ptr<'ctx, Item>,
-    src_array: Ptr<'ctx, Item>,
-    num_items: IntValue<'ctx>,
-) {
-    let itemsize = Item::default().get_type(generator, ctx.ctx).size_of().unwrap();
-    let totalsize = ctx.builder.build_int_mul(itemsize, num_items, "totalsize").unwrap(); // TODO: Int types may not match.
-    let is_volatile = ctx.ctx.bool_type().const_zero();
-    call_memcpy_generic(ctx, dst_array.value, src_array.value, totalsize, is_volatile);
-}
-
-/// Like [`gen_for_callback_incrementing`] with [`Model`] abstractions.
-/// The [`IntKind`] is automatically inferred.
-pub fn gen_for_model_auto<'ctx, 'a, G, F, I>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, 'a>,
-    start: Int<'ctx, I>,
-    stop: Int<'ctx, I>,
-    step: Int<'ctx, I>,
-    body: F,
-) -> Result<(), String>
-where
-    G: CodeGenerator + ?Sized,
-    F: FnOnce(
-        &mut G,
-        &mut CodeGenContext<'ctx, 'a>,
-        BreakContinueHooks<'ctx>,
-        Int<'ctx, I>,
-    ) -> Result<(), String>,
-    I: IntKind<'ctx> + Default,
-{
-    let int_model = IntModel(I::default());
-
-    gen_for_callback_incrementing(
-        generator,
-        ctx,
-        None,
-        start.value,
-        (stop.value, false),
-        |g, ctx, hooks, i| {
-            let i = int_model.believe_value(i);
-            body(g, ctx, hooks, i)
-        },
-        step.value,
-    )
-}

nac3core/src/codegen/numpy.rs

@@ -1,23 +1,32 @@
-use crate::{
-    codegen::{
-        classes::{
-            ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayType, NDArrayValue,
-            ProxyType, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
-            TypedArrayLikeMutator, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
-        },
+use inkwell::{
+    types::{AnyTypeEnum, BasicType, BasicTypeEnum, PointerType},
+    values::{BasicValue, BasicValueEnum, IntValue, PointerValue},
+    AddressSpace, IntPredicate, OptimizationLevel,
+};
+
+use nac3parser::ast::{Operator, StrRef};
+
+use super::{
     expr::gen_binop_expr_with_values,
     irrt::{
         calculate_len_for_slice_range, call_ndarray_calc_broadcast,
-            call_ndarray_calc_broadcast_index, call_ndarray_calc_nd_indices,
-            call_ndarray_calc_size,
+        call_ndarray_calc_broadcast_index, call_ndarray_calc_nd_indices, call_ndarray_calc_size,
     },
     llvm_intrinsics::{self, call_memcpy_generic},
+    macros::codegen_unreachable,
     stmt::{gen_for_callback_incrementing, gen_for_range_callback, gen_if_else_expr_callback},
-        CodeGenContext, CodeGenerator,
+    types::{ListType, NDArrayType, ProxyType},
+    values::{
+        ArrayLikeIndexer, ArrayLikeValue, ListValue, NDArrayValue, ProxyValue,
+        TypedArrayLikeAccessor, TypedArrayLikeAdapter, TypedArrayLikeMutator,
+        UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
     },
+    CodeGenContext, CodeGenerator,
+};
+use crate::{
     symbol_resolver::ValueEnum,
     toplevel::{
-        helper::PrimDef,
+        helper::{arraylike_flatten_element_type, PrimDef},
         numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         DefinitionId,
     },
@@ -26,16 +35,6 @@ use crate::{
         typedef::{FunSignature, Type, TypeEnum},
     },
 };
-use inkwell::{
-    types::BasicType,
-    values::{BasicValueEnum, IntValue, PointerValue},
-    AddressSpace, IntPredicate, OptimizationLevel,
-};
-use inkwell::{
-    types::{AnyTypeEnum, BasicTypeEnum, PointerType},
-    values::BasicValue,
-};
-use nac3parser::ast::{Operator, StrRef};
 
 /// Creates an uninitialized `NDArray` instance.
 fn create_ndarray_uninitialized<'ctx, G: CodeGenerator + ?Sized>(
@@ -43,6 +42,7 @@ fn create_ndarray_uninitialized<'ctx, G: CodeGenerator + ?Sized>(
     ctx: &mut CodeGenContext<'ctx, '_>,
     elem_ty: Type,
 ) -> Result<NDArrayValue<'ctx>, String> {
+    let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
     let ndarray_ty = make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(elem_ty), None);
 
     let llvm_usize = generator.get_size_type(ctx.ctx);
@@ -55,7 +55,7 @@ fn create_ndarray_uninitialized<'ctx, G: CodeGenerator + ?Sized>(
 
     let ndarray = generator.gen_var_alloc(ctx, llvm_ndarray_t.into(), None)?;
 
-    Ok(NDArrayValue::from_ptr_val(ndarray, llvm_usize, None))
+    Ok(NDArrayValue::from_pointer_value(ndarray, llvm_elem_ty, llvm_usize, None))
 }
 
 /// Creates an `NDArray` instance from a dynamic shape.
@@ -128,7 +128,7 @@ where
     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);
+    ndarray.create_shape(ctx, llvm_usize, ndarray_num_dims);
 
     // Copy the dimension sizes from shape to ndarray.dims
     let shape_len = shape_len_fn(generator, ctx, shape)?;
@@ -144,7 +144,7 @@ where
             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) };
+                unsafe { ndarray.shape().ptr_offset_unchecked(ctx, generator, &i, None) };
 
             ctx.builder.build_store(ndarray_pdim, shape_dim).unwrap();
 
@@ -195,12 +195,12 @@ pub fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
     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);
+    ndarray.create_shape(ctx, llvm_usize, ndarray_num_dims);
 
     for (i, &shape_dim) in shape.iter().enumerate() {
         let shape_dim = ctx.builder.build_int_z_extend(shape_dim, llvm_usize, "").unwrap();
         let ndarray_dim = unsafe {
-            ndarray.dim_sizes().ptr_offset_unchecked(
+            ndarray.shape().ptr_offset_unchecked(
                 ctx,
                 generator,
                 &llvm_usize.const_int(i as u64, true),
@@ -229,7 +229,7 @@ fn ndarray_init_data<'ctx, G: CodeGenerator + ?Sized>(
     let ndarray_num_elems = call_ndarray_calc_size(
         generator,
         ctx,
-        &ndarray.dim_sizes().as_slice_value(ctx, generator),
+        &ndarray.shape().as_slice_value(ctx, generator),
         (None, None),
     );
     ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
@@ -257,9 +257,9 @@ fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
     } 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, "").value.into()
+        ctx.gen_string(generator, "").into()
     } else {
-        unreachable!()
+        codegen_unreachable!(ctx)
     }
 }
 
@@ -285,9 +285,9 @@ fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
     } 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").value.into()
+        ctx.gen_string(generator, "1").into()
     } else {
-        unreachable!()
+        codegen_unreachable!(ctx)
     }
 }
 
@@ -315,11 +315,11 @@ fn call_ndarray_empty_impl<'ctx, G: CodeGenerator + ?Sized>(
 
     match shape {
         BasicValueEnum::PointerValue(shape_list_ptr)
-            if ListValue::is_instance(shape_list_ptr, llvm_usize).is_ok() =>
+            if ListValue::is_representable(shape_list_ptr, llvm_usize).is_ok() =>
         {
             // 1. A list of ints; e.g., `np.empty([600, 800, 3])`
 
-            let shape_list = ListValue::from_ptr_val(shape_list_ptr, llvm_usize, None);
+            let shape_list = ListValue::from_pointer_value(shape_list_ptr, llvm_usize, None);
             create_ndarray_dyn_shape(
                 generator,
                 ctx,
@@ -355,7 +355,7 @@ fn call_ndarray_empty_impl<'ctx, G: CodeGenerator + ?Sized>(
 
             create_ndarray_const_shape(generator, ctx, elem_ty, &[shape_int])
         }
-        _ => unreachable!(),
+        _ => codegen_unreachable!(ctx),
     }
 }
 
@@ -380,7 +380,7 @@ where
     let ndarray_num_elems = call_ndarray_calc_size(
         generator,
         ctx,
-        &ndarray.dim_sizes().as_slice_value(ctx, generator),
+        &ndarray.shape().as_slice_value(ctx, generator),
         (None, None),
     );
 
@@ -474,8 +474,8 @@ fn ndarray_broadcast_fill<'ctx, 'a, G, ValueFn>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, 'a>,
     res: NDArrayValue<'ctx>,
-    lhs: (BasicValueEnum<'ctx>, bool),
-    rhs: (BasicValueEnum<'ctx>, bool),
+    lhs: (Type, BasicValueEnum<'ctx>, bool),
+    rhs: (Type, BasicValueEnum<'ctx>, bool),
     value_fn: ValueFn,
 ) -> Result<NDArrayValue<'ctx>, String>
 where
@@ -488,8 +488,8 @@ where
 {
     let llvm_usize = generator.get_size_type(ctx.ctx);
 
-    let (lhs_val, lhs_scalar) = lhs;
-    let (rhs_val, rhs_scalar) = rhs;
+    let (lhs_ty, lhs_val, lhs_scalar) = lhs;
+    let (rhs_ty, rhs_val, rhs_scalar) = rhs;
 
     assert!(
         !(lhs_scalar && rhs_scalar),
@@ -500,12 +500,26 @@ where
 
     // Assert that all ndarray operands are broadcastable to the target size
     if !lhs_scalar {
-        let lhs_val = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
+        let lhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, lhs_ty);
+        let llvm_lhs_elem_ty = ctx.get_llvm_type(generator, lhs_dtype);
+        let lhs_val = NDArrayValue::from_pointer_value(
+            lhs_val.into_pointer_value(),
+            llvm_lhs_elem_ty,
+            llvm_usize,
+            None,
+        );
         ndarray_assert_is_broadcastable(generator, ctx, res, lhs_val);
     }
 
     if !rhs_scalar {
-        let rhs_val = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
+        let rhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, rhs_ty);
+        let llvm_rhs_elem_ty = ctx.get_llvm_type(generator, rhs_dtype);
+        let rhs_val = NDArrayValue::from_pointer_value(
+            rhs_val.into_pointer_value(),
+            llvm_rhs_elem_ty,
+            llvm_usize,
+            None,
+        );
         ndarray_assert_is_broadcastable(generator, ctx, res, rhs_val);
     }
 
@@ -513,7 +527,14 @@ where
         let lhs_elem = if lhs_scalar {
             lhs_val
         } else {
-            let lhs = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
+            let lhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, lhs_ty);
+            let llvm_lhs_elem_ty = ctx.get_llvm_type(generator, lhs_dtype);
+            let lhs = NDArrayValue::from_pointer_value(
+                lhs_val.into_pointer_value(),
+                llvm_lhs_elem_ty,
+                llvm_usize,
+                None,
+            );
             let lhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, lhs, idx);
 
             unsafe { lhs.data().get_unchecked(ctx, generator, &lhs_idx, None) }
@@ -522,7 +543,14 @@ where
         let rhs_elem = if rhs_scalar {
             rhs_val
         } else {
-            let rhs = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
+            let rhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, rhs_ty);
+            let llvm_rhs_elem_ty = ctx.get_llvm_type(generator, rhs_dtype);
+            let rhs = NDArrayValue::from_pointer_value(
+                rhs_val.into_pointer_value(),
+                llvm_rhs_elem_ty,
+                llvm_usize,
+                None,
+            );
             let rhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, rhs, idx);
 
             unsafe { rhs.data().get_unchecked(ctx, generator, &rhs_idx, None) }
@@ -626,7 +654,7 @@ fn call_ndarray_full_impl<'ctx, G: CodeGenerator + ?Sized>(
         } else if fill_value.is_int_value() || fill_value.is_float_value() {
             fill_value
         } else {
-            unreachable!()
+            codegen_unreachable!(ctx)
         };
 
         Ok(value)
@@ -648,11 +676,15 @@ fn llvm_ndlist_get_ndims<'ctx, G: CodeGenerator + ?Sized>(
 
     let ndims = llvm_usize.const_int(1, false);
     match list_elem_ty {
-        AnyTypeEnum::PointerType(ptr_ty) if ListType::is_type(ptr_ty, llvm_usize).is_ok() => {
+        AnyTypeEnum::PointerType(ptr_ty)
+            if ListType::is_representable(ptr_ty, llvm_usize).is_ok() =>
+        {
             ndims.const_add(llvm_ndlist_get_ndims(generator, ctx, ptr_ty))
         }
 
-        AnyTypeEnum::PointerType(ptr_ty) if NDArrayType::is_type(ptr_ty, llvm_usize).is_ok() => {
+        AnyTypeEnum::PointerType(ptr_ty)
+            if NDArrayType::is_representable(ptr_ty, llvm_usize).is_ok() =>
+        {
             todo!("Getting ndims for list[ndarray] not supported")
         }
 
@@ -664,16 +696,20 @@ fn llvm_ndlist_get_ndims<'ctx, G: CodeGenerator + ?Sized>(
 fn llvm_arraylike_get_ndims<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
-    value: BasicValueEnum<'ctx>,
+    (ty, value): (Type, BasicValueEnum<'ctx>),
 ) -> IntValue<'ctx> {
     let llvm_usize = generator.get_size_type(ctx.ctx);
 
     match value {
-        BasicValueEnum::PointerValue(v) if NDArrayValue::is_instance(v, llvm_usize).is_ok() => {
-            NDArrayValue::from_ptr_val(v, llvm_usize, None).load_ndims(ctx)
+        BasicValueEnum::PointerValue(v)
+            if NDArrayValue::is_representable(v, llvm_usize).is_ok() =>
+        {
+            let dtype = arraylike_flatten_element_type(&mut ctx.unifier, ty);
+            let llvm_elem_ty = ctx.get_llvm_type(generator, dtype);
+            NDArrayValue::from_pointer_value(v, llvm_elem_ty, llvm_usize, None).load_ndims(ctx)
         }
 
-        BasicValueEnum::PointerValue(v) if ListValue::is_instance(v, llvm_usize).is_ok() => {
+        BasicValueEnum::PointerValue(v) if ListValue::is_representable(v, llvm_usize).is_ok() => {
             llvm_ndlist_get_ndims(generator, ctx, v.get_type())
         }
 
@@ -685,7 +721,6 @@ fn llvm_arraylike_get_ndims<'ctx, G: CodeGenerator + ?Sized>(
 fn ndarray_from_ndlist_impl<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
     (dst_arr, dst_slice_ptr): (NDArrayValue<'ctx>, PointerValue<'ctx>),
     src_lst: ListValue<'ctx>,
     dim: u64,
@@ -696,13 +731,15 @@ fn ndarray_from_ndlist_impl<'ctx, G: CodeGenerator + ?Sized>(
     let list_elem_ty = src_lst.get_type().element_type();
 
     match list_elem_ty {
-        AnyTypeEnum::PointerType(ptr_ty) if ListType::is_type(ptr_ty, llvm_usize).is_ok() => {
+        AnyTypeEnum::PointerType(ptr_ty)
+            if ListType::is_representable(ptr_ty, llvm_usize).is_ok() =>
+        {
             // The stride of elements in this dimension, i.e. the number of elements between arr[i]
             // and arr[i + 1] in this dimension
             let stride = call_ndarray_calc_size(
                 generator,
                 ctx,
-                &dst_arr.dim_sizes(),
+                &dst_arr.shape(),
                 (Some(llvm_usize.const_int(dim + 1, false)), None),
             );
 
@@ -716,11 +753,25 @@ fn ndarray_from_ndlist_impl<'ctx, G: CodeGenerator + ?Sized>(
                 |_, _| Ok(llvm_usize.const_int(1, false)),
                 |generator, ctx, _, i| {
                     let offset = ctx.builder.build_int_mul(stride, i, "").unwrap();
+                    let offset = ctx
+                        .builder
+                        .build_int_mul(
+                            offset,
+                            ctx.builder
+                                .build_int_truncate_or_bit_cast(
+                                    dst_arr.get_type().element_type().size_of().unwrap(),
+                                    offset.get_type(),
+                                    "",
+                                )
+                                .unwrap(),
+                            "",
+                        )
+                        .unwrap();
 
                     let dst_ptr =
                         unsafe { ctx.builder.build_gep(dst_slice_ptr, &[offset], "").unwrap() };
 
-                    let nested_lst_elem = ListValue::from_ptr_val(
+                    let nested_lst_elem = ListValue::from_pointer_value(
                         unsafe { src_lst.data().get_unchecked(ctx, generator, &i, None) }
                             .into_pointer_value(),
                         llvm_usize,
@@ -730,7 +781,6 @@ fn ndarray_from_ndlist_impl<'ctx, G: CodeGenerator + ?Sized>(
                     ndarray_from_ndlist_impl(
                         generator,
                         ctx,
-                        elem_ty,
                         (dst_arr, dst_ptr),
                         nested_lst_elem,
                         dim + 1,
@@ -741,13 +791,15 @@ fn ndarray_from_ndlist_impl<'ctx, G: CodeGenerator + ?Sized>(
             )?;
         }
 
-        AnyTypeEnum::PointerType(ptr_ty) if NDArrayType::is_type(ptr_ty, llvm_usize).is_ok() => {
+        AnyTypeEnum::PointerType(ptr_ty)
+            if NDArrayType::is_representable(ptr_ty, llvm_usize).is_ok() =>
+        {
             todo!("Not implemented for list[ndarray]")
         }
 
         _ => {
             let lst_len = src_lst.load_size(ctx, None);
-            let sizeof_elem = ctx.get_llvm_type(generator, elem_ty).size_of().unwrap();
+            let sizeof_elem = dst_arr.get_type().element_type().size_of().unwrap();
             let sizeof_elem = ctx.builder.build_int_cast(sizeof_elem, llvm_usize, "").unwrap();
 
             let cpy_len = ctx
@@ -802,8 +854,9 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
     let object = object.into_pointer_value();
 
     // object is an NDArray instance - copy object unless copy=0 && ndmin < object.ndims
-    if NDArrayValue::is_instance(object, llvm_usize).is_ok() {
-        let object = NDArrayValue::from_ptr_val(object, llvm_usize, None);
+    if NDArrayValue::is_representable(object, llvm_usize).is_ok() {
+        let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
+        let object = NDArrayValue::from_pointer_value(object, llvm_elem_ty, llvm_usize, None);
 
         let ndarray = gen_if_else_expr_callback(
             generator,
@@ -865,7 +918,6 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
                 ndarray_sliced_copyto_impl(
                     generator,
                     ctx,
-                    elem_ty,
                     (ndarray, ndarray.data().base_ptr(ctx, generator)),
                     (object, object.data().base_ptr(ctx, generator)),
                     0,
@@ -877,16 +929,17 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
             |_, _| Ok(Some(object.as_base_value())),
         )?;
 
-        return Ok(NDArrayValue::from_ptr_val(
+        return Ok(NDArrayValue::from_pointer_value(
             ndarray.map(BasicValueEnum::into_pointer_value).unwrap(),
+            llvm_elem_ty,
             llvm_usize,
             None,
         ));
     }
 
     // Remaining case: TList
-    assert!(ListValue::is_instance(object, llvm_usize).is_ok());
-    let object = ListValue::from_ptr_val(object, llvm_usize, None);
+    assert!(ListValue::is_representable(object, llvm_usize).is_ok());
+    let object = ListValue::from_pointer_value(object, llvm_usize, None);
 
     // The number of dimensions to prepend 1's to
     let ndims = llvm_ndlist_get_ndims(generator, ctx, object.as_base_value().get_type());
@@ -941,7 +994,7 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
                         .build_store(
                             lst,
                             ctx.builder
-                                .build_bitcast(object.as_base_value(), llvm_plist_i8, "")
+                                .build_bit_cast(object.as_base_value(), llvm_plist_i8, "")
                                 .unwrap(),
                         )
                         .unwrap();
@@ -963,10 +1016,11 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
                                 .builder
                                 .build_load(lst, "")
                                 .map(BasicValueEnum::into_pointer_value)
-                                .map(|v| ctx.builder.build_bitcast(v, plist_plist_i8, "").unwrap())
+                                .map(|v| ctx.builder.build_bit_cast(v, plist_plist_i8, "").unwrap())
                                 .map(BasicValueEnum::into_pointer_value)
                                 .unwrap();
-                            let this_dim = ListValue::from_ptr_val(this_dim, llvm_usize, None);
+                            let this_dim =
+                                ListValue::from_pointer_value(this_dim, llvm_usize, None);
 
                             // TODO: Assert this_dim.sz != 0
 
@@ -982,7 +1036,9 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
                             ctx.builder
                                 .build_store(
                                     lst,
-                                    ctx.builder.build_bitcast(next_dim, llvm_plist_i8, "").unwrap(),
+                                    ctx.builder
+                                        .build_bit_cast(next_dim, llvm_plist_i8, "")
+                                        .unwrap(),
                                 )
                                 .unwrap();
 
@@ -990,7 +1046,7 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
                         },
                     )?;
 
-                    let lst = ListValue::from_ptr_val(
+                    let lst = ListValue::from_pointer_value(
                         ctx.builder
                             .build_load(lst, "")
                             .map(BasicValueEnum::into_pointer_value)
@@ -1010,7 +1066,6 @@ fn call_ndarray_array_impl<'ctx, G: CodeGenerator + ?Sized>(
     ndarray_from_ndlist_impl(
         generator,
         ctx,
-        elem_ty,
         (ndarray, ndarray.data().base_ptr(ctx, generator)),
         object,
         0,
@@ -1083,7 +1138,6 @@ fn call_ndarray_eye_impl<'ctx, G: CodeGenerator + ?Sized>(
 fn ndarray_sliced_copyto_impl<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
     (dst_arr, dst_slice_ptr): (NDArrayValue<'ctx>, PointerValue<'ctx>),
     (src_arr, src_slice_ptr): (NDArrayValue<'ctx>, PointerValue<'ctx>),
     dim: u64,
@@ -1092,14 +1146,16 @@ fn ndarray_sliced_copyto_impl<'ctx, G: CodeGenerator + ?Sized>(
     let llvm_i1 = ctx.ctx.bool_type();
     let llvm_usize = generator.get_size_type(ctx.ctx);
 
+    assert_eq!(dst_arr.get_type().element_type(), src_arr.get_type().element_type());
+
+    let sizeof_elem = dst_arr.get_type().element_type().size_of().unwrap();
+
     // If there are no (remaining) slice expressions, memcpy the entire dimension
     if slices.is_empty() {
-        let sizeof_elem = ctx.get_llvm_type(generator, elem_ty).size_of().unwrap();
-
         let stride = call_ndarray_calc_size(
             generator,
             ctx,
-            &src_arr.dim_sizes(),
+            &src_arr.shape(),
             (Some(llvm_usize.const_int(dim, false)), None),
         );
         let stride =
@@ -1117,13 +1173,13 @@ fn ndarray_sliced_copyto_impl<'ctx, G: CodeGenerator + ?Sized>(
     let src_stride = call_ndarray_calc_size(
         generator,
         ctx,
-        &src_arr.dim_sizes(),
+        &src_arr.shape(),
         (Some(llvm_usize.const_int(dim + 1, false)), None),
     );
     let dst_stride = call_ndarray_calc_size(
         generator,
         ctx,
-        &dst_arr.dim_sizes(),
+        &dst_arr.shape(),
         (Some(llvm_usize.const_int(dim + 1, false)), None),
     );
 
@@ -1146,9 +1202,29 @@ fn ndarray_sliced_copyto_impl<'ctx, G: CodeGenerator + ?Sized>(
         |generator, ctx, _, src_i| {
             // Calculate the offset of the active slice
             let src_data_offset = ctx.builder.build_int_mul(src_stride, src_i, "").unwrap();
+            let src_data_offset = ctx
+                .builder
+                .build_int_mul(
+                    src_data_offset,
+                    ctx.builder
+                        .build_int_cast(sizeof_elem, src_data_offset.get_type(), "")
+                        .unwrap(),
+                    "",
+                )
+                .unwrap();
             let dst_i =
                 ctx.builder.build_load(dst_i_addr, "").map(BasicValueEnum::into_int_value).unwrap();
             let dst_data_offset = ctx.builder.build_int_mul(dst_stride, dst_i, "").unwrap();
+            let dst_data_offset = ctx
+                .builder
+                .build_int_mul(
+                    dst_data_offset,
+                    ctx.builder
+                        .build_int_cast(sizeof_elem, dst_data_offset.get_type(), "")
+                        .unwrap(),
+                    "",
+                )
+                .unwrap();
 
             let (src_ptr, dst_ptr) = unsafe {
                 (
@@ -1160,7 +1236,6 @@ fn ndarray_sliced_copyto_impl<'ctx, G: CodeGenerator + ?Sized>(
             ndarray_sliced_copyto_impl(
                 generator,
                 ctx,
-                elem_ty,
                 (dst_arr, dst_ptr),
                 (src_arr, src_ptr),
                 dim + 1,
@@ -1203,7 +1278,7 @@ pub fn ndarray_sliced_copy<'ctx, G: CodeGenerator + ?Sized>(
             &this,
             |_, ctx, shape| Ok(shape.load_ndims(ctx)),
             |generator, ctx, shape, idx| unsafe {
-                Ok(shape.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None))
+                Ok(shape.shape().get_typed_unchecked(ctx, generator, &idx, None))
             },
         )?
     } else {
@@ -1211,7 +1286,7 @@ pub fn ndarray_sliced_copy<'ctx, G: CodeGenerator + ?Sized>(
         ndarray.store_ndims(ctx, generator, this.load_ndims(ctx));
 
         let ndims = this.load_ndims(ctx);
-        ndarray.create_dim_sizes(ctx, llvm_usize, ndims);
+        ndarray.create_shape(ctx, llvm_usize, ndims);
 
         // Populate the first slices.len() dimensions by computing the size of each dim slice
         for (i, (start, stop, step)) in slices.iter().enumerate() {
@@ -1243,7 +1318,7 @@ pub fn ndarray_sliced_copy<'ctx, G: CodeGenerator + ?Sized>(
                 ctx.builder.build_int_z_extend_or_bit_cast(slice_len, llvm_usize, "").unwrap();
 
             unsafe {
-                ndarray.dim_sizes().set_typed_unchecked(
+                ndarray.shape().set_typed_unchecked(
                     ctx,
                     generator,
                     &llvm_usize.const_int(i as u64, false),
@@ -1261,8 +1336,8 @@ pub fn ndarray_sliced_copy<'ctx, G: CodeGenerator + ?Sized>(
             (this.load_ndims(ctx), false),
             |generator, ctx, _, idx| {
                 unsafe {
-                    let dim_sz = this.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None);
-                    ndarray.dim_sizes().set_typed_unchecked(ctx, generator, &idx, dim_sz);
+                    let dim_sz = this.shape().get_typed_unchecked(ctx, generator, &idx, None);
+                    ndarray.shape().set_typed_unchecked(ctx, generator, &idx, dim_sz);
                 }
 
                 Ok(())
@@ -1277,7 +1352,6 @@ pub fn ndarray_sliced_copy<'ctx, G: CodeGenerator + ?Sized>(
     ndarray_sliced_copyto_impl(
         generator,
         ctx,
-        elem_ty,
         (ndarray, ndarray.data().base_ptr(ctx, generator)),
         (this, this.data().base_ptr(ctx, generator)),
         0,
@@ -1323,7 +1397,7 @@ where
             &operand,
             |_, ctx, v| Ok(v.load_ndims(ctx)),
             |generator, ctx, v, idx| unsafe {
-                Ok(v.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None))
+                Ok(v.shape().get_typed_unchecked(ctx, generator, &idx, None))
             },
         )
         .unwrap()
@@ -1360,8 +1434,8 @@ pub fn ndarray_elementwise_binop_impl<'ctx, 'a, G, ValueFn>(
     ctx: &mut CodeGenContext<'ctx, 'a>,
     elem_ty: Type,
     res: Option<NDArrayValue<'ctx>>,
-    lhs: (BasicValueEnum<'ctx>, bool),
-    rhs: (BasicValueEnum<'ctx>, bool),
+    lhs: (Type, BasicValueEnum<'ctx>, bool),
+    rhs: (Type, BasicValueEnum<'ctx>, bool),
     value_fn: ValueFn,
 ) -> Result<NDArrayValue<'ctx>, String>
 where
@@ -1374,8 +1448,8 @@ where
 {
     let llvm_usize = generator.get_size_type(ctx.ctx);
 
-    let (lhs_val, lhs_scalar) = lhs;
-    let (rhs_val, rhs_scalar) = rhs;
+    let (lhs_ty, lhs_val, lhs_scalar) = lhs;
+    let (rhs_ty, rhs_val, rhs_scalar) = rhs;
 
     assert!(
         !(lhs_scalar && rhs_scalar),
@@ -1386,10 +1460,22 @@ where
 
     let ndarray = res.unwrap_or_else(|| {
         if lhs_scalar && rhs_scalar {
-            let lhs_val =
-                NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
-            let rhs_val =
-                NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
+            let lhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, lhs_ty);
+            let llvm_lhs_elem_ty = ctx.get_llvm_type(generator, lhs_dtype);
+            let lhs_val = NDArrayValue::from_pointer_value(
+                lhs_val.into_pointer_value(),
+                llvm_lhs_elem_ty,
+                llvm_usize,
+                None,
+            );
+            let rhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, rhs_ty);
+            let llvm_rhs_elem_ty = ctx.get_llvm_type(generator, rhs_dtype);
+            let rhs_val = NDArrayValue::from_pointer_value(
+                rhs_val.into_pointer_value(),
+                llvm_rhs_elem_ty,
+                llvm_usize,
+                None,
+            );
 
             let ndarray_dims = call_ndarray_calc_broadcast(generator, ctx, lhs_val, rhs_val);
 
@@ -1405,8 +1491,14 @@ where
             )
             .unwrap()
         } else {
-            let ndarray = NDArrayValue::from_ptr_val(
+            let dtype = arraylike_flatten_element_type(
+                &mut ctx.unifier,
+                if lhs_scalar { rhs_ty } else { lhs_ty },
+            );
+            let llvm_elem_ty = ctx.get_llvm_type(generator, dtype);
+            let ndarray = NDArrayValue::from_pointer_value(
                 if lhs_scalar { rhs_val } else { lhs_val }.into_pointer_value(),
+                llvm_elem_ty,
                 llvm_usize,
                 None,
             );
@@ -1418,7 +1510,7 @@ where
                 &ndarray,
                 |_, ctx, v| Ok(v.load_ndims(ctx)),
                 |generator, ctx, v, idx| unsafe {
-                    Ok(v.dim_sizes().get_typed_unchecked(ctx, generator, &idx, None))
+                    Ok(v.shape().get_typed_unchecked(ctx, generator, &idx, None))
                 },
             )
             .unwrap()
@@ -1479,10 +1571,10 @@ pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
         if let Some(res) = res {
             let res_ndims = res.load_ndims(ctx);
             let res_dim0 = unsafe {
-                res.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
+                res.shape().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
             };
             let res_dim1 = unsafe {
-                res.dim_sizes().get_typed_unchecked(
+                res.shape().get_typed_unchecked(
                     ctx,
                     generator,
                     &llvm_usize.const_int(1, false),
@@ -1490,10 +1582,10 @@ pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
                 )
             };
             let lhs_dim0 = unsafe {
-                lhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
+                lhs.shape().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
             };
             let rhs_dim1 = unsafe {
-                rhs.dim_sizes().get_typed_unchecked(
+                rhs.shape().get_typed_unchecked(
                     ctx,
                     generator,
                     &llvm_usize.const_int(1, false),
@@ -1542,15 +1634,10 @@ pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
 
     if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
         let lhs_dim1 = unsafe {
-            lhs.dim_sizes().get_typed_unchecked(
-                ctx,
-                generator,
-                &llvm_usize.const_int(1, false),
-                None,
-            )
+            lhs.shape().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
         };
         let rhs_dim0 = unsafe {
-            rhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
+            rhs.shape().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
         };
 
         // lhs.dims[1] == rhs.dims[0]
@@ -1589,7 +1676,7 @@ pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
                     },
                     |generator, ctx| {
                         Ok(Some(unsafe {
-                            lhs.dim_sizes().get_typed_unchecked(
+                            lhs.shape().get_typed_unchecked(
                                 ctx,
                                 generator,
                                 &llvm_usize.const_zero(),
@@ -1599,7 +1686,7 @@ pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
                     },
                     |generator, ctx| {
                         Ok(Some(unsafe {
-                            rhs.dim_sizes().get_typed_unchecked(
+                            rhs.shape().get_typed_unchecked(
                                 ctx,
                                 generator,
                                 &llvm_usize.const_int(1, false),
@@ -1626,7 +1713,7 @@ pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
 
         let common_dim = {
             let lhs_idx1 = unsafe {
-                lhs.dim_sizes().get_typed_unchecked(
+                lhs.shape().get_typed_unchecked(
                     ctx,
                     generator,
                     &llvm_usize.const_int(1, false),
@@ -1634,7 +1721,7 @@ pub fn ndarray_matmul_2d<'ctx, G: CodeGenerator>(
                 )
             };
             let rhs_idx0 = unsafe {
-                rhs.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
+                rhs.shape().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
             };
 
             let idx = llvm_intrinsics::call_expect(ctx, rhs_idx0, lhs_idx1, None);
@@ -1965,11 +2052,18 @@ pub fn gen_ndarray_copy<'ctx>(
     let this_arg =
         obj.as_ref().unwrap().1.clone().to_basic_value_enum(context, generator, this_ty)?;
 
+    let llvm_elem_ty = context.get_llvm_type(generator, this_elem_ty);
+
     ndarray_copy_impl(
         generator,
         context,
         this_elem_ty,
-        NDArrayValue::from_ptr_val(this_arg.into_pointer_value(), llvm_usize, None),
+        NDArrayValue::from_pointer_value(
+            this_arg.into_pointer_value(),
+            llvm_elem_ty,
+            llvm_usize,
+            None,
+        ),
     )
     .map(NDArrayValue::into)
 }
@@ -1988,6 +2082,7 @@ pub fn gen_ndarray_fill<'ctx>(
     let llvm_usize = generator.get_size_type(context.ctx);
 
     let this_ty = obj.as_ref().unwrap().0;
+    let this_elem_ty = arraylike_flatten_element_type(&mut context.unifier, this_ty);
     let this_arg = obj
         .as_ref()
         .unwrap()
@@ -1998,10 +2093,12 @@ pub fn gen_ndarray_fill<'ctx>(
     let value_ty = fun.0.args[0].ty;
     let value_arg = args[0].1.clone().to_basic_value_enum(context, generator, value_ty)?;
 
+    let llvm_elem_ty = context.get_llvm_type(generator, this_elem_ty);
+
     ndarray_fill_flattened(
         generator,
         context,
-        NDArrayValue::from_ptr_val(this_arg, llvm_usize, None),
+        NDArrayValue::from_pointer_value(this_arg, llvm_elem_ty, llvm_usize, None),
         |generator, ctx, _| {
             let value = if value_arg.is_pointer_value() {
                 let llvm_i1 = ctx.ctx.bool_type();
@@ -2020,7 +2117,7 @@ pub fn gen_ndarray_fill<'ctx>(
             } else if value_arg.is_int_value() || value_arg.is_float_value() {
                 value_arg
             } else {
-                unreachable!()
+                codegen_unreachable!(ctx)
             };
 
             Ok(value)
@@ -2042,8 +2139,9 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
 
     if let BasicValueEnum::PointerValue(n1) = x1 {
         let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-        let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
+        let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
+        let n1 = NDArrayValue::from_pointer_value(n1, llvm_elem_ty, llvm_usize, None);
+        let n_sz = call_ndarray_calc_size(generator, ctx, &n1.shape(), (None, None));
 
         // Dimensions are reversed in the transposed array
         let out = create_ndarray_dyn_shape(
@@ -2058,7 +2156,7 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
                     .builder
                     .build_int_sub(new_idx, new_idx.get_type().const_int(1, false), "")
                     .unwrap();
-                unsafe { Ok(n.dim_sizes().get_typed_unchecked(ctx, generator, &new_idx, None)) }
+                unsafe { Ok(n.shape().get_typed_unchecked(ctx, generator, &new_idx, None)) }
             },
         )
         .unwrap();
@@ -2095,7 +2193,7 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
                             .build_int_sub(ndim_rev, llvm_usize.const_int(1, false), "")
                             .unwrap();
                         let dim = unsafe {
-                            n1.dim_sizes().get_typed_unchecked(ctx, generator, &ndim_rev, None)
+                            n1.shape().get_typed_unchecked(ctx, generator, &ndim_rev, None)
                         };
 
                         let rem_idx_val =
@@ -2129,7 +2227,8 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
 
         Ok(out.as_base_value().into())
     } else {
-        unreachable!(
+        codegen_unreachable!(
+            ctx,
             "{FN_NAME}() not supported for '{}'",
             format!("'{}'", ctx.unifier.stringify(x1_ty))
         )
@@ -2144,7 +2243,8 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
 ///   1. A list of `int32`;   e.g., `np.reshape(arr, [600, -1, 3])`
 ///   2. A tuple of `int32`;  e.g., `np.reshape(arr, (-1, 800, 3))`
 ///   3. A scalar `int32`;     e.g., `np.reshape(arr, 3)`
-/// Note that unlike other generating functions, one of the dimesions in the shape can be negative
+///
+/// Note that unlike other generating functions, one of the dimensions in the shape can be negative.
 pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -2159,8 +2259,9 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
 
     if let BasicValueEnum::PointerValue(n1) = x1 {
         let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-        let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
+        let llvm_elem_ty = ctx.get_llvm_type(generator, elem_ty);
+        let n1 = NDArrayValue::from_pointer_value(n1, llvm_elem_ty, llvm_usize, None);
+        let n_sz = call_ndarray_calc_size(generator, ctx, &n1.shape(), (None, None));
 
         let acc = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
         let num_neg = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
@@ -2169,11 +2270,11 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
 
         let out = match shape {
             BasicValueEnum::PointerValue(shape_list_ptr)
-                if ListValue::is_instance(shape_list_ptr, llvm_usize).is_ok() =>
+                if ListValue::is_representable(shape_list_ptr, llvm_usize).is_ok() =>
             {
                 // 1. A list of ints; e.g., `np.reshape(arr, [int64(600), int64(800, -1])`
 
-                let shape_list = ListValue::from_ptr_val(shape_list_ptr, llvm_usize, None);
+                let shape_list = ListValue::from_pointer_value(shape_list_ptr, llvm_usize, None);
                 // Check for -1 in dimensions
                 gen_for_callback_incrementing(
                     generator,
@@ -2370,7 +2471,7 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                 .into_int_value();
                 create_ndarray_const_shape(generator, ctx, elem_ty, &[shape_int])
             }
-            _ => unreachable!(),
+            _ => codegen_unreachable!(ctx),
         }
         .unwrap();
 
@@ -2388,7 +2489,7 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
         );
 
         // The new shape must be compatible with the old shape
-        let out_sz = call_ndarray_calc_size(generator, ctx, &out.dim_sizes(), (None, None));
+        let out_sz = call_ndarray_calc_size(generator, ctx, &out.shape(), (None, None));
         ctx.make_assert(
             generator,
             ctx.builder.build_int_compare(IntPredicate::EQ, out_sz, n_sz, "").unwrap(),
@@ -2414,7 +2515,8 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
 
         Ok(out.as_base_value().into())
     } else {
-        unreachable!(
+        codegen_unreachable!(
+            ctx,
             "{FN_NAME}() not supported for '{}'",
             format!("'{}'", ctx.unifier.stringify(x1_ty))
         )
@@ -2435,17 +2537,22 @@ pub fn ndarray_dot<'ctx, G: CodeGenerator + ?Sized>(
 ) -> Result<BasicValueEnum<'ctx>, String> {
     const FN_NAME: &str = "ndarray_dot";
     let (x1_ty, x1) = x1;
-    let (_, x2) = x2;
+    let (x2_ty, x2) = x2;
 
     let llvm_usize = generator.get_size_type(ctx.ctx);
 
     match (x1, x2) {
         (BasicValueEnum::PointerValue(n1), BasicValueEnum::PointerValue(n2)) => {
-            let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-            let n2 = NDArrayValue::from_ptr_val(n2, llvm_usize, None);
+            let n1_dtype = arraylike_flatten_element_type(&mut ctx.unifier, x1_ty);
+            let n2_dtype = arraylike_flatten_element_type(&mut ctx.unifier, x2_ty);
+            let llvm_n1_data_ty = ctx.get_llvm_type(generator, n1_dtype);
+            let llvm_n2_data_ty = ctx.get_llvm_type(generator, n2_dtype);
 
-            let n1_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
-            let n2_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
+            let n1 = NDArrayValue::from_pointer_value(n1, llvm_n1_data_ty, llvm_usize, None);
+            let n2 = NDArrayValue::from_pointer_value(n2, llvm_n2_data_ty, llvm_usize, None);
+
+            let n1_sz = call_ndarray_calc_size(generator, ctx, &n1.shape(), (None, None));
+            let n2_sz = call_ndarray_calc_size(generator, ctx, &n1.shape(), (None, None));
 
             ctx.make_assert(
                 generator,
@@ -2482,7 +2589,7 @@ pub fn ndarray_dot<'ctx, G: CodeGenerator + ?Sized>(
                             .build_float_mul(e1, elem2.into_float_value(), "")
                             .unwrap()
                             .as_basic_value_enum(),
-                        _ => unreachable!(),
+                        _ => codegen_unreachable!(ctx, "product: {}", elem1.get_type()),
                     };
                     let acc_val = ctx.builder.build_load(acc, "").unwrap();
                     let acc_val = match acc_val {
@@ -2496,7 +2603,7 @@ pub fn ndarray_dot<'ctx, G: CodeGenerator + ?Sized>(
                             .build_float_add(e1, product.into_float_value(), "")
                             .unwrap()
                             .as_basic_value_enum(),
-                        _ => unreachable!(),
+                        _ => codegen_unreachable!(ctx, "acc_val: {}", acc_val.get_type()),
                     };
                     ctx.builder.build_store(acc, acc_val).unwrap();
 
@@ -2513,7 +2620,8 @@ pub fn ndarray_dot<'ctx, G: CodeGenerator + ?Sized>(
         (BasicValueEnum::FloatValue(e1), BasicValueEnum::FloatValue(e2)) => {
             Ok(ctx.builder.build_float_mul(e1, e2, "").unwrap().as_basic_value_enum())
         }
-        _ => unreachable!(
+        _ => codegen_unreachable!(
+            ctx,
             "{FN_NAME}() not supported for '{}'",
             format!("'{}'", ctx.unifier.stringify(x1_ty))
         ),

nac3core/src/codegen/numpy_new.rs

@@ -1,210 +0,0 @@
-// TODO: Replace numpy.rs
-
-use inkwell::values::{BasicValue, BasicValueEnum};
-use nac3parser::ast::StrRef;
-
-use crate::{
-    codegen::object::{ndarray::scalar::split_scalar_or_ndarray, tuple::TupleObject},
-    symbol_resolver::ValueEnum,
-    toplevel::{helper::extract_ndims, numpy::unpack_ndarray_var_tys, DefinitionId},
-    typecheck::typedef::{FunSignature, Type},
-};
-
-use super::{
-    irrt::call_nac3_ndarray_util_assert_shape_no_negative,
-    model::*,
-    object::{
-        ndarray::{shape_util::parse_numpy_int_sequence, NDArrayObject},
-        AnyObject,
-    },
-    CodeGenContext, CodeGenerator,
-};
-
-/// Generates LLVM IR for `np.broadcast_to`.
-pub fn gen_ndarray_broadcast_to<'ctx>(
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<BasicValueEnum<'ctx>, String> {
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 2);
-
-    // Parse argument #1 input
-    let input_ty = fun.0.args[0].ty;
-    let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
-    let input = AnyObject { ty: input_ty, value: input };
-
-    // Parse argument #2 shape
-    let shape_ty = fun.0.args[1].ty;
-    let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
-    let shape = AnyObject { ty: shape_ty, value: shape };
-
-    // Define models
-    let sizet_model = IntModel(SizeT);
-
-    // Extract broadcast_ndims, this is the only way to get the
-    // ndims of the ndarray result statically.
-    let (_, broadcast_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
-    let broadcast_ndims = extract_ndims(&ctx.unifier, broadcast_ndims_ty);
-
-    // Process `input`
-    let in_ndarray = split_scalar_or_ndarray(generator, ctx, input).as_ndarray(generator, ctx);
-
-    // Process `shape`
-    let (_, broadcast_shape) = parse_numpy_int_sequence(generator, ctx, shape);
-    // NOTE: shape.size should equal to `broadcasted_ndims`.
-    let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
-    call_nac3_ndarray_util_assert_shape_no_negative(
-        generator,
-        ctx,
-        broadcast_ndims_llvm,
-        broadcast_shape,
-    );
-
-    // Create broadcast view
-    let broadcast_ndarray =
-        in_ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape);
-
-    Ok(broadcast_ndarray.instance.value.as_basic_value_enum())
-}
-
-/// Generates LLVM IR for `np.reshape`.
-pub fn gen_ndarray_reshape<'ctx>(
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<BasicValueEnum<'ctx>, String> {
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 2);
-
-    // Parse argument #1 input
-    let input_ty = fun.0.args[0].ty;
-    let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?;
-    let input = AnyObject { ty: input_ty, value: input };
-
-    // Parse argument #2 shape
-    let shape_ty = fun.0.args[1].ty;
-    let shape = args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
-    let shape = AnyObject { ty: shape_ty, value: shape };
-
-    // Extract reshaped_ndims
-    let (_, reshaped_ndims_ty) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
-    let reshaped_ndims = extract_ndims(&ctx.unifier, reshaped_ndims_ty);
-
-    // Process `input`
-    let in_ndarray = split_scalar_or_ndarray(generator, ctx, input).as_ndarray(generator, ctx);
-
-    // Process the shape input from user and resolve negative indices.
-    // The resulting `new_shape`'s size should be equal to reshaped_ndims.
-    // This is ensured by the typechecker.
-    let (_, new_shape) = parse_numpy_int_sequence(generator, ctx, shape);
-    let reshaped_ndarray = in_ndarray.reshape_or_copy(generator, ctx, reshaped_ndims, new_shape);
-
-    Ok(reshaped_ndarray.instance.value.as_basic_value_enum())
-}
-
-/// Generates LLVM IR for `np.arange`.
-pub fn gen_ndarray_arange<'ctx>(
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<BasicValueEnum<'ctx>, String> {
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 1);
-
-    // Parse argument #1 len
-    let input_ty = fun.0.args[0].ty;
-    let input = args[0].1.clone().to_basic_value_enum(ctx, generator, input_ty)?.into_int_value();
-
-    // Implementation
-    let input_dim = IntModel(SizeT).s_extend_or_bit_cast(generator, ctx, input, "input_dim");
-    let ndarray = NDArrayObject::from_np_arange(generator, ctx, input_dim);
-
-    Ok(ndarray.instance.value.as_basic_value_enum())
-}
-
-/// Generates LLVM IR for `np.size`.
-pub fn gen_ndarray_size<'ctx>(
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<BasicValueEnum<'ctx>, String> {
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 1);
-
-    let ndarray_ty = fun.0.args[0].ty;
-    let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
-    let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
-    let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
-
-    let size = ndarray.size(generator, ctx).truncate(generator, ctx, Int32, "size");
-    Ok(size.value.as_basic_value_enum())
-}
-
-/// Generates LLVM IR for `np.shape`.
-pub fn gen_ndarray_shape<'ctx>(
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<BasicValueEnum<'ctx>, String> {
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 1);
-
-    // Parse argument #1 ndarray
-    let ndarray_ty = fun.0.args[0].ty;
-    let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
-    let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
-
-    // Process ndarray
-    let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
-    Ok(ndarray.make_shape_tuple(generator, ctx).value.as_basic_value_enum())
-}
-
-/// Generates LLVM IR for `<ndarray>.strides`.
-pub fn gen_ndarray_strides<'ctx>(
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
-    fun: (&FunSignature, DefinitionId),
-    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
-    generator: &mut dyn CodeGenerator,
-) -> Result<BasicValueEnum<'ctx>, String> {
-    // TODO: Code duplication: This function looks exactly like `gen_ndarray_shapes`.
-
-    assert!(obj.is_none());
-    assert_eq!(args.len(), 1);
-
-    // Parse argument #1 ndarray
-    let ndarray_ty = fun.0.args[0].ty;
-    let ndarray = args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
-    let ndarray = AnyObject { ty: ndarray_ty, value: ndarray };
-
-    // Process ndarray
-    let ndarray = NDArrayObject::from_object(generator, ctx, ndarray);
-
-    let mut objects = Vec::with_capacity(ndarray.ndims as usize);
-
-    for i in 0..ndarray.ndims {
-        let dim = ndarray
-            .instance
-            .get(generator, ctx, |f| f.strides, "")
-            .offset_const(generator, ctx, i, "")
-            .load(generator, ctx, "dim");
-        let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
-
-        objects
-            .push(AnyObject { ty: ctx.primitives.int32, value: dim.value.as_basic_value_enum() });
-    }
-
-    let strides = TupleObject::create(generator, ctx, objects, "strides");
-    Ok(strides.value.as_basic_value_enum())
-}

nac3core/src/codegen/object/list.rs

@@ -1,121 +0,0 @@
-use crate::{
-    codegen::{
-        irrt::{call_nac3_list_slice_assign, list_slice_assignment},
-        model::*,
-        object::ndarray::indexing::UserSlice,
-        structure::List,
-        CodeGenContext, CodeGenerator,
-    },
-    typecheck::typedef::{iter_type_vars, Type, TypeEnum},
-};
-
-use super::{ndarray::indexing::RustUserSlice, AnyObject};
-
-/// A NAC3 Python List object.
-#[derive(Debug, Clone, Copy)]
-pub struct ListObject<'ctx> {
-    /// Typechecker type of the list items
-    pub item_type: Type,
-    pub instance: Ptr<'ctx, StructModel<List<AnyModel<'ctx>>>>,
-}
-
-impl<'ctx> ListObject<'ctx> {
-    /// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
-    pub fn from_object<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        object: AnyObject<'ctx>,
-    ) -> Self {
-        // Check typechecker type and extract `item_type`
-        let item_type = match &*ctx.unifier.get_ty(object.ty) {
-            TypeEnum::TObj { obj_id, params, .. }
-                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
-            {
-                iter_type_vars(params).next().unwrap().ty // Extract `item_type`
-            }
-            _ => {
-                panic!("Expecting type to be a list, but got {}", ctx.unifier.stringify(object.ty))
-            }
-        };
-
-        let item_model = AnyModel(ctx.get_llvm_type(generator, item_type));
-        let plist_model = PtrModel(StructModel(List { item: item_model }));
-
-        // Create object
-        let value = plist_model.check_value(generator, ctx.ctx, object.value).unwrap();
-        ListObject { item_type, instance: value }
-    }
-
-    /// Get the `items` field as an opaque pointer.
-    pub fn get_opaque_items_ptr<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Ptr<'ctx, IntModel<Byte>> {
-        self.instance.get(generator, ctx, |f| f.items, "items").pointer_cast(
-            generator,
-            ctx,
-            IntModel(Byte),
-            "items_opaque",
-        )
-    }
-
-    /// Get the value of this [`ListObject`] as a list with opaque items.
-    ///
-    /// This function allocates on the stack to create the list, but the
-    /// reference to the `items` are preserved.
-    pub fn get_opaque_list_ptr<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Ptr<'ctx, StructModel<List<IntModel<Byte>>>> {
-        let opaque_list_model = StructModel(List { item: IntModel(Byte) });
-        let opaque_list_ptr = opaque_list_model.alloca(generator, ctx, "opaque_list_ptr");
-
-        // Copy items pointer
-        let items = self.get_opaque_items_ptr(generator, ctx);
-        opaque_list_ptr.set(ctx, |f| f.items, items);
-
-        // Copy len
-        let len = self.instance.get(generator, ctx, |f| f.len, "len");
-        opaque_list_ptr.set(ctx, |f| f.len, len);
-
-        opaque_list_ptr
-    }
-
-    /// Get the `len()` of this list.
-    pub fn len<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, SizeT> {
-        self.instance.get(generator, ctx, |f| f.len, "list_len")
-    }
-
-    pub fn slice_assign_from<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        user_slice: &RustUserSlice<'ctx>,
-        source: ListObject<'ctx>,
-    ) {
-        // Sanity check
-        assert!(ctx.unifier.unioned(self.item_type, source.item_type));
-
-        let user_slice_model = StructModel(UserSlice);
-        let puser_slice = user_slice_model.alloca(generator, ctx, "user_slice");
-        user_slice.write_to_user_slice(generator, ctx, puser_slice);
-
-        let itemsize = self.instance.model.get_type(generator, ctx.ctx).size_of();
-
-        call_nac3_list_slice_assign(
-            generator,
-            ctx,
-            self.get_opaque_list_ptr(generator, ctx),
-            source.instance.value,
-            itemsize,
-            user_slice,
-        );
-        todo!()
-    }
-}

nac3core/src/codegen/object/mod.rs

@@ -1,608 +0,0 @@
-use inkwell::{
-    values::{BasicValue, BasicValueEnum, FloatValue, IntValue},
-    FloatPredicate, IntPredicate,
-};
-use itertools::Itertools;
-use list::ListObject;
-use ndarray::{NDArrayObject, NDArrayOut};
-use range::RangeObject;
-use tuple::TupleObject;
-
-use crate::{
-    toplevel::helper::PrimDef,
-    typecheck::typedef::{Type, TypeEnum},
-};
-
-use super::{llvm_intrinsics, model::*, CodeGenContext, CodeGenerator};
-
-pub mod list;
-pub mod ndarray;
-pub mod range;
-pub mod tuple;
-
-/// Convenience function to crash the program when types of arguments are not supported.
-/// Used to be debugged with a stacktrace.
-fn unsupported_type<I>(ctx: &CodeGenContext<'_, '_>, tys: I) -> !
-where
-    I: IntoIterator<Item = Type>,
-{
-    unreachable!(
-        "unsupported types found '{}'",
-        tys.into_iter().map(|ty| format!("'{}'", ctx.unifier.stringify(ty))).join(", "),
-    )
-}
-
-#[derive(Debug, Clone, Copy)]
-pub enum FloorOrCeil {
-    Floor,
-    Ceil,
-}
-
-#[derive(Debug, Clone, Copy)]
-pub enum MinOrMax {
-    Min,
-    Max,
-}
-
-fn signed_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
-    vec![ctx.primitives.int32, ctx.primitives.int64]
-}
-
-fn unsigned_ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
-    vec![ctx.primitives.uint32, ctx.primitives.uint64]
-}
-
-fn ints(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
-    vec![ctx.primitives.int32, ctx.primitives.int64, ctx.primitives.uint32, ctx.primitives.uint64]
-}
-
-fn int_like(ctx: &CodeGenContext<'_, '_>) -> Vec<Type> {
-    vec![
-        ctx.primitives.bool,
-        ctx.primitives.int32,
-        ctx.primitives.int64,
-        ctx.primitives.uint32,
-        ctx.primitives.uint64,
-    ]
-}
-
-#[derive(Debug, Clone, Copy)]
-pub struct AnyObject<'ctx> {
-    pub ty: Type,
-    pub value: BasicValueEnum<'ctx>,
-}
-
-impl<'ctx> AnyObject<'ctx> {
-    /// Returns true if this object's type is a [`TypeEnum::TObj`] and has the object ID as `prim`.
-    pub fn is_obj(&self, ctx: &mut CodeGenContext<'ctx, '_>, prim: PrimDef) -> bool {
-        match &*ctx.unifier.get_ty(self.ty) {
-            TypeEnum::TObj { obj_id, .. } => *obj_id == prim.id(),
-            _ => false,
-        }
-    }
-
-    /// Returns true if this object's type is a [`TypeEnum::TTuple`]
-    pub fn is_tuple(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        matches!(&*ctx.unifier.get_ty(self.ty), TypeEnum::TTuple { .. })
-    }
-
-    pub fn into_tuple() {}
-
-    pub fn is_int32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        ctx.unifier.unioned(self.ty, ctx.primitives.int32)
-    }
-
-    pub fn into_int32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Int<'ctx, Int32> {
-        assert!(self.is_int32(ctx));
-        IntModel(Int32).believe_value(self.value.into_int_value())
-    }
-
-    pub fn is_uint32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        ctx.unifier.unioned(self.ty, ctx.primitives.uint32)
-    }
-
-    pub fn is_int64(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        ctx.unifier.unioned(self.ty, ctx.primitives.int64)
-    }
-
-    pub fn is_uint64(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        ctx.unifier.unioned(self.ty, ctx.primitives.uint64)
-    }
-
-    pub fn is_bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        ctx.unifier.unioned(self.ty, ctx.primitives.bool)
-    }
-
-    /// Returns true if the object type is `bool`, `int32`, `int64`, `uint32`, or `uint64`.
-    pub fn is_int_like(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        ctx.unifier.unioned_any(self.ty, int_like(ctx))
-    }
-
-    /// Returns true if the object type is `int32`, `int64`.
-    pub fn is_signed_int(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        ctx.unifier.unioned_any(self.ty, signed_ints(ctx))
-    }
-
-    /// Returns true if the object type is `uint32`, `uint64`.
-    pub fn is_unsigned_int(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        ctx.unifier.unioned_any(self.ty, unsigned_ints(ctx))
-    }
-
-    pub fn into_int(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
-        assert!(self.is_int_like(ctx));
-        self.value.into_int_value()
-    }
-
-    pub fn is_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        self.is_obj(ctx, PrimDef::Float)
-    }
-
-    pub fn into_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Float<'ctx, Float64> {
-        assert!(self.is_float(ctx));
-        FloatModel(Float64).believe_value(self.value.into_float_value())
-    }
-
-    pub fn is_ndarray(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> bool {
-        self.is_obj(ctx, PrimDef::NDArray)
-    }
-
-    pub fn into_ndarray<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> NDArrayObject<'ctx> {
-        NDArrayObject::from_object(generator, ctx, *self)
-    }
-
-    /// Create an object from a boolean from an i1.
-    ///
-    /// NOTE: In NAC3, booleans are i8. This function does converts the input i1 to an i8.
-    pub fn from_bool(ctx: &mut CodeGenContext<'ctx, '_>, n: Int<'ctx, Bool>) -> AnyObject<'ctx> {
-        let llvm_i8 = ctx.ctx.i8_type();
-        let value = ctx.builder.build_int_z_extend(n.value, llvm_i8, "bool").unwrap();
-        AnyObject { value: value.as_basic_value_enum(), ty: ctx.primitives.bool }
-    }
-
-    /// Helper function to compare two scalars.
-    ///
-    /// Only int-to-int and float-to-float comparisons are allowed.
-    ///
-    /// Panic otherwise.
-    pub fn compare_int_or_float_by_predicate<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        lhs: AnyObject<'ctx>,
-        rhs: AnyObject<'ctx>,
-        int_predicate: IntPredicate,
-        float_predicate: FloatPredicate,
-        name: &str,
-    ) -> Int<'ctx, Bool> {
-        assert!(ctx.unifier.unioned(lhs.ty, rhs.ty), "lhs and rhs type should be the same");
-
-        let bool_model = IntModel(Bool);
-
-        let common_ty = lhs.ty;
-        let result = if lhs.is_float(ctx) {
-            let lhs = lhs.into_float(ctx);
-            let rhs = rhs.into_float(ctx);
-            ctx.builder.build_float_compare(float_predicate, lhs.value, rhs.value, name).unwrap()
-        } else if ctx.unifier.unioned_any(common_ty, int_like(ctx)) {
-            let lhs = lhs.into_int(ctx);
-            let rhs = rhs.into_int(ctx);
-            ctx.builder.build_int_compare(int_predicate, lhs, rhs, name).unwrap()
-        } else {
-            unsupported_type(ctx, [lhs.ty, rhs.ty]);
-        };
-
-        bool_model.check_value(generator, ctx.ctx, result).unwrap()
-    }
-
-    /// Helper function for `int32()`, `int64()`, `uint32()`, and `uint64()`.
-    ///
-    /// TODO: Document me
-    fn cast_to_int_conversion<'a, G, HandleFloatFn>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, 'a>,
-        ret_int_ty: Type,
-        handle_float: HandleFloatFn,
-    ) -> AnyObject<'ctx>
-    where
-        G: CodeGenerator + ?Sized,
-        HandleFloatFn:
-            FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, FloatValue<'ctx>) -> IntValue<'ctx>,
-    {
-        let ret_int_ty_llvm = ctx.get_llvm_type(generator, ret_int_ty).into_int_type();
-
-        let result = if self.is_float(ctx) {
-            // Handle float to int
-            let n = self.into_float(ctx);
-            handle_float(generator, ctx, n.value)
-        } else if self.is_int_like(ctx) {
-            // Handle int to a new int type
-            let n = self.into_int(ctx);
-            if n.get_type().get_bit_width() <= ret_int_ty_llvm.get_bit_width() {
-                ctx.builder.build_int_z_extend(n, ret_int_ty_llvm, "zext").unwrap()
-            } else {
-                ctx.builder.build_int_truncate(n, ret_int_ty_llvm, "trunc").unwrap()
-            }
-        } else {
-            unsupported_type(ctx, [self.ty]);
-        };
-
-        assert_eq!(ret_int_ty_llvm.get_bit_width(), result.get_type().get_bit_width()); // Sanity check
-        AnyObject { value: result.into(), ty: ret_int_ty }
-    }
-
-    /// Call `int32()` on this object.
-    #[must_use]
-    pub fn call_int32<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> AnyObject<'ctx> {
-        self.cast_to_int_conversion(
-            generator,
-            ctx,
-            ctx.primitives.int32,
-            |_generator, ctx, input| {
-                let n =
-                    ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap();
-                ctx.builder.build_int_truncate(n, ctx.ctx.i32_type(), "conv").unwrap()
-            },
-        )
-    }
-
-    /// Call `int64()` on this object.
-    #[must_use]
-    pub fn call_int64<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> AnyObject<'ctx> {
-        self.cast_to_int_conversion(
-            generator,
-            ctx,
-            ctx.primitives.int64,
-            |_generator, ctx, input| {
-                ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap()
-            },
-        )
-    }
-
-    /// Call `uint32()` on this object.
-    #[must_use]
-    pub fn call_uint32<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> AnyObject<'ctx> {
-        self.cast_to_int_conversion(generator, ctx, ctx.primitives.uint32, |_generator, ctx, n| {
-            let n_gez = ctx
-                .builder
-                .build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
-                .unwrap();
-
-            let to_int32 =
-                ctx.builder.build_float_to_signed_int(n, ctx.ctx.i32_type(), "").unwrap();
-            let to_uint64 =
-                ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
-
-            ctx.builder
-                .build_select(
-                    n_gez,
-                    ctx.builder.build_int_truncate(to_uint64, ctx.ctx.i32_type(), "").unwrap(),
-                    to_int32,
-                    "conv",
-                )
-                .unwrap()
-                .into_int_value()
-        })
-    }
-
-    /// Call `uint64()` on this object.
-    #[must_use]
-    pub fn call_uint64<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> AnyObject<'ctx> {
-        self.cast_to_int_conversion(generator, ctx, ctx.primitives.uint64, |_generator, ctx, n| {
-            let val_gez = ctx
-                .builder
-                .build_float_compare(FloatPredicate::OGE, n, n.get_type().const_zero(), "")
-                .unwrap();
-
-            let to_int64 =
-                ctx.builder.build_float_to_signed_int(n, ctx.ctx.i64_type(), "").unwrap();
-            let to_uint64 =
-                ctx.builder.build_float_to_unsigned_int(n, ctx.ctx.i64_type(), "").unwrap();
-
-            ctx.builder.build_select(val_gez, to_uint64, to_int64, "conv").unwrap().into_int_value()
-        })
-    }
-
-    // Get the `len()` of this object.
-    #[must_use]
-    pub fn call_len<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> AnyObject<'ctx> {
-        // TODO: Switch to returning SizeT
-        let result = match &*ctx.unifier.get_ty_immutable(self.ty) {
-            TypeEnum::TTuple { .. } => {
-                let tuple = TupleObject::from_object(ctx, *self);
-                tuple.len(generator, ctx).truncate(generator, ctx, Int32, "tuple_len_32")
-            }
-            TypeEnum::TObj { obj_id, .. }
-                if *obj_id == ctx.primitives.range.obj_id(&ctx.unifier).unwrap() =>
-            {
-                let range = RangeObject::from_object(generator, ctx, *self);
-                range.len(generator, ctx)
-            }
-            TypeEnum::TObj { obj_id, .. }
-                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
-            {
-                let list = ListObject::from_object(generator, ctx, *self);
-                list.len(generator, ctx).truncate(generator, ctx, Int32, "list_len_i32")
-            }
-            TypeEnum::TObj { obj_id, .. }
-                if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
-            {
-                let ndarray = NDArrayObject::from_object(generator, ctx, *self);
-                ndarray.len(generator, ctx).truncate(generator, ctx, Int32, "ndarray_len_i32")
-            }
-            _ => unreachable!(),
-        };
-
-        AnyObject { ty: ctx.primitives.int32, value: result.value.as_basic_value_enum() }
-    }
-
-    /// Like [`AnyObject::call_bool`] but this returns an `Int<'ctx, Bool>` instead of an object.
-    pub fn bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Int<'ctx, Bool> {
-        let bool_model = IntModel(Bool);
-        if self.is_int_like(ctx) {
-            let n = self.into_int(ctx);
-            let n = ctx
-                .builder
-                .build_int_compare(inkwell::IntPredicate::NE, n, n.get_type().const_zero(), "bool")
-                .unwrap();
-            bool_model.believe_value(n)
-        } else if self.is_float(ctx) {
-            let n = self.value.into_float_value();
-            let n = ctx
-                .builder
-                .build_float_compare(FloatPredicate::UNE, n, n.get_type().const_zero(), "bool")
-                .unwrap();
-            bool_model.believe_value(n)
-        } else {
-            unsupported_type(ctx, [self.ty])
-        }
-    }
-
-    /// Call `bool()` on this object.
-    #[must_use]
-    pub fn call_bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
-        let n = self.bool(ctx);
-        AnyObject::from_bool(ctx, n)
-    }
-
-    /// Call `float()` on this object.
-    #[must_use]
-    pub fn call_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
-        let f64_model = FloatModel(Float64);
-        let llvm_f64 = ctx.ctx.f64_type();
-
-        let result = if self.is_float(ctx) {
-            self.into_float(ctx)
-        } else if self.is_signed_int(ctx) || self.is_bool(ctx) {
-            let n = self.into_int(ctx);
-            let n = ctx.builder.build_signed_int_to_float(n, llvm_f64, "sitofp").unwrap();
-            f64_model.believe_value(n)
-        } else if self.is_unsigned_int(ctx) {
-            let n = self.into_int(ctx);
-            let n = ctx.builder.build_unsigned_int_to_float(n, llvm_f64, "uitofp").unwrap();
-            f64_model.believe_value(n)
-        } else {
-            unsupported_type(ctx, [self.ty]);
-        };
-
-        AnyObject { ty: ctx.primitives.float, value: result.value.as_basic_value_enum() }
-    }
-
-    // Call `abs()` on this object.
-    #[must_use]
-    pub fn call_abs<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Self {
-        if self.is_float(ctx) {
-            let n = self.value.into_float_value();
-            let n = llvm_intrinsics::call_float_fabs(ctx, n, Some("abs"));
-            AnyObject { value: n.into(), ty: ctx.primitives.float }
-        } else if self.is_unsigned_int(ctx) || self.is_signed_int(ctx) {
-            let is_poisoned = ctx.ctx.bool_type().const_zero(); // is_poisoned = false
-
-            let n = self.value.into_int_value();
-            let n = llvm_intrinsics::call_int_abs(ctx, n, is_poisoned, Some("abs"));
-            AnyObject { value: n.into(), ty: self.ty }
-        } else if self.is_ndarray(ctx) {
-            let ndarray = self.into_ndarray(generator, ctx);
-            ndarray
-                .map(
-                    generator,
-                    ctx,
-                    NDArrayOut::NewNDArray { dtype: ndarray.dtype },
-                    |generator, ctx, scalar| Ok(scalar.call_abs(generator, ctx)),
-                )
-                .unwrap()
-                .to_any_object(ctx)
-        } else {
-            unsupported_type(ctx, [self.ty])
-        }
-    }
-
-    // Call `round()` on this object.
-    //
-    // It is possible to specify which kind of int type to return.
-    #[must_use]
-    pub fn call_round<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        ret_int_ty: Type,
-    ) -> AnyObject<'ctx> {
-        let ret_int_ty_llvm = ctx.get_llvm_type(generator, ret_int_ty).into_int_type();
-
-        let result = if ctx.unifier.unioned(self.ty, ctx.primitives.float) {
-            let n = self.value.into_float_value();
-            let n = llvm_intrinsics::call_float_round(ctx, n, None);
-            ctx.builder.build_float_to_signed_int(n, ret_int_ty_llvm, "round").unwrap()
-        } else {
-            unsupported_type(ctx, [self.ty])
-        };
-        AnyObject { ty: ret_int_ty, value: result.as_basic_value_enum() }
-    }
-
-    /// Call `np_round()` on this object.
-    ///
-    /// NOTE: `np.round()` has different behaviors than `round()` when in comes to "tie" cases and return type.
-    #[must_use]
-    pub fn call_np_round<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> AnyObject<'ctx> {
-        if self.is_float(ctx) {
-            let n = self.into_float(ctx);
-            let n = llvm_intrinsics::call_float_rint(ctx, n.value, None);
-            AnyObject { ty: ctx.primitives.float, value: n.as_basic_value_enum() }
-        } else if self.is_ndarray(ctx) {
-            let ndarray = self.into_ndarray(generator, ctx);
-            ndarray
-                .map(
-                    generator,
-                    ctx,
-                    NDArrayOut::NewNDArray { dtype: ndarray.dtype },
-                    |generator, ctx, scalar| Ok(scalar.call_np_round(generator, ctx)),
-                )
-                .unwrap()
-                .to_any_object(ctx)
-        } else {
-            unsupported_type(ctx, [self.ty])
-        }
-    }
-
-    /// Call `min()` or `max()` on two objects.
-    #[must_use]
-    pub fn call_min_or_max(
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        kind: MinOrMax,
-        a: AnyObject<'ctx>,
-        b: AnyObject<'ctx>,
-    ) -> AnyObject<'ctx> {
-        if !ctx.unifier.unioned(a.ty, b.ty) {
-            unsupported_type(ctx, [a.ty, b.ty])
-        }
-
-        let common_ty = a.ty;
-
-        if a.is_float(ctx) {
-            let function = match kind {
-                MinOrMax::Min => llvm_intrinsics::call_float_minnum,
-                MinOrMax::Max => llvm_intrinsics::call_float_maxnum,
-            };
-
-            let a = a.into_float(ctx).value;
-            let b = b.into_float(ctx).value;
-            let result = function(ctx, a, b, None);
-            AnyObject { value: result.as_basic_value_enum(), ty: ctx.primitives.float }
-        } else if a.is_unsigned_int(ctx) || a.is_bool(ctx) {
-            // Treating bool has an unsigned int since that is convenient
-            let function = match kind {
-                MinOrMax::Min => llvm_intrinsics::call_int_umin,
-                MinOrMax::Max => llvm_intrinsics::call_int_umax,
-            };
-
-            let a = a.into_int(ctx);
-            let b = b.into_int(ctx);
-            let result = function(ctx, a, b, None);
-            AnyObject { value: result.as_basic_value_enum(), ty: common_ty }
-        } else if a.is_signed_int(ctx) {
-            let function = match kind {
-                MinOrMax::Min => llvm_intrinsics::call_int_smin,
-                MinOrMax::Max => llvm_intrinsics::call_int_smax,
-            };
-
-            let a = a.into_int(ctx);
-            let b = b.into_int(ctx);
-            let result = function(ctx, a, b, None);
-            AnyObject { value: result.as_basic_value_enum(), ty: common_ty }
-        } else {
-            unsupported_type(ctx, [common_ty])
-        }
-    }
-
-    /// Call `floor()` or `ceil()` on this object.
-    ///
-    /// It is possible to specify which kind of int type to return.
-    #[must_use]
-    pub fn call_floor_or_ceil<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        kind: FloorOrCeil,
-        ret_int_ty: Type,
-    ) -> Self {
-        let ret_int_ty_llvm = ctx.get_llvm_type(generator, ret_int_ty).into_int_type();
-
-        if self.is_float(ctx) {
-            let function = match kind {
-                FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
-                FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
-            };
-
-            let n = self.into_float(ctx).value;
-            let n = function(ctx, n, None);
-            let n = ctx.builder.build_float_to_signed_int(n, ret_int_ty_llvm, "").unwrap();
-            AnyObject { ty: ret_int_ty, value: n.as_basic_value_enum() }
-        } else {
-            unsupported_type(ctx, [self.ty])
-        }
-    }
-
-    /// Call `np_floor()` or `np_ceil()` on this object.
-    #[must_use]
-    pub fn call_np_floor_or_ceil<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        kind: FloorOrCeil,
-    ) -> Self {
-        // TODO:
-        if self.is_float(ctx) {
-            let function = match kind {
-                FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
-                FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
-            };
-            let n = self.into_float(ctx).value;
-            let n = function(ctx, n, None);
-            AnyObject { ty: ctx.primitives.float, value: n.as_basic_value_enum() }
-        } else if self.is_ndarray(ctx) {
-            let ndarray = self.into_ndarray(generator, ctx);
-            ndarray
-                .map(
-                    generator,
-                    ctx,
-                    NDArrayOut::NewNDArray { dtype: ctx.primitives.float },
-                    |generator, ctx, scalar| Ok(scalar.call_np_floor_or_ceil(generator, ctx, kind)),
-                )
-                .unwrap()
-                .to_any_object(ctx)
-        } else {
-            unsupported_type(ctx, [self.ty])
-        }
-    }
-}

nac3core/src/codegen/object/ndarray/array.rs

@@ -1,176 +0,0 @@
-use super::NDArrayObject;
-use crate::{
-    codegen::{
-        irrt::{call_nac3_array_set_and_validate_list_shape, call_nac3_array_write_list_to_array},
-        model::*,
-        object::{list::ListObject, AnyObject},
-        stmt::gen_if_else_expr_callback,
-        CodeGenContext, CodeGenerator,
-    },
-    toplevel::helper::{arraylike_flatten_element_type, arraylike_get_ndims},
-    typecheck::typedef::{Type, TypeEnum},
-};
-
-fn get_list_object_dtype_and_ndims<'ctx>(
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    list: ListObject<'ctx>,
-) -> (Type, u64) {
-    let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list.item_type);
-
-    let ndims = arraylike_get_ndims(&mut ctx.unifier, list.item_type);
-    let ndims = ndims + 1; // To count `list` itself.
-
-    (dtype, ndims)
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    fn from_np_array_list_copy_impl<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        list: ListObject<'ctx>,
-    ) -> Self {
-        let sizet_model = IntModel(SizeT);
-
-        let (dtype, ndims_int) = get_list_object_dtype_and_ndims(ctx, list);
-        let list_value = list.get_opaque_list_ptr(generator, ctx);
-
-        // Validate `list` has a consistent shape.
-        // Raise an exception if `list` is something abnormal like `[[1, 2], [3]]`.
-        // If `list` has a consistent shape, deduce the shape and write it to `shape`.
-        let ndims = sizet_model.constant(generator, ctx.ctx, ndims_int);
-        let shape = sizet_model.array_alloca(generator, ctx, ndims.value, "shape");
-        call_nac3_array_set_and_validate_list_shape(generator, ctx, list_value, ndims, shape);
-
-        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims_int, "ndarray_from_list");
-        ndarray.copy_shape_from_array(generator, ctx, shape);
-        ndarray.create_data(generator, ctx);
-
-        // Copy all contents from the list.
-        call_nac3_array_write_list_to_array(generator, ctx, list_value, ndarray.instance);
-
-        ndarray
-    }
-
-    fn from_np_array_list_try_no_copy_impl<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        list: ListObject<'ctx>,
-    ) -> Self {
-        // np_array without copy is only possible `list` is not nested.
-
-        // If `list` is `list[T]`, we can create an ndarray with `data` set
-        // to the array pointer of `list`.
-
-        let sizet_model = IntModel(SizeT);
-        let zero = sizet_model.const_0(generator, ctx.ctx);
-
-        let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
-        if ndims == 1 {
-            // `list` is not nested, does not need to copy.
-            let ndarray =
-                NDArrayObject::alloca(generator, ctx, dtype, 1, "ndarray_from_list_no_copy");
-
-            // Set data
-            let data = list.get_opaque_items_ptr(generator, ctx);
-            ndarray.instance.set(ctx, |f| f.data, data);
-
-            // Set shape
-            //   dim = list->len;
-            //   shape[0] = dim;
-            let shape = ndarray.instance.get(generator, ctx, |f| f.shape, "shape");
-            let dim = list.instance.get(generator, ctx, |f| f.len, "dim");
-            shape.offset(generator, ctx, zero.value, "pdim").store(ctx, dim);
-
-            // Set strides, the `data` is contiguous
-            ndarray.update_strides_by_shape(generator, ctx);
-
-            // Done
-            ndarray
-        } else {
-            // `list` is nested, it is impossible to not copy.
-            NDArrayObject::from_np_array_list_copy_impl(generator, ctx, list)
-        }
-    }
-
-    fn from_np_array_list_impl<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        list: ListObject<'ctx>,
-        copy: Int<'ctx, Bool>,
-    ) -> Self {
-        let (dtype, ndims) = get_list_object_dtype_and_ndims(ctx, list);
-
-        let ndarray = gen_if_else_expr_callback(
-            generator,
-            ctx,
-            |_generator, _ctx| Ok(copy.value),
-            |generator, ctx| {
-                let ndarray = NDArrayObject::from_np_array_list_copy_impl(generator, ctx, list);
-                Ok(Some(ndarray.instance.value))
-            },
-            |generator, ctx| {
-                let ndarray =
-                    NDArrayObject::from_np_array_list_try_no_copy_impl(generator, ctx, list);
-                Ok(Some(ndarray.instance.value))
-            },
-        )
-        .unwrap()
-        .unwrap();
-
-        NDArrayObject::from_value_and_unpacked_types(generator, ctx, ndarray, dtype, ndims)
-    }
-
-    pub fn from_np_array_ndarray_impl<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        ndarray: NDArrayObject<'ctx>,
-        copy: Int<'ctx, Bool>,
-    ) -> Self {
-        let ndarray_val = gen_if_else_expr_callback(
-            generator,
-            ctx,
-            |_generator, _ctx| Ok(copy.value),
-            |generator, ctx| {
-                let ndarray = ndarray.make_copy(generator, ctx, "np_array_copied_ndarray"); // Force copy
-                Ok(Some(ndarray.instance.value))
-            },
-            |_generator, _ctx| {
-                // No need to copy. Return `ndarray` itself.
-                Ok(Some(ndarray.instance.value))
-            },
-        )
-        .unwrap()
-        .unwrap();
-
-        NDArrayObject::from_value_and_unpacked_types(
-            generator,
-            ctx,
-            ndarray_val,
-            ndarray.dtype,
-            ndarray.ndims,
-        )
-    }
-
-    pub fn from_np_array<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        object: AnyObject<'ctx>,
-        copy: Int<'ctx, Bool>,
-    ) -> Self {
-        match &*ctx.unifier.get_ty(object.ty) {
-            TypeEnum::TObj { obj_id, .. }
-                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
-            {
-                let list = ListObject::from_object(generator, ctx, object);
-                NDArrayObject::from_np_array_list_impl(generator, ctx, list, copy)
-            }
-            TypeEnum::TObj { obj_id, .. }
-                if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
-            {
-                let ndarray = NDArrayObject::from_object(generator, ctx, object);
-                NDArrayObject::from_np_array_ndarray_impl(generator, ctx, ndarray, copy)
-            }
-            _ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object.ty)), // Typechecker ensures this
-        }
-    }
-}

nac3core/src/codegen/object/ndarray/broadcast.rs

@@ -1,161 +0,0 @@
-use itertools::Itertools;
-
-use crate::codegen::{
-    irrt::{
-        call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to,
-        call_nac3_ndarray_util_assert_shape_no_negative,
-    },
-    model::*,
-    CodeGenContext, CodeGenerator,
-};
-
-use super::NDArrayObject;
-
-/// Fields of [`ShapeEntry`]
-pub struct ShapeEntryFields<'ctx, F: FieldTraversal<'ctx>> {
-    pub ndims: F::Out<IntModel<SizeT>>,
-    pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
-}
-
-/// An IRRT structure used in broadcasting.
-#[derive(Debug, Clone, Copy, Default)]
-pub struct ShapeEntry;
-
-impl<'ctx> StructKind<'ctx> for ShapeEntry {
-    type Fields<F: FieldTraversal<'ctx>> = ShapeEntryFields<'ctx, F>;
-
-    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
-        Self::Fields { ndims: traversal.add_auto("ndims"), shape: traversal.add_auto("shape") }
-    }
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// Create a broadcast view on this ndarray with a target shape.
-    ///
-    /// The input shape will be checked to make sure that it contains no negative values.
-    ///
-    /// * `target_ndims` - The ndims type after broadcasting to the given shape.
-    /// The caller has to figure this out for this function.
-    /// * `target_shape` - An array pointer pointing to the target shape.
-    #[must_use]
-    pub fn broadcast_to<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        target_ndims: u64,
-        target_shape: Ptr<'ctx, IntModel<SizeT>>,
-    ) -> Self {
-        let target_ndims_llvm = IntModel(SizeT).constant(generator, ctx.ctx, target_ndims);
-        call_nac3_ndarray_util_assert_shape_no_negative(
-            generator,
-            ctx,
-            target_ndims_llvm,
-            target_shape,
-        );
-
-        let broadcast_ndarray = NDArrayObject::alloca(
-            generator,
-            ctx,
-            self.dtype,
-            target_ndims,
-            "broadcast_ndarray_to_dst",
-        );
-        broadcast_ndarray.copy_shape_from_array(generator, ctx, target_shape);
-
-        call_nac3_ndarray_broadcast_to(generator, ctx, self.instance, broadcast_ndarray.instance);
-        broadcast_ndarray
-    }
-}
-/// A result produced by [`broadcast_all_ndarrays`]
-#[derive(Debug, Clone)]
-pub struct BroadcastAllResult<'ctx> {
-    /// The statically known `ndims` of the broadcast result.
-    pub ndims: u64,
-    /// The broadcasting shape.
-    pub shape: Ptr<'ctx, IntModel<SizeT>>,
-    /// Broadcasted views on the inputs.
-    ///
-    /// All of them will have `shape` [`BroadcastAllResult::shape`] and
-    /// `ndims` [`BroadcastAllResult::ndims`]. The length of the vector
-    /// is the same as the input.
-    pub ndarrays: Vec<NDArrayObject<'ctx>>,
-}
-
-pub fn broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    in_entries: &[(Ptr<'ctx, IntModel<SizeT>>, u64)],
-    broadcast_ndims: u64,
-    broadcast_shape: Ptr<'ctx, IntModel<SizeT>>,
-) {
-    let sizet_model = IntModel(SizeT);
-    let shape_model = StructModel(ShapeEntry);
-
-    // Prepare input shape entries
-    let num_shape_entries =
-        sizet_model.constant(generator, ctx.ctx, u64::try_from(in_entries.len()).unwrap());
-    let shape_entries =
-        shape_model.array_alloca(generator, ctx, num_shape_entries.value, "shape_entries");
-    for (i, (in_shape, in_ndims)) in in_entries.iter().enumerate() {
-        let i = sizet_model.constant(generator, ctx.ctx, i as u64).value;
-        let pshape_entry = shape_entries.offset(generator, ctx, i, "shape_entry");
-
-        let in_ndims = sizet_model.constant(generator, ctx.ctx, *in_ndims);
-        pshape_entry.set(ctx, |f| f.ndims, in_ndims);
-
-        pshape_entry.set(ctx, |f| f.shape, *in_shape);
-    }
-
-    let broadcast_ndims = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
-
-    call_nac3_ndarray_broadcast_shapes(
-        generator,
-        ctx,
-        num_shape_entries,
-        shape_entries,
-        broadcast_ndims,
-        broadcast_shape,
-    );
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    // TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
-    pub fn broadcast<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        ndarrays: &[Self],
-    ) -> BroadcastAllResult<'ctx> {
-        assert!(!ndarrays.is_empty());
-
-        let sizet_model = IntModel(SizeT);
-
-        // Infer the broadcast output ndims.
-        let broadcast_ndims_int = ndarrays.iter().map(|ndarray| ndarray.ndims).max().unwrap();
-
-        let broadcast_ndims = sizet_model.constant(generator, ctx.ctx, broadcast_ndims_int);
-        let broadcast_shape =
-            sizet_model.array_alloca(generator, ctx, broadcast_ndims.value, "broadcast_shape");
-
-        let shape_entries = ndarrays
-            .iter()
-            .map(|ndarray| {
-                (ndarray.instance.get(generator, ctx, |f| f.shape, "shape"), ndarray.ndims)
-            })
-            .collect_vec();
-        broadcast_shapes(generator, ctx, &shape_entries, broadcast_ndims_int, broadcast_shape);
-
-        // Broadcast all the inputs to shape `dst_shape`.
-        let broadcast_ndarrays: Vec<_> = ndarrays
-            .iter()
-            .map(|ndarray| {
-                ndarray.broadcast_to(generator, ctx, broadcast_ndims_int, broadcast_shape)
-            })
-            .collect_vec();
-
-        BroadcastAllResult {
-            ndims: broadcast_ndims_int,
-            shape: broadcast_shape,
-            ndarrays: broadcast_ndarrays,
-        }
-    }
-}

nac3core/src/codegen/object/ndarray/factory.rs

@@ -1,238 +0,0 @@
-use inkwell::{values::BasicValueEnum, IntPredicate};
-
-use super::NDArrayObject;
-use crate::{
-    codegen::{
-        irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, object::AnyObject,
-        CodeGenContext, CodeGenerator,
-    },
-    typecheck::typedef::Type,
-};
-
-/// Get the zero value in `np.zeros()` of a `dtype`.
-fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    dtype: Type,
-) -> BasicValueEnum<'ctx> {
-    if [ctx.primitives.int32, ctx.primitives.uint32]
-        .iter()
-        .any(|ty| ctx.unifier.unioned(dtype, *ty))
-    {
-        ctx.ctx.i32_type().const_zero().into()
-    } else if [ctx.primitives.int64, ctx.primitives.uint64]
-        .iter()
-        .any(|ty| ctx.unifier.unioned(dtype, *ty))
-    {
-        ctx.ctx.i64_type().const_zero().into()
-    } else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
-        ctx.ctx.f64_type().const_zero().into()
-    } else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
-        ctx.ctx.bool_type().const_zero().into()
-    } else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
-        ctx.gen_string(generator, "").value.into()
-    } else {
-        panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
-    }
-}
-
-/// Get the one value in `np.ones()` of a `dtype`.
-fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    dtype: Type,
-) -> BasicValueEnum<'ctx> {
-    if [ctx.primitives.int32, ctx.primitives.uint32]
-        .iter()
-        .any(|ty| ctx.unifier.unioned(dtype, *ty))
-    {
-        let is_signed = ctx.unifier.unioned(dtype, 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(dtype, *ty))
-    {
-        let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int64);
-        ctx.ctx.i64_type().const_int(1, is_signed).into()
-    } else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
-        ctx.ctx.f64_type().const_float(1.0).into()
-    } else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
-        ctx.ctx.bool_type().const_int(1, false).into()
-    } else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
-        ctx.gen_string(generator, "1").value.into()
-    } else {
-        panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
-    }
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// Create an ndarray like `np.empty`.
-    pub fn from_np_empty<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        dtype: Type,
-        ndims: u64,
-        shape: Ptr<'ctx, IntModel<SizeT>>,
-    ) -> Self {
-        // Validate `shape`
-        // TODO: Should the caller be responsible for this instead?
-        let ndims_llvm = IntModel(SizeT).constant(generator, ctx.ctx, ndims);
-        call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims_llvm, shape);
-
-        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims, "full_ndarray");
-        ndarray.copy_shape_from_array(generator, ctx, shape);
-        ndarray.create_data(generator, ctx);
-
-        ndarray
-    }
-
-    /// Create an ndarray like `np.full`.
-    pub fn from_np_full<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        dtype: Type,
-        ndims: u64,
-        shape: Ptr<'ctx, IntModel<SizeT>>,
-        fill_value: AnyObject<'ctx>,
-    ) -> Self {
-        // Sanity check on `fill_value`'s dtype.
-        assert!(ctx.unifier.unioned(dtype, fill_value.ty));
-
-        let ndarray = NDArrayObject::from_np_empty(generator, ctx, dtype, ndims, shape);
-        ndarray.fill(generator, ctx, fill_value);
-        ndarray
-    }
-
-    /// Create an ndarray like `np.zero`.
-    pub fn from_np_zero<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        dtype: Type,
-        ndims: u64,
-        shape: Ptr<'ctx, IntModel<SizeT>>,
-    ) -> Self {
-        let fill_value = ndarray_zero_value(generator, ctx, dtype);
-        let fill_value = AnyObject { value: fill_value, ty: dtype };
-        NDArrayObject::from_np_full(generator, ctx, dtype, ndims, shape, fill_value)
-    }
-
-    /// Create an ndarray like `np.ones`.
-    pub fn from_np_ones<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        dtype: Type,
-        ndims: u64,
-        shape: Ptr<'ctx, IntModel<SizeT>>,
-    ) -> Self {
-        let fill_value = ndarray_one_value(generator, ctx, dtype);
-        let fill_value = AnyObject { value: fill_value, ty: dtype };
-        NDArrayObject::from_np_full(generator, ctx, dtype, ndims, shape, fill_value)
-    }
-
-    /// Create an ndarray like `np.arange`.
-    /// The returned ndarray's `dtype` is always `float`
-    pub fn from_np_arange<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        length: Int<'ctx, SizeT>,
-    ) -> Self {
-        let ndarray = NDArrayObject::alloca(
-            generator,
-            ctx,
-            ctx.primitives.float,
-            1, // ndims = 1
-            "arange_ndarray",
-        );
-
-        // `ndarray.shape[0] = length`
-        ndarray
-            .instance
-            .get(generator, ctx, |f| f.shape, "shape")
-            .offset_const(generator, ctx, 0, "dim")
-            .store(ctx, length);
-
-        // Create data and set elements
-        ndarray.create_data(generator, ctx);
-        ndarray
-            .foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
-                // Get the index of the current element, convert that index to float, and write it.
-                // This is how we get [0.0, 1.0, 2.0, ...].
-                let index = nditer.get_index(generator, ctx);
-                let pelement = nditer.get_pointer(generator, ctx);
-
-                let val = ctx
-                    .builder
-                    .build_unsigned_int_to_float(index.value, ctx.ctx.f64_type(), "val")
-                    .unwrap();
-                ctx.builder.build_store(pelement, val).unwrap();
-                Ok(())
-            })
-            .unwrap();
-
-        ndarray
-    }
-
-    /// Create an ndarray like `np.eye`.
-    pub fn from_np_eye<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        dtype: Type,
-        nrows: Int<'ctx, SizeT>,
-        ncols: Int<'ctx, SizeT>,
-        offset: Int<'ctx, SizeT>,
-    ) -> Self {
-        let ndzero = ndarray_zero_value(generator, ctx, dtype);
-        let ndone = ndarray_one_value(generator, ctx, dtype);
-
-        let ndarray = NDArrayObject::alloca_dynamic_shape(
-            generator,
-            ctx,
-            dtype,
-            &[nrows, ncols],
-            "eye_ndarray",
-        );
-
-        // Create data and make the matrix like look np.eye()
-        ndarray.create_data(generator, ctx);
-        ndarray
-            .foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
-                // NOTE: rows and cols can never be zero here, since this ndarray's `np.size` would be zero
-                // and this loop would not execute.
-
-                // Load up `row_i` and `col_i` from indices.
-                let row_i = nditer
-                    .get_indices()
-                    .offset_const(generator, ctx, 0, "")
-                    .load(generator, ctx, "row_i");
-                let col_i = nditer
-                    .get_indices()
-                    .offset_const(generator, ctx, 1, "")
-                    .load(generator, ctx, "col_i");
-
-                // Write to element
-                let be_one =
-                    row_i.add(ctx, offset, "").compare(ctx, IntPredicate::EQ, col_i, "write_one");
-                let value = ctx.builder.build_select(be_one.value, ndone, ndzero, "value").unwrap();
-
-                let p = nditer.get_pointer(generator, ctx);
-                ctx.builder.build_store(p, value).unwrap();
-
-                Ok(())
-            })
-            .unwrap();
-
-        ndarray
-    }
-
-    /// Create an ndarray like `np.identity`.
-    pub fn from_np_identity<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        dtype: Type,
-        size: Int<'ctx, SizeT>,
-    ) -> Self {
-        // Convenient implementation
-        let offset = IntModel(SizeT).const_0(generator, ctx.ctx);
-        NDArrayObject::from_np_eye(generator, ctx, dtype, size, size, offset)
-    }
-}

nac3core/src/codegen/object/ndarray/functions.rs

@@ -1,128 +0,0 @@
-use inkwell::{FloatPredicate, IntPredicate};
-
-use crate::codegen::{
-    model::*,
-    object::{AnyObject, MinOrMax},
-    stmt::gen_if_callback,
-    CodeGenContext, CodeGenerator,
-};
-
-use super::NDArrayObject;
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// Helper function to implement NAC3's builtin `np_min()`, `np_max()`, `np_argmin()`, and `np_argmax()`.
-    ///
-    /// Generate LLVM IR to find the extremum and index of the **first** extremum value.
-    ///
-    /// Care has also been taken to make the error messages match that of NumPy.
-    fn min_max_argmin_argmax_helper<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        kind: MinOrMax,
-        on_empty_err_msg: &str,
-    ) -> (AnyObject<'ctx>, Int<'ctx, SizeT>) {
-        let sizet_model = IntModel(SizeT);
-        let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
-
-        // If the ndarray is empty, throw an error.
-        let is_empty = self.is_empty(generator, ctx);
-        ctx.make_assert(
-            generator,
-            is_empty.value,
-            "0:ValueError",
-            on_empty_err_msg,
-            [None, None, None],
-            ctx.current_loc,
-        );
-
-        // Setup and initialize the extremum to be the first element in the ndarray
-        let pextremum_index = sizet_model.alloca(generator, ctx, "extremum_index");
-        let pextremum = ctx.builder.build_alloca(dtype_llvm, "extremum").unwrap();
-
-        let zero = sizet_model.const_0(generator, ctx.ctx);
-        pextremum_index.store(ctx, zero);
-
-        let first_scalar = self.get_nth_scalar(generator, ctx, zero);
-        ctx.builder.build_store(pextremum, first_scalar.value).unwrap();
-
-        self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
-            let old_extremum = ctx.builder.build_load(pextremum, "current_extremum").unwrap();
-            let old_extremum = AnyObject { ty: self.dtype, value: old_extremum };
-
-            let scalar = nditer.get_scalar(generator, ctx);
-            let new_extremum = AnyObject::call_min_or_max(ctx, kind, old_extremum, scalar);
-
-            gen_if_callback(
-                generator,
-                ctx,
-                |generator, ctx| {
-                    // Is new_extremum is more extreme than old_extremum?
-                    let cmp = AnyObject::compare_int_or_float_by_predicate(
-                        generator,
-                        ctx,
-                        new_extremum,
-                        old_extremum,
-                        IntPredicate::NE,
-                        FloatPredicate::ONE,
-                        "",
-                    );
-                    Ok(cmp.value)
-                },
-                |generator, ctx| {
-                    // Yes, update the extremum index
-                    let index = nditer.get_index(generator, ctx);
-                    pextremum_index.store(ctx, index);
-                    Ok(())
-                },
-                |_generator, _ctx| {
-                    // No, do nothing
-                    Ok(())
-                },
-            )
-        })
-        .unwrap();
-
-        // Finally return the extremum and extremum index.
-        let extremum_index = pextremum_index.load(generator, ctx, "extremum_index");
-
-        let extremum = ctx.builder.build_load(pextremum, "extremum_value").unwrap();
-        let extremum = AnyObject { ty: self.dtype, value: extremum };
-
-        (extremum, extremum_index)
-    }
-
-    /// Invoke NAC3's builtin `np_min()` or `np_max()`.
-    pub fn min_or_max<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        kind: MinOrMax,
-    ) -> AnyObject<'ctx> {
-        let on_empty_err_msg = format!(
-            "zero-size array to reduction operation {} which has no identity",
-            match kind {
-                MinOrMax::Min => "minimum",
-                MinOrMax::Max => "maximum",
-            }
-        );
-        self.min_max_argmin_argmax_helper(generator, ctx, kind, &on_empty_err_msg).0
-    }
-
-    /// Invoke NAC3's builtin `np_argmin()` or `np_argmax()`.
-    pub fn argmin_or_argmax<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        kind: MinOrMax,
-    ) -> Int<'ctx, SizeT> {
-        let on_empty_err_msg = format!(
-            "attempt to get {} of an empty sequence",
-            match kind {
-                MinOrMax::Min => "argmin",
-                MinOrMax::Max => "argmax",
-            }
-        );
-        self.min_max_argmin_argmax_helper(generator, ctx, kind, &on_empty_err_msg).1
-    }
-}

nac3core/src/codegen/object/ndarray/indexing.rs

@@ -1,321 +0,0 @@
-use crate::codegen::{irrt::call_nac3_ndarray_index, model::*, CodeGenContext, CodeGenerator};
-
-use super::NDArrayObject;
-
-pub type NDIndexType = Byte;
-
-/// Fields of [`NDIndex`]
-#[derive(Debug, Clone, Copy)]
-pub struct NDIndexFields<'ctx, F: FieldTraversal<'ctx>> {
-    pub type_: F::Out<IntModel<NDIndexType>>, // Defined to be uint8_t in IRRT
-    pub data: F::Out<PtrModel<IntModel<Byte>>>,
-}
-
-/// An IRRT representation fo an ndarray subscript index.
-#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
-pub struct NDIndex;
-
-impl<'ctx> StructKind<'ctx> for NDIndex {
-    type Fields<F: FieldTraversal<'ctx>> = NDIndexFields<'ctx, F>;
-
-    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
-        Self::Fields { type_: traversal.add_auto("type"), data: traversal.add_auto("data") }
-    }
-}
-
-/// Fields of [`UserSlice`]
-#[derive(Debug, Clone)]
-pub struct UserSliceFields<'ctx, F: FieldTraversal<'ctx>> {
-    pub start_defined: F::Out<IntModel<Bool>>,
-    pub start: F::Out<IntModel<Int32>>,
-    pub stop_defined: F::Out<IntModel<Bool>>,
-    pub stop: F::Out<IntModel<Int32>>,
-    pub step_defined: F::Out<IntModel<Bool>>,
-    pub step: F::Out<IntModel<Int32>>,
-}
-
-/// An IRRT representation of a user slice.
-#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
-pub struct UserSlice;
-
-impl<'ctx> StructKind<'ctx> for UserSlice {
-    type Fields<F: FieldTraversal<'ctx>> = UserSliceFields<'ctx, F>;
-
-    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
-        Self::Fields {
-            start_defined: traversal.add_auto("start_defined"),
-            start: traversal.add_auto("start"),
-            stop_defined: traversal.add_auto("stop_defined"),
-            stop: traversal.add_auto("stop"),
-            step_defined: traversal.add_auto("step_defined"),
-            step: traversal.add_auto("step"),
-        }
-    }
-}
-
-/// A convenience structure to prepare a [`UserSlice`].
-#[derive(Debug, Clone)]
-pub struct RustUserSlice<'ctx> {
-    pub start: Option<Int<'ctx, Int32>>,
-    pub stop: Option<Int<'ctx, Int32>>,
-    pub step: Option<Int<'ctx, Int32>>,
-}
-
-impl<'ctx> RustUserSlice<'ctx> {
-    /// Write the contents to an LLVM [`UserSlice`].
-    pub fn write_to_user_slice<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        dst_slice_ptr: Ptr<'ctx, StructModel<UserSlice>>,
-    ) {
-        let bool_model = IntModel(Bool);
-
-        let false_ = bool_model.constant(generator, ctx.ctx, 0);
-        let true_ = bool_model.constant(generator, ctx.ctx, 1);
-
-        // TODO: Code duplication. Probably okay...?
-
-        match self.start {
-            Some(start) => {
-                dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, true_);
-                dst_slice_ptr.gep(ctx, |f| f.start).store(ctx, start);
-            }
-            None => dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, false_),
-        }
-
-        match self.stop {
-            Some(stop) => {
-                dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, true_);
-                dst_slice_ptr.gep(ctx, |f| f.stop).store(ctx, stop);
-            }
-            None => dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, false_),
-        }
-
-        match self.step {
-            Some(step) => {
-                dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, true_);
-                dst_slice_ptr.gep(ctx, |f| f.step).store(ctx, step);
-            }
-            None => dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, false_),
-        }
-    }
-}
-
-// A convenience enum variant to store the content and type of an NDIndex in high level.
-#[derive(Debug, Clone)]
-pub enum RustNDIndex<'ctx> {
-    SingleElement(Int<'ctx, Int32>), // TODO: To be SizeT
-    Slice(RustUserSlice<'ctx>),
-    NewAxis,
-    Ellipsis,
-}
-
-impl<'ctx> RustNDIndex<'ctx> {
-    /// Get the value to set `NDIndex::type` for this variant.
-    fn get_type_id(&self) -> u64 {
-        // Defined in IRRT, must be in sync
-        match self {
-            RustNDIndex::SingleElement(_) => 0,
-            RustNDIndex::Slice(_) => 1,
-            RustNDIndex::NewAxis => 2,
-            RustNDIndex::Ellipsis => 3,
-        }
-    }
-
-    /// Write the contents to an LLVM [`NDIndex`].
-    fn write_to_ndindex<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        dst_ndindex_ptr: Ptr<'ctx, StructModel<NDIndex>>,
-    ) {
-        let ndindex_type_model = IntModel(NDIndexType::default());
-        let i32_model = IntModel(Int32);
-        let user_slice_model = StructModel(UserSlice);
-
-        // Set `dst_ndindex_ptr->type`
-        dst_ndindex_ptr
-            .gep(ctx, |f| f.type_)
-            .store(ctx, ndindex_type_model.constant(generator, ctx.ctx, self.get_type_id()));
-
-        // Set `dst_ndindex_ptr->data`
-        match self {
-            RustNDIndex::SingleElement(in_index) => {
-                let index_ptr = i32_model.alloca(generator, ctx, "index");
-                index_ptr.store(ctx, *in_index);
-
-                dst_ndindex_ptr
-                    .gep(ctx, |f| f.data)
-                    .store(ctx, index_ptr.pointer_cast(generator, ctx, IntModel(Byte), ""));
-            }
-            RustNDIndex::Slice(in_rust_slice) => {
-                let user_slice_ptr = user_slice_model.alloca(generator, ctx, "user_slice");
-                in_rust_slice.write_to_user_slice(generator, ctx, user_slice_ptr);
-
-                dst_ndindex_ptr
-                    .gep(ctx, |f| f.data)
-                    .store(ctx, user_slice_ptr.pointer_cast(generator, ctx, IntModel(Byte), ""));
-            }
-            RustNDIndex::NewAxis | RustNDIndex::Ellipsis => {}
-        }
-    }
-
-    /// Allocate an array of `NDIndex`es on the stack and return its stack pointer.
-    pub fn alloca_ndindexes<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        in_ndindexes: &[RustNDIndex<'ctx>],
-    ) -> (Int<'ctx, SizeT>, Ptr<'ctx, StructModel<NDIndex>>) {
-        let sizet_model = IntModel(SizeT);
-        let ndindex_model = StructModel(NDIndex);
-
-        let num_ndindexes = sizet_model.constant(generator, ctx.ctx, in_ndindexes.len() as u64);
-        let ndindexes =
-            ndindex_model.array_alloca(generator, ctx, num_ndindexes.value, "ndindexes");
-        for (i, in_ndindex) in in_ndindexes.iter().enumerate() {
-            let i = sizet_model.constant(generator, ctx.ctx, i as u64);
-            let pndindex = ndindexes.offset(generator, ctx, i.value, "");
-            in_ndindex.write_to_ndindex(generator, ctx, pndindex);
-        }
-
-        (num_ndindexes, ndindexes)
-    }
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// Get the ndims [`Type`] after indexing with a given slice.
-    #[must_use]
-    pub fn deduce_ndims_after_indexing_with(&self, indexes: &[RustNDIndex<'ctx>]) -> u64 {
-        let mut ndims = self.ndims;
-        for index in indexes {
-            match index {
-                RustNDIndex::SingleElement(_) => {
-                    ndims -= 1; // Single elements decrements ndims
-                }
-                RustNDIndex::NewAxis => {
-                    ndims += 1; // `np.newaxis` / `none` adds a new axis
-                }
-                RustNDIndex::Ellipsis | RustNDIndex::Slice(_) => {}
-            }
-        }
-        ndims
-    }
-
-    /// Index into the ndarray, and return a newly-allocated view on this ndarray.
-    ///
-    /// This function behaves like NumPy's ndarray indexing, but if the indexes index
-    /// into a single element, an unsized ndarray is returned.
-    #[must_use]
-    pub fn index<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        indexes: &[RustNDIndex<'ctx>],
-        name: &str,
-    ) -> Self {
-        let dst_ndims = self.deduce_ndims_after_indexing_with(indexes);
-        let dst_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, dst_ndims, name);
-
-        let (num_indexes, indexes) = RustNDIndex::alloca_ndindexes(generator, ctx, indexes);
-        call_nac3_ndarray_index(
-            generator,
-            ctx,
-            num_indexes,
-            indexes,
-            self.instance,
-            dst_ndarray.instance,
-        );
-
-        dst_ndarray
-    }
-}
-
-pub mod util {
-    use itertools::Itertools;
-    use nac3parser::ast::{Constant, Expr, ExprKind};
-
-    use crate::{
-        codegen::{expr::gen_slice, model::*, CodeGenContext, CodeGenerator},
-        typecheck::typedef::{Type, TypeEnum},
-    };
-
-    use super::{RustNDIndex, RustUserSlice};
-
-    /// Generate LLVM code to transform an ndarray subscript expression to
-    /// its list of [`RustNDIndex`]
-    ///
-    /// i.e.,
-    /// ```python
-    /// my_ndarray[::3, 1, :2:]
-    ///            ^^^^^^^^^^^ Then these into a three `RustNDIndex`es
-    /// ```
-    pub fn gen_ndarray_subscript_ndindexes<'ctx, G: CodeGenerator>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        subscript: &Expr<Option<Type>>,
-    ) -> Result<Vec<RustNDIndex<'ctx>>, String> {
-        // TODO: Support https://numpy.org/doc/stable/user/basics.indexing.html#dimensional-indexing-tools
-        let i32_model = IntModel(Int32);
-
-        // Annoying notes about `slice`
-        //  - `my_array[5]`
-        //    - slice is a `Constant`
-        //  - `my_array[:5]`
-        //    - slice is a `Slice`
-        //  - `my_array[:]`
-        //    - slice is a `Slice`, but lower upper step would all be `Option::None`
-        //  - `my_array[:, :]`
-        //    - slice is now a `Tuple` of two `Slice`-s
-        //
-        // In summary:
-        //  - when there is a comma "," within [], `slice` will be a `Tuple` of the entries.
-        //  - when there is not comma "," within [] (i.e., just a single entry), `slice` will be that entry itself.
-        //
-        // So we first "flatten" out the slice expression
-        let index_exprs = match &subscript.node {
-            ExprKind::Tuple { elts, .. } => elts.iter().collect_vec(),
-            _ => vec![subscript],
-        };
-
-        // Process all index expressions
-        let mut rust_ndindexes: Vec<RustNDIndex> = Vec::with_capacity(index_exprs.len()); // Not using iterators here because `?` is used here.
-        for index_expr in index_exprs {
-            // NOTE: Currently nac3core's slices do not have an object representation,
-            // so the code/implementation looks awkward - we have to do pattern matching on the expression
-            let ndindex = if let ExprKind::Slice { lower, upper, step } = &index_expr.node {
-                // Handle slices
-
-                // Helper function here to deduce code duplication
-                let (lower, upper, step) = gen_slice(generator, ctx, lower, upper, step)?;
-                RustNDIndex::Slice(RustUserSlice { start: lower, stop: upper, step })
-            } else if let ExprKind::Constant { value: Constant::Ellipsis, .. } = &index_expr.node {
-                // Handle '...'
-                RustNDIndex::Ellipsis
-            } else {
-                match &*ctx.unifier.get_ty(index_expr.custom.unwrap()) {
-                    TypeEnum::TObj { obj_id, .. }
-                        if *obj_id == ctx.primitives.option.obj_id(&ctx.unifier).unwrap() =>
-                    {
-                        // Handle `np.newaxis` / `None`
-                        RustNDIndex::NewAxis
-                    }
-                    _ => {
-                        // Treat and handle everything else as a single element index.
-                        let index =
-                            generator.gen_expr(ctx, index_expr)?.unwrap().to_basic_value_enum(
-                                ctx,
-                                generator,
-                                ctx.primitives.int32, // Must be int32, this checks for illegal values
-                            )?;
-                        let index = i32_model.check_value(generator, ctx.ctx, index).unwrap();
-
-                        RustNDIndex::SingleElement(index)
-                    }
-                }
-            };
-            rust_ndindexes.push(ndindex);
-        }
-        Ok(rust_ndindexes)
-    }
-}

nac3core/src/codegen/object/ndarray/mapping.rs

@@ -1,200 +0,0 @@
-use itertools::Itertools;
-
-use crate::{
-    codegen::{
-        object::ndarray::{AnyObject, NDArrayObject},
-        stmt::gen_for_callback,
-        CodeGenContext, CodeGenerator,
-    },
-    typecheck::typedef::Type,
-};
-
-use super::{nditer::NDIterHandle, scalar::ScalarOrNDArray, NDArrayOut};
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// TODO: Document me. Has complex behavior.
-    /// and explain why `ret_dtype` has to be specified beforehand.
-    pub fn broadcasting_starmap<'a, G, MappingFn>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, 'a>,
-        ndarrays: &[Self],
-        out: NDArrayOut<'ctx>,
-        mapping: MappingFn,
-    ) -> Result<Self, String>
-    where
-        G: CodeGenerator + ?Sized,
-        MappingFn: FnOnce(
-            &mut G,
-            &mut CodeGenContext<'ctx, 'a>,
-            &[AnyObject<'ctx>],
-        ) -> Result<AnyObject<'ctx>, String>,
-    {
-        // Broadcast inputs
-        let broadcast_result = NDArrayObject::broadcast(generator, ctx, ndarrays);
-
-        let out_ndarray = match out {
-            NDArrayOut::NewNDArray { dtype } => {
-                // Create a new ndarray based on the broadcast shape.
-                let result_ndarray = NDArrayObject::alloca(
-                    generator,
-                    ctx,
-                    dtype,
-                    broadcast_result.ndims,
-                    "mapped_ndarray",
-                );
-                result_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
-                result_ndarray.create_data(generator, ctx);
-                result_ndarray
-            }
-            NDArrayOut::WriteToNDArray { ndarray: result_ndarray } => {
-                // Use an existing ndarray.
-
-                // Check that its shape is compatible with the broadcast shape.
-                result_ndarray.check_can_be_written_by_out(
-                    generator,
-                    ctx,
-                    broadcast_result.ndims,
-                    broadcast_result.shape,
-                );
-                result_ndarray
-            }
-        };
-
-        // Map element-wise and store results into `mapped_ndarray`.
-        let nditer = NDIterHandle::new(generator, ctx, out_ndarray);
-        gen_for_callback(
-            generator,
-            ctx,
-            Some("broadcast_starmap"),
-            |generator, ctx| {
-                // Create NDIters for all broadcasted input ndarrays.
-                let other_nditers = broadcast_result
-                    .ndarrays
-                    .iter()
-                    .map(|ndarray| NDIterHandle::new(generator, ctx, *ndarray))
-                    .collect_vec();
-                Ok((nditer, other_nditers))
-            },
-            |generator, ctx, (out_nditer, _in_nditers)| {
-                // We can simply use `out_nditer`'s `has_next()`.
-                // `in_nditers`' `has_next()`s should return the same value.
-                Ok(out_nditer.has_next(generator, ctx).value)
-            },
-            |generator, ctx, _hooks, (out_nditer, in_nditers)| {
-                // Get all the scalars from the broadcasted input ndarrays, pass them to `mapping`,
-                // and write to `out_ndarray`.
-
-                let in_scalars =
-                    in_nditers.iter().map(|nditer| nditer.get_scalar(generator, ctx)).collect_vec();
-
-                let result = mapping(generator, ctx, &in_scalars)?;
-                // Sanity check on result's ty
-                assert!(ctx.unifier.unioned(result.ty, out_ndarray.dtype));
-
-                let p = out_nditer.get_pointer(generator, ctx);
-                ctx.builder.build_store(p, result.value).unwrap();
-
-                Ok(())
-            },
-            |generator, ctx, (out_nditer, in_nditers)| {
-                // Advance all iterators
-                out_nditer.next(generator, ctx);
-                in_nditers.iter().for_each(|nditer| nditer.next(generator, ctx));
-                Ok(())
-            },
-        )?;
-
-        Ok(out_ndarray)
-    }
-
-    pub fn map<'a, G, Mapping>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, 'a>,
-        out: NDArrayOut<'ctx>,
-        mapping: Mapping,
-    ) -> Result<Self, String>
-    where
-        G: CodeGenerator + ?Sized,
-        Mapping: FnOnce(
-            &mut G,
-            &mut CodeGenContext<'ctx, 'a>,
-            AnyObject<'ctx>,
-        ) -> Result<AnyObject<'ctx>, String>,
-    {
-        NDArrayObject::broadcasting_starmap(
-            generator,
-            ctx,
-            &[*self],
-            out,
-            |generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
-        )
-    }
-}
-
-impl<'ctx> ScalarOrNDArray<'ctx> {
-    /// TODO: Document me. Has complex behavior.
-    /// and explain why `ret_dtype` has to be specified beforehand.
-    pub fn broadcasting_starmap<'a, G, MappingFn>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, 'a>,
-        inputs: &[Self],
-        ret_dtype: Type,
-        mapping: MappingFn,
-    ) -> Result<Self, String>
-    where
-        G: CodeGenerator + ?Sized,
-        MappingFn: FnOnce(
-            &mut G,
-            &mut CodeGenContext<'ctx, 'a>,
-            &[AnyObject<'ctx>],
-        ) -> Result<AnyObject<'ctx>, String>,
-    {
-        // Check if all inputs are AnyObjects
-        let all_scalars: Option<Vec<_>> = inputs.iter().map(AnyObject::try_from).try_collect().ok();
-
-        if let Some(scalars) = all_scalars {
-            let scalar = mapping(generator, ctx, &scalars)?;
-
-            // Sanity check on scalar's type
-            assert!(ctx.unifier.unioned(scalar.ty, ret_dtype));
-
-            Ok(ScalarOrNDArray::Scalar(scalar))
-        } else {
-            // Promote all input to ndarrays and map through them.
-            let inputs = inputs.iter().map(|input| input.as_ndarray(generator, ctx)).collect_vec();
-            let ndarray = NDArrayObject::broadcasting_starmap(
-                generator,
-                ctx,
-                &inputs,
-                NDArrayOut::NewNDArray { dtype: ret_dtype },
-                mapping,
-            )?;
-            Ok(ScalarOrNDArray::NDArray(ndarray))
-        }
-    }
-
-    pub fn map<'a, G, Mapping>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, 'a>,
-        ret_dtype: Type,
-        mapping: Mapping,
-    ) -> Result<Self, String>
-    where
-        G: CodeGenerator + ?Sized,
-        Mapping: FnOnce(
-            &mut G,
-            &mut CodeGenContext<'ctx, 'a>,
-            AnyObject<'ctx>,
-        ) -> Result<AnyObject<'ctx>, String>,
-    {
-        ScalarOrNDArray::broadcasting_starmap(
-            generator,
-            ctx,
-            &[*self],
-            ret_dtype,
-            |generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
-        )
-    }
-}

nac3core/src/codegen/object/ndarray/mod.rs

@@ -1,831 +0,0 @@
-pub mod array;
-pub mod broadcast;
-pub mod factory;
-pub mod functions;
-pub mod indexing;
-pub mod mapping;
-pub mod nalgebra;
-pub mod nditer;
-pub mod product;
-pub mod scalar;
-pub mod shape_util;
-
-use crate::{
-    codegen::{
-        irrt::{
-            call_nac3_ndarray_copy_data, call_nac3_ndarray_get_nth_pelement,
-            call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_len, call_nac3_ndarray_nbytes,
-            call_nac3_ndarray_resolve_and_check_new_shape, call_nac3_ndarray_set_strides_by_shape,
-            call_nac3_ndarray_size, call_nac3_ndarray_transpose,
-            call_nac3_ndarray_util_assert_output_shape_same,
-        },
-        model::*,
-        stmt::{gen_for_callback, BreakContinueHooks},
-        structure::{NDArray, SimpleNDArray},
-        CodeGenContext, CodeGenerator,
-    },
-    toplevel::{
-        helper::{create_ndims, extract_ndims},
-        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
-    },
-    typecheck::typedef::Type,
-};
-use indexing::RustNDIndex;
-use inkwell::{
-    context::Context,
-    types::BasicType,
-    values::{BasicValue, PointerValue},
-    AddressSpace, IntPredicate,
-};
-use nditer::NDIterHandle;
-use scalar::ScalarOrNDArray;
-use util::call_memcpy_model;
-
-use super::{tuple::TupleObject, AnyObject};
-
-/// A NAC3 Python ndarray object.
-#[derive(Debug, Clone, Copy)]
-pub struct NDArrayObject<'ctx> {
-    pub dtype: Type,
-    pub ndims: u64,
-    pub instance: Ptr<'ctx, StructModel<NDArray>>,
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// Create an [`NDArrayObject`] from an LLVM value and its typechecker [`Type`].
-    pub fn from_object<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        object: AnyObject<'ctx>,
-    ) -> Self {
-        let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, object.ty);
-        let ndims = extract_ndims(&ctx.unifier, ndims);
-        Self::from_value_and_unpacked_types(generator, ctx, object.value, dtype, ndims)
-    }
-
-    /// Like [`NDArrayObject::from_object`] but you directly supply the ndarray's
-    /// `dtype` and `ndims`.
-    pub fn from_value_and_unpacked_types<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        value: V,
-        dtype: Type,
-        ndims: u64,
-    ) -> Self {
-        let pndarray_model = PtrModel(StructModel(NDArray));
-        let value = pndarray_model.check_value(generator, ctx.ctx, value).unwrap();
-        NDArrayObject { dtype, ndims, instance: value }
-    }
-
-    /// Forget that this is an ndarray and convert to an [`AnyObject`].
-    pub fn to_any_object(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> AnyObject<'ctx> {
-        let ty = self.get_ndarray_type(ctx);
-        AnyObject { value: self.instance.value.as_basic_value_enum(), ty }
-    }
-
-    /// Create a [`SimpleNDArray`] from the contents of this ndarray.
-    ///
-    /// This function may or may not be expensive depending on if this ndarray has contiguous data.
-    ///
-    /// If this ndarray is not C-contiguous, this function will allocate memory on the stack for the `data` field of
-    /// the returned [`SimpleNDArray`] and copy contents of this ndarray to there.
-    ///
-    /// If this ndarray is C-contiguous, contents of this ndarray will not be copied. The created [`SimpleNDArray`]
-    /// will have the same `data` field as this ndarray.
-    ///
-    /// The `item_model` sets the [`Model`] of the returned [`SimpleNDArray`]'s `Item` model, and should match the
-    /// `ctx.get_llvm_type()` of this ndarray's `dtype`. Otherwise this function panics.
-    pub fn make_simple_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        item_model: Item,
-        name: &str,
-    ) -> Ptr<'ctx, StructModel<SimpleNDArray<Item>>> {
-        // Sanity check on `self.dtype` and `item_model`.
-        let dtype_llvm = ctx.get_llvm_type(generator, self.dtype);
-        item_model.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
-
-        let simple_ndarray_model = StructModel(SimpleNDArray { item: item_model });
-
-        let current_bb = ctx.builder.get_insert_block().unwrap();
-        let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
-        let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
-        let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
-
-        // Allocate and setup the resulting [`SimpleNDArray`].
-        let result = simple_ndarray_model.alloca(generator, ctx, name);
-
-        // Set ndims and shape.
-        let ndims = self.get_ndims(generator, ctx.ctx);
-        result.set(ctx, |f| f.ndims, ndims);
-
-        let shape = self.instance.get(generator, ctx, |f| f.shape, "shape");
-        result.set(ctx, |f| f.shape, shape);
-
-        // Set data, but we do things differently if this ndarray is contiguous.
-        let is_contiguous = self.is_c_contiguous(generator, ctx);
-        ctx.builder.build_conditional_branch(is_contiguous.value, then_bb, else_bb).unwrap();
-
-        // Inserting into then_bb; This ndarray is contiguous.
-        let data = self.instance.get(generator, ctx, |f| f.data, "");
-        let data = data.pointer_cast(generator, ctx, item_model, "");
-        result.set(ctx, |f| f.data, data);
-        ctx.builder.build_unconditional_branch(end_bb).unwrap();
-
-        // Inserting into else_bb; This ndarray is not contiguous. Do a full-copy on `data`.
-        // TODO: Reimplement this? This method does give us the contiguous `data`, but
-        // this creates a few extra bytes of useless information because an entire NDArray
-        // is allocated. Though this is super convenient.
-        let data = self.make_copy(generator, ctx, "").instance.get(generator, ctx, |f| f.data, "");
-        let data = data.pointer_cast(generator, ctx, item_model, "");
-        result.set(ctx, |f| f.data, data);
-        ctx.builder.build_unconditional_branch(end_bb).unwrap();
-
-        // Reposition to end_bb for continuation
-        ctx.builder.position_at_end(end_bb);
-
-        result
-    }
-
-    /// Create an [`NDArrayObject`] from a [`SimpleNDArray`].
-    ///
-    /// This operation is super cheap. The newly created [`NDArray`] will not copy contents
-    /// from `simple_ndarray`, but only having its `data` and `shape` pointing to `simple_array`.
-    pub fn from_simple_ndarray<G: CodeGenerator + ?Sized, Item: Model<'ctx>>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        simple_ndarray: Ptr<'ctx, StructModel<SimpleNDArray<Item>>>,
-        dtype: Type,
-        ndims: u64,
-    ) -> Self {
-        // Sanity check on `dtype` and `simple_array`'s `Item` model.
-        let dtype_llvm = ctx.get_llvm_type(generator, dtype);
-        simple_ndarray.model.0 .0.item.check_type(generator, ctx.ctx, dtype_llvm).unwrap();
-
-        let byte_model = IntModel(Byte);
-
-        // TODO: Check if `ndims` is consistent with that in `simple_array`?
-
-        // Allocate the resulting ndarray.
-        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, ndims, "from_simple_ndarray");
-
-        // Set data, shape by simply copying addresses.
-        let data = simple_ndarray
-            .get(generator, ctx, |f| f.data, "")
-            .pointer_cast(generator, ctx, byte_model, "data");
-        ndarray.instance.set(ctx, |f| f.data, data);
-
-        let shape = simple_ndarray.get(generator, ctx, |f| f.shape, "shape");
-        ndarray.instance.set(ctx, |f| f.shape, shape);
-
-        // Set strides. We know `data` is contiguous.
-        ndarray.update_strides_by_shape(generator, ctx);
-
-        ndarray
-    }
-
-    /// Get the typechecker ndarray type of this [`NDArrayObject`].
-    pub fn get_ndarray_type(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Type {
-        let ndims = create_ndims(&mut ctx.unifier, self.ndims);
-        make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(self.dtype), Some(ndims))
-    }
-
-    /// Get the `np.size()` of this ndarray.
-    pub fn size<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, SizeT> {
-        call_nac3_ndarray_size(generator, ctx, self.instance)
-    }
-
-    /// Get the `ndarray.nbytes` of this ndarray.
-    pub fn nbytes<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, SizeT> {
-        call_nac3_ndarray_nbytes(generator, ctx, self.instance)
-    }
-
-    /// Get the `len()` of this ndarray.
-    pub fn len<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, SizeT> {
-        call_nac3_ndarray_len(generator, ctx, self.instance)
-    }
-
-    /// Check if this ndarray is C-contiguous.
-    ///
-    /// See NumPy's `flags["C_CONTIGUOUS"]`: <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags>
-    pub fn is_c_contiguous<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, Bool> {
-        call_nac3_ndarray_is_c_contiguous(generator, ctx, self.instance)
-    }
-
-    /// Get the pointer to the n-th (0-based) element.
-    ///
-    /// The returned pointer has the element type of the LLVM type of this ndarray's `dtype`.
-    ///
-    /// There is no out-of-bounds check.
-    pub fn get_nth_pointer<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        nth: Int<'ctx, SizeT>,
-        name: &str,
-    ) -> PointerValue<'ctx> {
-        let elem_ty = ctx.get_llvm_type(generator, self.dtype);
-
-        let p = call_nac3_ndarray_get_nth_pelement(generator, ctx, self.instance, nth);
-        ctx.builder
-            .build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
-            .unwrap()
-    }
-
-    /// Get the n-th (0-based) scalar.
-    ///
-    /// There is no out-of-bounds check.
-    pub fn get_nth_scalar<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        nth: Int<'ctx, SizeT>,
-    ) -> AnyObject<'ctx> {
-        let p = self.get_nth_pointer(generator, ctx, nth, "value");
-        let value = ctx.builder.build_load(p, "value").unwrap();
-        AnyObject { ty: self.dtype, value }
-    }
-
-    /// Set the n-th (0-based) scalar.
-    ///
-    /// There is no out-of-bounds check.
-    pub fn set_nth_scalar<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        nth: Int<'ctx, SizeT>,
-        scalar: AnyObject<'ctx>,
-    ) {
-        // Sanity check on scalar's `dtype`
-        assert!(ctx.unifier.unioned(scalar.ty, self.dtype));
-
-        let pscalar = self.get_nth_pointer(generator, ctx, nth, "pscalar");
-        ctx.builder.build_store(pscalar, scalar.value).unwrap();
-    }
-
-    /// Call [`call_nac3_ndarray_set_strides_by_shape`] on this ndarray to update `strides`.
-    ///
-    /// Please refer to the IRRT implementation to see its purpose.
-    pub fn update_strides_by_shape<G: CodeGenerator + ?Sized>(
-        self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) {
-        call_nac3_ndarray_set_strides_by_shape(generator, ctx, self.instance);
-    }
-
-    /// Copy data from another ndarray.
-    ///
-    /// This ndarray and `src` is that their `np.size()` should be the same. Their shapes
-    /// do not matter. The copying order is determined by how their flattened views look.
-    ///
-    /// Panics if the `dtype`s of ndarrays are different.
-    pub fn copy_data_from<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        src: NDArrayObject<'ctx>,
-    ) {
-        assert!(ctx.unifier.unioned(self.dtype, src.dtype), "self and src dtype should match");
-        call_nac3_ndarray_copy_data(generator, ctx, src.instance, self.instance);
-    }
-
-    /// Allocate an ndarray on the stack given its `ndims` and `dtype`.
-    ///
-    /// `shape` and `strides` will be automatically allocated on the stack.
-    //e
-    /// The returned ndarray's content will be:
-    /// - `data`: set to `nullptr`.
-    /// - `itemsize`: set to the `sizeof()` of `dtype`.
-    /// - `ndims`: set to the value of  `ndims`.
-    /// - `shape`: allocated with an array of length `ndims` with uninitialized values.
-    /// - `strides`: allocated with an array of length `ndims` with uninitialized values.
-    pub fn alloca<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        dtype: Type,
-        ndims: u64,
-        name: &str,
-    ) -> Self {
-        let sizet_model = IntModel(SizeT);
-        let ndarray_model = StructModel(NDArray);
-        let ndarray_data_model = PtrModel(IntModel(Byte));
-
-        let pndarray = ndarray_model.alloca(generator, ctx, name);
-
-        let data = ndarray_data_model.nullptr(generator, ctx.ctx);
-        pndarray.set(ctx, |f| f.data, data);
-
-        let itemsize = ctx.get_llvm_type(generator, dtype).size_of().unwrap();
-        let itemsize =
-            sizet_model.s_extend_or_bit_cast(generator, ctx, itemsize, "alloca_itemsize");
-        pndarray.set(ctx, |f| f.itemsize, itemsize);
-
-        let ndims_val = sizet_model.constant(generator, ctx.ctx, ndims);
-        pndarray.set(ctx, |f| f.ndims, ndims_val);
-
-        let shape = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_shape");
-        pndarray.set(ctx, |f| f.shape, shape);
-
-        let strides = sizet_model.array_alloca(generator, ctx, ndims_val.value, "alloca_strides");
-        pndarray.set(ctx, |f| f.strides, strides);
-
-        NDArrayObject { dtype, ndims, instance: pndarray }
-    }
-
-    /// Convenience function.
-    /// Like [`NDArrayObject::alloca_uninitialized`] but directly takes the typechecker type of the ndarray.
-    pub fn alloca_ndarray_type<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        ndarray_ty: Type,
-        name: &str,
-    ) -> Self {
-        let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ndarray_ty);
-        let ndims = extract_ndims(&ctx.unifier, ndims);
-        Self::alloca(generator, ctx, dtype, ndims, name)
-    }
-
-    /// Convenience function. Allocate an [`NDArrayObject`] with a statically known shape.
-    ///
-    /// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
-    pub fn alloca_constant_shape<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        dtype: Type,
-        shape: &[u64],
-        name: &str,
-    ) -> Self {
-        let sizet_model = IntModel(SizeT);
-
-        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64, name);
-
-        // Write shape
-        let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape, "shape");
-        for (i, dim) in shape.iter().enumerate() {
-            let dim = sizet_model.constant(generator, ctx.ctx, *dim);
-            dst_shape.offset_const(generator, ctx, i as u64, "").store(ctx, dim);
-        }
-
-        ndarray
-    }
-
-    /// Convenience function. Allocate an [`NDArrayObject`] with a dynamically known shape.
-    ///
-    /// The returned [`NDArrayObject`]'s `data` and `strides` are uninitialized.
-    pub fn alloca_dynamic_shape<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        dtype: Type,
-        shape: &[Int<'ctx, SizeT>],
-        name: &str,
-    ) -> Self {
-        let ndarray = NDArrayObject::alloca(generator, ctx, dtype, shape.len() as u64, name);
-
-        // Write shape
-        let dst_shape = ndarray.instance.get(generator, ctx, |f| f.shape, "shape");
-        for (i, dim) in shape.iter().enumerate() {
-            dst_shape.offset_const(generator, ctx, i as u64, "").store(ctx, *dim);
-        }
-
-        ndarray
-    }
-
-    /// Clone/Copy this ndarray - Allocate a new ndarray with the same shape as this ndarray and copy the contents over.
-    ///
-    /// The new ndarray will own its data and will be C-contiguous.
-    #[must_use]
-    pub fn make_copy<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: &str,
-    ) -> Self {
-        let clone = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims, name);
-
-        let shape = self.instance.gep(ctx, |f| f.shape).load(generator, ctx, "shape");
-        clone.copy_shape_from_array(generator, ctx, shape);
-        clone.create_data(generator, ctx);
-        clone.copy_data_from(generator, ctx, *self);
-        clone
-    }
-
-    /// Get this ndarray's `ndims` as an LLVM constant.
-    pub fn get_ndims<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-    ) -> Int<'ctx, SizeT> {
-        let sizet_model = IntModel(SizeT);
-        sizet_model.constant(generator, ctx, self.ndims)
-    }
-
-    /// Get if this ndarray's `np.size` is `0` - containing no content.
-    pub fn is_empty<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, Bool> {
-        let sizet_model = IntModel(SizeT);
-
-        let size = self.size(generator, ctx);
-        size.compare(ctx, IntPredicate::EQ, sizet_model.const_0(generator, ctx.ctx), "is_empty")
-    }
-
-    /// Return true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
-    ///
-    /// This is a staticially known property of ndarrays. This is why it is returning
-    /// a Rust boolean instead of a [`BasicValue`].
-    #[must_use]
-    pub fn is_unsized(&self) -> bool {
-        self.ndims == 0
-    }
-
-    /// If this ndarray is unsized, return its sole value as a [`AnyObject`]. Otherwise, do nothing.
-    pub fn split_unsized<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> ScalarOrNDArray<'ctx> {
-        if self.is_unsized() {
-            // NOTE: `np.size(self) == 0` here is never possible.
-            let sizet_model = IntModel(SizeT);
-            let zero = sizet_model.const_0(generator, ctx.ctx);
-            ScalarOrNDArray::Scalar(self.get_nth_scalar(generator, ctx, zero))
-        } else {
-            ScalarOrNDArray::NDArray(*self)
-        }
-    }
-
-    /// Initialize an ndarray's `data` by allocating a buffer on the stack.
-    /// The allocated data buffer is considered to be *owned* by the ndarray.
-    ///
-    /// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
-    ///
-    /// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
-    pub fn create_data<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) {
-        let byte_model = IntModel(Byte);
-
-        let nbytes = self.nbytes(generator, ctx);
-
-        let data = byte_model.array_alloca(generator, ctx, nbytes.value, "data");
-        self.instance.set(ctx, |f| f.data, data);
-
-        self.update_strides_by_shape(generator, ctx);
-    }
-
-    /// Copy shape dimensions from an array.
-    pub fn copy_shape_from_array<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        src_shape: Ptr<'ctx, IntModel<SizeT>>,
-    ) {
-        let dst_shape = self.instance.get(generator, ctx, |f| f.shape, "dst_shape");
-        let num_items = self.get_ndims(generator, ctx.ctx).value;
-        call_memcpy_model(generator, ctx, dst_shape, src_shape, num_items);
-    }
-
-    /// Copy shape dimensions from an ndarray.
-    /// Panics if `ndims` mismatches.
-    pub fn copy_shape_from_ndarray<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        src_ndarray: NDArrayObject<'ctx>,
-    ) {
-        assert_eq!(self.ndims, src_ndarray.ndims);
-        let src_shape = src_ndarray.instance.get(generator, ctx, |f| f.shape, "src_shape");
-        self.copy_shape_from_array(generator, ctx, src_shape);
-    }
-
-    /// Copy strides dimensions from an array.
-    pub fn copy_strides_from_array<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        src_strides: Ptr<'ctx, IntModel<SizeT>>,
-    ) {
-        let dst_strides = self.instance.get(generator, ctx, |f| f.strides, "dst_strides");
-        let num_items = self.get_ndims(generator, ctx.ctx).value;
-        call_memcpy_model(generator, ctx, dst_strides, src_strides, num_items);
-    }
-
-    /// Copy strides dimensions from an ndarray.
-    /// Panics if `ndims` mismatches.
-    pub fn copy_strides_from_ndarray<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        src_ndarray: NDArrayObject<'ctx>,
-    ) {
-        assert_eq!(self.ndims, src_ndarray.ndims);
-        let src_strides = src_ndarray.instance.get(generator, ctx, |f| f.strides, "src_strides");
-        self.copy_strides_from_array(generator, ctx, src_strides);
-    }
-
-    /// Iterate through every element in the ndarray.
-    ///
-    /// `body` also access to [`BreakContinueHooks`] to short-circuit.
-    pub fn foreach<'a, G, F>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, 'a>,
-        body: F,
-    ) -> Result<(), String>
-    where
-        G: CodeGenerator + ?Sized,
-        F: FnOnce(
-            &mut G,
-            &mut CodeGenContext<'ctx, 'a>,
-            BreakContinueHooks<'ctx>,
-            NDIterHandle<'ctx>,
-        ) -> Result<(), String>,
-    {
-        gen_for_callback(
-            generator,
-            ctx,
-            Some("ndarray_foreach"),
-            |generator, ctx| Ok(NDIterHandle::new(generator, ctx, *self)),
-            |generator, ctx, nditer| Ok(nditer.has_next(generator, ctx).value),
-            |generator, ctx, hooks, nditer| body(generator, ctx, hooks, nditer),
-            |generator, ctx, nditer| {
-                nditer.next(generator, ctx);
-                Ok(())
-            },
-        )
-    }
-
-    /// Make sure the ndarray is at least `ndmin`-dimensional.
-    ///
-    /// If this ndarray's `ndims` is less than `ndmin`, a view is created on this with 1s prepended to the shape.
-    /// If this ndarray's `ndims` is not less than `ndmin`, this function does nothing and return this ndarray.
-    #[must_use]
-    pub fn atleast_nd<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        ndmin: u64,
-    ) -> Self {
-        if self.ndims < ndmin {
-            let mut indices = vec![];
-            for _ in self.ndims..ndmin {
-                indices.push(RustNDIndex::NewAxis);
-            }
-            indices.push(RustNDIndex::Ellipsis);
-            self.index(generator, ctx, &indices, "atleast_nd_ndarray")
-        } else {
-            *self
-        }
-    }
-
-    /// Fill the ndarray with a scalar.
-    ///
-    /// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
-    pub fn fill<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        scalar: AnyObject<'ctx>,
-    ) {
-        // Sanity check on scalar's type.
-        assert!(ctx.unifier.unioned(self.dtype, scalar.ty));
-
-        self.foreach(generator, ctx, |generator, ctx, _hooks, nditer| {
-            let p = nditer.get_pointer(generator, ctx);
-            ctx.builder.build_store(p, scalar.value).unwrap();
-            Ok(())
-        })
-        .unwrap();
-    }
-
-    /// Create a reshaped view on this ndarray like `np.reshape()`.
-    ///
-    /// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be modified as a result.
-    ///
-    /// If reshape without copying is impossible, this function will allocate a new ndarray and copy contents.
-    ///
-    /// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
-    /// * `new_shape` - The target shape to do `np.reshape()`.
-    #[must_use]
-    pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        new_ndims: u64,
-        new_shape: Ptr<'ctx, IntModel<SizeT>>,
-    ) -> Self {
-        // TODO: The current criterion for whether to do a full copy or not is by checking `is_c_contiguous`,
-        // but this is not optimal - there are cases when the ndarray is not contiguous but could be reshaped
-        // without copying data. Look into how numpy does it.
-
-        let current_bb = ctx.builder.get_insert_block().unwrap();
-        let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then_bb");
-        let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
-        let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
-
-        let dst_ndarray =
-            NDArrayObject::alloca(generator, ctx, self.dtype, new_ndims, "reshaped_ndarray");
-        dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
-
-        // Reolsve negative indices
-        let size = self.size(generator, ctx);
-        let dst_ndims = dst_ndarray.get_ndims(generator, ctx.ctx);
-        let dst_shape =
-            dst_ndarray.instance.get(generator, ctx, |f| f.shape, "reshaped_ndarray_shape");
-        call_nac3_ndarray_resolve_and_check_new_shape(generator, ctx, size, dst_ndims, dst_shape);
-
-        let is_c_contiguous = self.is_c_contiguous(generator, ctx);
-        ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
-
-        // Inserting into then_bb: reshape is possible without copying
-        ctx.builder.position_at_end(then_bb);
-        dst_ndarray.update_strides_by_shape(generator, ctx);
-        dst_ndarray.instance.set(
-            ctx,
-            |f| f.data,
-            self.instance.get(generator, ctx, |f| f.data, "data"),
-        );
-        ctx.builder.build_unconditional_branch(end_bb).unwrap();
-
-        // Inserting into else_bb: reshape is impossible without copying
-        ctx.builder.position_at_end(else_bb);
-        dst_ndarray.create_data(generator, ctx);
-        dst_ndarray.copy_data_from(generator, ctx, *self);
-        ctx.builder.build_unconditional_branch(end_bb).unwrap();
-
-        // Reposition for continuation
-        ctx.builder.position_at_end(end_bb);
-
-        dst_ndarray
-    }
-
-    /// Create a flattened view of this ndarray, like `np.ravel()`.
-    ///
-    /// Uses [`NDArrayObject::reshape_or_copy`] under-the-hood so ndarray may or may not be copied.
-    #[must_use]
-    pub fn ravel_or_copy<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Self {
-        // Define models
-        let sizet_model = IntModel(SizeT);
-
-        let num0 = sizet_model.const_0(generator, ctx.ctx);
-        let num1 = sizet_model.const_1(generator, ctx.ctx);
-        let num_neg1 = sizet_model.const_all_1s(generator, ctx.ctx);
-
-        // Create `[-1]` and pass to `reshape_or_copy`.
-        let new_shape = sizet_model.array_alloca(generator, ctx, num1.value, "new_shape");
-        new_shape.offset(generator, ctx, num0.value, "").store(ctx, num_neg1);
-
-        self.reshape_or_copy(generator, ctx, 1, new_shape)
-    }
-
-    /// Create a transposed view on this ndarray like `np.transpose(<ndarray>, <axes> = None)`.
-    /// * `axes` - If specified, should be an array of the permutation (negative indices are **allowed**).
-    #[must_use]
-    pub fn transpose<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        axes: Option<Ptr<'ctx, IntModel<SizeT>>>,
-    ) -> Self {
-        // Define models
-        let sizet_model = IntModel(SizeT);
-
-        let transposed_ndarray =
-            NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims, "transposed_ndarray");
-
-        let num_axes = self.get_ndims(generator, ctx.ctx);
-
-        // `axes = nullptr` if `axes` is unspecified.
-        let axes = axes.unwrap_or_else(|| PtrModel(sizet_model).nullptr(generator, ctx.ctx));
-
-        call_nac3_ndarray_transpose(
-            generator,
-            ctx,
-            self.instance,
-            transposed_ndarray.instance,
-            num_axes,
-            axes,
-        );
-
-        transposed_ndarray
-    }
-
-    /// Check if this `NDArray` can be used as an `out` ndarray for an operation.
-    ///
-    /// Raise an exception if the shapes do not match.
-    pub fn check_can_be_written_by_out<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        out_ndims: u64,
-        out_shape: Ptr<'ctx, IntModel<SizeT>>,
-    ) {
-        let sizet_model = IntModel(SizeT);
-
-        let ndarray_ndims = self.get_ndims(generator, ctx.ctx);
-        let ndarray_shape = self.instance.get(generator, ctx, |f| f.shape, "shape");
-
-        let output_ndims = sizet_model.constant(generator, ctx.ctx, out_ndims);
-        let output_shape = out_shape;
-
-        call_nac3_ndarray_util_assert_output_shape_same(
-            generator,
-            ctx,
-            ndarray_ndims,
-            ndarray_shape,
-            output_ndims,
-            output_shape,
-        );
-    }
-
-    /// Create the shape tuple of this ndarray like `np.shape(<ndarray>)`.
-    ///
-    /// The returned integers in the tuple are in int32.
-    pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> TupleObject<'ctx> {
-        // TODO: Don't return a tuple of int32s.
-
-        let mut objects = Vec::with_capacity(self.ndims as usize);
-
-        for i in 0..self.ndims {
-            let dim = self
-                .instance
-                .get(generator, ctx, |f| f.shape, "")
-                .offset_const(generator, ctx, i, "")
-                .load(generator, ctx, "dim");
-            let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
-
-            objects.push(AnyObject {
-                ty: ctx.primitives.int32,
-                value: dim.value.as_basic_value_enum(),
-            });
-        }
-
-        TupleObject::create(generator, ctx, objects, "shape")
-    }
-
-    /// Create the strides tuple of this ndarray like `np.strides(<ndarray>)`.
-    ///
-    /// The returned integers in the tuple are in int32.
-    pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> TupleObject<'ctx> {
-        // TODO: Don't return a tuple of int32s.
-
-        let mut objects = Vec::with_capacity(self.ndims as usize);
-
-        for i in 0..self.ndims {
-            let dim = self
-                .instance
-                .get(generator, ctx, |f| f.strides, "")
-                .offset_const(generator, ctx, i, "")
-                .load(generator, ctx, "dim");
-            let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
-
-            objects.push(AnyObject {
-                ty: ctx.primitives.int32,
-                value: dim.value.as_basic_value_enum(),
-            });
-        }
-
-        TupleObject::create(generator, ctx, objects, "strides")
-    }
-}
-
-/// TODO: Document me
-#[derive(Debug, Clone, Copy)]
-pub enum NDArrayOut<'ctx> {
-    NewNDArray { dtype: Type },
-    WriteToNDArray { ndarray: NDArrayObject<'ctx> },
-}

nac3core/src/codegen/object/ndarray/nalgebra.rs

@@ -1,53 +0,0 @@
-use inkwell::values::{BasicValue, BasicValueEnum};
-
-use crate::codegen::{model::*, structure::SimpleNDArray, CodeGenContext, CodeGenerator};
-
-use super::NDArrayObject;
-
-pub fn perform_nalgebra_call<'ctx, 'a, const NUM_INPUTS: usize, const NUM_OUTPUTS: usize, G, F>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, 'a>,
-    inputs: [NDArrayObject<'ctx>; NUM_INPUTS],
-    output_ndims: [u64; NUM_OUTPUTS],
-    invoke_function: F,
-) -> [NDArrayObject<'ctx>; NUM_OUTPUTS]
-where
-    G: CodeGenerator + ?Sized,
-    F: FnOnce(
-        &mut CodeGenContext<'ctx, 'a>,
-        [BasicValueEnum<'ctx>; NUM_INPUTS],
-        [BasicValueEnum<'ctx>; NUM_OUTPUTS],
-    ),
-{
-    // TODO: Allow stacked inputs. See NumPy docs.
-
-    let f64_model = FloatModel(Float64);
-    let simple_ndarray_model = StructModel(SimpleNDArray { item: f64_model });
-
-    // Prepare inputs & outputs and invoke
-    let inputs = inputs.map(|input| {
-        // Sanity check. Typechecker ensures this.
-        assert!(ctx.unifier.unioned(input.dtype, ctx.primitives.float));
-
-        input
-            .make_simple_ndarray(generator, ctx, FloatModel(Float64), "nalgebra_input")
-            .value
-            .as_basic_value_enum()
-    });
-    let outputs = [simple_ndarray_model.alloca(generator, ctx, "nalgebra_output"); NUM_OUTPUTS];
-    invoke_function(ctx, inputs, outputs.map(|output| output.value.as_basic_value_enum()));
-
-    // Turn the outputs into strided NDArrays
-    let mut output_i = 0;
-    outputs.map(|output| {
-        let out = NDArrayObject::from_simple_ndarray(
-            generator,
-            ctx,
-            output,
-            ctx.primitives.float,
-            output_ndims[output_i],
-        );
-        output_i += 1;
-        out
-    })
-}

nac3core/src/codegen/object/ndarray/nditer.rs

@@ -1,88 +0,0 @@
-use inkwell::{types::BasicType, values::PointerValue, AddressSpace};
-
-use crate::codegen::{
-    irrt::{call_nac3_nditer_has_next, call_nac3_nditer_initialize, call_nac3_nditer_next},
-    model::*,
-    object::AnyObject,
-    structure::NDIter,
-    CodeGenContext, CodeGenerator,
-};
-
-use super::NDArrayObject;
-
-#[derive(Debug, Clone)]
-pub struct NDIterHandle<'ctx> {
-    ndarray: NDArrayObject<'ctx>,
-    instance: Ptr<'ctx, StructModel<NDIter>>,
-    indices: Ptr<'ctx, IntModel<SizeT>>,
-}
-
-impl<'ctx> NDIterHandle<'ctx> {
-    pub fn new<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        ndarray: NDArrayObject<'ctx>,
-    ) -> Self {
-        let nditer = StructModel(NDIter).alloca(generator, ctx, "nditer");
-        let ndims = ndarray.get_ndims(generator, ctx.ctx);
-        let indices = IntModel(SizeT).array_alloca(generator, ctx, ndims.value, "indices");
-        call_nac3_nditer_initialize(generator, ctx, nditer, ndarray.instance, indices);
-        NDIterHandle { ndarray, instance: nditer, indices }
-    }
-
-    #[must_use]
-    pub fn has_next<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, Bool> {
-        call_nac3_nditer_has_next(generator, ctx, self.instance)
-    }
-
-    pub fn next<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) {
-        call_nac3_nditer_next(generator, ctx, self.instance);
-    }
-
-    #[must_use]
-    pub fn get_pointer<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> PointerValue<'ctx> {
-        let elem_ty = ctx.get_llvm_type(generator, self.ndarray.dtype);
-
-        let p = self.instance.get(generator, ctx, |f| f.element, "element");
-        ctx.builder
-            .build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), "element")
-            .unwrap()
-    }
-
-    #[must_use]
-    pub fn get_scalar<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> AnyObject<'ctx> {
-        let p = self.get_pointer(generator, ctx);
-        let value = ctx.builder.build_load(p, "value").unwrap();
-        AnyObject { ty: self.ndarray.dtype, value }
-    }
-
-    #[must_use]
-    pub fn get_index<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, SizeT> {
-        self.instance.get(generator, ctx, |f| f.nth, "index")
-    }
-
-    #[must_use]
-    pub fn get_indices(&self) -> Ptr<'ctx, IntModel<SizeT>> {
-        self.indices
-    }
-}

nac3core/src/codegen/object/ndarray/product.rs

@@ -1,159 +0,0 @@
-use std::cmp::max;
-
-use crate::codegen::{
-    irrt::{
-        call_nac3_ndarray_float64_matmul_at_least_2d, call_nac3_ndarray_matmul_calculate_shapes,
-    },
-    model::*,
-    object::ndarray::indexing::RustNDIndex,
-    CodeGenContext, CodeGenerator,
-};
-
-use super::{NDArrayObject, NDArrayOut};
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// TODO: Document me
-    fn matmul_helper<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        a: Self,
-        b: Self,
-    ) -> Self {
-        assert!(a.ndims >= 2);
-        assert!(b.ndims >= 2);
-
-        assert!(ctx.unifier.unioned(ctx.primitives.float, a.dtype));
-        assert!(ctx.unifier.unioned(ctx.primitives.float, b.dtype));
-
-        let sizet_model = IntModel(SizeT);
-
-        let final_ndims_int = max(a.ndims, b.ndims);
-
-        let a_ndims = a.get_ndims(generator, ctx.ctx);
-        let a_shape = a.instance.get(generator, ctx, |f| f.shape, "a_shape");
-        let b_ndims = b.get_ndims(generator, ctx.ctx);
-        let b_shape = b.instance.get(generator, ctx, |f| f.shape, "b_shape");
-        let final_ndims = sizet_model.constant(generator, ctx.ctx, final_ndims_int);
-        let new_a_shape =
-            sizet_model.array_alloca(generator, ctx, final_ndims.value, "new_a_shape");
-        let new_b_shape =
-            sizet_model.array_alloca(generator, ctx, final_ndims.value, "new_b_shape");
-        let dst_shape = sizet_model.array_alloca(generator, ctx, final_ndims.value, "dst_shape");
-
-        call_nac3_ndarray_matmul_calculate_shapes(
-            generator,
-            ctx,
-            a_ndims,
-            a_shape,
-            b_ndims,
-            b_shape,
-            final_ndims,
-            new_a_shape,
-            new_b_shape,
-            dst_shape,
-        );
-
-        let new_a = a.broadcast_to(generator, ctx, final_ndims_int, new_a_shape);
-        let new_b = b.broadcast_to(generator, ctx, final_ndims_int, new_b_shape);
-        let dst = NDArrayObject::alloca(
-            generator,
-            ctx,
-            ctx.primitives.float,
-            final_ndims_int,
-            "matmul_result",
-        );
-        dst.copy_shape_from_array(generator, ctx, dst_shape);
-        dst.create_data(generator, ctx);
-
-        call_nac3_ndarray_float64_matmul_at_least_2d(
-            generator,
-            ctx,
-            new_a.instance,
-            new_b.instance,
-            dst.instance,
-        );
-
-        dst
-    }
-
-    /// Perform `np.matmul` according to the rules in
-    /// <https://numpy.org/doc/stable/reference/generated/numpy.matmul.html>.
-    ///
-    /// This function always return an [`NDArrayObject`]. You may want to use [`NDArrayObject::split_unsized`].
-    pub fn matmul<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        a: Self,
-        b: Self,
-        out: NDArrayOut<'ctx>,
-    ) -> Self {
-        // Sanity check, but type inference should prevent this.
-        assert!(a.ndims > 0 && b.ndims > 0, "np.matmul disallows scalar input");
-
-        /*
-            If both arguments are 2-D they are multiplied like conventional matrices.
-            If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly.
-            If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed.
-            If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed.
-        */
-
-        let new_a = if a.ndims == 1 {
-            // Prepend 1 to its dimensions
-            a.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis], "new_a")
-        } else {
-            a
-        };
-
-        let new_b = if b.ndims == 1 {
-            // Append 1 to its dimensions
-            b.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis], "new_a")
-        } else {
-            b
-        };
-
-        // NOTE: `result` will always be a newly allocated ndarray.
-        // Current implementation cannot do in-place matrix muliplication.
-        let mut result = NDArrayObject::matmul_helper(generator, ctx, new_a, new_b);
-
-        let i32_model = IntModel(Int32); // TODO: Upgrade to SizeT
-        let zero = i32_model.const_0(generator, ctx.ctx);
-
-        if a.ndims == 1 {
-            // Remove the prepended 1
-            result = result.index(
-                generator,
-                ctx,
-                &[RustNDIndex::SingleElement(zero), RustNDIndex::Ellipsis],
-                "result_no_prepend_1",
-            );
-        }
-
-        if b.ndims == 1 {
-            // Remove the appended 1
-            result = result.index(
-                generator,
-                ctx,
-                &[RustNDIndex::Ellipsis, RustNDIndex::SingleElement(zero)],
-                "result_no_append_1",
-            );
-        }
-
-        match out {
-            NDArrayOut::NewNDArray { dtype } => {
-                // We don't support auto-casting right now, nor anything other than float64.
-                // Force the output dtype to be float64.
-                assert!(ctx.unifier.unioned(ctx.primitives.float, dtype));
-                result
-            }
-            NDArrayOut::WriteToNDArray { ndarray: out_ndarray } => {
-                // TODO: It is possible to check the shapes before computing the matmul to save resources.
-                let result_shape = result.instance.get(generator, ctx, |f| f.shape, "result_shape");
-                out_ndarray.check_can_be_written_by_out(generator, ctx, result.ndims, result_shape);
-
-                // TODO: We can just set `out_ndarray.data` to `result.data`. Should we?
-                out_ndarray.copy_data_from(generator, ctx, result);
-                out_ndarray
-            }
-        }
-    }
-}

nac3core/src/codegen/object/ndarray/scalar.rs

@@ -1,131 +0,0 @@
-use inkwell::values::{BasicValue, BasicValueEnum};
-
-use crate::{
-    codegen::{model::*, object::AnyObject, CodeGenContext, CodeGenerator},
-    typecheck::typedef::{Type, TypeEnum},
-};
-
-use super::NDArrayObject;
-
-impl<'ctx> AnyObject<'ctx> {
-    /// Promote this scalar to an unsized ndarray (like doing `np.asarray`).
-    ///
-    /// The scalar value is allocated onto the stack, and the ndarray's `data` will point to that
-    /// allocated value.
-    pub fn as_ndarray<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> NDArrayObject<'ctx> {
-        let pbyte_model = PtrModel(IntModel(Byte));
-
-        // We have to put the value on the stack to get a data pointer.
-        let data = ctx.builder.build_alloca(self.value.get_type(), "as_ndarray_scalar").unwrap();
-        ctx.builder.build_store(data, self.value).unwrap();
-        let data = pbyte_model.pointer_cast(generator, ctx, data, "data");
-
-        let ndarray = NDArrayObject::alloca(generator, ctx, self.ty, 0, "scalar_ndarray");
-        ndarray.instance.set(ctx, |f| f.data, data);
-        ndarray
-    }
-}
-
-/// A convenience enum for implementing scalar/ndarray agnostic utilities.
-#[derive(Debug, Clone, Copy)]
-pub enum ScalarOrNDArray<'ctx> {
-    Scalar(AnyObject<'ctx>),
-    NDArray(NDArrayObject<'ctx>),
-}
-
-impl<'ctx> ScalarOrNDArray<'ctx> {
-    /// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
-    #[must_use]
-    pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
-        match self {
-            ScalarOrNDArray::Scalar(scalar) => scalar.value,
-            ScalarOrNDArray::NDArray(ndarray) => ndarray.instance.value.as_basic_value_enum(),
-        }
-    }
-
-    #[must_use]
-    pub fn into_scalar(&self) -> AnyObject<'ctx> {
-        match self {
-            ScalarOrNDArray::NDArray(_ndarray) => panic!("Got NDArray"),
-            ScalarOrNDArray::Scalar(scalar) => *scalar,
-        }
-    }
-
-    #[must_use]
-    pub fn into_ndarray(&self) -> NDArrayObject<'ctx> {
-        match self {
-            ScalarOrNDArray::NDArray(ndarray) => *ndarray,
-            ScalarOrNDArray::Scalar(_scalar) => panic!("Got Scalar"),
-        }
-    }
-
-    /// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
-    /// - If this is an ndarray, the ndarray is returned.
-    /// - If this is a scalar, an unsized ndarray view is created on it.
-    pub fn as_ndarray<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> NDArrayObject<'ctx> {
-        match self {
-            ScalarOrNDArray::NDArray(ndarray) => *ndarray,
-            ScalarOrNDArray::Scalar(scalar) => scalar.as_ndarray(generator, ctx),
-        }
-    }
-
-    #[must_use]
-    pub fn dtype(&self) -> Type {
-        match self {
-            ScalarOrNDArray::Scalar(scalar) => scalar.ty,
-            ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
-        }
-    }
-}
-
-impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for AnyObject<'ctx> {
-    type Error = ();
-
-    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
-        match value {
-            ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
-            ScalarOrNDArray::NDArray(_ndarray) => Err(()),
-        }
-    }
-}
-
-impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayObject<'ctx> {
-    type Error = ();
-
-    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
-        match value {
-            ScalarOrNDArray::Scalar(_scalar) => Err(()),
-            ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
-        }
-    }
-}
-
-/// Split an [`AnyObject`] into a [`ScalarOrNDArray`] depending on its [`Type`].
-pub fn split_scalar_or_ndarray<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    object: AnyObject<'ctx>,
-) -> ScalarOrNDArray<'ctx> {
-    // TODO: Automatically convert a list into an ndarray?
-
-    match &*ctx.unifier.get_ty(object.ty) {
-        TypeEnum::TObj { obj_id, .. }
-            if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
-        {
-            let ndarray = NDArrayObject::from_object(generator, ctx, object);
-            ScalarOrNDArray::NDArray(ndarray)
-        }
-        _ => {
-            let scalar = AnyObject { ty: object.ty, value: object.value };
-            ScalarOrNDArray::Scalar(scalar)
-        }
-    }
-}

nac3core/src/codegen/object/ndarray/shape_util.rs

@@ -1,111 +0,0 @@
-use util::gen_for_model_auto;
-
-use crate::{
-    codegen::{
-        model::*,
-        object::{list::ListObject, tuple::TupleObject, AnyObject},
-        CodeGenContext, CodeGenerator,
-    },
-    typecheck::typedef::TypeEnum,
-};
-
-/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
-///
-/// * `sequence` - The `sequence` parameter.
-/// * `sequence_ty` - The typechecker type of `sequence`
-///
-/// The `sequence` argument type may only be one of the following:
-///   1. A list of `int32`;   e.g., `np.empty([600, 800, 3])`
-///   2. A tuple of `int32`;  e.g., `np.empty((600, 800, 3))`
-///   3. A scalar `int32`;    e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
-///
-/// All `int32` values will be sign-extended to `SizeT`.
-pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    input_sequence: AnyObject<'ctx>,
-) -> (Int<'ctx, SizeT>, Ptr<'ctx, IntModel<SizeT>>) {
-    let sizet_model = IntModel(SizeT);
-
-    let zero = sizet_model.const_0(generator, ctx.ctx);
-    let one = sizet_model.const_1(generator, ctx.ctx);
-
-    // The result `list` to return.
-    match &*ctx.unifier.get_ty(input_sequence.ty) {
-        TypeEnum::TObj { obj_id, .. }
-            if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
-        {
-            // 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
-
-            // Check `input_sequence`
-            let input_sequence = ListObject::from_object(generator, ctx, input_sequence);
-
-            let len = input_sequence.instance.gep(ctx, |f| f.len).load(generator, ctx, "len");
-            let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
-
-            // Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
-            gen_for_model_auto(generator, ctx, zero, len, one, |generator, ctx, _hooks, i| {
-                // Load the i-th int32 in the input sequence
-                let int = input_sequence
-                    .instance
-                    .get(generator, ctx, |f| f.items, "")
-                    .offset(generator, ctx, i.value, "")
-                    .load(generator, ctx, "")
-                    .value
-                    .into_int_value();
-
-                // Cast to SizeT
-                let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
-
-                // Store
-                result.offset(generator, ctx, i.value, "int").store(ctx, int);
-
-                Ok(())
-            })
-            .unwrap();
-
-            (len, result)
-        }
-        TypeEnum::TTuple { .. } => {
-            // 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
-
-            let input_sequence = TupleObject::from_object(ctx, input_sequence);
-
-            let len_int = input_sequence.len_static();
-            let len = sizet_model.constant(generator, ctx.ctx, len_int as u64);
-
-            let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
-
-            for i in 0..len_int {
-                // Get the i-th element off of the tuple and load it into `result`.
-                let int = input_sequence.get(ctx, i, "dim").value.into_int_value();
-                let int = sizet_model.s_extend_or_bit_cast(generator, ctx, int, "int");
-
-                let offset = sizet_model.constant(generator, ctx.ctx, i as u64);
-                result.offset(generator, ctx, offset.value, "int").store(ctx, int);
-            }
-
-            (len, result)
-        }
-        TypeEnum::TObj { obj_id, .. }
-            if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
-        {
-            // 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
-            let input_int = input_sequence.value.into_int_value();
-
-            let len = sizet_model.const_1(generator, ctx.ctx);
-            let result = sizet_model.array_alloca(generator, ctx, len.value, "int_sequence");
-
-            let int = sizet_model.s_extend_or_bit_cast(generator, ctx, input_int, "int");
-
-            // Storing into result[0]
-            result.store(ctx, int);
-
-            (len, result)
-        }
-        _ => panic!(
-            "encountered unknown sequence type: {}",
-            ctx.unifier.stringify(input_sequence.ty)
-        ),
-    }
-}

nac3core/src/codegen/object/range.rs

@@ -1,41 +0,0 @@
-use crate::codegen::{
-    irrt::calculate_len_for_slice_range, model::*, structure::RangeModel, CodeGenContext,
-    CodeGenerator,
-};
-
-use super::AnyObject;
-
-/// A `range` in NAC3
-pub struct RangeObject<'ctx> {
-    pub instance: Ptr<'ctx, RangeModel>,
-}
-
-impl<'ctx> RangeObject<'ctx> {
-    pub fn from_object<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        object: AnyObject<'ctx>,
-    ) -> Self {
-        assert!(ctx.unifier.unioned(ctx.primitives.range, object.ty)); // Sanity check on type.
-
-        let model = PtrModel(RangeModel::default());
-        let instance = model.check_value(generator, ctx.ctx, object.value).unwrap();
-        RangeObject { instance }
-    }
-
-    /// Get the `len()` of this range.
-    pub fn len<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, Int32> {
-        let start = self.instance.gep_start(generator, ctx, "").load(generator, ctx, "start");
-        let stop = self.instance.gep_stop(generator, ctx, "").load(generator, ctx, "stop");
-        let step = self.instance.gep_step(generator, ctx, "").load(generator, ctx, "step");
-
-        // TODO: Refactor this
-        let len =
-            calculate_len_for_slice_range(generator, ctx, start.value, stop.value, step.value);
-        IntModel(Int32).check_value(generator, ctx.ctx, len).unwrap()
-    }
-}

nac3core/src/codegen/object/tuple.rs

@@ -1,113 +0,0 @@
-use core::panic;
-
-use inkwell::values::StructValue;
-use itertools::Itertools;
-
-use crate::{
-    codegen::{model::*, CodeGenContext, CodeGenerator},
-    typecheck::typedef::{Type, TypeEnum},
-};
-
-use super::AnyObject;
-
-/// A NAC3 tuple object.
-///
-/// NOTE: This struct has no copy trait.
-#[derive(Debug, Clone)]
-pub struct TupleObject<'ctx> {
-    /// The type of the tuple.
-    pub tys: Vec<Type>,
-    /// The underlying LLVM value of this tuple.
-    pub value: StructValue<'ctx>,
-}
-
-impl<'ctx> TupleObject<'ctx> {
-    // NOTE: There is no Model abstraction for Tuples with arbitrary lengths.
-    // Everything has to be done raw with Inkwell.
-
-    pub fn from_object(ctx: &mut CodeGenContext<'ctx, '_>, object: AnyObject<'ctx>) -> Self {
-        // TODO: Keep `is_vararg_ctx` from TTuple?
-
-        // Sanity check on object type.
-        let TypeEnum::TTuple { ty: tys, .. } = &*ctx.unifier.get_ty(object.ty) else {
-            panic!(
-                "Expected type to be a TypeEnum::TTuple, got {}",
-                ctx.unifier.stringify(object.ty)
-            );
-        };
-
-        let value = object.value.into_struct_value();
-        let value_num_fields = value.get_type().count_fields() as usize;
-        assert!(
-            value_num_fields == tys.len(),
-            "Tuple type has {} item(s), but the LLVM struct value has {} field(s)",
-            tys.len(),
-            value_num_fields
-        );
-
-        TupleObject { tys: tys.clone(), value }
-    }
-
-    /// Convenience function. Create a [`TupleObject`] from an iterator of objects.
-    pub fn create<I, G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        objects: I,
-        name: &str,
-    ) -> Self
-    where
-        I: IntoIterator<Item = AnyObject<'ctx>>,
-    {
-        let (values, tys): (Vec<_>, Vec<_>) =
-            objects.into_iter().map(|object| (object.value, object.ty)).unzip();
-
-        let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
-        let llvm_tuple_ty = ctx.ctx.struct_type(&llvm_tys, false);
-
-        let pllvm_tuple = ctx.builder.build_alloca(llvm_tuple_ty, "tuple").unwrap();
-        for (i, val) in values.into_iter().enumerate() {
-            let pval = ctx.builder.build_struct_gep(pllvm_tuple, i as u32, "value").unwrap();
-            ctx.builder.build_store(pval, val).unwrap();
-        }
-
-        let value = ctx.builder.build_load(pllvm_tuple, name).unwrap().into_struct_value();
-        TupleObject { tys, value }
-    }
-
-    /// Get the `len()` of this tuple statically.
-    ///
-    /// We statically know the lengths of tuples in NAC3 when compiling.
-    #[must_use]
-    pub fn len_static(&self) -> usize {
-        self.tys.len()
-    }
-
-    /// Get the `len()` of this tuple.
-    #[must_use]
-    pub fn len<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Int<'ctx, SizeT> {
-        IntModel(SizeT).constant(generator, ctx.ctx, self.len_static() as u64)
-    }
-
-    /// Check if this tuple is an empty/unit tuple.
-    #[must_use]
-    pub fn is_empty(&self) -> bool {
-        self.len_static() == 0
-    }
-
-    /// Get the `i`-th (0-based) object in this tuple.
-    pub fn get(&self, ctx: &mut CodeGenContext<'ctx, '_>, i: usize, name: &str) -> AnyObject<'ctx> {
-        assert!(
-            i < self.len_static(),
-            "Tuple object with length {} have index {i}",
-            self.len_static()
-        );
-
-        let value = ctx.builder.build_extract_value(self.value, i as u32, name).unwrap();
-        let ty = self.tys[i];
-        AnyObject { ty, value }
-    }
-}

nac3core/src/codegen/stmt.rs

@@ -1,27 +1,3 @@
-use super::model::*;
-use super::object::ndarray::indexing::util::gen_ndarray_subscript_ndindexes;
-use super::object::ndarray::scalar::split_scalar_or_ndarray;
-use super::object::ndarray::NDArrayObject;
-use super::object::AnyObject;
-use super::{
-    super::symbol_resolver::ValueEnum,
-    expr::destructure_range,
-    irrt::{handle_slice_indices, list_slice_assignment},
-    structure::{CSlice, Exception},
-    CodeGenContext, CodeGenerator, Int32, IntModel, Ptr, StructModel,
-};
-use crate::{
-    codegen::{
-        classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
-        expr::gen_binop_expr,
-        gen_in_range_check,
-    },
-    toplevel::{DefinitionId, TopLevelDef},
-    typecheck::{
-        magic_methods::Binop,
-        typedef::{iter_type_vars, FunSignature, Type, TypeEnum},
-    },
-};
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
     basic_block::BasicBlock,
@@ -30,10 +6,28 @@ use inkwell::{
     IntPredicate,
 };
 use itertools::{izip, Itertools};
+
 use nac3parser::ast::{
     Constant, ExcepthandlerKind, Expr, ExprKind, Location, Stmt, StmtKind, StrRef,
 };
 
+use super::{
+    expr::{destructure_range, gen_binop_expr},
+    gen_in_range_check,
+    irrt::{handle_slice_indices, list_slice_assignment},
+    macros::codegen_unreachable,
+    values::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
+    CodeGenContext, CodeGenerator,
+};
+use crate::{
+    symbol_resolver::ValueEnum,
+    toplevel::{DefinitionId, TopLevelDef},
+    typecheck::{
+        magic_methods::Binop,
+        typedef::{iter_type_vars, FunSignature, Type, TypeEnum},
+    },
+};
+
 /// See [`CodeGenerator::gen_var_alloc`].
 pub fn gen_var<'ctx>(
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -127,7 +121,7 @@ pub fn gen_store_target<'ctx, G: CodeGenerator>(
                 return Ok(None);
             };
             let BasicValueEnum::PointerValue(ptr) = val else {
-                unreachable!();
+                codegen_unreachable!(ctx);
             };
             unsafe {
                 ctx.builder.build_in_bounds_gep(
@@ -141,7 +135,7 @@ pub fn gen_store_target<'ctx, G: CodeGenerator>(
             }
             .unwrap()
         }
-        _ => unreachable!(),
+        _ => codegen_unreachable!(ctx),
     }))
 }
 
@@ -182,6 +176,14 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
                 }
             }
             let val = value.to_basic_value_enum(ctx, generator, target.custom.unwrap())?;
+
+            // Perform i1 <-> i8 conversion as needed
+            let val = if ctx.unifier.unioned(target.custom.unwrap(), ctx.primitives.bool) {
+                generator.bool_to_i8(ctx, val.into_int_value()).into()
+            } else {
+                val
+            };
+
             ctx.builder.build_store(ptr, val).unwrap();
         }
     };
@@ -199,12 +201,12 @@ pub fn gen_assign_target_list<'ctx, G: CodeGenerator>(
     // Deconstruct the tuple `value`
     let BasicValueEnum::StructValue(tuple) = value.to_basic_value_enum(ctx, generator, value_ty)?
     else {
-        unreachable!()
+        codegen_unreachable!(ctx)
     };
 
     // NOTE: Currently, RHS's type is forced to be a Tuple by the type inferencer.
     let TypeEnum::TTuple { ty: tuple_tys, .. } = &*ctx.unifier.get_ty(value_ty) else {
-        unreachable!();
+        codegen_unreachable!(ctx);
     };
 
     assert_eq!(tuple.get_type().count_fields() as usize, tuple_tys.len());
@@ -264,7 +266,7 @@ pub fn gen_assign_target_list<'ctx, G: CodeGenerator>(
             // Now assign with that sub-tuple to the starred target.
             generator.gen_assign(ctx, target, ValueEnum::Dynamic(sub_tuple_val), sub_tuple_ty)?;
         } else {
-            unreachable!() // The typechecker ensures this
+            codegen_unreachable!(ctx) // The typechecker ensures this
         }
 
         // Handle assignment after the starred target
@@ -308,11 +310,13 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
                 .unwrap()
                 .to_basic_value_enum(ctx, generator, target_ty)?
                 .into_pointer_value();
-            let target = ListValue::from_ptr_val(target, llvm_usize, None);
+            let target = ListValue::from_pointer_value(target, llvm_usize, None);
 
             if let ExprKind::Slice { .. } = &key.node {
                 // Handle assigning to a slice
-                let ExprKind::Slice { lower, upper, step } = &key.node else { unreachable!() };
+                let ExprKind::Slice { lower, upper, step } = &key.node else {
+                    codegen_unreachable!(ctx)
+                };
                 let Some((start, end, step)) = handle_slice_indices(
                     lower,
                     upper,
@@ -327,7 +331,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
 
                 let value =
                     value.to_basic_value_enum(ctx, generator, value_ty)?.into_pointer_value();
-                let value = ListValue::from_ptr_val(value, llvm_usize, None);
+                let value = ListValue::from_pointer_value(value, llvm_usize, None);
 
                 let target_item_ty = ctx.get_llvm_type(generator, target_item_ty);
                 let Some(src_ind) = handle_slice_indices(
@@ -407,45 +411,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
             if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
         {
             // Handle NDArray item assignment
-            // Process target
-            let target = generator
-                .gen_expr(ctx, target)?
-                .unwrap()
-                .to_basic_value_enum(ctx, generator, target_ty)?;
-            let target = AnyObject { value: target, ty: target_ty };
-
-            // Process key
-            let key = gen_ndarray_subscript_ndindexes(generator, ctx, key)?;
-
-            // Process value
-            let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
-            let value = AnyObject { value, ty: value_ty };
-
-            /*
-                Reference code:
-                ```python
-                target = target[key]
-                value = np.asarray(value)
-
-                shape = np.broadcast_shape((target, value))
-
-                target = np.broadcast_to(target, shape)
-                value = np.broadcast_to(value, shape)
-
-                ...and finally copy 1-1 from value to target.
-                ```
-            */
-            let target = NDArrayObject::from_object(generator, ctx, target);
-            let target = target.index(generator, ctx, &key, "assign_target_ndarray");
-
-            let value = split_scalar_or_ndarray(generator, ctx, value).as_ndarray(generator, ctx);
-
-            let broadcast_result = NDArrayObject::broadcast(generator, ctx, &[target, value]);
-
-            let target = broadcast_result.ndarrays[0];
-            let value = broadcast_result.ndarrays[1];
-
-            target.copy_data_from(generator, ctx, value);
+            todo!("ndarray subscript assignment is not yet implemented");
         }
         _ => {
             panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
@@ -460,7 +426,9 @@ pub fn gen_for<G: CodeGenerator>(
     ctx: &mut CodeGenContext<'_, '_>,
     stmt: &Stmt<Option<Type>>,
 ) -> Result<(), String> {
-    let StmtKind::For { iter, target, body, orelse, .. } = &stmt.node else { unreachable!() };
+    let StmtKind::For { iter, target, body, orelse, .. } = &stmt.node else {
+        codegen_unreachable!(ctx)
+    };
 
     // var_assignment static values may be changed in another branch
     // if so, remove the static value as it may not be correct in this branch
@@ -495,14 +463,15 @@ pub fn gen_for<G: CodeGenerator>(
         TypeEnum::TObj { obj_id, .. }
             if *obj_id == ctx.primitives.range.obj_id(&ctx.unifier).unwrap() =>
         {
-            let iter_val = RangeValue::from_ptr_val(iter_val.into_pointer_value(), Some("range"));
+            let iter_val =
+                RangeValue::from_pointer_value(iter_val.into_pointer_value(), Some("range"));
             // Internal variable for loop; Cannot be assigned
             let i = generator.gen_var_alloc(ctx, int32.into(), Some("for.i.addr"))?;
             // Variable declared in "target" expression of the loop; Can be reassigned *or* shadowed
             let Some(target_i) =
                 generator.gen_store_target(ctx, target, Some("for.target.addr"))?
             else {
-                unreachable!()
+                codegen_unreachable!(ctx)
             };
             let (start, stop, step) = destructure_range(ctx, iter_val);
 
@@ -847,7 +816,7 @@ where
     BodyFn: FnOnce(
         &mut G,
         &mut CodeGenContext<'ctx, 'a>,
-        BreakContinueHooks,
+        BreakContinueHooks<'ctx>,
         IntValue<'ctx>,
     ) -> Result<(), String>,
 {
@@ -945,7 +914,7 @@ pub fn gen_while<G: CodeGenerator>(
     ctx: &mut CodeGenContext<'_, '_>,
     stmt: &Stmt<Option<Type>>,
 ) -> Result<(), String> {
-    let StmtKind::While { test, body, orelse, .. } = &stmt.node else { unreachable!() };
+    let StmtKind::While { test, body, orelse, .. } = &stmt.node else { codegen_unreachable!(ctx) };
 
     // var_assignment static values may be changed in another branch
     // if so, remove the static value as it may not be correct in this branch
@@ -975,7 +944,7 @@ pub fn gen_while<G: CodeGenerator>(
 
         return Ok(());
     };
-    let BasicValueEnum::IntValue(test) = test else { unreachable!() };
+    let BasicValueEnum::IntValue(test) = test else { codegen_unreachable!(ctx) };
 
     ctx.builder
         .build_conditional_branch(generator.bool_to_i1(ctx, test), body_bb, orelse_bb)
@@ -1123,7 +1092,7 @@ pub fn gen_if<G: CodeGenerator>(
     ctx: &mut CodeGenContext<'_, '_>,
     stmt: &Stmt<Option<Type>>,
 ) -> Result<(), String> {
-    let StmtKind::If { test, body, orelse, .. } = &stmt.node else { unreachable!() };
+    let StmtKind::If { test, body, orelse, .. } = &stmt.node else { codegen_unreachable!(ctx) };
 
     // var_assignment static values may be changed in another branch
     // if so, remove the static value as it may not be correct in this branch
@@ -1246,11 +1215,11 @@ pub fn exn_constructor<'ctx>(
     let zelf_id = if let TypeEnum::TObj { obj_id, .. } = &*ctx.unifier.get_ty(zelf_ty) {
         obj_id.0
     } else {
-        unreachable!()
+        codegen_unreachable!(ctx)
     };
     let defs = ctx.top_level.definitions.read();
     let def = defs[zelf_id].read();
-    let TopLevelDef::Class { name: zelf_name, .. } = &*def else { unreachable!() };
+    let TopLevelDef::Class { name: zelf_name, .. } = &*def else { codegen_unreachable!(ctx) };
     let exception_name = format!("{}:{}", ctx.resolver.get_exception_id(zelf_id), zelf_name);
     unsafe {
         let id_ptr = ctx.builder.build_in_bounds_gep(zelf, &[zero, zero], "exn.id").unwrap();
@@ -1307,36 +1276,47 @@ pub fn exn_constructor<'ctx>(
 pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
-    exception: Option<Ptr<'ctx, StructModel<Exception>>>,
+    exception: Option<&BasicValueEnum<'ctx>>,
     loc: Location,
 ) {
-    if let Some(pexn) = exception {
-        let i32_model = IntModel(Int32);
-        let cslice_model = StructModel(CSlice);
+    if let Some(exception) = exception {
+        unsafe {
+            let int32 = ctx.ctx.i32_type();
+            let zero = int32.const_zero();
+            let exception = exception.into_pointer_value();
+            let file_ptr = ctx
+                .builder
+                .build_in_bounds_gep(exception, &[zero, int32.const_int(1, false)], "file_ptr")
+                .unwrap();
+            let filename = ctx.gen_string(generator, loc.file.0);
+            ctx.builder.build_store(file_ptr, filename).unwrap();
+            let row_ptr = ctx
+                .builder
+                .build_in_bounds_gep(exception, &[zero, int32.const_int(2, false)], "row_ptr")
+                .unwrap();
+            ctx.builder.build_store(row_ptr, int32.const_int(loc.row as u64, false)).unwrap();
+            let col_ptr = ctx
+                .builder
+                .build_in_bounds_gep(exception, &[zero, int32.const_int(3, false)], "col_ptr")
+                .unwrap();
+            ctx.builder.build_store(col_ptr, int32.const_int(loc.column as u64, false)).unwrap();
 
-        // Get and store filename
-        let filename = loc.file.0;
-        let filename = ctx.gen_string(generator, &String::from(filename)).value;
-        let filename = cslice_model.check_value(generator, ctx.ctx, filename).unwrap();
-        pexn.set(ctx, |f| f.filename, filename);
-
-        let row = i32_model.constant(generator, ctx.ctx, loc.row as u64);
-        pexn.set(ctx, |f| f.line, row);
-
-        let column = i32_model.constant(generator, ctx.ctx, loc.column as u64);
-        pexn.set(ctx, |f| f.column, column);
-
-        let current_fn = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
-        let fn_name = ctx.gen_string(generator, current_fn.get_name().to_str().unwrap());
-        pexn.set(ctx, |f| f.function, fn_name);
+            let current_fun = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
+            let fun_name = ctx.gen_string(generator, current_fun.get_name().to_str().unwrap());
+            let name_ptr = ctx
+                .builder
+                .build_in_bounds_gep(exception, &[zero, int32.const_int(4, false)], "name_ptr")
+                .unwrap();
+            ctx.builder.build_store(name_ptr, fun_name).unwrap();
+        }
 
         let raise = get_builtins(generator, ctx, "__nac3_raise");
-        ctx.build_call_or_invoke(raise, &[pexn.value.into()], "raise");
+        let exception = *exception;
+        ctx.build_call_or_invoke(raise, &[exception], "raise");
     } else {
         let resume = get_builtins(generator, ctx, "__nac3_resume");
         ctx.build_call_or_invoke(resume, &[], "resume");
     }
-
     ctx.builder.build_unreachable().unwrap();
 }
 
@@ -1347,7 +1327,7 @@ pub fn gen_try<'ctx, 'a, G: CodeGenerator>(
     target: &Stmt<Option<Type>>,
 ) -> Result<(), String> {
     let StmtKind::Try { body, handlers, orelse, finalbody, .. } = &target.node else {
-        unreachable!()
+        codegen_unreachable!(ctx)
     };
 
     // if we need to generate anything related to exception, we must have personality defined
@@ -1424,7 +1404,7 @@ pub fn gen_try<'ctx, 'a, G: CodeGenerator>(
             if let TypeEnum::TObj { obj_id, .. } = &*ctx.unifier.get_ty(type_.custom.unwrap()) {
                 *obj_id
             } else {
-                unreachable!()
+                codegen_unreachable!(ctx)
             };
         let exception_name = format!("{}:{}", ctx.resolver.get_exception_id(obj_id.0), exn_name);
         let exn_id = ctx.resolver.get_string_id(&exception_name);
@@ -1696,6 +1676,23 @@ pub fn gen_return<G: CodeGenerator>(
     } else {
         None
     };
+
+    // Remap boolean return type into i1
+    let value = value.map(|ret_val| {
+        // The "return type" of a sret function is in the first parameter
+        let expected_ty = if ctx.need_sret {
+            func.get_type().get_param_types()[0]
+        } else {
+            func.get_type().get_return_type().unwrap()
+        };
+
+        if matches!(expected_ty, BasicTypeEnum::IntType(ty) if ty.get_bit_width() == 1) {
+            generator.bool_to_i1(ctx, ret_val.into_int_value()).into()
+        } else {
+            ret_val
+        }
+    });
+
     if let Some(return_target) = ctx.return_target {
         if let Some(value) = value {
             ctx.builder.build_store(ctx.return_buffer.unwrap(), value).unwrap();
@@ -1706,25 +1703,6 @@ pub fn gen_return<G: CodeGenerator>(
         ctx.builder.build_store(ctx.return_buffer.unwrap(), value.unwrap()).unwrap();
         ctx.builder.build_return(None).unwrap();
     } else {
-        // Remap boolean return type into i1
-        let value = value.map(|v| {
-            let expected_ty = func.get_type().get_return_type().unwrap();
-            let ret_val = v.as_basic_value_enum();
-
-            if expected_ty.is_int_type() && ret_val.is_int_value() {
-                let ret_type = expected_ty.into_int_type();
-                let ret_val = ret_val.into_int_value();
-
-                if ret_type.get_bit_width() == 1 && ret_val.get_type().get_bit_width() != 1 {
-                    generator.bool_to_i1(ctx, ret_val)
-                } else {
-                    ret_val
-                }
-                .into()
-            } else {
-                ret_val
-            }
-        });
         let value = value.as_ref().map(|v| v as &dyn BasicValue);
         ctx.builder.build_return(value).unwrap();
     }
@@ -1793,52 +1771,95 @@ pub fn gen_stmt<G: CodeGenerator>(
         StmtKind::Try { .. } => gen_try(generator, ctx, stmt)?,
         StmtKind::Raise { exc, .. } => {
             if let Some(exc) = exc {
-                let exc = if let Some(v) = generator.gen_expr(ctx, exc)? {
+                let exn = if let ExprKind::Name { id, .. } = &exc.node {
+                    // Handle "raise Exception" short form
+                    let def_id = ctx.resolver.get_identifier_def(*id).map_err(|e| {
+                        format!("{} (at {})", e.iter().next().unwrap(), exc.location)
+                    })?;
+                    let def = ctx.top_level.definitions.read();
+                    let TopLevelDef::Class { constructor, .. } = *def[def_id.0].read() else {
+                        return Err(format!("Failed to resolve symbol {id} (at {})", exc.location));
+                    };
+
+                    let TypeEnum::TFunc(signature) =
+                        ctx.unifier.get_ty(constructor.unwrap()).as_ref().clone()
+                    else {
+                        return Err(format!("Failed to resolve symbol {id} (at {})", exc.location));
+                    };
+
+                    generator
+                        .gen_call(ctx, None, (&signature, def_id), Vec::default())?
+                        .map(Into::into)
+                } else {
+                    generator.gen_expr(ctx, exc)?
+                };
+
+                let exc = if let Some(v) = exn {
                     v.to_basic_value_enum(ctx, generator, exc.custom.unwrap())?
                 } else {
                     return Ok(());
                 };
-
-                let pexn_model = PtrModel(StructModel(Exception));
-                let exn = pexn_model.check_value(generator, ctx.ctx, exc).unwrap();
-
-                gen_raise(generator, ctx, Some(exn), stmt.location);
+                gen_raise(generator, ctx, Some(&exc), stmt.location);
             } else {
                 gen_raise(generator, ctx, None, stmt.location);
             }
         }
         StmtKind::Assert { test, msg, .. } => {
-            let byte_model = IntModel(Byte);
-            let cslice_model = StructModel(CSlice);
-
-            let Some(test) = generator.gen_expr(ctx, test)? else {
+            let test = if let Some(v) = generator.gen_expr(ctx, test)? {
+                v.to_basic_value_enum(ctx, generator, test.custom.unwrap())?
+            } else {
                 return Ok(());
             };
-            let test = test.to_basic_value_enum(ctx, generator, ctx.primitives.bool)?;
-            let test = byte_model.check_value(generator, ctx.ctx, test).unwrap(); // Python `bool` is represented as `i8` in nac3core
-
-            // Check `msg`
             let err_msg = match msg {
                 Some(msg) => {
-                    let Some(msg) = generator.gen_expr(ctx, msg)? else {
+                    if let Some(v) = generator.gen_expr(ctx, msg)? {
+                        v.to_basic_value_enum(ctx, generator, msg.custom.unwrap())?
+                    } else {
                         return Ok(());
-                    };
-
-                    let msg = msg.to_basic_value_enum(ctx, generator, ctx.primitives.str)?;
-                    cslice_model.check_value(generator, ctx.ctx, msg).unwrap()
                     }
-                None => ctx.gen_string(generator, ""),
+                }
+                None => ctx.gen_string(generator, "").into(),
             };
-
             ctx.make_assert_impl(
                 generator,
-                test.value,
+                generator.bool_to_i1(ctx, test.into_int_value()),
                 "0:AssertionError",
                 err_msg,
                 [None, None, None],
                 stmt.location,
             );
         }
+        StmtKind::Global { names, .. } => {
+            let registered_globals = ctx
+                .top_level
+                .definitions
+                .read()
+                .iter()
+                .filter_map(|def| {
+                    if let TopLevelDef::Variable { simple_name, ty, .. } = &*def.read() {
+                        Some((*simple_name, *ty))
+                    } else {
+                        None
+                    }
+                })
+                .collect_vec();
+
+            for id in names {
+                let Some((_, ty)) = registered_globals.iter().find(|(name, _)| name == id) else {
+                    return Err(format!("{id} is not a global at {}", stmt.location));
+                };
+
+                let resolver = ctx.resolver.clone();
+                let ptr = resolver
+                    .get_symbol_value(*id, ctx, generator)
+                    .map(|val| val.to_basic_value_enum(ctx, generator, *ty))
+                    .transpose()?
+                    .map(BasicValueEnum::into_pointer_value)
+                    .unwrap();
+
+                ctx.var_assignment.insert(*id, (ptr, None, 0));
+            }
+        }
         _ => unimplemented!(),
     };
     Ok(())