.clang-format

@@ -1,3 +0,0 @@
-BasedOnStyle: Google
-IndentWidth: 4
-ReflowComments: false

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 ];

nac3artiq/src/lib.rs

@@ -33,7 +33,6 @@ use inkwell::{
     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::{TypeEnum, Unifier, VarMap};
@@ -499,11 +498,6 @@ impl Nac3 {
             .register_top_level(synthesized.pop().unwrap(), Some(resolver.clone()), "", false)
             .unwrap();
 
-        // Process IRRT
-        let context = inkwell::context::Context::create();
-        let irrt = load_irrt(&context);
-        setup_irrt_exceptions(&context, &irrt, resolver.as_ref());
-
         let fun_signature =
             FunSignature { args: vec![], ret: self.primitive.none, vars: VarMap::new() };
         let mut store = ConcreteTypeStore::new();
@@ -674,7 +668,7 @@ impl Nac3 {
             membuffer.lock().push(buffer);
         });
 
-        // Link all modules into `main`.
+        let context = inkwell::context::Context::create();
         let buffers = membuffers.lock();
         let main = context
             .create_module_from_ir(MemoryBuffer::create_from_memory_range(&buffers[0], "main"))
@@ -703,7 +697,8 @@ impl Nac3 {
             )
             .unwrap();
 
-        main.link_in_module(irrt).map_err(|err| CompileError::new_err(err.to_string()))?;
+        main.link_in_module(load_irrt(&context))
+            .map_err(|err| CompileError::new_err(err.to_string()))?;
 
         let mut function_iter = main.get_first_function();
         while let Some(func) = function_iter {

nac3core/Cargo.toml

@@ -1,6 +1,3 @@
-[features]
-test = []
-
 [package]
 name = "nac3core"
 version = "0.1.0"

nac3core/build.rs

@@ -3,63 +3,43 @@ use std::{
     env,
     fs::File,
     io::Write,
-    path::{Path, PathBuf},
+    path::Path,
     process::{Command, Stdio},
 };
 
-const CMD_IRRT_CLANG: &str = "clang-irrt";
+fn main() {
-const CMD_IRRT_CLANG_TEST: &str = "clang-irrt-test";
+    const FILE: &str = "src/codegen/irrt/irrt.cpp";
-const CMD_IRRT_LLVM_AS: &str = "llvm-as-irrt";
-
-fn get_out_dir() -> PathBuf {
-    PathBuf::from(env::var("OUT_DIR").unwrap())
-}
-
-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 (opt_flag, debug_assert_flag) = match env::var("PROFILE").as_deref() {
-        Ok("debug") => ("-O0", "-DIRRT_DEBUG_ASSERT=true"),
-        Ok("release") => ("-O3", "-DIRRT_DEBUG_ASSERT=false"),
-        flavor => panic!("Unknown or missing build flavor {flavor:?}"),
-    };
 
     /*
      * 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 flags: &[&str] = &[
         "--target=wasm32",
+        FILE,
         "-x",
         "c++",
         "-fno-discard-value-names",
         "-fno-exceptions",
         "-fno-rtti",
-        opt_flag,
+        match env::var("PROFILE").as_deref() {
-        debug_assert_flag,
+            Ok("debug") => "-O0",
+            Ok("release") => "-O3",
+            flavor => panic!("Unknown or missing build flavor {flavor:?}"),
+        },
         "-emit-llvm",
         "-S",
         "-Wall",
         "-Wextra",
         "-o",
         "-",
-        "-I",
-        irrt_dir.to_str().unwrap(),
-        irrt_cpp_path.to_str().unwrap(),
     ];
 
-    // Tell Cargo to rerun if any file under `irrt_dir` (recursive) changes
+    println!("cargo:rerun-if-changed={FILE}");
-    println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
+    let out_dir = env::var("OUT_DIR").unwrap();
+    let out_path = Path::new(&out_dir);
 
-    // Compile IRRT and capture the LLVM IR output
+    let output = Command::new("clang-irrt")
-    let output = Command::new(CMD_IRRT_CLANG)
         .args(flags)
         .output()
         .map(|o| {
@@ -72,17 +52,7 @@ fn compile_irrt_cpp() {
     let output = std::str::from_utf8(&output.stdout).unwrap().replace("\r\n", "\n");
     let mut filtered_output = String::with_capacity(output.len());
 
-    // Filter out irrelevant IR
+    let regex_filter = Regex::new(r"(?ms:^define.*?\}$)|(?m:^declare.*?$)").unwrap();
-    //
-    // Regex:
-    // - `(?ms:^define.*?\}$)` captures LLVM `define` blocks
-    // - `(?m:^declare.*?$)` captures LLVM `declare` lines
-    // - `(?m:^%.+?=\s*type\s*\{.+?\}$)` captures LLVM `type` declarations
-    // - `(?m:^@.+?=.+$)` captures global constants
-    let regex_filter = Regex::new(
-        r"(?ms:^define.*?\}$)|(?m:^declare.*?$)|(?m:^%.+?=\s*type\s*\{.+?\}$)|(?m:^@.+?=.+$)",
-    )
-    .unwrap();
     for f in regex_filter.captures_iter(&output) {
         assert_eq!(f.len(), 1);
         filtered_output.push_str(&f[0]);
@@ -93,71 +63,20 @@ fn compile_irrt_cpp() {
         .unwrap()
         .replace_all(&filtered_output, "");
 
-    // For debugging
+    println!("cargo:rerun-if-env-changed=DEBUG_DUMP_IRRT");
-    // Doing `DEBUG_DUMP_IRRT=1 cargo build -p nac3core` dumps the LLVM IR generated
+    if env::var("DEBUG_DUMP_IRRT").is_ok() {
-    const DEBUG_DUMP_IRRT: &str = "DEBUG_DUMP_IRRT";
+        let mut file = File::create(out_path.join("irrt.ll")).unwrap();
-    println!("cargo:rerun-if-env-changed={DEBUG_DUMP_IRRT}");
-    if env::var(DEBUG_DUMP_IRRT).is_ok() {
-        let mut file = File::create(out_dir.join("irrt.ll")).unwrap();
         file.write_all(output.as_bytes()).unwrap();
-
+        let mut file = File::create(out_path.join("irrt-filtered.ll")).unwrap();
-        let mut file = File::create(out_dir.join("irrt-filtered.ll")).unwrap();
         file.write_all(filtered_output.as_bytes()).unwrap();
     }
 
-    // Assemble the emitted and filtered IR to .bc
+    let mut llvm_as = Command::new("llvm-as-irrt")
-    // That .bc will be integrated into nac3core's codegen
-    let mut llvm_as = Command::new(CMD_IRRT_LLVM_AS)
         .stdin(Stdio::piped())
         .arg("-o")
-        .arg(out_dir.join("irrt.bc"))
+        .arg(out_path.join("irrt.bc"))
         .spawn()
         .unwrap();
     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 +0,0 @@
-#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.
- */

nac3core/irrt/irrt/cslice.hpp

@@ -1,9 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-
-template <typename SizeT>
-struct CSlice {
-    uint8_t* base;
-    SizeT len;
-};

nac3core/irrt/irrt/exception.hpp

@@ -1,123 +0,0 @@
-#pragma once
-
-#include <irrt/cslice.hpp>
-#include <irrt/int_defs.hpp>
-#include <irrt/util.hpp>
-
-/**
- * @brief The int type of ARTIQ exception IDs.
- *
- * It is always `int32_t`
- */
-typedef int32_t ExceptionId;
-
-/*
- * A set of exceptions 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
-
-namespace {
-/**
- * @brief NAC3's Exception struct
- */
-template <typename SizeT>
-struct Exception {
-    ExceptionId id;
-    CSlice<SizeT> filename;
-    int32_t line;
-    int32_t column;
-    CSlice<SizeT> function;
-    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
-
-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) {
-    Exception<SizeT> e = {
-        .id = id,
-        .filename = {.base = (uint8_t*)filename,
-                     .len = (int32_t)cstr_utils::length(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)},
-    };
-    e.params[0] = param0;
-    e.params[1] = param1;
-    e.params[2] = param2;
-    __nac3_raise((void*)&e);
-    __builtin_unreachable();
-}
-
-/**
- * @brief Raise an exception with location details (location in the IRRT source files).
- * @param SizeT The runtime `size_t` type.
- * @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
- * `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>(); }
-}

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/ndarray/basic.hpp

@@ -1,297 +0,0 @@
-#pragma once
-
-#include <irrt/exception.hpp>
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/def.hpp>
-
-namespace {
-namespace ndarray {
-namespace basic {
-namespace util {
-/**
- * @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 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;
-    }
-}
-}  // namespace util
-
-/**
- * @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 util::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;
-}
-
-int counter = 0;
-
-/**
- * @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++) {
-        int 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
-
-    __builtin_assume(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) {
-    util::assert_shape_no_negative(ndims, shape);
-}
-
-void __nac3_ndarray_util_assert_shape_no_negative64(int64_t ndims,
-                                                    int64_t* shape) {
-    util::assert_shape_no_negative(ndims, 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

@@ -1,157 +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 {
-namespace util {
-/**
- * @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`
- */
-template <typename SizeT>
-void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT>* shapes,
-                      SizeT dst_ndims, SizeT* 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.
-    // `dst_shape` must be pre-allocated.
-    // `dst_shape` does not have to be initialized
-    for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
-        dst_shape[dst_axis] = 1;
-    }
-
-    for (SizeT i = 0; i < num_shapes; i++) {
-        ShapeEntry<SizeT> entry = shapes[i];
-
-        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);
-            }
-        }
-    }
-}
-}  // namespace util
-
-/**
- * @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::util::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",
-                        dst_ndarray->shape[0], src_ndarray->shape[0], 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) {
-    ndarray::broadcast::util::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) {
-    ndarray::broadcast::util::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

@@ -1,284 +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 {
-namespace util {
-
-/**
- * @brief Return the expected rank of the resulting ndarray
- * created by indexing an ndarray of rank `ndims` using `indexes`.
- *
- * An IndexError is raised if the indexes are invalid.
- */
-template <typename SizeT>
-SizeT validate_and_deduce_ndims_after_indexing(SizeT ndims, SizeT num_indexes,
-                                               const NDIndex* indexes) {
-    // 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) {
-            ndims--;
-        } else if (indexes[i].type == ND_INDEX_TYPE_SLICE) {
-            // Nothing
-        } else if (indexes[i].type == ND_INDEX_TYPE_NEWAXIS) {
-            ndims++;
-        } else if (indexes[i].type == ND_INDEX_TYPE_ELLIPSIS) {
-            // `...` doesn't do anything to `ndims`.
-            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();
-        }
-    }
-
-    if (num_indexes - num_ellipsis > ndims) {
-        raise_exception(SizeT, EXN_INDEX_ERROR,
-                        "too many indices for array: array is {0}-dimensional, "
-                        "but {1} were indexed",
-                        ndims, num_indexes, NO_PARAM);
-    }
-
-    return ndims;
-}
-}  // namespace util
-
-/**
- * @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();
-        }
-    }
-
-    if (IRRT_DEBUG_ASSERT) {
-        if (expected_dst_ndims != dst_ndarray->ndims) {
-            raise_exception(
-                SizeT, EXN_ASSERTION_ERROR,
-                "IRRT expected_dst_ndims is {0}, but dst_ndarray->ndims is {1}",
-                expected_dst_ndims, dst_ndarray->ndims, NO_PARAM);
-        }
-    }
-
-    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], &slice);
-
-            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];
-    }
-
-    if (IRRT_DEBUG_ASSERT) {
-        if (dst_ndarray->ndims != dst_axis) {
-            raise_exception(SizeT, EXN_ASSERTION_ERROR,
-                            "IRRT dst_ndarray->ndims ({0}) != dst_axis ({1})",
-                            dst_ndarray->ndims, dst_axis, NO_PARAM);
-        }
-        if (src_ndarray->ndims != src_axis) {
-            raise_exception(SizeT, EXN_ASSERTION_ERROR,
-                            "IRRT src_ndarray->ndims ({0}) != src_axis ({1})",
-                            src_ndarray->ndims, src_axis, NO_PARAM);
-        }
-    }
-}
-}  // 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/product.hpp

@@ -1,26 +0,0 @@
-
-namespace {
-namespace ndarray {
-namespace matmul {
-namespace util {
-
-template <typename SizeT>
-void broadcast_shape(SizeT a_ndims, SizeT* a_shape, SizeT b_ndims,
-                     SizeT* b_shape, SizeT* dst_shape) {
-    __builtin_assume(!(a_ndims == 1 && b_ndims == 1));
-    __builtin_assume(a_ndims >= 1);
-    __builtin_assume(b_ndims >= 1);
-}
-}  // namespace util
-
-template <typename SizeT, typename T>
-void matmul_at_least_2d(NDArray<SizeT>* a_ndarray, NDArray<SizeT>* b_ndarray,
-            NDArray<SizeT>* dst_ndarray) {
-    __builtin_assume(sizeof(T) == a_ndarray->itemsize);
-    __builtin_assume(sizeof(T) == b_ndarray->itemsize);
-
-    // See https://numpy.org/doc/stable/reference/generated/numpy.matmul.html#numpy-matmul
-}
-}  // namespace matmul
-}  // namespace ndarray
-}  // namespace

nac3core/irrt/irrt/ndarray/reshape.hpp

@@ -1,111 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/def.hpp>
-
-namespace {
-namespace ndarray {
-namespace reshape {
-namespace util {
-
-/**
- * @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 util
-}  // 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::util::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::util::resolve_and_check_new_shape(size, new_ndims,
-                                                        new_shape);
-}
-}

nac3core/irrt/irrt/ndarray/transpose.hpp

@@ -1,148 +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 {
-namespace util {
-
-/**
- * @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;
-    }
-}
-}  // namespace util
-
-/**
- * @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) {
-    __builtin_assume(src_ndarray->ndims == dst_ndarray->ndims);
-    const auto ndims = src_ndarray->ndims;
-
-    if (axes != nullptr) util::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

@@ -1,165 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-#include <irrt/slice.hpp>
-#include <irrt/util.hpp>
-
-#include "exception.hpp"
-
-// 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;
-        if (diff > 0 && step > 0) {
-            return ((diff - 1) / step) + 1;
-        } else if (diff < 0 && step < 0) {
-            return ((diff + 1) / step) + 1;
-        } 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>
-    void indices_checked(SliceIndex length, Slice* result) {
-        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);
-        }
-
-        *result = 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

@@ -1,16 +0,0 @@
-#pragma once
-
-#ifndef IRRT_DEBUG_ASSERT
-#error IRRT_DEBUG_ASSERT flag is missing!! Please define it to 'false' or 'true'.
-#endif
-
-#include <irrt/core.hpp>
-#include <irrt/exception.hpp>
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/basic.hpp>
-#include <irrt/ndarray/broadcast.hpp>
-#include <irrt/ndarray/def.hpp>
-#include <irrt/ndarray/indexing.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/src/codegen/expr.rs

@@ -2,7 +2,7 @@ use crate::{
     codegen::{
         classes::{
             ArrayLikeIndexer, ArrayLikeValue, ListType, ListValue, NDArrayValue, ProxyType,
-            ProxyValue, RangeValue, UntypedArrayLikeAccessor,
+            ProxyValue, RangeValue, TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
         },
         concrete_type::{ConcreteFuncArg, ConcreteTypeEnum, ConcreteTypeStore},
         gen_in_range_check, get_llvm_abi_type, get_llvm_type, get_va_count_arg_name,
@@ -16,11 +16,14 @@ use crate::{
             gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
             gen_var,
         },
-        structure::ndarray::NDArrayObject,
         CodeGenContext, CodeGenTask, CodeGenerator,
     },
     symbol_resolver::{SymbolValue, ValueEnum},
-    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, DefinitionId, TopLevelDef},
+    toplevel::{
+        helper::PrimDef,
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
+        DefinitionId, TopLevelDef,
+    },
     typecheck::{
         magic_methods::{Binop, BinopVariant, HasOpInfo},
         typedef::{FunSignature, FuncArg, Type, TypeEnum, TypeVarId, Unifier, VarMap},
@@ -40,12 +43,6 @@ use nac3parser::ast::{
 use std::iter::{repeat, repeat_with};
 use std::{collections::HashMap, convert::TryInto, iter::once, iter::zip};
 
-use super::structure::{cslice::CSlice, ndarray::indexing::util::gen_ndarray_subscript_ndindexes};
-use super::{
-    model::*,
-    structure::exception::{Exception, ExceptionId},
-};
-
 pub fn get_subst_key(
     unifier: &mut Unifier,
     obj: Option<Type>,
@@ -287,7 +284,24 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
                     None
                 }
             }
-            Constant::Str(s) => Some(self.gen_string(generator, s).value.into()),
+            Constant::Str(v) => {
+                assert!(self.unifier.unioned(ty, self.primitives.str));
+                if let Some(v) = self.const_strings.get(v) {
+                    Some(*v)
+                } else {
+                    let str_ptr = self
+                        .builder
+                        .build_global_string_ptr(v, "const")
+                        .map(|v| v.as_pointer_value().into())
+                        .unwrap();
+                    let size = generator.get_size_type(self.ctx).const_int(v.len() as u64, false);
+                    let ty = self.get_llvm_type(generator, self.primitives.str);
+                    let val =
+                        ty.into_struct_type().const_named_struct(&[str_ptr, size.into()]).into();
+                    self.const_strings.insert(v.to_string(), val);
+                    Some(val)
+                }
+            }
             Constant::Ellipsis => {
                 let msg = self.gen_string(generator, "NotImplementedError");
 
@@ -554,70 +568,69 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
     }
 
     /// Helper function for generating a LLVM variable storing a [String].
-    pub fn gen_string<G>(&mut self, generator: &mut G, string: &str) -> Struct<'ctx, CSlice>
+    pub fn gen_string<G, S>(&mut self, generator: &mut G, s: S) -> BasicValueEnum<'ctx>
     where
         G: CodeGenerator + ?Sized,
+        S: Into<String>,
     {
-        self.const_strings.get(string).copied().unwrap_or_else(|| {
+        self.gen_const(generator, &Constant::Str(s.into()), self.primitives.str).unwrap()
-            let sizet_model = IntModel(SizeT);
-            let pbyte_model = PtrModel(IntModel(Byte));
-            let cslice_model = StructModel(CSlice);
-
-            let base = self.builder.build_global_string_ptr(string, "constant_string").unwrap();
-            let base = pbyte_model.believe_value(base.as_pointer_value());
-
-            let len = sizet_model.constant(generator, self.ctx, string.len() as u64);
-
-            let cslice = cslice_model.create_const(generator, self.ctx, base, len);
-
-            self.const_strings.insert(string.to_owned(), cslice);
-
-            cslice
-        })
     }
 
     pub fn raise_exn<G: CodeGenerator + ?Sized>(
         &mut self,
         generator: &mut G,
         name: &str,
-        msg: Struct<'ctx, CSlice>,
+        msg: BasicValueEnum<'ctx>,
-        params: [Option<Int<'ctx, Int64>>; 3],
+        params: [Option<IntValue<'ctx>>; 3],
         loc: Location,
     ) {
-        let exn_model = StructModel(Exception);
+        let zelf = if let Some(exception_val) = self.exception_val {
-        let exn_id_model = IntModel(ExceptionId::default());
+            exception_val
-
+        } else {
-        let exn_id =
+            let ty = self.get_llvm_type(generator, self.primitives.exception).into_pointer_type();
-            exn_id_model.constant(generator, self.ctx, self.resolver.get_string_id(name) as u64);
+            let zelf_ty: BasicTypeEnum = ty.get_element_type().into_struct_type().into();
-        let exn = self.exception_val.unwrap_or_else(|| {
+            let zelf = generator.gen_var_alloc(self, zelf_ty, Some("exn")).unwrap();
-            let exn = exn_model.var_alloca(generator, self, Some("exn")).unwrap();
+            *self.exception_val.insert(zelf)
-            *self.exception_val.insert(exn)
+        };
-        });
+        let int32 = self.ctx.i32_type();
-
+        let zero = int32.const_zero();
-        exn.set(self, |f| f.id, exn_id);
+        unsafe {
-        exn.set(self, |f| f.msg, msg);
+            let id_ptr = self.builder.build_in_bounds_gep(zelf, &[zero, zero], "exn.id").unwrap();
-        for (i, param) in params.iter().enumerate() {
+            let id = self.resolver.get_string_id(name);
-            if let Some(param) = param {
+            self.builder.build_store(id_ptr, int32.const_int(id as u64, false)).unwrap();
-                exn.set(self, |f| f.params[i], *param);
+            let ptr = self
+                .builder
+                .build_in_bounds_gep(zelf, &[zero, int32.const_int(5, false)], "exn.msg")
+                .unwrap();
+            self.builder.build_store(ptr, msg).unwrap();
+            let i64_zero = self.ctx.i64_type().const_zero();
+            for (i, attr_ind) in [6, 7, 8].iter().enumerate() {
+                let ptr = self
+                    .builder
+                    .build_in_bounds_gep(
+                        zelf,
+                        &[zero, int32.const_int(*attr_ind, false)],
+                        "exn.param",
+                    )
+                    .unwrap();
+                let val = params[i].map_or(i64_zero, |v| {
+                    self.builder.build_int_s_extend(v, self.ctx.i64_type(), "sext").unwrap()
+                });
+                self.builder.build_store(ptr, val).unwrap();
             }
         }
-
+        gen_raise(generator, self, Some(&zelf.into()), loc);
-        gen_raise(generator, self, Some(exn), loc);
     }
 
     pub fn make_assert<G: CodeGenerator + ?Sized>(
         &mut self,
         generator: &mut G,
-        cond: IntValue<'ctx>, // IntType can have arbitrary bit width
+        cond: IntValue<'ctx>,
         err_name: &str,
         err_msg: &str,
         params: [Option<IntValue<'ctx>>; 3],
         loc: Location,
     ) {
-        let param_model = IntModel(Int64);
-        let params =
-            params.map(|p| p.map(|p| param_model.check_value(generator, self.ctx, p).unwrap()));
-
         let err_msg = self.gen_string(generator, err_msg);
         self.make_assert_impl(generator, cond, err_name, err_msg, params, loc);
     }
@@ -625,36 +638,26 @@ impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
     pub fn make_assert_impl<G: CodeGenerator + ?Sized>(
         &mut self,
         generator: &mut G,
-        cond: IntValue<'ctx>, // IntType can have arbitrary bit width
+        cond: IntValue<'ctx>,
         err_name: &str,
-        err_msg: Struct<'ctx, CSlice>,
+        err_msg: BasicValueEnum<'ctx>,
-        params: [Option<Int<'ctx, Int64>>; 3],
+        params: [Option<IntValue<'ctx>>; 3],
         loc: Location,
     ) {
-        let bool_model = IntModel(Bool);
+        let i1 = self.ctx.bool_type();
-
+        let i1_true = i1.const_all_ones();
-        // We assume that the condition is most probably true, so the normal path is the most
+        // we assume that the condition is most probably true, so the normal path is the most
-        // probable path even if this assumption is violated, it does not matter as exception unwinding is.
+        // probable path
-        let cond = call_expect(
+        // even if this assumption is violated, it does not matter as exception unwinding is
-            self,
+        // slow anyway...
-            generator.bool_to_i1(self, cond),
+        let cond = call_expect(self, cond, i1_true, Some("expect"));
-            bool_model.const_true(generator, self.ctx).value,
-            Some("expect"),
-        );
-
         let current_bb = self.builder.get_insert_block().unwrap();
         let current_fun = current_bb.get_parent().unwrap();
-
         let then_block = self.ctx.insert_basic_block_after(current_bb, "succ");
         let exn_block = self.ctx.append_basic_block(current_fun, "fail");
-
         self.builder.build_conditional_branch(cond, then_block, exn_block).unwrap();
-
-        // Inserting into `exn_block`
         self.builder.position_at_end(exn_block);
         self.raise_exn(generator, err_name, err_msg, params, loc);
-
-        // Continuation
         self.builder.position_at_end(then_block);
     }
 }
@@ -2247,6 +2250,338 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
     )
 }
 
+/// Generates code for a subscript expression on an `ndarray`.
+///
+/// * `ty` - The `Type` of the `NDArray` elements.
+/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
+/// * `v` - The `NDArray` value.
+/// * `slice` - The slice expression used to subscript into the `ndarray`.
+fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ty: Type,
+    ndims: Type,
+    v: NDArrayValue<'ctx>,
+    slice: &Expr<Option<Type>>,
+) -> Result<Option<ValueEnum<'ctx>>, String> {
+    let llvm_i1 = ctx.ctx.bool_type();
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims) else {
+        unreachable!()
+    };
+
+    let ndims = values
+        .iter()
+        .map(|ndim| u64::try_from(ndim.clone()).map_err(|()| ndim.clone()))
+        .collect::<Result<Vec<_>, _>>()
+        .map_err(|val| {
+            format!(
+                "Expected non-negative literal for ndarray.ndims, got {}",
+                i128::try_from(val).unwrap()
+            )
+        })?;
+
+    assert!(!ndims.is_empty());
+
+    // The number of dimensions subscripted by the index expression.
+    // Slicing a ndarray will yield the same number of dimensions, whereas indexing into a
+    // dimension will remove a dimension.
+    let subscripted_dims = match &slice.node {
+        ExprKind::Tuple { elts, .. } => elts.iter().fold(0, |acc, value_subexpr| {
+            if let ExprKind::Slice { .. } = &value_subexpr.node {
+                acc
+            } else {
+                acc + 1
+            }
+        }),
+
+        ExprKind::Slice { .. } => 0,
+        _ => 1,
+    };
+
+    let ndarray_ndims_ty = ctx.unifier.get_fresh_literal(
+        ndims.iter().map(|v| SymbolValue::U64(v - subscripted_dims)).collect(),
+        None,
+    );
+    let ndarray_ty =
+        make_ndarray_ty(&mut ctx.unifier, &ctx.primitives, Some(ty), Some(ndarray_ndims_ty));
+    let llvm_pndarray_t = ctx.get_llvm_type(generator, ndarray_ty).into_pointer_type();
+    let llvm_ndarray_t = llvm_pndarray_t.get_element_type().into_struct_type();
+    let llvm_ndarray_data_t = ctx.get_llvm_type(generator, ty).as_basic_type_enum();
+    let sizeof_elem = llvm_ndarray_data_t.size_of().unwrap();
+
+    // Check that len is non-zero
+    let len = v.load_ndims(ctx);
+    ctx.make_assert(
+        generator,
+        ctx.builder.build_int_compare(IntPredicate::SGT, len, llvm_usize.const_zero(), "").unwrap(),
+        "0:IndexError",
+        "too many indices for array: array is {0}-dimensional but 1 were indexed",
+        [Some(len), None, None],
+        slice.location,
+    );
+
+    // Normalizes a possibly-negative index to its corresponding positive index
+    let normalize_index = |generator: &mut G,
+                           ctx: &mut CodeGenContext<'ctx, '_>,
+                           index: IntValue<'ctx>,
+                           dim: u64| {
+        gen_if_else_expr_callback(
+            generator,
+            ctx,
+            |_, ctx| {
+                Ok(ctx
+                    .builder
+                    .build_int_compare(IntPredicate::SGE, index, index.get_type().const_zero(), "")
+                    .unwrap())
+            },
+            |_, _| Ok(Some(index)),
+            |generator, ctx| {
+                let llvm_i32 = ctx.ctx.i32_type();
+
+                let len = unsafe {
+                    v.dim_sizes().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(dim, true),
+                        None,
+                    )
+                };
+
+                let index = ctx
+                    .builder
+                    .build_int_add(
+                        len,
+                        ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
+                        "",
+                    )
+                    .unwrap();
+
+                Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
+            },
+        )
+        .map(|v| v.map(BasicValueEnum::into_int_value))
+    };
+
+    // Converts a slice expression into a slice-range tuple
+    let expr_to_slice = |generator: &mut G,
+                         ctx: &mut CodeGenContext<'ctx, '_>,
+                         node: &ExprKind<Option<Type>>,
+                         dim: u64| {
+        match node {
+            ExprKind::Constant { value: Constant::Int(v), .. } => {
+                let Some(index) =
+                    normalize_index(generator, ctx, llvm_i32.const_int(*v as u64, true), dim)?
+                else {
+                    return Ok(None);
+                };
+
+                Ok(Some((index, index, llvm_i32.const_int(1, true))))
+            }
+
+            ExprKind::Slice { lower, upper, step } => {
+                let dim_sz = unsafe {
+                    v.dim_sizes().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(dim, false),
+                        None,
+                    )
+                };
+
+                handle_slice_indices(lower, upper, step, ctx, generator, dim_sz)
+            }
+
+            _ => {
+                let Some(index) = generator.gen_expr(ctx, slice)? else { return Ok(None) };
+                let index = index
+                    .to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
+                    .into_int_value();
+                let Some(index) = normalize_index(generator, ctx, index, dim)? else {
+                    return Ok(None);
+                };
+
+                Ok(Some((index, index, llvm_i32.const_int(1, true))))
+            }
+        }
+    };
+
+    let make_indices_arr = |generator: &mut G,
+                            ctx: &mut CodeGenContext<'ctx, '_>|
+     -> Result<_, String> {
+        Ok(if let ExprKind::Tuple { elts, .. } = &slice.node {
+            let llvm_int_ty = ctx.get_llvm_type(generator, elts[0].custom.unwrap());
+            let index_addr = generator.gen_array_var_alloc(
+                ctx,
+                llvm_int_ty,
+                llvm_usize.const_int(elts.len() as u64, false),
+                None,
+            )?;
+
+            for (i, elt) in elts.iter().enumerate() {
+                let Some(index) = generator.gen_expr(ctx, elt)? else {
+                    return Ok(None);
+                };
+
+                let index = index
+                    .to_basic_value_enum(ctx, generator, elt.custom.unwrap())?
+                    .into_int_value();
+                let Some(index) = normalize_index(generator, ctx, index, 0)? else {
+                    return Ok(None);
+                };
+
+                let store_ptr = unsafe {
+                    index_addr.ptr_offset_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(i as u64, false),
+                        None,
+                    )
+                };
+                ctx.builder.build_store(store_ptr, index).unwrap();
+            }
+
+            Some(index_addr)
+        } else if let Some(index) = generator.gen_expr(ctx, slice)? {
+            let llvm_int_ty = ctx.get_llvm_type(generator, slice.custom.unwrap());
+            let index_addr = generator.gen_array_var_alloc(
+                ctx,
+                llvm_int_ty,
+                llvm_usize.const_int(1u64, false),
+                None,
+            )?;
+
+            let index =
+                index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
+            let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
+
+            let store_ptr = unsafe {
+                index_addr.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
+            };
+            ctx.builder.build_store(store_ptr, index).unwrap();
+
+            Some(index_addr)
+        } else {
+            None
+        })
+    };
+
+    Ok(Some(if ndims.len() == 1 && ndims[0] - subscripted_dims == 0 {
+        let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
+
+        v.data().get(ctx, generator, &index_addr, None).into()
+    } else {
+        match &slice.node {
+            ExprKind::Tuple { elts, .. } => {
+                let slices = elts
+                    .iter()
+                    .enumerate()
+                    .map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
+                    .take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
+                    .collect::<Result<Vec<_>, _>>()?;
+                if slices.len() < elts.len() {
+                    return Ok(None);
+                }
+
+                let slices = slices.into_iter().map(Option::unwrap).collect_vec();
+
+                numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
+            }
+
+            ExprKind::Slice { .. } => {
+                let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
+                    return Ok(None);
+                };
+
+                numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
+            }
+
+            _ => {
+                // Accessing an element from a multi-dimensional `ndarray`
+
+                let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
+
+                // Create a new array, remove the top dimension from the dimension-size-list, and copy the
+                // elements over
+                let subscripted_ndarray =
+                    generator.gen_var_alloc(ctx, llvm_ndarray_t.into(), None)?;
+                let ndarray = NDArrayValue::from_ptr_val(subscripted_ndarray, llvm_usize, None);
+
+                let num_dims = v.load_ndims(ctx);
+                ndarray.store_ndims(
+                    ctx,
+                    generator,
+                    ctx.builder
+                        .build_int_sub(num_dims, llvm_usize.const_int(1, false), "")
+                        .unwrap(),
+                );
+
+                let ndarray_num_dims = ndarray.load_ndims(ctx);
+                ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
+
+                let ndarray_num_dims = ctx
+                    .builder
+                    .build_int_z_extend_or_bit_cast(
+                        ndarray.load_ndims(ctx),
+                        llvm_usize.size_of().get_type(),
+                        "",
+                    )
+                    .unwrap();
+                let v_dims_src_ptr = unsafe {
+                    v.dim_sizes().ptr_offset_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(1, false),
+                        None,
+                    )
+                };
+                call_memcpy_generic(
+                    ctx,
+                    ndarray.dim_sizes().base_ptr(ctx, generator),
+                    v_dims_src_ptr,
+                    ctx.builder
+                        .build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
+                        .map(Into::into)
+                        .unwrap(),
+                    llvm_i1.const_zero(),
+                );
+
+                let ndarray_num_elems = call_ndarray_calc_size(
+                    generator,
+                    ctx,
+                    &ndarray.dim_sizes().as_slice_value(ctx, generator),
+                    (None, None),
+                );
+                let ndarray_num_elems = ctx
+                    .builder
+                    .build_int_z_extend_or_bit_cast(ndarray_num_elems, sizeof_elem.get_type(), "")
+                    .unwrap();
+                ndarray.create_data(ctx, llvm_ndarray_data_t, ndarray_num_elems);
+
+                let v_data_src_ptr = v.data().ptr_offset(ctx, generator, &index_addr, None);
+                call_memcpy_generic(
+                    ctx,
+                    ndarray.data().base_ptr(ctx, generator),
+                    v_data_src_ptr,
+                    ctx.builder
+                        .build_int_mul(
+                            ndarray_num_elems,
+                            llvm_ndarray_data_t.size_of().unwrap(),
+                            "",
+                        )
+                        .map(Into::into)
+                        .unwrap(),
+                    llvm_i1.const_zero(),
+                );
+
+                ndarray.as_base_value().into()
+            }
+        }
+    }))
+}
+
 /// See [`CodeGenerator::gen_expr`].
 pub fn gen_expr<'ctx, G: CodeGenerator>(
     generator: &mut G,
@@ -2886,20 +3221,18 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
                         v.data().get(ctx, generator, &index, None).into()
                     }
                 }
-                TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
+                TypeEnum::TObj { obj_id, params, .. } if *obj_id == PrimDef::NDArray.id() => {
-                    let Some(ndarray) = generator.gen_expr(ctx, value)? else {
+                    let (ty, ndims) = params.iter().map(|(_, ty)| ty).collect_tuple().unwrap();
+
+                    let v = if let Some(v) = generator.gen_expr(ctx, value)? {
+                        v.to_basic_value_enum(ctx, generator, value.custom.unwrap())?
+                            .into_pointer_value()
+                    } else {
                         return Ok(None);
                     };
+                    let v = NDArrayValue::from_ptr_val(v, usize, None);
 
-                    let ndarray_ty = value.custom.unwrap();
+                    return gen_ndarray_subscript_expr(generator, ctx, *ty, *ndims, v, slice);
-                    let ndarray = ndarray.to_basic_value_enum(ctx, generator, ndarray_ty)?;
-                    let ndarray =
-                        NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
-
-                    let indexes = gen_ndarray_subscript_ndindexes(generator, ctx, slice)?;
-                    let result = ndarray.index_or_scalar(generator, ctx, &indexes, "index_result");
-                    let result = result.to_basic_value_enum();
-                    return Ok(Some(ValueEnum::Dynamic(result)));
                 }
                 TypeEnum::TTuple { .. } => {
                     let index: u32 =
@@ -2942,43 +3275,3 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
         _ => unimplemented!(),
     }))
 }
-
-/// Generate LLVM IR for an [`ExprKind::Slice`]
-pub fn gen_slice<'ctx, G: CodeGenerator>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    lower: &Option<Box<Expr<Option<Type>>>>,
-    upper: &Option<Box<Expr<Option<Type>>>>,
-    step: &Option<Box<Expr<Option<Type>>>>,
-) -> Result<
-    (
-        Option<Instance<'ctx, IntModel<Int32>>>,
-        Option<Instance<'ctx, IntModel<Int32>>>,
-        Option<Instance<'ctx, IntModel<Int32>>>,
-    ),
-    String,
-> {
-    let i32_model = IntModel(Int32); // TODO: Switch to usize
-
-    let mut help = |value_expr: &Option<Box<Expr<Option<Type>>>>| -> Result<_, String> {
-        Ok(match value_expr {
-            None => None,
-            Some(value_expr) => {
-                let value_expr = generator
-                    .gen_expr(ctx, value_expr)?
-                    .unwrap()
-                    .to_basic_value_enum(ctx, generator, ctx.primitives.int32)?;
-
-                let value_expr = i32_model.check_value(generator, ctx.ctx, value_expr).unwrap();
-
-                Some(value_expr)
-            }
-        })
-    };
-
-    let lower = help(lower)?;
-    let upper = help(upper)?;
-    let step = help(step)?;
-
-    Ok((lower, upper, step))
-}

nac3core/src/codegen/irrt/irrt.cpp

@@ -1,17 +1,27 @@
-#pragma once
+using int8_t = _BitInt(8);
-
+using uint8_t = unsigned _BitInt(8);
-#include <irrt/int_defs.hpp>
+using int32_t = _BitInt(32);
-#include <irrt/util.hpp>
+using uint32_t = unsigned _BitInt(32);
+using int64_t = _BitInt(64);
+using uint64_t = unsigned _BitInt(64);
 
 // NDArray indices are always `uint32_t`.
-using NDIndexInt = uint32_t;
+using NDIndex = uint32_t;
-// The type of an index or a value describing the length of a
+// The type of an index or a value describing the length of a range/slice is always `int32_t`.
-// range/slice is always `int32_t`.
 using SliceIndex = int32_t;
 
 namespace {
-// adapted from GNU Scientific Library:
+template <typename T>
-// https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
+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;
+}
+
+// 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) {
@@ -28,8 +38,12 @@ T __nac3_int_exp_impl(T base, T exp) {
 }
 
 template <typename SizeT>
-SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len,
+SizeT __nac3_ndarray_calc_size_impl(
-                                    SizeT begin_idx, SizeT end_idx) {
+    const SizeT* list_data,
+    SizeT list_len,
+    SizeT begin_idx,
+    SizeT end_idx
+) {
     __builtin_assume(end_idx <= list_len);
 
     SizeT num_elems = 1;
@@ -42,8 +56,12 @@ SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len,
 }
 
 template <typename SizeT>
-void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
+void __nac3_ndarray_calc_nd_indices_impl(
-                                         SizeT num_dims, NDIndexInt* idxs) {
+    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;
@@ -54,9 +72,12 @@ void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
 }
 
 template <typename SizeT>
-SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims,
+SizeT __nac3_ndarray_flatten_index_impl(
-                                        const NDIndexInt* indices,
+    const SizeT* dims,
-                                        SizeT num_indices) {
+    SizeT num_dims,
+    const NDIndex* indices,
+    SizeT num_indices
+) {
     SizeT idx = 0;
     SizeT stride = 1;
     for (SizeT i = 0; i < num_dims; ++i) {
@@ -72,17 +93,18 @@ SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims,
 }
 
 template <typename SizeT>
-void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims, SizeT lhs_ndims,
+void __nac3_ndarray_calc_broadcast_impl(
-                                        const SizeT* rhs_dims, SizeT rhs_ndims,
+    const SizeT* lhs_dims,
-                                        SizeT* out_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 =
+        const SizeT* lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
-            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;
-        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) {
@@ -102,10 +124,12 @@ void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims, SizeT lhs_ndims,
 }
 
 template <typename SizeT>
-void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
+void __nac3_ndarray_calc_broadcast_idx_impl(
-                                            SizeT src_ndims,
+    const SizeT* src_dims,
-                                            const NDIndexInt* in_idx,
+    SizeT src_ndims,
-                                            NDIndexInt* out_idx) {
+    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];
@@ -114,15 +138,15 @@ void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
 }  // namespace
 
 extern "C" {
-#define DEF_nac3_int_exp_(T)                   \
+#define DEF_nac3_int_exp_(T) \
-    T __nac3_int_exp_##T(T base, T exp) {      \
+    T __nac3_int_exp_##T(T base, T exp) {\
-        return __nac3_int_exp_impl(base, exp); \
+        return __nac3_int_exp_impl(base, exp);\
     }
 
-DEF_nac3_int_exp_(int32_t);
+DEF_nac3_int_exp_(int32_t)
-DEF_nac3_int_exp_(int64_t);
+DEF_nac3_int_exp_(int64_t)
-DEF_nac3_int_exp_(uint32_t);
+DEF_nac3_int_exp_(uint32_t)
-DEF_nac3_int_exp_(uint64_t);
+DEF_nac3_int_exp_(uint64_t)
 
 SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
     if (i < 0) {
@@ -136,8 +160,11 @@ SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
     return i;
 }
 
-SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end,
+SliceIndex __nac3_range_slice_len(
-                                  const SliceIndex step) {
+    const SliceIndex start,
+    const SliceIndex end,
+    const SliceIndex step
+) {
     SliceIndex diff = end - start;
     if (diff > 0 && step > 0) {
         return ((diff - 1) / step) + 1;
@@ -153,52 +180,62 @@ SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end,
 // - 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
+// be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
-// len(src) == len(dest)
 SliceIndex __nac3_list_slice_assign_var_size(
-    SliceIndex dest_start, SliceIndex dest_end, SliceIndex dest_step,
+    SliceIndex dest_start,
-    uint8_t* dest_arr, SliceIndex dest_arr_len, SliceIndex src_start,
+    SliceIndex dest_end,
-    SliceIndex src_end, SliceIndex src_step, uint8_t* src_arr,
+    SliceIndex dest_step,
-    SliceIndex src_arr_len, const SliceIndex size) {
+    uint8_t* dest_arr,
-    /* if dest_arr_len == 0, do nothing since we do not support
+    SliceIndex dest_arr_len,
-     * extending list
+    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
+    /* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
-     * the list, and shrink size */
     if (src_step == dest_step && dest_step == 1) {
-        const SliceIndex src_len =
+        const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
-            (src_end >= src_start) ? (src_end - src_start + 1) : 0;
+        const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_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,
+            __builtin_memmove(
-                              src_arr + src_start * size, src_len * size);
+                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,
+            __builtin_memmove(
-                              dest_arr + (dest_end + 1) * size,
+                dest_arr + (dest_start + src_len) * size,
-                              (dest_arr_len - dest_end - 1) * 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) &&
+        (dest_arr == src_arr)
-        !(max(dest_start, dest_end) < min(src_start, src_end) ||
+        && !(
-          max(src_start, src_end) < min(dest_start, dest_end));
+            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 =
+        uint8_t* tmp = reinterpret_cast<uint8_t *>(__builtin_alloca(src_arr_len * size));
-            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);
+    for (;
-         src_ind += src_step, dest_ind += dest_step) {
+        (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);
@@ -207,26 +244,30 @@ SliceIndex __nac3_list_slice_assign_var_size(
         } 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
+            /* memcpy for var size, cannot overlap after previous alloca */
-             * alloca */
+            __builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
-            __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 + dest_ind * size,
-            (dest_arr_len - dest_end - 1) * size + size + size + size);
+            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;
 }
 
-int32_t __nac3_isinf(double x) { return __builtin_isinf(x); }
+int32_t __nac3_isinf(double x) {
+    return __builtin_isinf(x);
+}
 
-int32_t __nac3_isnan(double x) { return __builtin_isnan(x); }
+int32_t __nac3_isnan(double x) {
+    return __builtin_isnan(x);
+}
 
 double tgamma(double arg);
 
@@ -279,71 +320,95 @@ double __nac3_j0(double x) {
     return j0(x);
 }
 
-uint32_t __nac3_ndarray_calc_size(const uint32_t* list_data, uint32_t list_len,
+uint32_t __nac3_ndarray_calc_size(
-                                  uint32_t begin_idx, uint32_t end_idx) {
+    const uint32_t* list_data,
-    return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx,
+    uint32_t list_len,
-                                         end_idx);
+    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 __nac3_ndarray_calc_size64(
-                                    uint64_t list_len, uint64_t begin_idx,
+    const uint64_t* list_data,
-                                    uint64_t end_idx) {
+    uint64_t list_len,
-    return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx,
+    uint64_t begin_idx,
-                                         end_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,
+void __nac3_ndarray_calc_nd_indices(
-                                    uint32_t num_dims, NDIndexInt* idxs) {
+    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,
+void __nac3_ndarray_calc_nd_indices64(
-                                      uint64_t num_dims, NDIndexInt* idxs) {
+    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,
+uint32_t __nac3_ndarray_flatten_index(
-                                      const NDIndexInt* indices,
+    const uint32_t* dims,
-                                      uint32_t num_indices) {
+    uint32_t num_dims,
-    return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
+    const NDIndex* indices,
-                                             num_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,
+uint64_t __nac3_ndarray_flatten_index64(
-                                        const NDIndexInt* indices,
+    const uint64_t* dims,
-                                        uint64_t num_indices) {
+    uint64_t num_dims,
-    return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
+    const NDIndex* indices,
-                                             num_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,
+void __nac3_ndarray_calc_broadcast(
-                                   const uint32_t* rhs_dims, uint32_t rhs_ndims,
+    const uint32_t* lhs_dims,
-                                   uint32_t* out_dims) {
+    uint32_t lhs_ndims,
-    return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims,
+    const uint32_t* rhs_dims,
-                                              rhs_ndims, out_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,
+void __nac3_ndarray_calc_broadcast64(
-                                     uint64_t lhs_ndims,
+    const uint64_t* lhs_dims,
-                                     const uint64_t* rhs_dims,
+    uint64_t lhs_ndims,
-                                     uint64_t rhs_ndims, uint64_t* out_dims) {
+    const uint64_t* rhs_dims,
-    return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims,
+    uint64_t rhs_ndims,
-                                              rhs_ndims, out_dims);
+    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,
+void __nac3_ndarray_calc_broadcast_idx(
-                                       uint32_t src_ndims,
+    const uint32_t* src_dims,
-                                       const NDIndexInt* in_idx,
+    uint32_t src_ndims,
-                                       NDIndexInt* out_idx) {
+    const NDIndex* in_idx,
-    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
+    NDIndex* out_idx
-                                           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,
+void __nac3_ndarray_calc_broadcast_idx64(
-                                         uint64_t src_ndims,
+    const uint64_t* src_dims,
-                                         const NDIndexInt* in_idx,
+    uint64_t src_ndims,
-                                         NDIndexInt* out_idx) {
+    const NDIndex* in_idx,
-    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
+    NDIndex* out_idx
-                                           out_idx);
+) {
+    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
 }
 }  // extern "C"

nac3core/src/codegen/irrt/mod.rs

@@ -1,13 +1,5 @@
-use crate::symbol_resolver::SymbolResolver;
 use crate::typecheck::typedef::Type;
 
-mod test;
-pub mod util;
-
-use super::model::*;
-use super::structure::ndarray::broadcast::ShapeEntry;
-use super::structure::ndarray::indexing::NDIndex;
-use super::structure::ndarray::NpArray;
 use super::{
     classes::{
         ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
@@ -17,7 +9,6 @@ use super::{
 };
 use crate::codegen::classes::TypedArrayLikeAccessor;
 use crate::codegen::stmt::gen_for_callback_incrementing;
-use inkwell::values::BasicValue;
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
     context::Context,
@@ -29,8 +20,6 @@ use inkwell::{
 };
 use itertools::Either;
 use nac3parser::ast::Expr;
-use util::function::CallFunction;
-use util::get_sizet_dependent_function_name;
 
 #[must_use]
 pub fn load_irrt(ctx: &Context) -> Module {
@@ -425,27 +414,14 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
         .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(
-    // TODO: Temporary fix. Rewrite `list_slice_assignment` later
+        generator,
-    // Exception params should have been i64
+        cond,
-    {
+        "0:ValueError",
-        let param_model = IntModel(Int64);
+        "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)],
-        let src_slice_len =
+        ctx.current_loc,
-            param_model.s_extend_or_bit_cast(generator, ctx, src_slice_len, "src_slice_len");
+    );
-        let dest_slice_len =
-            param_model.s_extend_or_bit_cast(generator, ctx, dest_slice_len, "dest_slice_len");
-        let dest_idx_2 = param_model.s_extend_or_bit_cast(generator, ctx, dest_idx.2, "dest_idx_2");
-
-        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.value), Some(dest_slice_len.value), Some(dest_idx_2.value)],
-            ctx.current_loc,
-        );
-    }
 
     let new_len = {
         let args = vec![
@@ -897,7 +873,7 @@ pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
 }
 
 /// 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 broadcast
+/// 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,
@@ -952,212 +928,3 @@ pub fn call_ndarray_calc_broadcast_index<
         Box::new(|_, v| v.into()),
     )
 }
-
-pub fn call_nac3_throw_dummy_error<G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &CodeGenContext<'_, '_>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_throw_dummy_error");
-    CallFunction::begin(generator, ctx, &name).returning_void();
-}
-
-/// Initialize all global `EXN_*` exception IDs in IRRT with the [`SymbolResolver`].
-pub fn setup_irrt_exceptions<'ctx>(
-    ctx: &'ctx Context,
-    module: &Module<'ctx>,
-    symbol_resolver: &dyn SymbolResolver,
-) {
-    let exn_id_type = ctx.i32_type();
-
-    let errors = &[
-        ("EXN_INDEX_ERROR", "0:IndexError"),
-        ("EXN_VALUE_ERROR", "0:ValueError"),
-        ("EXN_ASSERTION_ERROR", "0:AssertionError"),
-        ("EXN_RUNTIME_ERROR", "0:RuntimeError"),
-        ("EXN_TYPE_ERROR", "0:TypeError"),
-    ];
-
-    for (irrt_name, symbol_name) in errors {
-        let exn_id = symbol_resolver.get_string_id(symbol_name);
-        let exn_id = exn_id_type.const_int(exn_id as u64, false).as_basic_value_enum();
-
-        let global = module.get_global(irrt_name).unwrap_or_else(|| {
-            panic!("Exception symbol name '{irrt_name}' should exist in the IRRT LLVM module")
-        });
-        global.set_initializer(&exn_id);
-    }
-}
-
-pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ndims: Int<'ctx, SizeT>,
-    shape: Ptr<'ctx, IntModel<SizeT>>,
-) {
-    let name = get_sizet_dependent_function_name(
-        generator,
-        ctx,
-        "__nac3_ndarray_util_assert_shape_no_negative",
-    );
-    CallFunction::begin(generator, ctx, &name)
-        .arg("ndims", ndims)
-        .arg("shape", shape)
-        .returning_void();
-}
-
-pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    pndarray: Ptr<'ctx, StructModel<NpArray>>,
-) -> Int<'ctx, SizeT> {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_size");
-    CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("size")
-}
-
-pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    pndarray: Ptr<'ctx, StructModel<NpArray>>,
-) -> Int<'ctx, SizeT> {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_nbytes");
-    CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("nbytes")
-}
-
-pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    pndarray: Ptr<'ctx, StructModel<NpArray>>,
-) -> Int<'ctx, SizeT> {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_len");
-    CallFunction::begin(generator, ctx, &name).arg("ndarray", pndarray).returning_auto("len")
-}
-
-pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray_ptr: Ptr<'ctx, StructModel<NpArray>>,
-) -> Int<'ctx, Bool> {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_is_c_contiguous");
-    CallFunction::begin(generator, ctx, &name)
-        .arg("ndarray", ndarray_ptr)
-        .returning_auto("is_c_contiguous")
-}
-
-pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    pndarray: Ptr<'ctx, StructModel<NpArray>>,
-    index: Int<'ctx, SizeT>,
-) -> Ptr<'ctx, IntModel<Byte>> {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
-    CallFunction::begin(generator, ctx, &name)
-        .arg("ndarray", pndarray)
-        .arg("index", index)
-        .returning_auto("pelement")
-}
-
-pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    pdnarray: Ptr<'ctx, StructModel<NpArray>>,
-) {
-    let name =
-        get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_set_strides_by_shape");
-    CallFunction::begin(generator, ctx, &name).arg("ndarray", pdnarray).returning_void();
-}
-
-pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
-    dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_copy_data");
-    CallFunction::begin(generator, ctx, &name)
-        .arg("src_ndarray", src_ndarray)
-        .arg("dst_ndarray", dst_ndarray)
-        .returning_void();
-}
-
-pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    num_indexes: Int<'ctx, SizeT>,
-    indexes: Ptr<'ctx, StructModel<NDIndex>>,
-    src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
-    dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_index");
-    CallFunction::begin(generator, ctx, &name)
-        .arg("num_indexes", num_indexes)
-        .arg("indexes", indexes)
-        .arg("src_ndarray", src_ndarray)
-        .arg("dst_ndarray", dst_ndarray)
-        .returning_void();
-}
-
-pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
-    dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
-    CallFunction::begin(generator, ctx, &name)
-        .arg("src_ndarray", src_ndarray)
-        .arg("dst_ndarray", dst_ndarray)
-        .returning_void();
-}
-
-pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    num_shape_entries: Int<'ctx, SizeT>,
-    shape_entries: Ptr<'ctx, StructModel<ShapeEntry>>,
-    dst_ndims: Int<'ctx, SizeT>,
-    dst_shape: Ptr<'ctx, IntModel<SizeT>>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
-    CallFunction::begin(generator, ctx, &name)
-        .arg("num_shapes", num_shape_entries)
-        .arg("shapes", shape_entries)
-        .arg("dst_ndims", dst_ndims)
-        .arg("dst_shape", dst_shape)
-        .returning_void();
-}
-
-pub fn call_nac3_ndarray_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    size: Int<'ctx, SizeT>,
-    new_ndims: Int<'ctx, SizeT>,
-    new_shape: Ptr<'ctx, IntModel<SizeT>>,
-) {
-    let name = get_sizet_dependent_function_name(
-        generator,
-        ctx,
-        "__nac3_ndarray_resolve_and_check_new_shape",
-    );
-    CallFunction::begin(generator, ctx, &name)
-        .arg("size", size)
-        .arg("new_ndims", new_ndims)
-        .arg("new_shape", new_shape)
-        .returning_void();
-}
-
-pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
-    dst_ndarray: Ptr<'ctx, StructModel<NpArray>>,
-    num_axes: Int<'ctx, SizeT>,
-    axes: Ptr<'ctx, IntModel<SizeT>>,
-) {
-    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
-    CallFunction::begin(generator, ctx, &name)
-        .arg("src_ndarray", src_ndarray)
-        .arg("dst_ndarray", dst_ndarray)
-        .arg("num_axes", num_axes)
-        .arg("axes", axes)
-        .returning_void();
-}

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/irrt/util.rs

@@ -1,109 +0,0 @@
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-// 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
-}
-
-pub mod function {
-    use crate::codegen::{model::*, CodeGenContext, CodeGenerator};
-    use inkwell::{
-        types::{BasicMetadataTypeEnum, BasicType, FunctionType},
-        values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum, CallSiteValue},
-    };
-    use itertools::Itertools;
-
-    #[derive(Debug, Clone, Copy)]
-    struct Arg<'ctx> {
-        ty: BasicMetadataTypeEnum<'ctx>,
-        val: BasicMetadataValueEnum<'ctx>,
-    }
-
-    /// Helper structure to reduce IRRT Inkwell function call boilerplate
-    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>>,
-    }
-
-    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() }
-        }
-
-        /// Push a call argument to the function call.
-        ///
-        /// The `_name` parameter is there for self-documentation purposes.
-        #[allow(clippy::needless_pass_by_value)]
-        #[must_use]
-        pub fn arg<M: Model<'ctx>>(mut self, _name: &str, 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, declare the function if it doesn't exist - it will be defined by other
-            // components of NAC3.
-            let func = self.ctx.module.get_function(self.name).unwrap_or_else(|| {
-                let tys = self.args.iter().map(|arg| arg.ty).collect_vec();
-                let fn_type = make_fn_type(&tys);
-                self.ctx.module.add_function(self.name, fn_type, None)
-            });
-
-            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/mod.rs

@@ -1,7 +1,7 @@
 use crate::{
-    codegen::classes::{ListType, ProxyType, RangeType},
+    codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
     symbol_resolver::{StaticValue, SymbolResolver},
-    toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
+    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
     typecheck::{
         type_inferencer::{CodeLocation, PrimitiveStore},
         typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
@@ -24,7 +24,6 @@ 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};
@@ -33,7 +32,6 @@ use std::sync::{
     Arc,
 };
 use std::thread;
-use structure::{cslice::CSlice, exception::Exception, ndarray::NpArray};
 
 pub mod builtin_fns;
 pub mod classes;
@@ -43,11 +41,8 @@ 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 stmt;
-pub mod structure;
 
 #[cfg(test)]
 mod test;
@@ -173,11 +168,11 @@ 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 +489,12 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                         }
 
                         TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                            let pndarray_model = PtrModel(StructModel(NpArray));
+                            let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
-                            pndarray_model.get_type(generator, ctx).as_basic_type_enum()
+                            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!(
@@ -701,19 +700,43 @@ pub fn gen_func_impl<
         ..primitives
     };
 
-    let cslice_model = StructModel(CSlice);
+    let mut type_cache: HashMap<_, _> = [
-    let pexn_model = PtrModel(StructModel(Exception));
-
-    let mut type_cache: HashMap<_, BasicTypeEnum<'ctx>> = [
         (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()

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/core.rs

@@ -1,121 +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.
-    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.
-    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/int.rs

@@ -1,276 +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>;
-
-    #[must_use]
-    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

@@ -1,12 +0,0 @@
-mod any;
-mod core;
-mod int;
-mod ptr;
-mod structure;
-pub mod util;
-
-pub use any::*;
-pub use core::*;
-pub use int::*;
-pub use ptr::*;
-pub use structure::*;

nac3core/src/codegen/model/ptr.rs

@@ -1,143 +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()
-    }
-
-    // Load the `i`-th element (0-based) on the array with [`inkwell::builder::Builder::build_in_bounds_gep`].
-    pub fn ix<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &CodeGenContext<'ctx, '_>,
-        i: IntValue<'ctx>,
-        name: &str,
-    ) -> Instance<'ctx, Element> {
-        self.offset(generator, ctx, i, name).load(generator, ctx, 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 transmute<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

@@ -1,203 +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.
-    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>> 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);
-    }
-}

nac3core/src/codegen/model/util.rs

@@ -1,91 +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,
-    )
-}
-
-/// Like [`gen_if_callback`] with [`Model`] abstractions and without the `else` block.
-pub fn gen_if_model<'ctx, 'a, G, ThenFn>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, 'a>,
-    cond: Int<'ctx, Bool>,
-    then: ThenFn,
-) -> Result<(), String>
-where
-    G: CodeGenerator + ?Sized,
-    ThenFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<(), String>,
-{
-    let current_bb = ctx.builder.get_insert_block().unwrap();
-    let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "if.then");
-    let end_bb = ctx.ctx.insert_basic_block_after(then_bb, "if.end");
-
-    // Inserting into `current_bb`.
-    ctx.builder.build_conditional_branch(cond.value, then_bb, end_bb).unwrap();
-
-    // Inserting into `then_bb`
-    ctx.builder.position_at_end(then_bb);
-    then(generator, ctx)?;
-    ctx.builder.build_unconditional_branch(end_bb).unwrap();
-
-    // Reposition to `end_bb` for continuation.
-    ctx.builder.position_at_end(end_bb);
-
-    Ok(())
-}

nac3core/src/codegen/numpy.rs

@@ -257,7 +257,7 @@ 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, "")
     } else {
         unreachable!()
     }
@@ -285,7 +285,7 @@ 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")
     } else {
         unreachable!()
     }

nac3core/src/codegen/numpy_new.rs

@@ -1,480 +0,0 @@
-// TODO: Replace numpy.rs
-
-use inkwell::values::{BasicValue, BasicValueEnum};
-use nac3parser::ast::StrRef;
-
-use crate::{
-    codegen::structure::{
-        ndarray::{
-            scalar::split_scalar_or_ndarray, shape_util::parse_numpy_int_sequence, NDArrayObject,
-        },
-        tuple::TupleObject,
-    },
-    symbol_resolver::ValueEnum,
-    toplevel::{
-        numpy::{extract_ndims, unpack_ndarray_var_tys},
-        DefinitionId,
-    },
-    typecheck::typedef::{FunSignature, Type},
-};
-
-use super::{
-    irrt::call_nac3_ndarray_util_assert_shape_no_negative, model::*, CodeGenContext, CodeGenerator,
-};
-
-/// 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 {
-        unreachable!()
-    }
-}
-
-/// 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, '_>,
-    elem_ty: Type,
-) -> BasicValueEnum<'ctx> {
-    if [ctx.primitives.int32, ctx.primitives.uint32]
-        .iter()
-        .any(|ty| ctx.unifier.unioned(elem_ty, *ty))
-    {
-        let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int32);
-        ctx.ctx.i32_type().const_int(1, is_signed).into()
-    } else if [ctx.primitives.int64, ctx.primitives.uint64]
-        .iter()
-        .any(|ty| ctx.unifier.unioned(elem_ty, *ty))
-    {
-        let is_signed = ctx.unifier.unioned(elem_ty, ctx.primitives.int64);
-        ctx.ctx.i64_type().const_int(1, is_signed).into()
-    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.float) {
-        ctx.ctx.f64_type().const_float(1.0).into()
-    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.bool) {
-        ctx.ctx.bool_type().const_int(1, false).into()
-    } else if ctx.unifier.unioned(elem_ty, ctx.primitives.str) {
-        ctx.gen_string(generator, "1").value.into()
-    } else {
-        unreachable!()
-    }
-}
-
-/// Helper function to create an ndarray with uninitialized values.
-///
-/// * `ndarray_ty` - The [`Type`] of the ndarray
-/// * `shape` - The user input shape argument
-/// * `shape_ty` - The [`Type`] of the shape argument
-///
-/// This function does data validation the `shape` input.
-fn create_empty_ndarray<'ctx, G>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray_ty: Type,
-    shape: BasicValueEnum<'ctx>,
-    shape_ty: Type,
-) -> NDArrayObject<'ctx>
-where
-    G: CodeGenerator + ?Sized,
-{
-    let (_, shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
-
-    let ndarray =
-        NDArrayObject::alloca_uninitialized_of_type(generator, ctx, ndarray_ty, "ndarray");
-
-    // Validate `shape`
-    let ndims = ndarray.get_ndims(generator, ctx.ctx);
-    call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, ndims, shape);
-
-    // Setup `ndarray` with `shape`
-    ndarray.copy_shape_from_array(generator, ctx, shape);
-    ndarray.create_data(generator, ctx); // `shape` has to be set
-
-    ndarray
-}
-
-/// Generates LLVM IR for `np.empty`.
-pub fn gen_ndarray_empty<'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 arguments
-    let shape_ty = fun.0.args[0].ty;
-    let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
-
-    // Implementation
-    let ndarray_ty = fun.0.ret;
-    let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
-
-    Ok(ndarray.value.value.as_basic_value_enum())
-}
-
-/// Generates LLVM IR for `np.zero`.
-pub fn gen_ndarray_zeros<'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 arguments
-    let shape_ty = fun.0.args[0].ty;
-    let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
-
-    // Implementation
-    let ndarray_ty = fun.0.ret;
-    let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
-
-    let fill_value = ndarray_zero_value(generator, ctx, ndarray.dtype);
-    ndarray.fill(generator, ctx, fill_value);
-
-    Ok(ndarray.value.value.as_basic_value_enum())
-}
-
-/// Generates LLVM IR for `np.ones`.
-pub fn gen_ndarray_ones<'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 arguments
-    let shape_ty = fun.0.args[0].ty;
-    let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
-
-    // Implementation
-    let ndarray_ty = fun.0.ret;
-    let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
-
-    let fill_value = ndarray_zero_value(generator, ctx, ndarray.dtype);
-    ndarray.fill(generator, ctx, fill_value);
-
-    Ok(ndarray.value.value.as_basic_value_enum())
-}
-
-/// Generates LLVM IR for `np.full`.
-pub fn gen_ndarray_full<'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 shape
-    let shape_ty = fun.0.args[0].ty;
-    let shape = args[0].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
-
-    // Parse argument #2 fill_value
-    let fill_value_ty = fun.0.args[1].ty;
-    let fill_value = args[1].1.clone().to_basic_value_enum(ctx, generator, fill_value_ty)?;
-
-    // Implementation
-    let ndarray_ty = fun.0.ret;
-    let ndarray = create_empty_ndarray(generator, ctx, ndarray_ty, shape, shape_ty);
-
-    ndarray.fill(generator, ctx, fill_value);
-
-    Ok(ndarray.value.value.as_basic_value_enum())
-}
-
-/// 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)?;
-
-    // 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)?;
-
-    // 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, input_ty).as_ndarray(generator, ctx);
-
-    // Process `shape`
-    let (_, broadcast_shape) = parse_numpy_int_sequence(generator, ctx, shape, shape_ty);
-    // 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.value.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)?;
-
-    // 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)?;
-
-    // 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, input_ty).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, shape_ty);
-    let reshaped_ndarray = in_ndarray.reshape_or_copy(generator, ctx, reshaped_ndims, new_shape);
-
-    Ok(reshaped_ndarray.value.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();
-
-    // Define models
-    let sizet_model = IntModel(SizeT);
-
-    // Process input
-    let input = sizet_model.s_extend_or_bit_cast(generator, ctx, input, "input_dim");
-
-    // Allocate the resulting ndarray
-    let ndarray = NDArrayObject::alloca_uninitialized(
-        generator,
-        ctx,
-        ctx.primitives.float,
-        1, // ndims = 1
-        "arange_ndarray",
-    );
-
-    // `ndarray.shape[0] = input`
-    let zero = sizet_model.const_0(generator, ctx.ctx);
-    ndarray
-        .value
-        .get(generator, ctx, |f| f.shape, "shape")
-        .offset(generator, ctx, zero.value, "dim")
-        .store(ctx, input);
-
-    // Create data and set elements
-    ndarray.create_data(generator, ctx);
-    ndarray.foreach_pointer(generator, ctx, |_generator, ctx, _hooks, i, pelement| {
-        let val =
-            ctx.builder.build_unsigned_int_to_float(i.value, ctx.ctx.f64_type(), "val").unwrap();
-        ctx.builder.build_store(pelement, val).unwrap();
-        Ok(())
-    })?;
-
-    Ok(ndarray.value.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 = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
-
-    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)?;
-
-    // Define models
-    let sizet_model = IntModel(SizeT);
-
-    // Process ndarray
-    let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
-
-    let mut items = Vec::with_capacity(ndarray.ndims as usize);
-
-    for i in 0..ndarray.ndims {
-        let i = sizet_model.constant(generator, ctx.ctx, i);
-        let dim =
-            ndarray.value.get(generator, ctx, |f| f.shape, "").ix(generator, ctx, i.value, "dim");
-        let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
-
-        items.push((dim.value.as_basic_value_enum(), ctx.primitives.int32));
-    }
-
-    let shape = TupleObject::create(generator, ctx, items, "shape");
-    Ok(shape.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: This function looks exactly like `gen_ndarray_shapes`, code duplication?
-
-    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)?;
-
-    // Define models
-    let sizet_model = IntModel(SizeT);
-
-    // Process ndarray
-    let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
-
-    let mut items = Vec::with_capacity(ndarray.ndims as usize);
-
-    for i in 0..ndarray.ndims {
-        let i = sizet_model.constant(generator, ctx.ctx, i);
-        let dim =
-            ndarray.value.get(generator, ctx, |f| f.strides, "").ix(generator, ctx, i.value, "dim");
-        let dim = dim.truncate(generator, ctx, Int32, "dim"); // TODO: keep using SizeT
-
-        items.push((dim.value.as_basic_value_enum(), ctx.primitives.int32));
-    }
-
-    let strides = TupleObject::create(generator, ctx, items, "strides");
-    Ok(strides.value.as_basic_value_enum())
-}
-
-/// Generates LLVM IR for `np.transpose`.
-pub fn gen_ndarray_transpose<'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: The implementation will be changed once default values start working again.
-    // Read the comment on this function in BuiltinBuilder.
-
-    // TODO: Change axes values to `SizeT`
-
-    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)?;
-
-    // Implementation
-    let ndarray = NDArrayObject::from_value_and_type(generator, ctx, ndarray, ndarray_ty);
-
-    let has_axes = args.len() >= 2;
-    let transposed_ndarray = if has_axes {
-        // Parse argument #2 axes
-        let in_axes_ty = fun.0.args[1].ty;
-        let in_axes = args[1].1.clone().to_basic_value_enum(ctx, generator, in_axes_ty)?;
-
-        let (_, axes) = parse_numpy_int_sequence(generator, ctx, in_axes, in_axes_ty);
-
-        ndarray.transpose(generator, ctx, Some(axes))
-    } else {
-        ndarray.transpose(generator, ctx, None)
-    };
-
-    Ok(transposed_ndarray.value.value.as_basic_value_enum())
-}

nac3core/src/codegen/stmt.rs

@@ -1,15 +1,9 @@
-use super::model::*;
-use super::structure::cslice::CSlice;
 use super::{
     super::symbol_resolver::ValueEnum,
     expr::destructure_range,
     irrt::{handle_slice_indices, list_slice_assignment},
-    structure::exception::Exception,
+    CodeGenContext, CodeGenerator,
-    CodeGenContext, CodeGenerator, Int32, IntModel, Ptr, StructModel,
 };
-use crate::codegen::structure::ndarray::indexing::util::gen_ndarray_subscript_ndindexes;
-use crate::codegen::structure::ndarray::scalar::split_scalar_or_ndarray;
-use crate::codegen::structure::ndarray::NDArrayObject;
 use crate::{
     codegen::{
         classes::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
@@ -407,43 +401,7 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
             if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
         {
             // Handle NDArray item assignment
-            // Process target
+            todo!("ndarray subscript assignment is not yet implemented");
-            let target = generator
-                .gen_expr(ctx, target)?
-                .unwrap()
-                .to_basic_value_enum(ctx, generator, target_ty)?;
-            let target = NDArrayObject::from_value_and_type(generator, ctx, target, 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)?;
-
-            /*
-                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 = target.index(generator, ctx, &key, "assign_target_ndarray");
-            let value =
-                split_scalar_or_ndarray(generator, ctx, value, value_ty).as_ndarray(generator, ctx);
-
-            let broadcast_result = NDArrayObject::broadcast_all(generator, ctx, &[target, value]);
-
-            let target = broadcast_result.ndarrays[0];
-            let value = broadcast_result.ndarrays[1];
-
-            target.copy_data_from(generator, ctx, value);
         }
         _ => {
             panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));
@@ -680,12 +638,8 @@ where
     I: Clone,
     InitFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>) -> Result<I, String>,
     CondFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<IntValue<'ctx>, String>,
-    BodyFn: FnOnce(
+    BodyFn:
-        &mut G,
+        FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, BreakContinueHooks, I) -> Result<(), String>,
-        &mut CodeGenContext<'ctx, 'a>,
-        BreakContinueHooks<'ctx>,
-        I,
-    ) -> Result<(), String>,
     UpdateFn: FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, I) -> Result<(), String>,
 {
     let label = label.unwrap_or("for");
@@ -765,7 +719,7 @@ where
     BodyFn: FnOnce(
         &mut G,
         &mut CodeGenContext<'ctx, 'a>,
-        BreakContinueHooks<'ctx>,
+        BreakContinueHooks,
         IntValue<'ctx>,
     ) -> Result<(), String>,
 {
@@ -1305,36 +1259,47 @@ 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 {
+    if let Some(exception) = exception {
-        let i32_model = IntModel(Int32);
+        unsafe {
-        let cslice_model = StructModel(CSlice);
+            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 current_fun = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
-        let filename = loc.file.0;
+            let fun_name = ctx.gen_string(generator, current_fun.get_name().to_str().unwrap());
-        let filename = ctx.gen_string(generator, &String::from(filename)).value;
+            let name_ptr = ctx
-        let filename = cslice_model.check_value(generator, ctx.ctx, filename).unwrap();
+                .builder
-        pexn.set(ctx, |f| f.filename, filename);
+                .build_in_bounds_gep(exception, &[zero, int32.const_int(4, false)], "name_ptr")
-
+                .unwrap();
-        let row = i32_model.constant(generator, ctx.ctx, loc.row as u64);
+            ctx.builder.build_store(name_ptr, fun_name).unwrap();
-        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 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();
 }
 
@@ -1796,41 +1761,30 @@ pub fn gen_stmt<G: CodeGenerator>(
                 } else {
                     return Ok(());
                 };
-
+                gen_raise(generator, ctx, Some(&exc), stmt.location);
-                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);
             } else {
                 gen_raise(generator, ctx, None, stmt.location);
             }
         }
         StmtKind::Assert { test, msg, .. } => {
-            let byte_model = IntModel(Byte);
+            let test = if let Some(v) = generator.gen_expr(ctx, test)? {
-            let cslice_model = StructModel(CSlice);
+                v.to_basic_value_enum(ctx, generator, test.custom.unwrap())?
-
+            } else {
-            let Some(test) = generator.gen_expr(ctx, test)? 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, ""),
             };
-
             ctx.make_assert_impl(
                 generator,
-                test.value,
+                generator.bool_to_i1(ctx, test.into_int_value()),
                 "0:AssertionError",
                 err_msg,
                 [None, None, None],

nac3core/src/codegen/structure/cslice.rs

@@ -1,45 +0,0 @@
-use inkwell::context::Context;
-
-use crate::codegen::{model::*, CodeGenerator};
-
-/// Fields of [`CSlice<'ctx>`].
-pub struct CSliceFields<'ctx, F: FieldTraversal<'ctx>> {
-    /// Pointer to data.
-    pub base: F::Out<PtrModel<IntModel<Byte>>>,
-    /// Number of bytes of data.
-    pub len: F::Out<IntModel<SizeT>>,
-}
-
-/// See <https://crates.io/crates/cslice>.
-///
-/// Additionally, see <https://github.com/m-labs/artiq/blob/b0d2705c385f64b6e6711c1726cd9178f40b598e/artiq/firmware/libeh/eh_artiq.rs>)
-/// for ARTIQ-specific notes.
-#[derive(Debug, Clone, Copy, Default)]
-pub struct CSlice;
-
-impl<'ctx> StructKind<'ctx> for CSlice {
-    type Fields<F: FieldTraversal<'ctx>> = CSliceFields<'ctx, F>;
-
-    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
-        Self::Fields { base: traversal.add_auto("base"), len: traversal.add_auto("len") }
-    }
-}
-
-impl StructModel<CSlice> {
-    /// Create a [`CSlice`].
-    ///
-    /// `base` and `len` must be LLVM global constants.
-    pub fn create_const<'ctx, G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &'ctx Context,
-        base: Ptr<'ctx, IntModel<Byte>>,
-        len: Int<'ctx, SizeT>,
-    ) -> Struct<'ctx, CSlice> {
-        let value = self
-            .0
-            .get_struct_type(generator, ctx)
-            .const_named_struct(&[base.value.into(), len.value.into()]);
-        self.believe_value(value)
-    }
-}

nac3core/src/codegen/structure/exception.rs

@@ -1,61 +0,0 @@
-use crate::codegen::model::*;
-
-use super::cslice::CSlice;
-
-/// The LLVM int type of an Exception ID.
-pub type ExceptionId = Int32;
-
-/// Fields of [`Exception<'ctx>`]
-///
-/// The definition came from `pub struct Exception<'a>` in
-/// <https://github.com/m-labs/artiq/blob/master/artiq/firmware/libeh/eh_artiq.rs>.
-pub struct ExceptionFields<'ctx, F: FieldTraversal<'ctx>> {
-    /// nac3core's ID of the exception
-    pub id: F::Out<IntModel<ExceptionId>>,
-    /// The name of the file this `Exception` was raised in.
-    pub filename: F::Out<StructModel<CSlice>>,
-    /// The line number in the file this `Exception` was raised in.
-    pub line: F::Out<IntModel<Int32>>,
-    /// The column number in the file this `Exception` was raised in.
-    pub column: F::Out<IntModel<Int32>>,
-    /// The name of the Python function this `Exception` was raised in.
-    pub function: F::Out<StructModel<CSlice>>,
-    /// The message of this Exception.
-    ///
-    /// The message can optionally contain integer parameters `{0}`, `{1}`, and `{2}` in its string,
-    /// where they will be substituted by `params[0]`, `params[1]`, and `params[2]` respectively (as `int64_t`s).
-    /// Here is an example:
-    ///
-    /// ```ignore
-    /// "Index {0} is out of bounds! List only has {1} element(s)."
-    /// ```
-    ///
-    /// In this case, `params[0]` and `params[1]` must be specified, and `params[2]` is ***unused***.
-    /// Having only 3 parameters is a constraint in ARTIQ.
-    pub msg: F::Out<StructModel<CSlice>>,
-    pub params: [F::Out<IntModel<Int64>>; 3],
-}
-
-/// nac3core & ARTIQ's Exception
-#[derive(Debug, Clone, Copy, Default)]
-pub struct Exception;
-
-impl<'ctx> StructKind<'ctx> for Exception {
-    type Fields<F: FieldTraversal<'ctx>> = ExceptionFields<'ctx, F>;
-
-    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
-        Self::Fields {
-            id: traversal.add_auto("id"),
-            filename: traversal.add_auto("filename"),
-            line: traversal.add_auto("line"),
-            column: traversal.add_auto("column"),
-            function: traversal.add_auto("function"),
-            msg: traversal.add_auto("msg"),
-            params: [
-                traversal.add_auto("params[0]"),
-                traversal.add_auto("params[1]"),
-                traversal.add_auto("params[2]"),
-            ],
-        }
-    }
-}

nac3core/src/codegen/structure/list.rs

@@ -1,70 +0,0 @@
-use inkwell::values::BasicValue;
-
-use crate::{
-    codegen::{model::*, CodeGenContext, CodeGenerator},
-    typecheck::typedef::{iter_type_vars, Type, TypeEnum},
-};
-
-/// Fields of [`List`]
-pub struct ListFields<'ctx, F: FieldTraversal<'ctx>, Item: Model<'ctx>, Size: IntKind<'ctx>> {
-    /// Array pointer to content
-    pub items: F::Out<PtrModel<Item>>,
-    /// Number of items in the array
-    pub len: F::Out<IntModel<Size>>,
-}
-
-/// A list in NAC3.
-#[derive(Debug, Clone, Copy, Default)]
-pub struct List<Item, Len> {
-    /// Model of the list items
-    pub item: Item,
-    /// Model of type of integer storing the number of items on the list
-    pub len: Len,
-}
-
-impl<'ctx, Item: Model<'ctx>, Size: IntKind<'ctx>> StructKind<'ctx> for List<Item, Size> {
-    type Fields<F: FieldTraversal<'ctx>> = ListFields<'ctx, F, Item, Size>;
-
-    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
-        Self::Fields {
-            items: traversal.add("data", PtrModel(self.item)),
-            len: traversal.add("len", IntModel(self.len)),
-        }
-    }
-}
-
-/// A NAC3 Python List object.
-pub struct ListObject<'ctx> {
-    /// Typechecker type of the list items
-    pub item_type: Type,
-    pub value: Ptr<'ctx, StructModel<List<AnyModel<'ctx>, SizeT>>>,
-}
-
-impl<'ctx> ListObject<'ctx> {
-    /// Create a [`ListObject`] from an LLVM value and its typechecker [`Type`].
-    pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        list_val: V,
-        list_type: Type,
-    ) -> Self {
-        // Check typechecker type and extract `item_type`
-        let item_type = match &*ctx.unifier.get_ty(list_type) {
-            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(list_type))
-            }
-        };
-
-        let item_model = AnyModel(ctx.get_llvm_type(generator, item_type));
-        let plist_model = PtrModel(StructModel(List { item: item_model, len: SizeT }));
-
-        // Create object
-        let value = plist_model.check_value(generator, ctx.ctx, list_val).unwrap();
-        ListObject { item_type, value }
-    }
-}

nac3core/src/codegen/structure/mod.rs

@@ -1,5 +0,0 @@
-pub mod cslice;
-pub mod exception;
-pub mod list;
-pub mod ndarray;
-pub mod tuple;

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

@@ -1,134 +0,0 @@
-use itertools::Itertools;
-
-use crate::{
-    codegen::{
-        irrt::{call_nac3_ndarray_broadcast_shapes, call_nac3_ndarray_broadcast_to},
-        model::*,
-        CodeGenContext, CodeGenerator,
-    },
-    toplevel::numpy::get_broadcast_all_ndims,
-};
-
-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.
-    ///
-    /// * `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 broadcast_ndarray = NDArrayObject::alloca_uninitialized(
-            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.value, broadcast_ndarray.value);
-        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>>,
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    // TODO: DOCUMENT: Behaves like `np.broadcast()`, except returns results differently.
-    pub fn broadcast_all<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        ndarrays: &[Self],
-    ) -> BroadcastAllResult<'ctx> {
-        assert!(!ndarrays.is_empty());
-
-        let sizet_model = IntModel(SizeT);
-        let shape_model = StructModel(ShapeEntry);
-
-        let broadcast_ndims = get_broadcast_all_ndims(ndarrays.iter().map(|ndarray| ndarray.ndims));
-
-        // Prepare input shape entries
-        let num_shape_entries =
-            sizet_model.constant(generator, ctx.ctx, u64::try_from(ndarrays.len()).unwrap());
-        let shape_entries =
-            shape_model.array_alloca(generator, ctx, num_shape_entries.value, "shape_entries");
-        for (i, ndarray) in ndarrays.iter().enumerate() {
-            let i = sizet_model.constant(generator, ctx.ctx, i as u64).value;
-
-            let shape_entry = shape_entries.offset(generator, ctx, i, "shape_entry");
-
-            let this_ndims = ndarray.value.get(generator, ctx, |f| f.ndims, "this_ndims");
-            shape_entry.set(ctx, |f| f.ndims, this_ndims);
-
-            let this_shape = ndarray.value.get(generator, ctx, |f| f.shape, "this_shape");
-            shape_entry.set(ctx, |f| f.shape, this_shape);
-        }
-
-        // Prepare destination
-        let broadcast_ndims_llvm = sizet_model.constant(generator, ctx.ctx, broadcast_ndims);
-        let broadcast_shape =
-            sizet_model.array_alloca(generator, ctx, broadcast_ndims_llvm.value, "dst_shape");
-
-        // Compute the target broadcast shape `dst_shape` for all ndarrays.
-        call_nac3_ndarray_broadcast_shapes(
-            generator,
-            ctx,
-            num_shape_entries,
-            shape_entries,
-            broadcast_ndims_llvm,
-            broadcast_shape,
-        );
-
-        // Now that we know about the broadcasting shape, broadcast all the inputs.
-
-        // Broadcast all the inputs to shape `dst_shape`.
-        let broadcast_ndarrays: Vec<_> = ndarrays
-            .iter()
-            .map(|ndarray| ndarray.broadcast_to(generator, ctx, broadcast_ndims, broadcast_shape))
-            .collect_vec();
-
-        BroadcastAllResult {
-            ndims: broadcast_ndims,
-            shape: broadcast_shape,
-            ndarrays: broadcast_ndarrays,
-        }
-    }
-}

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

@@ -1,562 +0,0 @@
-use inkwell::{
-    values::{BasicValue, FloatValue, IntValue},
-    FloatPredicate, IntPredicate,
-};
-use itertools::Itertools;
-
-use crate::{
-    codegen::{
-        llvm_intrinsics,
-        model::{
-            util::{gen_for_model_auto, gen_if_model},
-            *,
-        },
-        CodeGenContext, CodeGenerator,
-    },
-    typecheck::typedef::Type,
-};
-
-use super::{scalar::ScalarObject, NDArrayObject};
-
-/// 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,
-    ]
-}
-
-fn cast_to_int_conversion<'ctx, 'a, G, HandleFloatFn>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, 'a>,
-    scalar: ScalarObject<'ctx>,
-    ret_int_dtype: Type,
-    handle_float: HandleFloatFn,
-) -> ScalarObject<'ctx>
-where
-    G: CodeGenerator + ?Sized,
-    HandleFloatFn:
-        FnOnce(&mut G, &mut CodeGenContext<'ctx, 'a>, FloatValue<'ctx>) -> IntValue<'ctx>,
-{
-    let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
-
-    let result = if ctx.unifier.unioned(scalar.dtype, ctx.primitives.float) {
-        // Special handling for floats
-        let n = scalar.value.into_float_value();
-        handle_float(generator, ctx, n)
-    } else if ctx.unifier.unioned_any(scalar.dtype, int_like(ctx)) {
-        let n = scalar.value.into_int_value();
-
-        if n.get_type().get_bit_width() <= ret_int_dtype_llvm.get_bit_width() {
-            ctx.builder.build_int_z_extend(n, ret_int_dtype_llvm, "zext").unwrap()
-        } else {
-            ctx.builder.build_int_truncate(n, ret_int_dtype_llvm, "trunc").unwrap()
-        }
-    } else {
-        unsupported_type(ctx, [scalar.dtype]);
-    };
-
-    assert_eq!(ret_int_dtype_llvm.get_bit_width(), result.get_type().get_bit_width()); // Sanity check
-    ScalarObject { value: result.into(), dtype: ret_int_dtype }
-}
-
-impl<'ctx> ScalarObject<'ctx> {
-    /// Convenience function. Assume this scalar has typechecker type float64, get its underlying LLVM value.
-    ///
-    /// Panic if the type is wrong.
-    pub fn into_float64(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> FloatValue<'ctx> {
-        if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
-            self.value.into_float_value() // self.value must be a FloatValue
-        } else {
-            panic!("not a float type")
-        }
-    }
-
-    /// Convenience function. Assume this scalar has typechecker type int32, get its underlying LLVM value.
-    ///
-    /// Panic if the type is wrong.
-    pub fn into_int32(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
-        if ctx.unifier.unioned(self.dtype, ctx.primitives.int32) {
-            let value = self.value.into_int_value();
-            debug_assert_eq!(value.get_type().get_bit_width(), 32); // Sanity check
-            value
-        } else {
-            panic!("not a float type")
-        }
-    }
-
-    /// Compare two scalars. Only int-to-int and float-to-float comparisons are allowed.
-    /// Panic otherwise.
-    pub fn compare<G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        lhs: ScalarObject<'ctx>,
-        rhs: ScalarObject<'ctx>,
-        int_predicate: IntPredicate,
-        float_predicate: FloatPredicate,
-        name: &str,
-    ) -> Int<'ctx, Bool> {
-        if !ctx.unifier.unioned(lhs.dtype, rhs.dtype) {
-            unsupported_type(ctx, [lhs.dtype, rhs.dtype]);
-        }
-
-        let bool_model = IntModel(Bool);
-
-        let common_ty = lhs.dtype;
-        let result = if ctx.unifier.unioned(common_ty, ctx.primitives.float) {
-            let lhs = lhs.value.into_float_value();
-            let rhs = rhs.value.into_float_value();
-            ctx.builder.build_float_compare(float_predicate, lhs, rhs, name).unwrap()
-        } else if ctx.unifier.unioned_any(common_ty, int_like(ctx)) {
-            let lhs = lhs.value.into_int_value();
-            let rhs = rhs.value.into_int_value();
-            ctx.builder.build_int_compare(int_predicate, lhs, rhs, name).unwrap()
-        } else {
-            unsupported_type(ctx, [lhs.dtype, rhs.dtype]);
-        };
-
-        bool_model.check_value(generator, ctx.ctx, result).unwrap()
-    }
-
-    /// Invoke NAC3's builtin `int32()`.
-    #[must_use]
-    pub fn cast_to_int32<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Self {
-        cast_to_int_conversion(
-            generator,
-            ctx,
-            *self,
-            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()
-            },
-        )
-    }
-
-    /// Invoke NAC3's builtin `int64()`.
-    #[must_use]
-    pub fn cast_to_int64<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Self {
-        cast_to_int_conversion(
-            generator,
-            ctx,
-            *self,
-            ctx.primitives.int64,
-            |_generator, ctx, input| {
-                ctx.builder.build_float_to_signed_int(input, ctx.ctx.i64_type(), "").unwrap()
-            },
-        )
-    }
-
-    /// Invoke NAC3's builtin `uint32()`.
-    #[must_use]
-    pub fn cast_to_uint32<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Self {
-        cast_to_int_conversion(
-            generator,
-            ctx,
-            *self,
-            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()
-            },
-        )
-    }
-
-    /// Invoke NAC3's builtin `uint64()`.
-    #[must_use]
-    pub fn cast_to_uint64<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-    ) -> Self {
-        cast_to_int_conversion(
-            generator,
-            ctx,
-            *self,
-            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()
-            },
-        )
-    }
-
-    /// Invoke NAC3's builtin `bool()`.
-    #[must_use]
-    pub fn cast_to_bool(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
-        // TODO: Why is the original code being so lax about i1 and i8 for the returned int type?
-        let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.bool) {
-            self.value.into_int_value()
-        } else if ctx.unifier.unioned_any(self.dtype, ints(ctx)) {
-            let n = self.value.into_int_value();
-            ctx.builder
-                .build_int_compare(inkwell::IntPredicate::NE, n, n.get_type().const_zero(), "bool")
-                .unwrap()
-        } else if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
-            let n = self.value.into_float_value();
-            ctx.builder
-                .build_float_compare(FloatPredicate::UNE, n, n.get_type().const_zero(), "bool")
-                .unwrap()
-        } else {
-            unsupported_type(ctx, [self.dtype])
-        };
-
-        ScalarObject { dtype: ctx.primitives.bool, value: result.as_basic_value_enum() }
-    }
-
-    /// Invoke NAC3's builtin `float()`.
-    #[must_use]
-    pub fn cast_to_float(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
-        let llvm_f64 = ctx.ctx.f64_type();
-
-        let result: FloatValue<'_> = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
-            self.value.into_float_value()
-        } else if ctx
-            .unifier
-            .unioned_any(self.dtype, [signed_ints(ctx).as_slice(), &[ctx.primitives.bool]].concat())
-        {
-            let n = self.value.into_int_value();
-            ctx.builder.build_signed_int_to_float(n, llvm_f64, "sitofp").unwrap()
-        } else if ctx.unifier.unioned_any(self.dtype, unsigned_ints(ctx)) {
-            let n = self.value.into_int_value();
-            ctx.builder.build_unsigned_int_to_float(n, llvm_f64, "uitofp").unwrap()
-        } else {
-            unsupported_type(ctx, [self.dtype]);
-        };
-
-        ScalarObject { value: result.as_basic_value_enum(), dtype: ctx.primitives.float }
-    }
-
-    /// Invoke NAC3's builtin `round()`.
-    #[must_use]
-    pub fn round<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        ret_int_dtype: Type,
-    ) -> Self {
-        let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
-
-        let result = if ctx.unifier.unioned(self.dtype, 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_dtype_llvm, "round").unwrap()
-        } else {
-            unsupported_type(ctx, [self.dtype, ret_int_dtype])
-        };
-        ScalarObject { dtype: ret_int_dtype, value: result.as_basic_value_enum() }
-    }
-
-    /// Invoke NAC3's builtin `np_round()`.
-    ///
-    /// NOTE: `np.round()` has different behaviors than `round()` in terms of their result
-    /// on "tie" cases and return type.
-    #[must_use]
-    pub fn np_round(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
-        let result = if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
-            let n = self.value.into_float_value();
-            llvm_intrinsics::call_float_rint(ctx, n, None)
-        } else {
-            unsupported_type(ctx, [self.dtype])
-        };
-        ScalarObject { dtype: ctx.primitives.float, value: result.as_basic_value_enum() }
-    }
-
-    /// Invoke NAC3's builtin `min()` or `max()`.
-    pub fn min_or_max(
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        kind: MinOrMax,
-        a: Self,
-        b: Self,
-    ) -> Self {
-        if !ctx.unifier.unioned(a.dtype, b.dtype) {
-            unsupported_type(ctx, [a.dtype, b.dtype])
-        }
-
-        let common_dtype = a.dtype;
-
-        if ctx.unifier.unioned(common_dtype, ctx.primitives.float) {
-            let function = match kind {
-                MinOrMax::Min => llvm_intrinsics::call_float_minnum,
-                MinOrMax::Max => llvm_intrinsics::call_float_maxnum,
-            };
-            let result =
-                function(ctx, a.value.into_float_value(), b.value.into_float_value(), None);
-            ScalarObject { value: result.as_basic_value_enum(), dtype: ctx.primitives.float }
-        } else if ctx.unifier.unioned_any(
-            common_dtype,
-            [unsigned_ints(ctx).as_slice(), &[ctx.primitives.bool]].concat(),
-        ) {
-            // 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 result = function(ctx, a.value.into_int_value(), b.value.into_int_value(), None);
-            ScalarObject { value: result.as_basic_value_enum(), dtype: common_dtype }
-        } else {
-            unsupported_type(ctx, [common_dtype])
-        }
-    }
-
-    /// Invoke NAC3's builtin `floor()` or `ceil()`.
-    ///
-    /// * `ret_int_dtype` - The type of int to return.
-    ///
-    /// Takes in a float/int and returns an int of type `ret_int_dtype`
-    #[must_use]
-    pub fn floor_or_ceil<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        kind: FloorOrCeil,
-        ret_int_dtype: Type,
-    ) -> Self {
-        let ret_int_dtype_llvm = ctx.get_llvm_type(generator, ret_int_dtype).into_int_type();
-
-        if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
-            let function = match kind {
-                FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
-                FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
-            };
-            let n = self.value.into_float_value();
-            let n = function(ctx, n, None);
-
-            let n = ctx.builder.build_float_to_signed_int(n, ret_int_dtype_llvm, "").unwrap();
-            ScalarObject { dtype: ret_int_dtype, value: n.as_basic_value_enum() }
-        } else {
-            unsupported_type(ctx, [self.dtype])
-        }
-    }
-
-    /// Invoke NAC3's builtin `np_floor()`/ `np_ceil()`.
-    ///
-    /// Takes in a float/int and returns a float64 result.
-    #[must_use]
-    pub fn np_floor_or_ceil(&self, ctx: &mut CodeGenContext<'ctx, '_>, kind: FloorOrCeil) -> Self {
-        if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
-            let function = match kind {
-                FloorOrCeil::Floor => llvm_intrinsics::call_float_floor,
-                FloorOrCeil::Ceil => llvm_intrinsics::call_float_ceil,
-            };
-            let n = self.value.into_float_value();
-            let n = function(ctx, n, None);
-            ScalarObject { dtype: ctx.primitives.float, value: n.as_basic_value_enum() }
-        } else {
-            unsupported_type(ctx, [self.dtype])
-        }
-    }
-
-    /// Invoke NAC3's builtin `abs()`.
-    pub fn abs(&self, ctx: &mut CodeGenContext<'ctx, '_>) -> Self {
-        if ctx.unifier.unioned(self.dtype, ctx.primitives.float) {
-            let n = self.value.into_float_value();
-            let n = llvm_intrinsics::call_float_fabs(ctx, n, Some("abs"));
-            ScalarObject { value: n.into(), dtype: ctx.primitives.float }
-        } else if ctx.unifier.unioned_any(self.dtype, ints(ctx)) {
-            let n = self.value.into_int_value();
-
-            let is_poisoned = ctx.ctx.bool_type().const_zero(); // is_poisoned = false
-            let n = llvm_intrinsics::call_int_abs(ctx, n, is_poisoned, Some("abs"));
-
-            ScalarObject { value: n.into(), dtype: self.dtype }
-        } else {
-            unsupported_type(ctx, [self.dtype])
-        }
-    }
-}
-
-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,
-    ) -> (ScalarObject<'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(generator, ctx, zero);
-        ctx.builder.build_store(pextremum, first_scalar.value).unwrap();
-
-        // Find extremum
-        let start = sizet_model.const_1(generator, ctx.ctx); // Start on 1
-        let stop = self.size(generator, ctx);
-        let step = sizet_model.const_1(generator, ctx.ctx);
-        gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, _hooks, i| {
-            // Worth reading on "Notes" in <https://numpy.org/doc/stable/reference/generated/numpy.min.html#numpy.min>
-            // on how `NaN` values have to be handled.
-
-            let scalar = self.get_nth(generator, ctx, i);
-
-            let old_extremum = ctx.builder.build_load(pextremum, "current_extremum").unwrap();
-            let old_extremum = ScalarObject { dtype: self.dtype, value: old_extremum };
-
-            let new_extremum = ScalarObject::min_or_max(ctx, kind, old_extremum, scalar);
-
-            // Check if new_extremum is more extreme than old_extremum.
-            let update_index = ScalarObject::compare(
-                generator,
-                ctx,
-                new_extremum,
-                old_extremum,
-                IntPredicate::NE,
-                FloatPredicate::ONE,
-                "",
-            );
-
-            gen_if_model(generator, ctx, update_index, |_generator, ctx| {
-                pextremum_index.store(ctx, i);
-                Ok(())
-            })
-            .unwrap();
-            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 = ScalarObject { dtype: 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,
-    ) -> ScalarObject<'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/structure/ndarray/indexing.rs

@@ -1,346 +0,0 @@
-use crate::codegen::{irrt::call_nac3_ndarray_index, model::*, CodeGenContext, CodeGenerator};
-
-use super::{scalar::ScalarOrNDArray, 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.transmute(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.transmute(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::Slice(_) => {}
-                RustNDIndex::NewAxis => {
-                    ndims += 1; // `np.newaxis` / `none` adds a new axis
-                }
-                RustNDIndex::Ellipsis => {}
-            }
-        }
-        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_uninitialized(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.value,
-            dst_ndarray.value,
-        );
-
-        dst_ndarray
-    }
-
-    /// Like [`NDArrayObject::index`] but returns a scalar if the indexes index
-    /// into a single element.
-    #[must_use]
-    pub fn index_or_scalar<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        indexes: &[RustNDIndex<'ctx>],
-        name: &str,
-    ) -> ScalarOrNDArray<'ctx> {
-        let sizet_model = IntModel(SizeT);
-        let zero = sizet_model.const_0(generator, ctx.ctx);
-
-        let subndarray = self.index(generator, ctx, indexes, name);
-        if subndarray.is_unsized() {
-            // NOTE: `np.size(self) == 0` here is never possible.
-            ScalarOrNDArray::Scalar(subndarray.get_nth(generator, ctx, zero))
-        } else {
-            ScalarOrNDArray::NDArray(subndarray)
-        }
-    }
-}
-
-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/structure/ndarray/mapping.rs

@@ -1,158 +0,0 @@
-use inkwell::values::BasicValueEnum;
-use itertools::Itertools;
-use util::gen_for_model_auto;
-
-use crate::{
-    codegen::{
-        model::*,
-        structure::ndarray::{scalar::ScalarObject, NDArrayObject},
-        CodeGenContext, CodeGenerator,
-    },
-    typecheck::typedef::Type,
-};
-
-use super::scalar::ScalarOrNDArray;
-
-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],
-        ret_dtype: Type,
-        mapping: MappingFn,
-    ) -> Result<Self, String>
-    where
-        G: CodeGenerator + ?Sized,
-        MappingFn: FnOnce(
-            &mut G,
-            &mut CodeGenContext<'ctx, 'a>,
-            Int<'ctx, SizeT>,
-            &[ScalarObject<'ctx>],
-        ) -> Result<BasicValueEnum<'ctx>, String>,
-    {
-        let sizet_model = IntModel(SizeT);
-
-        // Broadcast inputs
-        let broadcast_result = NDArrayObject::broadcast_all(generator, ctx, ndarrays);
-
-        // Allocate the resulting ndarray
-        let mapped_ndarray = NDArrayObject::alloca_uninitialized(
-            generator,
-            ctx,
-            ret_dtype,
-            broadcast_result.ndims,
-            "mapped_ndarray",
-        );
-        mapped_ndarray.copy_shape_from_array(generator, ctx, broadcast_result.shape);
-        mapped_ndarray.create_data(generator, ctx);
-
-        // Map element-wise and store results into `mapped_ndarray`.
-        let start = sizet_model.const_0(generator, ctx.ctx);
-        let stop = broadcast_result.ndarrays[0].size(generator, ctx); // They all should have the same `np.size`.
-        let step = sizet_model.const_1(generator, ctx.ctx);
-        gen_for_model_auto(generator, ctx, start, stop, step, move |generator, ctx, _hooks, i| {
-            let elements =
-                ndarrays.iter().map(|ndarray| ndarray.get_nth(generator, ctx, i)).collect_vec();
-
-            let ret = mapping(generator, ctx, i, &elements)?;
-
-            let pret = mapped_ndarray.get_nth_pointer(generator, ctx, i, "pret");
-            ctx.builder.build_store(pret, ret).unwrap();
-            Ok(())
-        })?;
-
-        Ok(mapped_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>,
-            Int<'ctx, SizeT>,
-            ScalarObject<'ctx>,
-        ) -> Result<BasicValueEnum<'ctx>, String>,
-    {
-        NDArrayObject::broadcasting_starmap(
-            generator,
-            ctx,
-            &[*self],
-            ret_dtype,
-            |generator, ctx, i, scalars| mapping(generator, ctx, i, 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>,
-            Int<'ctx, SizeT>,
-            &[ScalarObject<'ctx>],
-        ) -> Result<BasicValueEnum<'ctx>, String>,
-    {
-        let sizet_model = IntModel(SizeT);
-
-        // Check if all inputs are ScalarObjects
-        let all_scalars: Option<Vec<_>> =
-            inputs.iter().map(ScalarObject::try_from).try_collect().ok();
-
-        if let Some(scalars) = all_scalars {
-            let i = sizet_model.const_0(generator, ctx.ctx); // Pass 0 as the index
-            let scalar =
-                ScalarObject { value: mapping(generator, ctx, i, &scalars)?, dtype: 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, 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>,
-            Int<'ctx, SizeT>,
-            ScalarObject<'ctx>,
-        ) -> Result<BasicValueEnum<'ctx>, String>,
-    {
-        ScalarOrNDArray::broadcasting_starmap(
-            generator,
-            ctx,
-            &[*self],
-            ret_dtype,
-            |generator, ctx, i, scalars| mapping(generator, ctx, i, scalars[0]),
-        )
-    }
-}

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

@@ -1,543 +0,0 @@
-pub mod broadcast;
-pub mod functions;
-pub mod indexing;
-pub mod mapping;
-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,
-        },
-        model::*,
-        stmt::BreakContinueHooks,
-        CodeGenContext, CodeGenerator,
-    },
-    toplevel::numpy::{extract_ndims, unpack_ndarray_var_tys},
-    typecheck::typedef::Type,
-};
-use inkwell::{
-    context::Context,
-    types::BasicType,
-    values::{BasicValue, BasicValueEnum, PointerValue},
-    AddressSpace, IntPredicate,
-};
-use scalar::ScalarObject;
-use util::{call_memcpy_model, gen_for_model_auto};
-
-pub struct NpArrayFields<'ctx, F: FieldTraversal<'ctx>> {
-    pub data: F::Out<PtrModel<IntModel<Byte>>>,
-    pub itemsize: F::Out<IntModel<SizeT>>,
-    pub ndims: F::Out<IntModel<SizeT>>,
-    pub shape: F::Out<PtrModel<IntModel<SizeT>>>,
-    pub strides: F::Out<PtrModel<IntModel<SizeT>>>,
-}
-
-// TODO: Rename to `NDArray` when the old NDArray is removed.
-#[derive(Debug, Clone, Copy, Default)]
-pub struct NpArray;
-
-impl<'ctx> StructKind<'ctx> for NpArray {
-    type Fields<F: FieldTraversal<'ctx>> = NpArrayFields<'ctx, F>;
-
-    fn traverse_fields<F: FieldTraversal<'ctx>>(&self, traversal: &mut F) -> Self::Fields<F> {
-        Self::Fields {
-            data: traversal.add_auto("data"),
-            itemsize: traversal.add_auto("itemsize"),
-            ndims: traversal.add_auto("ndims"),
-            shape: traversal.add_auto("shape"),
-            strides: traversal.add_auto("strides"),
-        }
-    }
-}
-
-/// A NAC3 Python ndarray object.
-#[derive(Debug, Clone, Copy)]
-pub struct NDArrayObject<'ctx> {
-    pub dtype: Type,
-    pub ndims: u64,
-    pub value: Ptr<'ctx, StructModel<NpArray>>,
-}
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// Create an [`NDArrayObject`] from an LLVM value and its typechecker [`Type`].
-    pub fn from_value_and_type<V: BasicValue<'ctx>, G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        value: V,
-        ty: Type,
-    ) -> Self {
-        let pndarray_model = PtrModel(StructModel(NpArray));
-
-        let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
-        let ndims = extract_ndims(&ctx.unifier, ndims);
-        let value = pndarray_model.check_value(generator, ctx.ctx, value).unwrap();
-        NDArrayObject { dtype, ndims, value }
-    }
-
-    /// 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.value)
-    }
-
-    /// 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.value)
-    }
-
-    /// 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.value)
-    }
-
-    /// 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.value)
-    }
-
-    /// 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`.
-    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.value, nth);
-        ctx.builder
-            .build_pointer_cast(p.value, elem_ty.ptr_type(AddressSpace::default()), name)
-            .unwrap()
-    }
-
-    /// Get the n-th (0-based) scalar.
-    pub fn get_nth<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        nth: Int<'ctx, SizeT>,
-    ) -> ScalarObject<'ctx> {
-        let p = self.get_nth_pointer(generator, ctx, nth, "value");
-        let value = ctx.builder.build_load(p, "value").unwrap();
-        ScalarObject { dtype: self.dtype, value }
-    }
-
-    /// 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.value);
-    }
-
-    /// 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.value, self.value);
-    }
-
-    /// Allocate an ndarray on the stack given its `ndims` and `dtype`.
-    ///
-    /// `shape` and `strides` will be automatically allocated on the stack.
-    ///
-    /// 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_uninitialized<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(NpArray);
-        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, value: pndarray }
-    }
-
-    /// Convenience function.
-    /// Like [`NDArrayObject::alloca_uninitialized`] but directly takes the typechecker type of the ndarray.
-    pub fn alloca_uninitialized_of_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_uninitialized(generator, ctx, dtype, ndims, name)
-    }
-
-    /// Clone this ndaarray - 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.
-    pub fn make_clone<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: &str,
-    ) -> Self {
-        let clone =
-            NDArrayObject::alloca_uninitialized(generator, ctx, self.dtype, self.ndims, name);
-
-        let shape = self.value.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
-    }
-
-    /// 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.value.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.value.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.value.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.value.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.value.get(generator, ctx, |f| f.strides, "src_strides");
-        self.copy_strides_from_array(generator, ctx, src_strides);
-    }
-
-    /// Iterate through every element pointer in the ndarray in its flatten view.
-    ///
-    /// `body` also access to [`BreakContinueHooks`] to short-circuit and an element's
-    /// index. The given element pointer also has been casted to the LLVM type of this ndarray's `dtype`.
-    pub fn foreach_pointer<'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>,
-            Int<'ctx, SizeT>,
-            PointerValue<'ctx>,
-        ) -> Result<(), String>,
-    {
-        let sizet_model = IntModel(SizeT);
-
-        let start = sizet_model.const_0(generator, ctx.ctx);
-        let stop = self.size(generator, ctx);
-        let step = sizet_model.const_1(generator, ctx.ctx);
-
-        gen_for_model_auto(generator, ctx, start, stop, step, |generator, ctx, hooks, i| {
-            let pelement = self.get_nth_pointer(generator, ctx, i, "element");
-            body(generator, ctx, hooks, i, pelement)
-        })
-    }
-
-    /// Iterate through every scalar in this ndarray.
-    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>,
-            Int<'ctx, SizeT>,
-            ScalarObject<'ctx>,
-        ) -> Result<(), String>,
-    {
-        self.foreach_pointer(generator, ctx, |generator, ctx, hooks, i, p| {
-            let value = ctx.builder.build_load(p, "value").unwrap();
-            let scalar = ScalarObject { dtype: self.dtype, value };
-            body(generator, ctx, hooks, i, scalar)
-        })
-    }
-
-    /// Fill the ndarray with a value.
-    ///
-    /// `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, '_>,
-        fill_value: BasicValueEnum<'ctx>,
-    ) {
-        self.foreach_pointer(generator, ctx, |_generator, ctx, _hooks, _i, pelement| {
-            ctx.builder.build_store(pelement, fill_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. 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_uninitialized(
-            generator,
-            ctx,
-            self.dtype,
-            new_ndims,
-            "reshaped_ndarray",
-        );
-        dst_ndarray.copy_shape_from_array(generator, ctx, new_shape);
-
-        let size = self.size(generator, ctx);
-        let new_ndims = dst_ndarray.get_ndims(generator, ctx.ctx);
-        call_nac3_ndarray_resolve_and_check_new_shape(generator, ctx, size, new_ndims, new_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.value.set(ctx, |f| f.data, self.value.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.
-    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**).
-    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_uninitialized(
-            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.value,
-            transposed_ndarray.value,
-            num_axes,
-            axes,
-        );
-
-        transposed_ndarray
-    }
-}

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

@@ -1,18 +0,0 @@
-use crate::codegen::{CodeGenContext, CodeGenerator};
-
-use super::NDArrayObject;
-
-impl<'ctx> NDArrayObject<'ctx> {
-    /// TODO: Document me
-    pub fn matmul_at_least_2d<G: CodeGenerator + ?Sized>(
-        generator: &G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        a: Self,
-        b: Self,
-    ) -> Self {
-        assert!(a.ndims >= 2);
-        assert!(b.ndims >= 2);
-
-        todo!()
-    }
-}

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

@@ -1,143 +0,0 @@
-use inkwell::values::{BasicValue, BasicValueEnum};
-
-use crate::{
-    codegen::{model::*, CodeGenContext, CodeGenerator},
-    typecheck::typedef::{Type, TypeEnum},
-};
-
-use super::NDArrayObject;
-
-/// An LLVM numpy scalar with its [`Type`].
-///
-/// Intended to be used with [`ScalarOrNDArray`].
-///
-/// A scalar does not have to be an actual number. It could be arbitrary objects.
-#[derive(Debug, Clone, Copy)]
-pub struct ScalarObject<'ctx> {
-    pub dtype: Type,
-    pub value: BasicValueEnum<'ctx>,
-}
-
-impl<'ctx> ScalarObject<'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_uninitialized(generator, ctx, self.dtype, 0, "scalar_ndarray");
-        ndarray.value.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(ScalarObject<'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.value.value.as_basic_value_enum(),
-        }
-    }
-
-    #[must_use]
-    pub fn into_scalar(&self) -> ScalarObject<'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.dtype,
-            ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
-        }
-    }
-}
-
-impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for ScalarObject<'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 [`BasicValueEnum<'ctx>`] into a [`ScalarOrNDArray`] depending
-/// on its [`Type`].
-pub fn split_scalar_or_ndarray<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    input: BasicValueEnum<'ctx>,
-    input_ty: Type,
-) -> ScalarOrNDArray<'ctx> {
-    match &*ctx.unifier.get_ty(input_ty) {
-        TypeEnum::TObj { obj_id, .. }
-            if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
-        {
-            let ndarray = NDArrayObject::from_value_and_type(generator, ctx, input, input_ty);
-            ScalarOrNDArray::NDArray(ndarray)
-        }
-        _ => {
-            let scalar = ScalarObject { dtype: input_ty, value: input };
-            ScalarOrNDArray::Scalar(scalar)
-        }
-    }
-}

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

@@ -1,112 +0,0 @@
-use inkwell::values::BasicValueEnum;
-use util::gen_for_model_auto;
-
-use crate::{
-    codegen::{model::*, structure::list::ListObject, CodeGenContext, CodeGenerator},
-    typecheck::typedef::{Type, 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: BasicValueEnum<'ctx>,
-    input_sequence_ty: Type,
-) -> (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_value_and_type(generator, ctx, input_sequence, input_sequence_ty);
-
-            let len = input_sequence.value.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
-                    .value
-                    .get(generator, ctx, |f| f.items, "int")
-                    .ix(generator, ctx, i.value, "int")
-                    .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 { ty: tuple_types, .. } => {
-            // 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
-            let input_sequence = input_sequence.into_struct_value(); // A tuple is a struct
-
-            let len_int = tuple_types.len();
-
-            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 = ctx
-                    .builder
-                    .build_extract_value(input_sequence, i as u32, "int")
-                    .unwrap()
-                    .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.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/structure/tuple.rs

@@ -1,39 +0,0 @@
-use inkwell::values::{BasicValueEnum, StructValue};
-use itertools::Itertools;
-
-use crate::{
-    codegen::{CodeGenContext, CodeGenerator},
-    typecheck::typedef::Type,
-};
-
-pub struct TupleObject<'ctx> {
-    pub tys: Vec<Type>,
-    pub value: StructValue<'ctx>,
-}
-
-impl<'ctx> TupleObject<'ctx> {
-    pub fn create<I, G: CodeGenerator + ?Sized>(
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        items: I,
-        name: &str,
-    ) -> Self
-    where
-        I: IntoIterator<Item = (BasicValueEnum<'ctx>, Type)>,
-    {
-        let (vals, tys): (Vec<_>, Vec<_>) = items.into_iter().unzip();
-
-        // let tuple_ty = ctx.unifier.add_ty(TypeEnum::TTuple { ty: tys });
-        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 vals.into_iter().enumerate() {
-            // Store the dim value into the tuple
-            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 }
-    }
-}

nac3core/src/toplevel/builtins.rs

@@ -13,27 +13,15 @@ use strum::IntoEnumIterator;
 
 use crate::{
     codegen::{
-        builtin_fns::{self},
+        builtin_fns,
-        classes::{ProxyValue, RangeValue},
+        classes::{ArrayLikeValue, NDArrayValue, ProxyValue, RangeValue, TypedArrayLikeAccessor},
         expr::destructure_range,
-        extern_fns,
+        irrt::*,
-        irrt::{self, *},
-        llvm_intrinsics,
-        model::Int32,
         numpy::*,
-        numpy_new::{self, gen_ndarray_transpose},
         stmt::exn_constructor,
-        structure::ndarray::{
-            functions::{FloorOrCeil, MinOrMax},
-            scalar::{split_scalar_or_ndarray, ScalarObject, ScalarOrNDArray},
-            NDArrayObject,
-        },
     },
     symbol_resolver::SymbolValue,
-    toplevel::{
+    toplevel::{helper::PrimDef, numpy::make_ndarray_ty},
-        helper::PrimDef,
-        numpy::{create_ndims, make_ndarray_ty},
-    },
     typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
 };
 
@@ -523,16 +511,7 @@ impl<'a> BuiltinBuilder<'a> {
             PrimDef::FunNpArray
             | PrimDef::FunNpFull
             | PrimDef::FunNpEye
-            | PrimDef::FunNpIdentity
+            | PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
-            | PrimDef::FunNpArange => self.build_ndarray_other_factory_function(prim),
-
-            PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape | PrimDef::FunNpTranspose => {
-                self.build_ndarray_view_function(prim)
-            }
-
-            PrimDef::FunNpSize | PrimDef::FunNpShape | PrimDef::FunNpStrides => {
-                self.build_ndarray_property_getter_function(prim)
-            }
 
             PrimDef::FunStr => self.build_str_function(),
 
@@ -599,6 +578,10 @@ impl<'a> BuiltinBuilder<'a> {
             | PrimDef::FunNpHypot
             | PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
 
+            PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
+                self.build_np_sp_ndarray_function(prim)
+            }
+
             PrimDef::FunNpDot
             | PrimDef::FunNpLinalgCholesky
             | PrimDef::FunNpLinalgQr
@@ -1089,34 +1072,16 @@ impl<'a> BuiltinBuilder<'a> {
                     let arg_ty = fun.0.args[0].ty;
                     let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
 
-                    let ret_dtype = match prim {
+                    let func = match prim {
-                        PrimDef::FunInt32 => ctx.primitives.int32,
+                        PrimDef::FunInt32 => builtin_fns::call_int32,
-                        PrimDef::FunInt64 => ctx.primitives.int64,
+                        PrimDef::FunInt64 => builtin_fns::call_int64,
-                        PrimDef::FunUInt32 => ctx.primitives.uint32,
+                        PrimDef::FunUInt32 => builtin_fns::call_uint32,
-                        PrimDef::FunUInt64 => ctx.primitives.uint64,
+                        PrimDef::FunUInt64 => builtin_fns::call_uint64,
-                        PrimDef::FunFloat => ctx.primitives.float,
+                        PrimDef::FunFloat => builtin_fns::call_float,
-                        PrimDef::FunBool => ctx.primitives.bool,
+                        PrimDef::FunBool => builtin_fns::call_bool,
                         _ => unreachable!(),
                     };
-
+                    Ok(Some(func(generator, ctx, (arg_ty, arg))?))
-                    let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
-                        generator,
-                        ctx,
-                        ret_dtype,
-                        |generator, ctx, _i, scalar| {
-                            let result = match prim {
-                                PrimDef::FunInt32 => scalar.cast_to_int32(generator, ctx),
-                                PrimDef::FunInt64 => scalar.cast_to_int64(generator, ctx),
-                                PrimDef::FunUInt32 => scalar.cast_to_uint32(generator, ctx),
-                                PrimDef::FunUInt64 => scalar.cast_to_uint64(generator, ctx),
-                                PrimDef::FunFloat => scalar.cast_to_float(ctx),
-                                PrimDef::FunBool => scalar.cast_to_bool(ctx),
-                                _ => unreachable!(),
-                            };
-                            Ok(result.value)
-                        },
-                    )?;
-                    Ok(Some(result.to_basic_value_enum()))
                 },
             )))),
             loc: None,
@@ -1167,23 +1132,20 @@ impl<'a> BuiltinBuilder<'a> {
                 let arg_ty = fun.0.args[0].ty;
                 let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
 
-                let ret_int_dtype = size_variant.of_int(&ctx.primitives);
+                let ret_elem_ty = size_variant.of_int(&ctx.primitives);
-
+                Ok(Some(builtin_fns::call_round(generator, ctx, (arg_ty, arg), ret_elem_ty)?))
-                let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
-                    generator,
-                    ctx,
-                    ret_int_dtype,
-                    |generator, ctx, _i, scalar| {
-                        Ok(scalar.round(generator, ctx, ret_int_dtype).value)
-                    },
-                )?;
-                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
 
     /// Build the functions `ceil()` and `floor()` and their 64 bit variants.
     fn build_ceil_floor_function(&mut self, prim: PrimDef) -> TopLevelDef {
+        #[derive(Clone, Copy)]
+        enum Kind {
+            Floor,
+            Ceil,
+        }
+
         debug_assert_prim_is_allowed(
             prim,
             &[PrimDef::FunFloor, PrimDef::FunFloor64, PrimDef::FunCeil, PrimDef::FunCeil64],
@@ -1191,10 +1153,10 @@ impl<'a> BuiltinBuilder<'a> {
 
         let (size_variant, kind) = {
             match prim {
-                PrimDef::FunFloor => (SizeVariant::Bits32, FloorOrCeil::Floor),
+                PrimDef::FunFloor => (SizeVariant::Bits32, Kind::Floor),
-                PrimDef::FunFloor64 => (SizeVariant::Bits64, FloorOrCeil::Floor),
+                PrimDef::FunFloor64 => (SizeVariant::Bits64, Kind::Floor),
-                PrimDef::FunCeil => (SizeVariant::Bits32, FloorOrCeil::Ceil),
+                PrimDef::FunCeil => (SizeVariant::Bits32, Kind::Ceil),
-                PrimDef::FunCeil64 => (SizeVariant::Bits64, FloorOrCeil::Ceil),
+                PrimDef::FunCeil64 => (SizeVariant::Bits64, Kind::Ceil),
                 _ => unreachable!(),
             }
         };
@@ -1234,15 +1196,12 @@ impl<'a> BuiltinBuilder<'a> {
                 let arg_ty = fun.0.args[0].ty;
                 let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
 
-                let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
+                let ret_elem_ty = size_variant.of_int(&ctx.primitives);
-                    generator,
+                let func = match kind {
-                    ctx,
+                    Kind::Ceil => builtin_fns::call_ceil,
-                    int_sized,
+                    Kind::Floor => builtin_fns::call_floor,
-                    |generator, ctx, _i, scalar| {
+                };
-                        Ok(scalar.floor_or_ceil(generator, ctx, kind, int_sized).value)
+                Ok(Some(func(generator, ctx, (arg_ty, arg), ret_elem_ty)?))
-                    },
-                )?;
-                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
@@ -1289,9 +1248,9 @@ impl<'a> BuiltinBuilder<'a> {
             &[(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
             Box::new(move |ctx, obj, fun, args, generator| {
                 let func = match prim {
-                    PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => numpy_new::gen_ndarray_empty,
+                    PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => gen_ndarray_empty,
-                    PrimDef::FunNpZeros => numpy_new::gen_ndarray_zeros,
+                    PrimDef::FunNpZeros => gen_ndarray_zeros,
-                    PrimDef::FunNpOnes => numpy_new::gen_ndarray_ones,
+                    PrimDef::FunNpOnes => gen_ndarray_ones,
                     _ => unreachable!(),
                 };
                 func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
@@ -1305,13 +1264,7 @@ impl<'a> BuiltinBuilder<'a> {
     fn build_ndarray_other_factory_function(&mut self, prim: PrimDef) -> TopLevelDef {
         debug_assert_prim_is_allowed(
             prim,
-            &[
+            &[PrimDef::FunNpArray, PrimDef::FunNpFull, PrimDef::FunNpEye, PrimDef::FunNpIdentity],
-                PrimDef::FunNpArray,
-                PrimDef::FunNpFull,
-                PrimDef::FunNpEye,
-                PrimDef::FunNpIdentity,
-                PrimDef::FunNpArange,
-            ],
         );
 
         let PrimitiveStore { int32, bool, ndarray, .. } = *self.primitives;
@@ -1372,7 +1325,7 @@ impl<'a> BuiltinBuilder<'a> {
                     // type variable
                     &[(self.list_int32, "shape"), (tv.ty, "fill_value")],
                     Box::new(move |ctx, obj, fun, args, generator| {
-                        numpy_new::gen_ndarray_full(ctx, &obj, fun, &args, generator)
+                        gen_ndarray_full(ctx, &obj, fun, &args, generator)
                             .map(|val| Some(val.as_basic_value_enum()))
                     }),
                 )
@@ -1430,152 +1383,6 @@ impl<'a> BuiltinBuilder<'a> {
                         .map(|val| Some(val.as_basic_value_enum()))
                 }),
             ),
-            PrimDef::FunNpArange => {
-                // TODO: Support `np.arange(start, stop, step)`
-                let ndims1 = create_ndims(self.unifier, 1);
-                let ndarray_float_1d = make_ndarray_ty(
-                    self.unifier,
-                    self.primitives,
-                    Some(self.primitives.float),
-                    Some(ndims1),
-                );
-                create_fn_by_codegen(
-                    self.unifier,
-                    &VarMap::new(),
-                    prim.name(),
-                    ndarray_float_1d,
-                    &[(int32, "n")],
-                    Box::new(|ctx, obj, fun, args, generator| {
-                        numpy_new::gen_ndarray_arange(ctx, &obj, fun, &args, generator)
-                            .map(|val| Some(val.as_basic_value_enum()))
-                    }),
-                )
-            }
-            _ => unreachable!(),
-        }
-    }
-
-    fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
-        debug_assert_prim_is_allowed(
-            prim,
-            &[PrimDef::FunNpBroadcastTo, PrimDef::FunNpReshape, PrimDef::FunNpTranspose],
-        );
-
-        match prim {
-            PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape => {
-                // `array_ty` can be ndarrays and arbitrary scalars and objects.
-                let array_tvar = self.unifier.get_dummy_var();
-
-                // The return type is handled by special folding in the type inferencer,
-                // since the returned `ndims` depends on input shape.
-                let return_tvar = self.unifier.get_dummy_var();
-
-                create_fn_by_codegen(
-                    self.unifier,
-                    &into_var_map([array_tvar, return_tvar]),
-                    prim.name(),
-                    return_tvar.ty,
-                    &[
-                        (array_tvar.ty, "array"),
-                        (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"),
-                    ],
-                    Box::new(move |ctx, obj, fun, args, generator| {
-                        let f = match prim {
-                            PrimDef::FunNpBroadcastTo => numpy_new::gen_ndarray_broadcast_to,
-                            PrimDef::FunNpReshape => numpy_new::gen_ndarray_reshape,
-                            _ => unreachable!(),
-                        };
-                        f(ctx, &obj, fun, &args, generator).map(Some)
-                    }),
-                )
-            }
-            PrimDef::FunNpTranspose => {
-                // TODO: Allow tuple inputs.
-                // TODO: Support scalar inputs (difficult)
-
-                // TODO: Default values don't work for some reason.
-                // `axes` should have been `Option[List[int32]]` with default `None`.
-                // Workaround with some bogus types and values for now.
-
-                let axes_ty = self.list_int32;
-
-                TopLevelDef::Function {
-                    name: prim.name().into(),
-                    simple_name: prim.simple_name().into(),
-                    signature: self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
-                        args: vec![
-                            FuncArg {
-                                name: "a".into(),
-                                ty: self.primitives.ndarray,
-                                default_value: None,
-                                is_vararg: false,
-                            },
-                            FuncArg {
-                                name: "axes".into(),
-                                ty: axes_ty,
-                                default_value: Some(SymbolValue::OptionNone), // Bogus
-                                is_vararg: false,
-                            },
-                        ],
-                        ret: self.primitives.ndarray,
-                        vars: VarMap::new(),
-                    })),
-                    var_id: Vec::default(),
-                    instance_to_symbol: HashMap::default(),
-                    instance_to_stmt: HashMap::default(),
-                    resolver: None,
-                    codegen_callback: Some(Arc::new(GenCall::new(Box::new(
-                        |ctx, obj, fun, args, generator| {
-                            gen_ndarray_transpose(ctx, &obj, fun, &args, generator).map(Some)
-                        },
-                    )))),
-                    loc: None,
-                }
-            }
-            _ => unreachable!(),
-        }
-    }
-
-    fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
-        debug_assert_prim_is_allowed(
-            prim,
-            &[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
-        );
-
-        match prim {
-            PrimDef::FunNpSize => {
-                // TODO: Make the return type usize
-                create_fn_by_codegen(
-                    self.unifier,
-                    &VarMap::new(),
-                    prim.name(),
-                    self.primitives.int32,
-                    &[(self.primitives.ndarray, "a")],
-                    Box::new(|ctx, obj, fun, args, generator| {
-                        numpy_new::gen_ndarray_size(ctx, &obj, fun, &args, generator).map(Some)
-                    }),
-                )
-            }
-            PrimDef::FunNpShape | PrimDef::FunNpStrides => {
-                // The return type is a tuple of variable length depending on the ndims
-                // of the input ndarray.
-                let ret_ty = self.unifier.get_dummy_var().ty;
-                create_fn_by_codegen(
-                    self.unifier,
-                    &VarMap::new(),
-                    prim.name(),
-                    ret_ty,
-                    &[(self.primitives.ndarray, "a")],
-                    Box::new(move |ctx, obj, fun, args, generator| {
-                        let f = match prim {
-                            PrimDef::FunNpShape => numpy_new::gen_ndarray_shape,
-                            PrimDef::FunNpStrides => numpy_new::gen_ndarray_strides,
-                            _ => unreachable!(),
-                        };
-                        f(ctx, &obj, fun, &args, generator).map(Some)
-                    }),
-                )
-            }
             _ => unreachable!(),
         }
     }
@@ -1627,22 +1434,12 @@ impl<'a> BuiltinBuilder<'a> {
                 let arg_ty = fun.0.args[0].ty;
                 let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
 
-                let kind = match prim {
+                let func = match prim {
-                    PrimDef::FunNpFloor => FloorOrCeil::Floor,
+                    PrimDef::FunNpCeil => builtin_fns::call_ceil,
-                    PrimDef::FunNpCeil => FloorOrCeil::Ceil,
+                    PrimDef::FunNpFloor => builtin_fns::call_floor,
                     _ => unreachable!(),
                 };
-
+                Ok(Some(func(generator, ctx, (arg_ty, arg), ctx.primitives.float)?))
-                let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
-                    generator,
-                    ctx,
-                    ctx.primitives.float,
-                    move |_generator, ctx, _i, scalar| {
-                        let result = scalar.np_floor_or_ceil(ctx, kind);
-                        Ok(result.value)
-                    },
-                )?;
-                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
@@ -1660,17 +1457,7 @@ impl<'a> BuiltinBuilder<'a> {
             Box::new(|ctx, _, fun, args, generator| {
                 let arg_ty = fun.0.args[0].ty;
                 let arg = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
-
+                Ok(Some(builtin_fns::call_numpy_round(generator, ctx, (arg_ty, arg))?))
-                let result = split_scalar_or_ndarray(generator, ctx, arg, arg_ty).map(
-                    generator,
-                    ctx,
-                    ctx.primitives.float,
-                    |_generator, ctx, _i, scalar| {
-                        let result = scalar.np_round(ctx);
-                        Ok(result.value)
-                    },
-                )?;
-                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
@@ -1747,11 +1534,51 @@ impl<'a> BuiltinBuilder<'a> {
                                 }
                             }
                             TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                                let ndarray =
+                                let llvm_i32 = ctx.ctx.i32_type();
-                                    NDArrayObject::from_value_and_type(generator, ctx, arg, arg_ty);
+                                let llvm_usize = generator.get_size_type(ctx.ctx);
-                                let len = ndarray.len(generator, ctx);
+
-                                let len = len.truncate(generator, ctx, Int32, "len"); // TODO: Currently `len()` returns an int32. It should have been SizeT
+                                let arg = NDArrayValue::from_ptr_val(
-                                Some(len.value.as_basic_value_enum())
+                                    arg.into_pointer_value(),
+                                    llvm_usize,
+                                    None,
+                                );
+
+                                let ndims = arg.dim_sizes().size(ctx, generator);
+                                ctx.make_assert(
+                                    generator,
+                                    ctx.builder
+                                        .build_int_compare(
+                                            IntPredicate::NE,
+                                            ndims,
+                                            llvm_usize.const_zero(),
+                                            "",
+                                        )
+                                        .unwrap(),
+                                    "0:TypeError",
+                                    &format!("{name}() of unsized object", name = prim.name()),
+                                    [None, None, None],
+                                    ctx.current_loc,
+                                );
+
+                                let len = unsafe {
+                                    arg.dim_sizes().get_typed_unchecked(
+                                        ctx,
+                                        generator,
+                                        &llvm_usize.const_zero(),
+                                        None,
+                                    )
+                                };
+
+                                if len.get_type().get_bit_width() == 32 {
+                                    Some(len.into())
+                                } else {
+                                    Some(
+                                        ctx.builder
+                                            .build_int_truncate(len, llvm_i32, "len")
+                                            .map(Into::into)
+                                            .unwrap(),
+                                    )
+                                }
                             }
                             _ => unreachable!(),
                         }
@@ -1794,21 +1621,16 @@ impl<'a> BuiltinBuilder<'a> {
             codegen_callback: Some(Arc::new(GenCall::new(Box::new(
                 move |ctx, _, fun, args, generator| {
                     let m_ty = fun.0.args[0].ty;
-                    let m_val = args[0].1.clone().to_basic_value_enum(ctx, generator, m_ty)?;
-
                     let n_ty = fun.0.args[1].ty;
+                    let m_val = args[0].1.clone().to_basic_value_enum(ctx, generator, m_ty)?;
                     let n_val = args[1].1.clone().to_basic_value_enum(ctx, generator, n_ty)?;
 
-                    let kind = match prim {
+                    let func = match prim {
-                        PrimDef::FunMin => MinOrMax::Min,
+                        PrimDef::FunMin => builtin_fns::call_min,
-                        PrimDef::FunMax => MinOrMax::Max,
+                        PrimDef::FunMax => builtin_fns::call_max,
                         _ => unreachable!(),
                     };
-
+                    Ok(Some(func(ctx, (m_ty, m_val), (n_ty, n_val))))
-                    let m = ScalarObject { dtype: m_ty, value: m_val };
-                    let n = ScalarObject { dtype: n_ty, value: n_val };
-                    let result = ScalarObject::min_or_max(ctx, kind, m, n);
-                    Ok(Some(result.value))
                 },
             )))),
             loc: None,
@@ -1850,25 +1672,7 @@ impl<'a> BuiltinBuilder<'a> {
                 let a_ty = fun.0.args[0].ty;
                 let a = args[0].1.clone().to_basic_value_enum(ctx, generator, a_ty)?;
 
-                let a = split_scalar_or_ndarray(generator, ctx, a, a_ty).as_ndarray(generator, ctx);
+                Ok(Some(builtin_fns::call_numpy_max_min(generator, ctx, (a_ty, a), prim.name())?))
-                let result = match prim {
-                    PrimDef::FunNpArgmin => a
-                        .argmin_or_argmax(generator, ctx, MinOrMax::Min)
-                        .value
-                        .as_basic_value_enum(),
-                    PrimDef::FunNpArgmax => a
-                        .argmin_or_argmax(generator, ctx, MinOrMax::Max)
-                        .value
-                        .as_basic_value_enum(),
-                    PrimDef::FunNpMin => {
-                        a.min_or_max(generator, ctx, MinOrMax::Min).value.as_basic_value_enum()
-                    }
-                    PrimDef::FunNpMax => {
-                        a.min_or_max(generator, ctx, MinOrMax::Max).value.as_basic_value_enum()
-                    }
-                    _ => unreachable!(),
-                };
-                Ok(Some(result))
             }),
         )
     }
@@ -1908,32 +1712,13 @@ impl<'a> BuiltinBuilder<'a> {
                     let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
                     let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
 
-                    let kind = match prim {
+                    let func = match prim {
-                        PrimDef::FunNpMinimum => MinOrMax::Min,
+                        PrimDef::FunNpMinimum => builtin_fns::call_numpy_minimum,
-                        PrimDef::FunNpMaximum => MinOrMax::Max,
+                        PrimDef::FunNpMaximum => builtin_fns::call_numpy_maximum,
                         _ => unreachable!(),
                     };
 
-                    let x1 = split_scalar_or_ndarray(generator, ctx, x1_val, x1_ty);
+                    Ok(Some(func(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
-                    let x2 = split_scalar_or_ndarray(generator, ctx, x2_val, x2_ty);
-
-                    // NOTE: x1.dtype() and x2.dtype() should be the same
-                    let common_ty = x1.dtype();
-
-                    let result = ScalarOrNDArray::broadcasting_starmap(
-                        generator,
-                        ctx,
-                        &[x1, x2],
-                        common_ty,
-                        |_generator, ctx, _i, scalars| {
-                            let x1 = scalars[0];
-                            let x2 = scalars[1];
-
-                            let result = ScalarObject::min_or_max(ctx, kind, x1, x2);
-                            Ok(result.value)
-                        },
-                    )?;
-                    Ok(Some(result.to_basic_value_enum()))
                 },
             )))),
             loc: None,
@@ -1944,7 +1729,6 @@ impl<'a> BuiltinBuilder<'a> {
     fn build_abs_function(&mut self) -> TopLevelDef {
         let prim = PrimDef::FunAbs;
 
-        let num_ty = self.num_ty; // To move into codegen_callback
         TopLevelDef::Function {
             name: prim.name().into(),
             simple_name: prim.simple_name().into(),
@@ -1963,17 +1747,11 @@ impl<'a> BuiltinBuilder<'a> {
             instance_to_stmt: HashMap::default(),
             resolver: None,
             codegen_callback: Some(Arc::new(GenCall::new(Box::new(
-                move |ctx, _, fun, args, generator| {
+                |ctx, _, fun, args, generator| {
                     let n_ty = fun.0.args[0].ty;
                     let n_val = args[0].1.clone().to_basic_value_enum(ctx, generator, n_ty)?;
 
-                    let result = split_scalar_or_ndarray(generator, ctx, n_val, n_ty).map(
+                    Ok(Some(builtin_fns::call_abs(generator, ctx, (n_ty, n_val))?))
-                        generator,
-                        ctx,
-                        num_ty.ty,
-                        |_generator, ctx, _i, scalar| Ok(scalar.abs(ctx).value),
-                    )?;
-                    Ok(Some(result.to_basic_value_enum()))
                 },
             )))),
             loc: None,
@@ -1996,23 +1774,13 @@ impl<'a> BuiltinBuilder<'a> {
                 let x_ty = fun.0.args[0].ty;
                 let x_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x_ty)?;
 
-                let function = match prim {
+                let func = match prim {
-                    PrimDef::FunNpIsInf => irrt::call_isnan,
+                    PrimDef::FunNpIsInf => builtin_fns::call_numpy_isinf,
-                    PrimDef::FunNpIsNan => irrt::call_isinf,
+                    PrimDef::FunNpIsNan => builtin_fns::call_numpy_isnan,
                     _ => unreachable!(),
                 };
 
-                let result = split_scalar_or_ndarray(generator, ctx, x_val, x_ty).map(
+                Ok(Some(func(generator, ctx, (x_ty, x_val))?))
-                    generator,
-                    ctx,
-                    ctx.primitives.bool,
-                    |generator, ctx, _i, scalar| {
-                        let n = scalar.into_float64(ctx);
-                        let n = function(generator, ctx, n);
-                        Ok(n.as_basic_value_enum())
-                    },
-                )?;
-                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
@@ -2070,58 +1838,49 @@ impl<'a> BuiltinBuilder<'a> {
                 let arg_ty = fun.0.args[0].ty;
                 let arg_val = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
 
-                let result = split_scalar_or_ndarray(generator, ctx, arg_val, arg_ty).map(
+                let func = match prim {
-                    generator,
+                    PrimDef::FunNpSin => builtin_fns::call_numpy_sin,
-                    ctx,
+                    PrimDef::FunNpCos => builtin_fns::call_numpy_cos,
-                    ctx.primitives.float,
+                    PrimDef::FunNpTan => builtin_fns::call_numpy_tan,
-                    |_generator, ctx, _i, scalar| {
-                        let n = scalar.into_float64(ctx);
-                        let n = match prim {
-                            PrimDef::FunNpSin => llvm_intrinsics::call_float_sin(ctx, n, None),
-                            PrimDef::FunNpCos => llvm_intrinsics::call_float_cos(ctx, n, None),
-                            PrimDef::FunNpTan => extern_fns::call_tan(ctx, n, None),
 
-                            PrimDef::FunNpArcsin => extern_fns::call_asin(ctx, n, None),
+                    PrimDef::FunNpArcsin => builtin_fns::call_numpy_arcsin,
-                            PrimDef::FunNpArccos => extern_fns::call_acos(ctx, n, None),
+                    PrimDef::FunNpArccos => builtin_fns::call_numpy_arccos,
-                            PrimDef::FunNpArctan => extern_fns::call_atan(ctx, n, None),
+                    PrimDef::FunNpArctan => builtin_fns::call_numpy_arctan,
 
-                            PrimDef::FunNpSinh => extern_fns::call_sinh(ctx, n, None),
+                    PrimDef::FunNpSinh => builtin_fns::call_numpy_sinh,
-                            PrimDef::FunNpCosh => extern_fns::call_cosh(ctx, n, None),
+                    PrimDef::FunNpCosh => builtin_fns::call_numpy_cosh,
-                            PrimDef::FunNpTanh => extern_fns::call_tanh(ctx, n, None),
+                    PrimDef::FunNpTanh => builtin_fns::call_numpy_tanh,
 
-                            PrimDef::FunNpArcsinh => extern_fns::call_asinh(ctx, n, None),
+                    PrimDef::FunNpArcsinh => builtin_fns::call_numpy_arcsinh,
-                            PrimDef::FunNpArccosh => extern_fns::call_acosh(ctx, n, None),
+                    PrimDef::FunNpArccosh => builtin_fns::call_numpy_arccosh,
-                            PrimDef::FunNpArctanh => extern_fns::call_atanh(ctx, n, None),
+                    PrimDef::FunNpArctanh => builtin_fns::call_numpy_arctanh,
 
-                            PrimDef::FunNpExp => llvm_intrinsics::call_float_exp(ctx, n, None),
+                    PrimDef::FunNpExp => builtin_fns::call_numpy_exp,
-                            PrimDef::FunNpExp2 => llvm_intrinsics::call_float_exp2(ctx, n, None),
+                    PrimDef::FunNpExp2 => builtin_fns::call_numpy_exp2,
-                            PrimDef::FunNpExpm1 => extern_fns::call_expm1(ctx, n, None),
+                    PrimDef::FunNpExpm1 => builtin_fns::call_numpy_expm1,
 
-                            PrimDef::FunNpLog => llvm_intrinsics::call_float_log(ctx, n, None),
+                    PrimDef::FunNpLog => builtin_fns::call_numpy_log,
-                            PrimDef::FunNpLog2 => llvm_intrinsics::call_float_log2(ctx, n, None),
+                    PrimDef::FunNpLog2 => builtin_fns::call_numpy_log2,
-                            PrimDef::FunNpLog10 => llvm_intrinsics::call_float_log10(ctx, n, None),
+                    PrimDef::FunNpLog10 => builtin_fns::call_numpy_log10,
 
-                            PrimDef::FunNpSqrt => llvm_intrinsics::call_float_sqrt(ctx, n, None),
+                    PrimDef::FunNpSqrt => builtin_fns::call_numpy_sqrt,
-                            PrimDef::FunNpCbrt => extern_fns::call_cbrt(ctx, n, None),
+                    PrimDef::FunNpCbrt => builtin_fns::call_numpy_cbrt,
 
-                            PrimDef::FunNpFabs => llvm_intrinsics::call_float_fabs(ctx, n, None),
+                    PrimDef::FunNpFabs => builtin_fns::call_numpy_fabs,
-                            PrimDef::FunNpRint => llvm_intrinsics::call_float_rint(ctx, n, None),
+                    PrimDef::FunNpRint => builtin_fns::call_numpy_rint,
 
-                            PrimDef::FunSpSpecErf => extern_fns::call_erf(ctx, n, None),
+                    PrimDef::FunSpSpecErf => builtin_fns::call_scipy_special_erf,
-                            PrimDef::FunSpSpecErfc => extern_fns::call_erfc(ctx, n, None),
+                    PrimDef::FunSpSpecErfc => builtin_fns::call_scipy_special_erfc,
 
-                            PrimDef::FunSpSpecGamma => irrt::call_gamma(ctx, n),
+                    PrimDef::FunSpSpecGamma => builtin_fns::call_scipy_special_gamma,
-                            PrimDef::FunSpSpecGammaln => irrt::call_gammaln(ctx, n),
+                    PrimDef::FunSpSpecGammaln => builtin_fns::call_scipy_special_gammaln,
 
-                            PrimDef::FunSpSpecJ0 => irrt::call_j0(ctx, n),
+                    PrimDef::FunSpSpecJ0 => builtin_fns::call_scipy_special_j0,
-                            PrimDef::FunSpSpecJ1 => extern_fns::call_j1(ctx, n, None),
+                    PrimDef::FunSpSpecJ1 => builtin_fns::call_scipy_special_j1,
 
-                            _ => unreachable!(),
+                    _ => unreachable!(),
-                        };
+                };
-                        Ok(n.as_basic_value_enum())
+                Ok(Some(func(generator, ctx, (arg_ty, arg_val))?))
-                    },
-                )?;
-                Ok(Some(result.to_basic_value_enum()))
             }),
         )
     }
@@ -2143,20 +1902,20 @@ impl<'a> BuiltinBuilder<'a> {
 
         let PrimitiveStore { float, int32, .. } = *self.primitives;
 
-        // The argument types of the two input arguments + the return type
+        // The argument types of the two input arguments are controlled here.
-        let (x1_dtype, x2_dtype, ret_dtype) = match prim {
+        let (x1_ty, x2_ty) = match prim {
             PrimDef::FunNpArctan2
             | PrimDef::FunNpCopysign
             | PrimDef::FunNpFmax
             | PrimDef::FunNpFmin
             | PrimDef::FunNpHypot
-            | PrimDef::FunNpNextAfter => (float, float, float),
+            | PrimDef::FunNpNextAfter => (float, float),
-            PrimDef::FunNpLdExp => (float, int32, float),
+            PrimDef::FunNpLdExp => (float, int32),
             _ => unreachable!(),
         };
 
-        let x1_ty = self.new_type_or_ndarray_ty(x1_dtype);
+        let x1_ty = self.new_type_or_ndarray_ty(x1_ty);
-        let x2_ty = self.new_type_or_ndarray_ty(x2_dtype);
+        let x2_ty = self.new_type_or_ndarray_ty(x2_ty);
 
         let param_ty = &[(x1_ty.ty, "x1"), (x2_ty.ty, "x2")];
         let ret_ty = self.unifier.get_fresh_var(None, None);
@@ -2185,78 +1944,78 @@ impl<'a> BuiltinBuilder<'a> {
                 move |ctx, _, fun, args, generator| {
                     let x1_ty = fun.0.args[0].ty;
                     let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
-
                     let x2_ty = fun.0.args[1].ty;
                     let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
 
-                    let x1 = split_scalar_or_ndarray(generator, ctx, x1_val, x1_ty);
+                    let func = match prim {
-                    let x2 = split_scalar_or_ndarray(generator, ctx, x2_val, x2_ty);
+                        PrimDef::FunNpArctan2 => builtin_fns::call_numpy_arctan2,
+                        PrimDef::FunNpCopysign => builtin_fns::call_numpy_copysign,
+                        PrimDef::FunNpFmax => builtin_fns::call_numpy_fmax,
+                        PrimDef::FunNpFmin => builtin_fns::call_numpy_fmin,
+                        PrimDef::FunNpLdExp => builtin_fns::call_numpy_ldexp,
+                        PrimDef::FunNpHypot => builtin_fns::call_numpy_hypot,
+                        PrimDef::FunNpNextAfter => builtin_fns::call_numpy_nextafter,
+                        _ => unreachable!(),
+                    };
 
-                    let result = ScalarOrNDArray::broadcasting_starmap(
+                    Ok(Some(func(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
-                        generator,
-                        ctx,
-                        &[x1, x2],
-                        ret_dtype,
-                        |_generator, ctx, _i, scalars| {
-                            let x1 = scalars[0];
-                            let x2 = scalars[1];
-
-                            // TODO: This looks ugly
-                            let result = match prim {
-                                PrimDef::FunNpArctan2 => {
-                                    let x1 = x1.into_float64(ctx);
-                                    let x2 = x2.into_float64(ctx);
-                                    extern_fns::call_atan2(ctx, x1, x2, None).as_basic_value_enum()
-                                }
-                                PrimDef::FunNpCopysign => {
-                                    let x1 = x1.into_float64(ctx);
-                                    let x2 = x2.into_float64(ctx);
-                                    llvm_intrinsics::call_float_copysign(ctx, x1, x2, None)
-                                        .as_basic_value_enum()
-                                }
-                                PrimDef::FunNpFmax => {
-                                    let x1 = x1.into_float64(ctx);
-                                    let x2 = x2.into_float64(ctx);
-                                    llvm_intrinsics::call_float_maxnum(ctx, x1, x2, None)
-                                        .as_basic_value_enum()
-                                }
-                                PrimDef::FunNpFmin => {
-                                    let x1 = x1.into_float64(ctx);
-                                    let x2 = x2.into_float64(ctx);
-                                    llvm_intrinsics::call_float_minnum(ctx, x1, x2, None)
-                                        .as_basic_value_enum()
-                                }
-                                PrimDef::FunNpHypot => {
-                                    let x1 = x1.into_float64(ctx);
-                                    let x2 = x2.into_float64(ctx);
-                                    llvm_intrinsics::call_float_minnum(ctx, x1, x2, None)
-                                        .as_basic_value_enum()
-                                }
-                                PrimDef::FunNpNextAfter => {
-                                    let x1 = x1.into_float64(ctx);
-                                    let x2 = x2.into_float64(ctx);
-                                    extern_fns::call_nextafter(ctx, x1, x2, None)
-                                        .as_basic_value_enum()
-                                }
-                                PrimDef::FunNpLdExp => {
-                                    let x1 = x1.into_float64(ctx);
-                                    let x2 = x2.into_int32(ctx);
-                                    extern_fns::call_ldexp(ctx, x1, x2, None).as_basic_value_enum()
-                                }
-                                _ => unreachable!(),
-                            };
-
-                            Ok(result)
-                        },
-                    )?;
-
-                    Ok(Some(result.to_basic_value_enum()))
                 },
             )))),
             loc: None,
         }
     }
 
+    /// Build np/sp functions that take as input `NDArray` only
+    fn build_np_sp_ndarray_function(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
+
+        match prim {
+            PrimDef::FunNpTranspose => {
+                let ndarray_ty = self.unifier.get_fresh_var_with_range(
+                    &[self.ndarray_num_ty],
+                    Some("T".into()),
+                    None,
+                );
+                create_fn_by_codegen(
+                    self.unifier,
+                    &into_var_map([ndarray_ty]),
+                    prim.name(),
+                    ndarray_ty.ty,
+                    &[(ndarray_ty.ty, "x")],
+                    Box::new(move |ctx, _, fun, args, generator| {
+                        let arg_ty = fun.0.args[0].ty;
+                        let arg_val =
+                            args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
+                        Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
+                    }),
+                )
+            }
+
+            // NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
+            // the `param_ty` for `create_fn_by_codegen`.
+            //
+            // Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
+            // to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
+            // and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
+            PrimDef::FunNpReshape => create_fn_by_codegen(
+                self.unifier,
+                &VarMap::new(),
+                prim.name(),
+                self.ndarray_num_ty,
+                &[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
+                Box::new(move |ctx, _, fun, args, generator| {
+                    let x1_ty = fun.0.args[0].ty;
+                    let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
+                    let x2_ty = fun.0.args[1].ty;
+                    let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
+                    Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
+                }),
+            ),
+
+            _ => unreachable!(),
+        }
+    }
+
     /// Build `np_linalg` and `sp_linalg` functions
     ///
     /// The input to these functions must be floating point `NDArray`

nac3core/src/toplevel/helper.rs

@@ -51,17 +51,6 @@ pub enum PrimDef {
     FunNpArray,
     FunNpEye,
     FunNpIdentity,
-    FunNpArange,
-
-    // NumPy view functions
-    FunNpBroadcastTo,
-    FunNpReshape,
-    FunNpTranspose,
-
-    // NumPy NDArray property getters
-    FunNpSize,
-    FunNpShape,
-    FunNpStrides,
 
     // Miscellaneous NumPy & SciPy functions
     FunNpRound,
@@ -110,6 +99,8 @@ pub enum PrimDef {
     FunNpLdExp,
     FunNpHypot,
     FunNpNextAfter,
+    FunNpTranspose,
+    FunNpReshape,
 
     // Linalg functions
     FunNpDot,
@@ -246,17 +237,6 @@ impl PrimDef {
             PrimDef::FunNpArray => fun("np_array", None),
             PrimDef::FunNpEye => fun("np_eye", None),
             PrimDef::FunNpIdentity => fun("np_identity", None),
-            PrimDef::FunNpArange => fun("np_arange", None),
-
-            // NumPy view functions
-            PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
-            PrimDef::FunNpReshape => fun("np_reshape", None),
-            PrimDef::FunNpTranspose => fun("np_transpose", None),
-
-            // NumPy NDArray property getters
-            PrimDef::FunNpSize => fun("np_size", None),
-            PrimDef::FunNpShape => fun("np_shape", None),
-            PrimDef::FunNpStrides => fun("np_strides", None),
 
             // Miscellaneous NumPy & SciPy functions
             PrimDef::FunNpRound => fun("np_round", None),
@@ -305,6 +285,8 @@ impl PrimDef {
             PrimDef::FunNpLdExp => fun("np_ldexp", None),
             PrimDef::FunNpHypot => fun("np_hypot", None),
             PrimDef::FunNpNextAfter => fun("np_nextafter", None),
+            PrimDef::FunNpTranspose => fun("np_transpose", None),
+            PrimDef::FunNpReshape => fun("np_reshape", None),
 
             // Linalg functions
             PrimDef::FunNpDot => fun("np_dot", None),

nac3core/src/toplevel/mod.rs

@@ -31,7 +31,6 @@ pub mod builtins;
 pub mod composer;
 pub mod helper;
 pub mod numpy;
-pub mod option;
 pub mod type_annotation;
 use composer::*;
 use type_annotation::*;

nac3core/src/toplevel/numpy.rs

@@ -1,5 +1,4 @@
 use crate::{
-    symbol_resolver::SymbolValue,
     toplevel::helper::PrimDef,
     typecheck::{
         type_inferencer::PrimitiveStore,
@@ -84,33 +83,3 @@ pub fn unpack_ndarray_var_ids(unifier: &mut Unifier, ndarray: Type) -> (TypeVarI
 pub fn unpack_ndarray_var_tys(unifier: &mut Unifier, ndarray: Type) -> (Type, Type) {
     unpack_ndarray_tvars(unifier, ndarray).into_iter().map(|v| v.1).collect_tuple().unwrap()
 }
-
-/// Extract an ndarray's `ndims` [type][`Type`] in `u64`. Panic if not possible.
-/// The `ndims` must only contain 1 value.
-#[must_use]
-pub fn extract_ndims(unifier: &Unifier, ndims_ty: Type) -> u64 {
-    let ndims_ty_enum = unifier.get_ty_immutable(ndims_ty);
-    let TypeEnum::TLiteral { values, .. } = &*ndims_ty_enum else {
-        panic!("ndims_ty should be a TLiteral");
-    };
-
-    assert_eq!(values.len(), 1, "ndims_ty TLiteral should only contain 1 value");
-
-    let ndims = values[0].clone();
-    u64::try_from(ndims).unwrap()
-}
-
-/// Return an ndarray's `ndims` as a typechecker [`Type`] from its `u64` value.
-pub fn create_ndims(unifier: &mut Unifier, ndims: u64) -> Type {
-    unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None)
-}
-
-/// Return the ndims after broadcasting ndarrays of different ndims.
-///
-/// Panics if the input list is empty.
-pub fn get_broadcast_all_ndims<I>(ndims: I) -> u64
-where
-    I: IntoIterator<Item = u64>,
-{
-    ndims.into_iter().max().unwrap()
-}

nac3core/src/toplevel/option.rs

@@ -1,46 +0,0 @@
-use itertools::Itertools;
-
-use crate::{
-    toplevel::helper::PrimDef,
-    typecheck::{
-        type_inferencer::PrimitiveStore,
-        typedef::{Type, TypeEnum, Unifier, VarMap},
-    },
-};
-
-// TODO: This entire module is duplicated code (numpy.rs also has these kinds of things)
-
-/// Creates a `option` [`Type`] with the given type arguments.
-///
-/// * `dtype` - The element type of the `option`, or [`None`] if the type variable is not
-/// specialized.
-/// * `ndims` - The number of dimensions of the `option`, or [`None`] if the type variable is not
-/// specialized.
-pub fn make_option_ty(
-    unifier: &mut Unifier,
-    primitives: &PrimitiveStore,
-    dtype: Option<Type>,
-) -> Type {
-    subst_option_tvars(unifier, primitives.option, dtype)
-}
-
-/// Substitutes type variables in `option`.
-///
-/// * `dtype` - The element type of the `option`, or [`None`] if the type variable is not
-/// specialized.
-pub fn subst_option_tvars(unifier: &mut Unifier, option: Type, dtype: Option<Type>) -> Type {
-    let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(option) else {
-        panic!("Expected `option` to be TObj, but got {}", unifier.stringify(option))
-    };
-    debug_assert_eq!(*obj_id, PrimDef::Option.id());
-
-    let tvar_ids = params.iter().map(|(obj_id, _)| *obj_id).collect_vec();
-    debug_assert_eq!(tvar_ids.len(), 1);
-
-    let mut tvar_subst = VarMap::new();
-    if let Some(dtype) = dtype {
-        tvar_subst.insert(tvar_ids[0], dtype);
-    }
-
-    unifier.subst(option, &tvar_subst).unwrap_or(option)
-}

nac3core/src/typecheck/function_check.rs

@@ -80,7 +80,7 @@ impl<'a> Inferencer<'a> {
                 return Err(HashSet::from([format!(
                     "expected concrete type at {} but got {}",
                     expr.location,
-                    self.unifier.stringify(*ty)
+                    self.unifier.get_ty(*ty).get_type_name()
                 )]));
             }
         }

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -1,6 +1,6 @@
 use std::collections::{HashMap, HashSet};
 use std::convert::{From, TryInto};
-use std::iter::{self, once};
+use std::iter::once;
 use std::{cell::RefCell, sync::Arc};
 
 use super::{
@@ -11,18 +11,16 @@ use super::{
         RecordField, RecordKey, Type, TypeEnum, TypeVar, Unifier, VarMap,
     },
 };
-use crate::toplevel::option::make_option_ty;
 use crate::{
     symbol_resolver::{SymbolResolver, SymbolValue},
     toplevel::{
         helper::{arraylike_flatten_element_type, arraylike_get_ndims, PrimDef},
-        numpy::{extract_ndims, make_ndarray_ty, unpack_ndarray_var_tys},
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         TopLevelContext, TopLevelDef,
     },
     typecheck::typedef::Mapping,
 };
 use itertools::{izip, Itertools};
-use nac3parser::ast::Constant;
 use nac3parser::ast::{
     self,
     fold::{self, Fold},
@@ -1274,7 +1272,7 @@ impl<'a> Inferencer<'a> {
                 arg_ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
             }) {
                 // typeof_ndarray_broadcast requires both dtypes to be the same, but ldexp accepts
-                // (float, int32), so convert it to align with t#he dtype of the first arg
+                // (float, int32), so convert it to align with the dtype of the first arg
                 let arg1_ty = if id == &"np_ldexp".into() {
                     if arg1_ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) {
                         let (_, ndims) = unpack_ndarray_var_tys(self.unifier, arg1_ty);
@@ -1450,7 +1448,7 @@ impl<'a> Inferencer<'a> {
                 },
             }));
         }
-
+        // 2-argument ndarray n-dimensional factory functions
         if id == &"np_reshape".into() && args.len() == 2 {
             let arg0 = self.fold_expr(args.remove(0))?;
 
@@ -1495,51 +1493,6 @@ impl<'a> Inferencer<'a> {
                 },
             }));
         }
-
-        if ["np_shape".into(), "np_strides".into()].contains(id) && args.len() == 1 {
-            // Output tuple size depends on input ndarray's ndims.
-
-            let ndarray = self.fold_expr(args.remove(0))?;
-
-            let (_, ndims) = unpack_ndarray_var_tys(self.unifier, ndarray.custom.unwrap());
-            let ndims = extract_ndims(self.unifier, ndims);
-
-            // Create a tuple of size `ndims` full of int32
-            // TODO: Make it usize
-
-            let ret_ty = TypeEnum::TTuple {
-                ty: iter::repeat(self.primitives.int32).take(ndims as usize).collect_vec(),
-                is_vararg_ctx: false,
-            };
-            let ret_ty = self.unifier.add_ty(ret_ty);
-
-            let func_ty = TypeEnum::TFunc(FunSignature {
-                args: vec![FuncArg {
-                    name: "a".into(),
-                    default_value: None,
-                    ty: ndarray.custom.unwrap(),
-                    is_vararg: false,
-                }],
-                ret: ret_ty,
-                vars: VarMap::new(),
-            });
-            let func_ty = self.unifier.add_ty(func_ty);
-
-            return Ok(Some(Located {
-                location,
-                custom: Some(ret_ty),
-                node: ExprKind::Call {
-                    func: Box::new(Located {
-                        custom: Some(func_ty),
-                        location: func.location,
-                        node: ExprKind::Name { id: *id, ctx: *ctx },
-                    }),
-                    args: vec![ndarray],
-                    keywords: vec![],
-                },
-            }));
-        }
-
         // 2-argument ndarray n-dimensional creation functions
         if id == &"np_full".into() && args.len() == 2 {
             let ExprKind::List { elts, .. } = &args[0].node else {
@@ -2257,39 +2210,14 @@ impl<'a> Inferencer<'a> {
         // We will also take the opportunity to deduce `dims_to_subtract` as well
         let mut dims_to_subtract: i128 = 0;
         for index in indices {
-            match &index.node {
+            if let ExprKind::Slice { lower, upper, step } = &index.node {
-                ExprKind::Slice { lower, upper, step } => {
+                for v in [lower.as_ref(), upper.as_ref(), step.as_ref()].iter().flatten() {
-                    // Handle slices
+                    self.constrain(v.custom.unwrap(), self.primitives.int32, &v.location)?;
-                    for v in [lower.as_ref(), upper.as_ref(), step.as_ref()].iter().flatten() {
-                        self.constrain(v.custom.unwrap(), self.primitives.int32, &v.location)?;
-                    }
-                }
-                ExprKind::Constant { value: Constant::Ellipsis, .. } => {
-                    // Handle `...`.
-
-                    // See https://git.m-labs.hk/M-Labs/nac3/issues/486
-                    // Force `...` to have `()` (completely bogus) to make it concrete.
-                    let empty_tuple = TypeEnum::TTuple { ty: vec![], is_vararg_ctx: false };
-                    let empty_tuple = self.unifier.add_ty(empty_tuple);
-                    self.unify(index.custom.unwrap(), empty_tuple, &index.location)?;
-                }
-                ExprKind::Name { id, .. } if id == &"none".into() => {
-                    // Handle `np.newaxis` / `None`.
-                    dims_to_subtract -= 1;
-
-                    // "none" itself has type `Option[T]`, and since we have a stray `T` (non-concrete type).
-                    // We will force the type to be `Option[()]` to make it concrete. (TODO: is there a void type?)
-                    let empty_tuple = TypeEnum::TTuple { ty: vec![], is_vararg_ctx: false };
-                    let empty_tuple = self.unifier.add_ty(empty_tuple);
-                    let expected_type =
-                        make_option_ty(self.unifier, self.primitives, Some(empty_tuple));
-                    self.unify(index.custom.unwrap(), expected_type, &index.location)?;
-                }
-                _ => {
-                    // Treat anything else as an integer index, and force unify their type to int32.
-                    dims_to_subtract += 1;
-                    self.unify(index.custom.unwrap(), self.primitives.int32, &index.location)?;
                 }
+            } else {
+                // Treat anything else as an integer index, and force unify their type to int32.
+                self.unify(index.custom.unwrap(), self.primitives.int32, &index.location)?;
+                dims_to_subtract += 1;
             }
         }
 

nac3core/src/typecheck/typedef/mod.rs

@@ -342,14 +342,6 @@ impl Unifier {
         self.unification_table.unioned(a, b)
     }
 
-    /// Determine if a type unions with a type in `tys`.
-    pub fn unioned_any<I>(&mut self, a: Type, tys: I) -> bool
-    where
-        I: IntoIterator<Item = Type>,
-    {
-        tys.into_iter().any(|ty| self.unioned(a, ty))
-    }
-
     pub fn from_shared_unifier(unifier: &SharedUnifier) -> Unifier {
         let lock = unifier.lock().unwrap();
         Unifier {

nac3standalone/demo/interpret_demo.py

@@ -218,16 +218,8 @@ def patch(module):
     module.np_ldexp = np.ldexp
     module.np_hypot = np.hypot
     module.np_nextafter = np.nextafter
-
-    # NumPy view functions
-    module.np_broadcast_to = np.broadcast_to
-    module.np_reshape = np.reshape
     module.np_transpose = np.transpose
-
+    module.np_reshape = np.reshape
-    # NumPy NDArray property getter functions
-    module.np_size = np.size
-    module.np_shape = np.shape
-    module.np_strides = lambda ndarray: ndarray.strides
 
     # SciPy Math functions
     module.sp_spec_erf = special.erf

nac3standalone/src/main.rs

@@ -14,7 +14,6 @@ use inkwell::{
     memory_buffer::MemoryBuffer, passes::PassBuilderOptions, support::is_multithreaded, targets::*,
     OptimizationLevel,
 };
-use nac3core::codegen::irrt::setup_irrt_exceptions;
 use nac3core::{
     codegen::{
         concrete_type::ConcreteTypeStore, irrt::load_irrt, CodeGenLLVMOptions,
@@ -315,16 +314,6 @@ fn main() {
     let resolver =
         Arc::new(Resolver(internal_resolver.clone())) as Arc<dyn SymbolResolver + Send + Sync>;
 
-    let context = inkwell::context::Context::create();
-
-    // Process IRRT
-    let irrt = load_irrt(&context);
-    setup_irrt_exceptions(&context, &irrt, resolver.as_ref());
-    if emit_llvm {
-        irrt.write_bitcode_to_path(Path::new("irrt.bc"));
-    }
-
-    // Process the Python script
     let parser_result = parser::parse_program(&program, file_name.into()).unwrap();
 
     for stmt in parser_result {
@@ -429,8 +418,8 @@ fn main() {
     registry.add_task(task);
     registry.wait_tasks_complete(handles);
 
-    // Link all modules together into `main`
     let buffers = membuffers.lock();
+    let context = inkwell::context::Context::create();
     let main = context
         .create_module_from_ir(MemoryBuffer::create_from_memory_range(&buffers[0], "main"))
         .unwrap();
@@ -450,9 +439,12 @@ fn main() {
         main.link_in_module(other).unwrap();
     }
 
+    let irrt = load_irrt(&context);
+    if emit_llvm {
+        irrt.write_bitcode_to_path(Path::new("irrt.bc"));
+    }
     main.link_in_module(irrt).unwrap();
 
-    // Private all functions except "run"
     let mut function_iter = main.get_first_function();
     while let Some(func) = function_iter {
         if func.count_basic_blocks() > 0 && func.get_name().to_str().unwrap() != "run" {
@@ -461,7 +453,6 @@ fn main() {
         function_iter = func.get_next_function();
     }
 
-    // Optimize `main`
     let target_machine = llvm_options
         .target
         .create_target_machine(llvm_options.opt_level)
@@ -475,7 +466,6 @@ fn main() {
         panic!("Failed to run optimization for module `main`: {}", err.to_string());
     }
 
-    // Write output
     target_machine
         .write_to_file(&main, FileType::Object, Path::new("module.o"))
         .expect("couldn't write module to file");