a

Needed by ndarray indexing

.clang-format

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

Cargo.lock

@@ -117,9 +117,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
 
 [[package]]
 name = "cc"
-version = "1.1.0"
+version = "1.1.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "eaff6f8ce506b9773fa786672d63fc7a191ffea1be33f72bbd4aeacefca9ffc8"
+checksum = "2aba8f4e9906c7ce3c73463f62a7f0c65183ada1a2d47e397cc8810827f9694f"
 
 [[package]]
 name = "cfg-if"
@@ -158,7 +158,7 @@ dependencies = [
  "heck 0.5.0",
  "proc-macro2",
  "quote",
- "syn 2.0.70",
+ "syn 2.0.71",
 ]
 
 [[package]]
@@ -421,7 +421,7 @@ checksum = "4fa4d8d74483041a882adaa9a29f633253a66dde85055f0495c121620ac484b2"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.70",
+ "syn 2.0.71",
 ]
 
 [[package]]
@@ -513,9 +513,9 @@ checksum = "97b3888a4aecf77e811145cadf6eef5901f4782c53886191b2f693f24761847c"
 
 [[package]]
 name = "libloading"
-version = "0.8.4"
+version = "0.8.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e310b3a6b5907f99202fcdb4960ff45b93735d7c7d96b760fcff8db2dc0e103d"
+checksum = "4979f22fdb869068da03c9f7528f8297c6fd2606bc3a4affe42e6a823fdb8da4"
 dependencies = [
  "cfg-if",
  "windows-targets",
@@ -749,7 +749,7 @@ dependencies = [
  "phf_shared 0.11.2",
  "proc-macro2",
  "quote",
- "syn 2.0.70",
+ "syn 2.0.71",
 ]
 
 [[package]]
@@ -778,9 +778,9 @@ checksum = "5be167a7af36ee22fe3115051bc51f6e6c7054c9348e28deb4f49bd6f705a315"
 
 [[package]]
 name = "portable-atomic"
-version = "1.6.0"
+version = "1.7.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7170ef9988bc169ba16dd36a7fa041e5c4cbeb6a35b76d4c03daded371eae7c0"
+checksum = "da544ee218f0d287a911e9c99a39a8c9bc8fcad3cb8db5959940044ecfc67265"
 
 [[package]]
 name = "ppv-lite86"
@@ -850,7 +850,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-macros-backend",
  "quote",
- "syn 2.0.70",
+ "syn 2.0.71",
 ]
 
 [[package]]
@@ -863,7 +863,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-build-config",
  "quote",
- "syn 2.0.70",
+ "syn 2.0.71",
 ]
 
 [[package]]
@@ -927,9 +927,9 @@ dependencies = [
 
 [[package]]
 name = "redox_syscall"
-version = "0.5.2"
+version = "0.5.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c82cf8cff14456045f55ec4241383baeff27af886adb72ffb2162f99911de0fd"
+checksum = "2a908a6e00f1fdd0dfd9c0eb08ce85126f6d8bbda50017e74bc4a4b7d4a926a4"
 dependencies = [
  "bitflags",
 ]
@@ -1044,7 +1044,7 @@ checksum = "e0cd7e117be63d3c3678776753929474f3b04a43a080c744d6b0ae2a8c28e222"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.70",
+ "syn 2.0.71",
 ]
 
 [[package]]
@@ -1134,7 +1134,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustversion",
- "syn 2.0.70",
+ "syn 2.0.71",
 ]
 
 [[package]]
@@ -1150,9 +1150,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.70"
+version = "2.0.71"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2f0209b68b3613b093e0ec905354eccaedcfe83b8cb37cbdeae64026c3064c16"
+checksum = "b146dcf730474b4bcd16c311627b31ede9ab149045db4d6088b3becaea046462"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -1203,22 +1203,22 @@ dependencies = [
 
 [[package]]
 name = "thiserror"
-version = "1.0.61"
+version = "1.0.63"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c546c80d6be4bc6a00c0f01730c08df82eaa7a7a61f11d656526506112cc1709"
+checksum = "c0342370b38b6a11b6cc11d6a805569958d54cfa061a29969c3b5ce2ea405724"
 dependencies = [
  "thiserror-impl",
 ]
 
 [[package]]
 name = "thiserror-impl"
-version = "1.0.61"
+version = "1.0.63"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "46c3384250002a6d5af4d114f2845d37b57521033f30d5c3f46c4d70e1197533"
+checksum = "a4558b58466b9ad7ca0f102865eccc95938dca1a74a856f2b57b6629050da261"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.70",
+ "syn 2.0.71",
 ]
 
 [[package]]
@@ -1486,5 +1486,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.70",
+ "syn 2.0.71",
 ]

flake.nix

@@ -151,7 +151,7 @@
         buildInputs = with pkgs; [
           # build dependencies
           packages.x86_64-linux.llvm-nac3
-          llvmPackages_14.clang llvmPackages_14.llvm.out  # for running nac3standalone demos
+          llvmPackages_14.clang llvmPackages_14.llvm.out llvmPackages_14.lldb.out # for running nac3standalone demos
           packages.x86_64-linux.llvm-tools-irrt
           cargo
           rustc
@@ -165,7 +165,6 @@
           rustfmt
           rust-analyzer
         ];
-        # https://nixos.wiki/wiki/Rust#Shell.nix_example
         RUST_SRC_PATH = "${pkgs.rust.packages.stable.rustPlatform.rustLibSrc}";
       };
       devShells.x86_64-linux.msys2 = pkgs.mkShell {

nac3artiq/demo/list_cmp.py

@@ -0,0 +1,24 @@
+from min_artiq import *
+from numpy import int32
+
+
+@nac3
+class EmptyList:
+    core: KernelInvariant[Core]
+
+    def __init__(self):
+        self.core = Core()
+
+    @rpc
+    def get_empty(self) -> list[int32]:
+        return []
+
+    @kernel
+    def run(self):
+        a: list[int32] = self.get_empty()
+        if a != []:
+            raise ValueError
+
+
+if __name__ == "__main__":
+    EmptyList().run()

nac3artiq/src/symbol_resolver.rs

@@ -991,8 +991,15 @@ impl InnerResolver {
                 }
                 _ => unreachable!("must be list"),
             };
-            let ty = ctx.get_llvm_type(generator, elem_ty);
             let size_t = generator.get_size_type(ctx.ctx);
+            let ty = if len == 0
+                && matches!(&*ctx.unifier.get_ty_immutable(elem_ty), TypeEnum::TVar { .. })
+            {
+                // The default type for zero-length lists of unknown element type is size_t
+                size_t.into()
+            } else {
+                ctx.get_llvm_type(generator, elem_ty)
+            };
             let arr_ty = ctx
                 .ctx
                 .struct_type(&[ty.ptr_type(AddressSpace::default()).into(), size_t.into()], false);

nac3core/build.rs

@@ -3,20 +3,34 @@ use std::{
     env,
     fs::File,
     io::Write,
-    path::Path,
+    path::{Path, PathBuf},
     process::{Command, Stdio},
 };
 
-fn compile_irrt(irrt_dir: &Path, out_dir: &Path) {
-    let irrt_cpp_path = irrt_dir.join("irrt.cpp");
+const CMD_IRRT_CLANG: &str = "clang-irrt";
+const CMD_IRRT_CLANG_TEST: &str = "clang-irrt-test";
+const CMD_IRRT_LLVM_AS: &str = "llvm-as-irrt";
+
+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();
 
     /*
      * 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",
-        irrt_cpp_path.to_str().unwrap(),
         "-x",
         "c++",
         "-fno-discard-value-names",
@@ -31,16 +45,18 @@ fn compile_irrt(irrt_dir: &Path, out_dir: &Path) {
         "-S",
         "-Wall",
         "-Wextra",
-        "-Werror=return-type",
-        "-I",
-        irrt_dir.to_str().unwrap(),
         "-o",
         "-",
+        "-I",
+        irrt_dir.to_str().unwrap(),
+        irrt_cpp_path.to_str().unwrap(),
     ];
 
-    println!("cargo:rerun-if-changed={}", out_dir.to_str().unwrap());
+    // Tell Cargo to rerun if any file under `irrt_dir` (recursive) changes
+    println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
 
-    let output = Command::new("clang-irrt")
+    // Compile IRRT and capture the LLVM IR output
+    let output = Command::new(CMD_IRRT_CLANG)
         .args(flags)
         .output()
         .map(|o| {
@@ -53,11 +69,17 @@ fn compile_irrt(irrt_dir: &Path, out_dir: &Path) {
     let output = std::str::from_utf8(&output.stdout).unwrap().replace("\r\n", "\n");
     let mut filtered_output = String::with_capacity(output.len());
 
-    // (?ms:^define.*?\}$) to capture `define` blocks
-    // (?m:^declare.*?$) to capture `declare` blocks
-    // (?m:^%.+?=\s*type\s*\{.+?\}$) to capture `type` declarations
-    let regex_filter =
-        Regex::new(r"(?ms:^define.*?\}$)|(?m:^declare.*?$)|(?m:^%.+?=\s*type\s*\{.+?\}$)").unwrap();
+    // Filter out irrelevant IR
+    //
+    // 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]);
@@ -68,15 +90,21 @@ fn compile_irrt(irrt_dir: &Path, out_dir: &Path) {
         .unwrap()
         .replace_all(&filtered_output, "");
 
-    println!("cargo:rerun-if-env-changed=DEBUG_DUMP_IRRT");
-    if env::var("DEBUG_DUMP_IRRT").is_ok() {
+    // For debugging
+    // Doing `DEBUG_DUMP_IRRT=1 cargo build -p nac3core` dumps the LLVM IR generated
+    const DEBUG_DUMP_IRRT: &str = "DEBUG_DUMP_IRRT";
+    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_dir.join("irrt-filtered.ll")).unwrap();
         file.write_all(filtered_output.as_bytes()).unwrap();
     }
 
-    let mut llvm_as = Command::new("llvm-as-irrt")
+    // Assemble the emitted and filtered IR to .bc
+    // That .bc will be integrated into nac3core's codegen
+    let mut llvm_as = Command::new(CMD_IRRT_LLVM_AS)
         .stdin(Stdio::piped())
         .arg("-o")
         .arg(out_dir.join("irrt.bc"))
@@ -86,10 +114,13 @@ fn compile_irrt(irrt_dir: &Path, out_dir: &Path) {
     assert!(llvm_as.wait().unwrap().success());
 }
 
-fn compile_irrt_test(irrt_dir: &Path, out_dir: &Path) {
-    let irrt_test_cpp_path = irrt_dir.join("irrt_test.cpp");
-    let exe_path = out_dir.join("irrt_test.out");
+/// 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",
@@ -107,7 +138,7 @@ fn compile_irrt_test(irrt_dir: &Path, out_dir: &Path) {
         exe_path.to_str().unwrap(),
     ];
 
-    Command::new("clang-irrt-test")
+    Command::new(CMD_IRRT_CLANG_TEST)
         .args(flags)
         .output()
         .map(|o| {
@@ -115,20 +146,15 @@ fn compile_irrt_test(irrt_dir: &Path, out_dir: &Path) {
             o
         })
         .unwrap();
-    println!("cargo:rerun-if-changed={}", out_dir.to_str().unwrap());
+    println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
 }
 
 fn main() {
-    let out_dir = env::var("OUT_DIR").unwrap();
-    let out_dir = Path::new(&out_dir);
-
-    let irrt_dir = Path::new("./irrt");
-
-    compile_irrt(irrt_dir, out_dir);
+    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(irrt_dir, out_dir);
+        compile_irrt_test_cpp();
     }
 }

nac3core/irrt/irrt.cpp

@@ -1,5 +1,10 @@
-#include "irrt_everything.hpp"
+#define IRRT_DEFINE_TYPEDEF_INTS
+#include <irrt_everything.hpp>
 
 /*
-    This file will be read by `clang-irrt` to conveniently produce LLVM IR for `nac3core/codegen`.
-*/
+ * 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.hpp

@@ -1,437 +0,0 @@
-#ifndef IRRT_DONT_TYPEDEF_INTS
-typedef _BitInt(8) int8_t;
-typedef unsigned _BitInt(8) uint8_t;
-typedef _BitInt(32) int32_t;
-typedef unsigned _BitInt(32) uint32_t;
-typedef _BitInt(64) int64_t;
-typedef unsigned _BitInt(64) uint64_t;
-#endif
-
-// NDArray indices are always `uint32_t`.
-typedef uint32_t NDIndex;
-// The type of an index or a value describing the length of a range/slice is
-// always `int32_t`.
-typedef int32_t SliceIndex;
-
-template <typename T>
-static T max(T a, T b) {
-    return a > b ? a : b;
-}
-
-template <typename T>
-static T min(T a, 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>
-static T __nac3_int_exp_impl(T base, T exp) {
-    T res = 1;
-    /* repeated squaring method */
-    do {
-        if (exp & 1) {
-            res *= base; /* for n odd */
-        }
-        exp >>= 1;
-        base *= base;
-    } while (exp);
-    return res;
-}
-
-template <typename SizeT>
-static SizeT __nac3_ndarray_calc_size_impl(
-    const SizeT *list_data,
-    SizeT list_len,
-    SizeT begin_idx,
-    SizeT end_idx
-) {
-    __builtin_assume(end_idx <= list_len);
-
-    SizeT num_elems = 1;
-    for (SizeT i = begin_idx; i < end_idx; ++i) {
-        SizeT val = list_data[i];
-        __builtin_assume(val > 0);
-        num_elems *= val;
-    }
-    return num_elems;
-}
-
-template <typename SizeT>
-static void __nac3_ndarray_calc_nd_indices_impl(
-    SizeT index,
-    const SizeT *dims,
-    SizeT num_dims,
-    NDIndex *idxs
-) {
-    SizeT stride = 1;
-    for (SizeT dim = 0; dim < num_dims; dim++) {
-        SizeT i = num_dims - dim - 1;
-        __builtin_assume(dims[i] > 0);
-        idxs[i] = (index / stride) % dims[i];
-        stride *= dims[i];
-    }
-}
-
-template <typename SizeT>
-static SizeT __nac3_ndarray_flatten_index_impl(
-    const SizeT *dims,
-    SizeT num_dims,
-    const NDIndex *indices,
-    SizeT num_indices
-) {
-    SizeT idx = 0;
-    SizeT stride = 1;
-    for (SizeT i = 0; i < num_dims; ++i) {
-        SizeT ri = num_dims - i - 1;
-        if (ri < num_indices) {
-            idx += stride * indices[ri];
-        }
-
-        __builtin_assume(dims[i] > 0);
-        stride *= dims[ri];
-    }
-    return idx;
-}
-
-template <typename SizeT>
-static void __nac3_ndarray_calc_broadcast_impl(
-    const SizeT *lhs_dims,
-    SizeT lhs_ndims,
-    const SizeT *rhs_dims,
-    SizeT rhs_ndims,
-    SizeT *out_dims
-) {
-    SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
-
-    for (SizeT i = 0; i < max_ndims; ++i) {
-        const SizeT *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
-        const SizeT *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
-        SizeT *out_dim = &out_dims[max_ndims - i - 1];
-
-        if (lhs_dim_sz == nullptr) {
-            *out_dim = *rhs_dim_sz;
-        } else if (rhs_dim_sz == nullptr) {
-            *out_dim = *lhs_dim_sz;
-        } else if (*lhs_dim_sz == 1) {
-            *out_dim = *rhs_dim_sz;
-        } else if (*rhs_dim_sz == 1) {
-            *out_dim = *lhs_dim_sz;
-        } else if (*lhs_dim_sz == *rhs_dim_sz) {
-            *out_dim = *lhs_dim_sz;
-        } else {
-            __builtin_unreachable();
-        }
-    }
-}
-
-template <typename SizeT>
-static void __nac3_ndarray_calc_broadcast_idx_impl(
-    const SizeT *src_dims,
-    SizeT src_ndims,
-    const NDIndex *in_idx,
-    NDIndex *out_idx
-) {
-    for (SizeT i = 0; i < src_ndims; ++i) {
-        SizeT src_i = src_ndims - i - 1;
-        out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
-    }
-}
-
-template<typename SizeT>
-static void __nac3_ndarray_strides_from_shape_impl(
-    SizeT ndims,
-    SizeT *shape,
-    SizeT *dst_strides
-) {
-    SizeT stride_product = 1;
-    for (SizeT i = 0; i < ndims; i++) {
-        int dim_i = ndims - i - 1;
-        dst_strides[dim_i] = stride_product;
-        stride_product *= shape[dim_i];
-    }
-}
-
-extern "C" {
-    #define DEF_nac3_int_exp_(T) \
-        T __nac3_int_exp_##T(T base, T exp) {\
-            return __nac3_int_exp_impl(base, exp);\
-        }
-
-    DEF_nac3_int_exp_(int32_t)
-    DEF_nac3_int_exp_(int64_t)
-    DEF_nac3_int_exp_(uint32_t)
-    DEF_nac3_int_exp_(uint64_t)
-
-    SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
-        if (i < 0) {
-            i = len + i;
-        }
-        if (i < 0) {
-            return 0;
-        } else if (i > len) {
-            return len;
-        }
-        return i;
-    }
-
-    SliceIndex __nac3_range_slice_len(
-        const SliceIndex start,
-        const SliceIndex end,
-        const SliceIndex step
-    ) {
-        SliceIndex diff = end - start;
-        if (diff > 0 && step > 0) {
-            return ((diff - 1) / step) + 1;
-        } else if (diff < 0 && step < 0) {
-            return ((diff + 1) / step) + 1;
-        } else {
-            return 0;
-        }
-    }
-
-    // Handle list assignment and dropping part of the list when
-    // both dest_step and src_step are +1.
-    // - All the index must *not* be out-of-bound or negative,
-    // - The end index is *inclusive*,
-    // - The length of src and dest slice size should already
-    // be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
-    SliceIndex __nac3_list_slice_assign_var_size(
-        SliceIndex dest_start,
-        SliceIndex dest_end,
-        SliceIndex dest_step,
-        uint8_t *dest_arr,
-        SliceIndex dest_arr_len,
-        SliceIndex src_start,
-        SliceIndex src_end,
-        SliceIndex src_step,
-        uint8_t *src_arr,
-        SliceIndex src_arr_len,
-        const SliceIndex size
-    ) {
-        /* if dest_arr_len == 0, do nothing since we do not support extending list */
-        if (dest_arr_len == 0) return dest_arr_len;
-        /* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
-        if (src_step == dest_step && dest_step == 1) {
-            const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
-            const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
-            if (src_len > 0) {
-                __builtin_memmove(
-                    dest_arr + dest_start * size,
-                    src_arr + src_start * size,
-                    src_len * size
-                );
-            }
-            if (dest_len > 0) {
-                /* dropping */
-                __builtin_memmove(
-                    dest_arr + (dest_start + src_len) * size,
-                    dest_arr + (dest_end + 1) * size,
-                    (dest_arr_len - dest_end - 1) * size
-                );
-            }
-            /* shrink size */
-            return dest_arr_len - (dest_len - src_len);
-        }
-        /* if two range overlaps, need alloca */
-        uint8_t need_alloca =
-            (dest_arr == src_arr)
-            && !(
-                max(dest_start, dest_end) < min(src_start, src_end)
-                || max(src_start, src_end) < min(dest_start, dest_end)
-            );
-        if (need_alloca) {
-            uint8_t *tmp = reinterpret_cast<uint8_t *>(__builtin_alloca(src_arr_len * size));
-            __builtin_memcpy(tmp, src_arr, src_arr_len * size);
-            src_arr = tmp;
-        }
-        SliceIndex src_ind = src_start;
-        SliceIndex dest_ind = dest_start;
-        for (;
-            (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
-            src_ind += src_step, dest_ind += dest_step
-        ) {
-            /* for constant optimization */
-            if (size == 1) {
-                __builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
-            } else if (size == 4) {
-                __builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
-            } else if (size == 8) {
-                __builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
-            } else {
-                /* memcpy for var size, cannot overlap after previous alloca */
-                __builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
-            }
-        }
-        /* only dest_step == 1 can we shrink the dest list. */
-        /* size should be ensured prior to calling this function */
-        if (dest_step == 1 && dest_end >= dest_start) {
-            __builtin_memmove(
-                dest_arr + dest_ind * size,
-                dest_arr + (dest_end + 1) * size,
-                (dest_arr_len - dest_end - 1) * size
-            );
-            return dest_arr_len - (dest_end - dest_ind) - 1;
-        }
-        return dest_arr_len;
-    }
-
-    int32_t __nac3_isinf(double x) {
-        return __builtin_isinf(x);
-    }
-
-    int32_t __nac3_isnan(double x) {
-        return __builtin_isnan(x);
-    }
-
-    double tgamma(double arg);
-
-    double __nac3_gamma(double z) {
-        // Handling for denormals
-        //     | x                 | Python gamma(x) | C tgamma(x) |
-        // --- | ----------------- | --------------- | ----------- |
-        // (1) | nan               | nan             | nan         |
-        // (2) | -inf              | -inf            | inf         |
-        // (3) | inf               | inf             | inf         |
-        // (4) | 0.0               | inf             | inf         |
-        // (5) | {-1.0, -2.0, ...} | inf             | nan         |
-
-        // (1)-(3)
-        if (__builtin_isinf(z) || __builtin_isnan(z)) {
-            return z;
-        }
-
-        double v = tgamma(z);
-
-        // (4)-(5)
-        return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
-    }
-
-    double lgamma(double arg);
-
-    double __nac3_gammaln(double x) {
-        // libm's handling of value overflows differs from scipy:
-        // - scipy: gammaln(-inf) -> -inf
-        // - libm : lgamma(-inf) -> inf
-
-        if (__builtin_isinf(x)) {
-            return x;
-        }
-
-        return lgamma(x);
-    }
-
-    double j0(double x);
-
-    double __nac3_j0(double x) {
-        // libm's handling of value overflows differs from scipy:
-        // - scipy: j0(inf) -> nan
-        // - libm : j0(inf) -> 0.0
-
-        if (__builtin_isinf(x)) {
-            return __builtin_nan("");
-        }
-
-        return j0(x);
-    }
-
-    uint32_t __nac3_ndarray_calc_size(
-        const uint32_t *list_data,
-        uint32_t list_len,
-        uint32_t begin_idx,
-        uint32_t end_idx
-    ) {
-        return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
-    }
-
-    uint64_t __nac3_ndarray_calc_size64(
-        const uint64_t *list_data,
-        uint64_t list_len,
-        uint64_t begin_idx,
-        uint64_t end_idx
-    ) {
-        return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx, end_idx);
-    }
-
-    void __nac3_ndarray_calc_nd_indices(
-        uint32_t index,
-        const uint32_t* dims,
-        uint32_t num_dims,
-        NDIndex* idxs
-    ) {
-        __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
-    }
-
-    void __nac3_ndarray_calc_nd_indices64(
-        uint64_t index,
-        const uint64_t* dims,
-        uint64_t num_dims,
-        NDIndex* idxs
-    ) {
-        __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
-    }
-
-    uint32_t __nac3_ndarray_flatten_index(
-        const uint32_t* dims,
-        uint32_t num_dims,
-        const NDIndex* indices,
-        uint32_t num_indices
-    ) {
-        return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
-    }
-
-    uint64_t __nac3_ndarray_flatten_index64(
-        const uint64_t* dims,
-        uint64_t num_dims,
-        const NDIndex* indices,
-        uint64_t num_indices
-    ) {
-        return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices, num_indices);
-    }
-
-    void __nac3_ndarray_calc_broadcast(
-        const uint32_t *lhs_dims,
-        uint32_t lhs_ndims,
-        const uint32_t *rhs_dims,
-        uint32_t rhs_ndims,
-        uint32_t *out_dims
-    ) {
-        return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
-    }
-
-    void __nac3_ndarray_calc_broadcast64(
-        const uint64_t *lhs_dims,
-        uint64_t lhs_ndims,
-        const uint64_t *rhs_dims,
-        uint64_t rhs_ndims,
-        uint64_t *out_dims
-    ) {
-        return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims, rhs_ndims, out_dims);
-    }
-
-    void __nac3_ndarray_calc_broadcast_idx(
-        const uint32_t *src_dims,
-        uint32_t src_ndims,
-        const NDIndex *in_idx,
-        NDIndex *out_idx
-    ) {
-        __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
-    }
-
-    void __nac3_ndarray_calc_broadcast_idx64(
-        const uint64_t *src_dims,
-        uint64_t src_ndims,
-        const NDIndex *in_idx,
-        NDIndex *out_idx
-    ) {
-        __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx, out_idx);
-    }
-
-    void __nac3_ndarray_strides_from_shape(uint32_t ndims, uint32_t* shape, uint32_t* dst_strides) {
-        __nac3_ndarray_strides_from_shape_impl(ndims, shape, dst_strides);
-    }
-
-    void __nac3_ndarray_strides_from_shape64(uint64_t ndims, uint64_t* shape, uint64_t* dst_strides) {
-        __nac3_ndarray_strides_from_shape_impl(ndims, shape, dst_strides);
-    }
-}

nac3core/irrt/irrt/artiq_defs.hpp

@@ -0,0 +1,39 @@
+#pragma once
+
+#include <irrt/int_defs.hpp>
+
+/*
+This file defines all ARTIQ-specific structures
+*/
+
+/**
+ * @brief ARTIQ's `cslice` object
+ *
+ * See https://docs.rs/cslice/0.3.0/src/cslice/lib.rs.html#33-37
+ */
+template <typename SizeT>
+struct CSlice {
+    const char *base;
+    SizeT len;
+};
+
+/**
+ * @brief Int type of ARTIQ's `Exception` IDs.
+ */
+typedef uint32_t ExceptionId;
+
+/**
+ * @brief ARTIQ's `Exception` object
+ *
+ * See https://github.com/m-labs/artiq/blob/b0d2705c385f64b6e6711c1726cd9178f40b598e/artiq/firmware/libeh/eh_artiq.rs#L1C1-L17C1
+ */
+template <typename SizeT>
+struct Exception {
+    ExceptionId id;
+    CSlice<SizeT> file;
+    uint32_t line;
+    uint32_t column;
+    CSlice<SizeT> function;
+    CSlice<SizeT> message;
+    uint32_t param;
+};

nac3core/irrt/irrt/core.hpp

@@ -0,0 +1,347 @@
+#pragma once
+
+#include <irrt/int_defs.hpp>
+#include <irrt/slice.hpp>
+#include <irrt/utils.hpp>
+
+// NDArray indices are always `uint32_t`.
+using NDIndexInt = uint32_t;
+
+namespace {
+// adapted from GNU Scientific Library:
+// https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
+// need to make sure `exp >= 0` before calling this function
+template <typename T>
+T __nac3_int_exp_impl(T base, T exp) {
+    T res = 1;
+    /* repeated squaring method */
+    do {
+        if (exp & 1) {
+            res *= base; /* for n odd */
+        }
+        exp >>= 1;
+        base *= base;
+    } while (exp);
+    return res;
+}
+
+template <typename SizeT>
+SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len,
+                                    SizeT begin_idx, SizeT end_idx) {
+    __builtin_assume(end_idx <= list_len);
+
+    SizeT num_elems = 1;
+    for (SizeT i = begin_idx; i < end_idx; ++i) {
+        SizeT val = list_data[i];
+        __builtin_assume(val > 0);
+        num_elems *= val;
+    }
+    return num_elems;
+}
+
+template <typename SizeT>
+void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims,
+                                         SizeT num_dims, NDIndexInt* idxs) {
+    SizeT stride = 1;
+    for (SizeT dim = 0; dim < num_dims; dim++) {
+        SizeT i = num_dims - dim - 1;
+        __builtin_assume(dims[i] > 0);
+        idxs[i] = (index / stride) % dims[i];
+        stride *= dims[i];
+    }
+}
+
+template <typename SizeT>
+SizeT __nac3_ndarray_flatten_index_impl(const SizeT* dims, SizeT num_dims,
+                                        const NDIndexInt* indices,
+                                        SizeT num_indices) {
+    SizeT idx = 0;
+    SizeT stride = 1;
+    for (SizeT i = 0; i < num_dims; ++i) {
+        SizeT ri = num_dims - i - 1;
+        if (ri < num_indices) {
+            idx += stride * indices[ri];
+        }
+
+        __builtin_assume(dims[i] > 0);
+        stride *= dims[ri];
+    }
+    return idx;
+}
+
+template <typename SizeT>
+void __nac3_ndarray_calc_broadcast_impl(const SizeT* lhs_dims, SizeT lhs_ndims,
+                                        const SizeT* rhs_dims, SizeT rhs_ndims,
+                                        SizeT* out_dims) {
+    SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
+
+    for (SizeT i = 0; i < max_ndims; ++i) {
+        const SizeT* lhs_dim_sz =
+            i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
+        const SizeT* rhs_dim_sz =
+            i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
+
+        SizeT* out_dim = &out_dims[max_ndims - i - 1];
+
+        if (lhs_dim_sz == nullptr) {
+            *out_dim = *rhs_dim_sz;
+        } else if (rhs_dim_sz == nullptr) {
+            *out_dim = *lhs_dim_sz;
+        } else if (*lhs_dim_sz == 1) {
+            *out_dim = *rhs_dim_sz;
+        } else if (*rhs_dim_sz == 1) {
+            *out_dim = *lhs_dim_sz;
+        } else if (*lhs_dim_sz == *rhs_dim_sz) {
+            *out_dim = *lhs_dim_sz;
+        } else {
+            __builtin_unreachable();
+        }
+    }
+}
+
+template <typename SizeT>
+void __nac3_ndarray_calc_broadcast_idx_impl(const SizeT* src_dims,
+                                            SizeT src_ndims,
+                                            const NDIndexInt* in_idx,
+                                            NDIndexInt* out_idx) {
+    for (SizeT i = 0; i < src_ndims; ++i) {
+        SizeT src_i = src_ndims - i - 1;
+        out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
+    }
+}
+}  // namespace
+
+extern "C" {
+#define DEF_nac3_int_exp_(T)                   \
+    T __nac3_int_exp_##T(T base, T exp) {      \
+        return __nac3_int_exp_impl(base, exp); \
+    }
+
+DEF_nac3_int_exp_(int32_t);
+DEF_nac3_int_exp_(int64_t);
+DEF_nac3_int_exp_(uint32_t);
+DEF_nac3_int_exp_(uint64_t);
+
+SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
+    if (i < 0) {
+        i = len + i;
+    }
+    if (i < 0) {
+        return 0;
+    } else if (i > len) {
+        return len;
+    }
+    return i;
+}
+
+SliceIndex __nac3_range_slice_len(const SliceIndex start, const SliceIndex end,
+                                  const SliceIndex step) {
+    SliceIndex diff = end - start;
+    if (diff > 0 && step > 0) {
+        return ((diff - 1) / step) + 1;
+    } else if (diff < 0 && step < 0) {
+        return ((diff + 1) / step) + 1;
+    } else {
+        return 0;
+    }
+}
+
+// Handle list assignment and dropping part of the list when
+// both dest_step and src_step are +1.
+// - All the index must *not* be out-of-bound or negative,
+// - The end index is *inclusive*,
+// - The length of src and dest slice size should already
+// be checked: if dest.step == 1 then len(src) <= len(dest) else
+// len(src) == len(dest)
+SliceIndex __nac3_list_slice_assign_var_size(
+    SliceIndex dest_start, SliceIndex dest_end, SliceIndex dest_step,
+    uint8_t* dest_arr, SliceIndex dest_arr_len, SliceIndex src_start,
+    SliceIndex src_end, SliceIndex src_step, uint8_t* src_arr,
+    SliceIndex src_arr_len, const SliceIndex size) {
+    /* if dest_arr_len == 0, do nothing since we do not support
+     * extending list
+     */
+    if (dest_arr_len == 0) return dest_arr_len;
+    /* if both step is 1, memmove directly, handle the dropping of
+     * the list, and shrink size */
+    if (src_step == dest_step && dest_step == 1) {
+        const SliceIndex src_len =
+            (src_end >= src_start) ? (src_end - src_start + 1) : 0;
+        const SliceIndex dest_len =
+            (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
+        if (src_len > 0) {
+            __builtin_memmove(dest_arr + dest_start * size,
+                              src_arr + src_start * size, src_len * size);
+        }
+        if (dest_len > 0) {
+            /* dropping */
+            __builtin_memmove(dest_arr + (dest_start + src_len) * size,
+                              dest_arr + (dest_end + 1) * size,
+                              (dest_arr_len - dest_end - 1) * size);
+        }
+        /* shrink size */
+        return dest_arr_len - (dest_len - src_len);
+    }
+    /* if two range overlaps, need alloca */
+    uint8_t need_alloca =
+        (dest_arr == src_arr) &&
+        !(max(dest_start, dest_end) < min(src_start, src_end) ||
+          max(src_start, src_end) < min(dest_start, dest_end));
+    if (need_alloca) {
+        uint8_t* tmp =
+            reinterpret_cast<uint8_t*>(__builtin_alloca(src_arr_len * size));
+        __builtin_memcpy(tmp, src_arr, src_arr_len * size);
+        src_arr = tmp;
+    }
+    SliceIndex src_ind = src_start;
+    SliceIndex dest_ind = dest_start;
+    for (; (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
+         src_ind += src_step, dest_ind += dest_step) {
+        /* for constant optimization */
+        if (size == 1) {
+            __builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
+        } else if (size == 4) {
+            __builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
+        } else if (size == 8) {
+            __builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
+        } else {
+            /* memcpy for var size, cannot overlap after previous
+             * alloca */
+            __builtin_memcpy(dest_arr + dest_ind * size,
+                             src_arr + src_ind * size, size);
+        }
+    }
+    /* only dest_step == 1 can we shrink the dest list. */
+    /* size should be ensured prior to calling this function */
+    if (dest_step == 1 && dest_end >= dest_start) {
+        __builtin_memmove(
+            dest_arr + dest_ind * size, dest_arr + (dest_end + 1) * size,
+            (dest_arr_len - dest_end - 1) * size + size + size + size);
+        return dest_arr_len - (dest_end - dest_ind) - 1;
+    }
+    return dest_arr_len;
+}
+
+int32_t __nac3_isinf(double x) { return __builtin_isinf(x); }
+
+int32_t __nac3_isnan(double x) { return __builtin_isnan(x); }
+
+double tgamma(double arg);
+
+double __nac3_gamma(double z) {
+    // Handling for denormals
+    //     | x                 | Python gamma(x) | C tgamma(x) |
+    // --- | ----------------- | --------------- | ----------- |
+    // (1) | nan               | nan             | nan         |
+    // (2) | -inf              | -inf            | inf         |
+    // (3) | inf               | inf             | inf         |
+    // (4) | 0.0               | inf             | inf         |
+    // (5) | {-1.0, -2.0, ...} | inf             | nan         |
+
+    // (1)-(3)
+    if (__builtin_isinf(z) || __builtin_isnan(z)) {
+        return z;
+    }
+
+    double v = tgamma(z);
+
+    // (4)-(5)
+    return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
+}
+
+double lgamma(double arg);
+
+double __nac3_gammaln(double x) {
+    // libm's handling of value overflows differs from scipy:
+    // - scipy: gammaln(-inf) -> -inf
+    // - libm : lgamma(-inf) -> inf
+
+    if (__builtin_isinf(x)) {
+        return x;
+    }
+
+    return lgamma(x);
+}
+
+double j0(double x);
+
+double __nac3_j0(double x) {
+    // libm's handling of value overflows differs from scipy:
+    // - scipy: j0(inf) -> nan
+    // - libm : j0(inf) -> 0.0
+
+    if (__builtin_isinf(x)) {
+        return __builtin_nan("");
+    }
+
+    return j0(x);
+}
+
+uint32_t __nac3_ndarray_calc_size(const uint32_t* list_data, uint32_t list_len,
+                                  uint32_t begin_idx, uint32_t end_idx) {
+    return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx,
+                                         end_idx);
+}
+
+uint64_t __nac3_ndarray_calc_size64(const uint64_t* list_data,
+                                    uint64_t list_len, uint64_t begin_idx,
+                                    uint64_t end_idx) {
+    return __nac3_ndarray_calc_size_impl(list_data, list_len, begin_idx,
+                                         end_idx);
+}
+
+void __nac3_ndarray_calc_nd_indices(uint32_t index, const uint32_t* dims,
+                                    uint32_t num_dims, NDIndexInt* idxs) {
+    __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
+}
+
+void __nac3_ndarray_calc_nd_indices64(uint64_t index, const uint64_t* dims,
+                                      uint64_t num_dims, NDIndexInt* idxs) {
+    __nac3_ndarray_calc_nd_indices_impl(index, dims, num_dims, idxs);
+}
+
+uint32_t __nac3_ndarray_flatten_index(const uint32_t* dims, uint32_t num_dims,
+                                      const NDIndexInt* indices,
+                                      uint32_t num_indices) {
+    return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
+                                             num_indices);
+}
+
+uint64_t __nac3_ndarray_flatten_index64(const uint64_t* dims, uint64_t num_dims,
+                                        const NDIndexInt* indices,
+                                        uint64_t num_indices) {
+    return __nac3_ndarray_flatten_index_impl(dims, num_dims, indices,
+                                             num_indices);
+}
+
+void __nac3_ndarray_calc_broadcast(const uint32_t* lhs_dims, uint32_t lhs_ndims,
+                                   const uint32_t* rhs_dims, uint32_t rhs_ndims,
+                                   uint32_t* out_dims) {
+    return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims,
+                                              rhs_ndims, out_dims);
+}
+
+void __nac3_ndarray_calc_broadcast64(const uint64_t* lhs_dims,
+                                     uint64_t lhs_ndims,
+                                     const uint64_t* rhs_dims,
+                                     uint64_t rhs_ndims, uint64_t* out_dims) {
+    return __nac3_ndarray_calc_broadcast_impl(lhs_dims, lhs_ndims, rhs_dims,
+                                              rhs_ndims, out_dims);
+}
+
+void __nac3_ndarray_calc_broadcast_idx(const uint32_t* src_dims,
+                                       uint32_t src_ndims,
+                                       const NDIndexInt* in_idx,
+                                       NDIndexInt* out_idx) {
+    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
+                                           out_idx);
+}
+
+void __nac3_ndarray_calc_broadcast_idx64(const uint64_t* src_dims,
+                                         uint64_t src_ndims,
+                                         const NDIndexInt* in_idx,
+                                         NDIndexInt* out_idx) {
+    __nac3_ndarray_calc_broadcast_idx_impl(src_dims, src_ndims, in_idx,
+                                           out_idx);
+}
+}  // extern "C"

nac3core/irrt/irrt/error_context.hpp

@@ -0,0 +1,94 @@
+#pragma once
+
+#include <irrt/artiq_defs.hpp>
+#include <irrt/int_defs.hpp>
+#include <irrt/utils.hpp>
+
+namespace {
+/**
+ * @brief A (limited) set of known Error IDs
+ */
+struct ErrorIds {
+    ExceptionId index_error;
+    ExceptionId value_error;
+    ExceptionId assertion_error;
+    ExceptionId runtime_error;
+    ExceptionId type_error;
+};
+
+/**
+ * @brief The IRRT error context object
+ *
+ * This object contains all the details needed to propagate Python-like Exceptions in
+ * IRRT - within IRRT itself or propagate out of extern calls from nac3core.
+ */
+struct ErrorContext {
+    /**
+     * @brief The set of all
+     */
+    const ErrorIds *error_ids;
+
+    // Error thrown by IRRT
+    ExceptionId error_id;
+    const char *message_template;
+    uint64_t param1;
+    uint64_t param2;
+    uint64_t param3;
+
+    void initialize(const ErrorIds *error_ids) {
+        this->error_ids = error_ids;
+        clear_error();
+    }
+
+    void clear_error() {
+        // Point the message_template to an empty str. Don't set it to nullptr
+        // as a sentinel
+        this->message_template = "";
+    }
+
+    void set_error(ExceptionId error_id, const char *message,
+                   uint64_t param1 = 0, uint64_t param2 = 0,
+                   uint64_t param3 = 0) {
+        this->error_id = error_id;
+        this->message_template = message;
+        this->param1 = param1;
+        this->param2 = param2;
+        this->param3 = param3;
+    }
+
+    bool has_error() { return !cstr_utils::is_empty(message_template); }
+
+    template <typename SizeT>
+    void get_error_str(CSlice<SizeT> *dst_str) {
+        dst_str->base = message_template;
+        dst_str->len = (SizeT)cstr_utils::length(message_template);
+    }
+};
+}  // namespace
+
+extern "C" {
+void __nac3_error_context_initialize(ErrorContext *errctx,
+                                     ErrorIds *error_ids) {
+    errctx->initialize(error_ids);
+}
+
+bool __nac3_error_context_has_error(ErrorContext *errctx) {
+    return errctx->has_error();
+}
+
+void __nac3_error_context_get_error_str(ErrorContext *errctx,
+                                        CSlice<int32_t> *dst_str) {
+    errctx->get_error_str<int32_t>(dst_str);
+}
+
+void __nac3_error_context_get_error_str64(ErrorContext *errctx,
+                                          CSlice<int64_t> *dst_str) {
+    errctx->get_error_str<int64_t>(dst_str);
+}
+
+// Used for testing
+void __nac3_error_dummy_raise(ErrorContext *errctx) {
+    errctx->set_error(errctx->error_ids->runtime_error,
+                      "Error thrown from __nac3_error_dummy_raise");
+}
+}

nac3core/irrt/irrt/int_defs.hpp

@@ -0,0 +1,12 @@
+#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

@@ -0,0 +1,305 @@
+#pragma once
+
+#include <irrt/error_context.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(ErrorContext* errctx, SizeT ndims,
+                              const SizeT* shape) {
+    for (SizeT axis = 0; axis < ndims; axis++) {
+        if (shape[axis] < 0) {
+            errctx->set_error(errctx->error_ids->value_error,
+                              "negative dimensions are not allowed; axis {0} "
+                              "has dimension {1}",
+                              axis, shape[axis]);
+            return;
+        }
+    }
+}
+
+/**
+ * @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 (int32_t i = 0; i < ndims; i++) {
+        int32_t axis = ndims - i - 1;
+        int32_t 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 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 Return the pointer to the element indexed by `indices`.
+ */
+template <typename SizeT>
+uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray,
+                                 const SizeT* indices) {
+    uint8_t* element = ndarray->data;
+    for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
+        element += indices[dim_i] * ndarray->strides[dim_i];
+    return element;
+}
+
+/**
+ * @brief Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
+ */
+template <typename SizeT>
+uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
+    SizeT* indices = (SizeT*)__builtin_alloca(sizeof(SizeT) * ndarray->ndims);
+    util::set_indices_by_nth(ndarray->ndims, ndarray->shape, indices, nth);
+    return get_pelement_by_indices(ndarray, indices);
+}
+
+/**
+ * @brief Like `get_nth_pelement` but asserts that `nth` is in bounds.
+ */
+template <typename SizeT>
+uint8_t* checked_get_nth_pelement(ErrorContext* errctx,
+                                  const NDArray<SizeT>* ndarray, SizeT nth) {
+    SizeT arr_size = ndarray->size();
+    if (!(0 <= nth && nth < arr_size)) {
+        errctx->set_error(
+            errctx->error_ids->index_error,
+            "index {0} is out of bounds, valid range is {1} <= index < {2}",
+            nth, 0, arr_size);
+        return 0;
+    }
+    return get_nth_pelement(ndarray, nth);
+}
+
+/**
+ * @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 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>
+void len(ErrorContext* errctx, const NDArray<SizeT>* ndarray,
+         SliceIndex* dst_length) {
+    // numpy prohibits `__len__` on unsized objects
+    if (ndarray->ndims == 0) {
+        errctx->set_error(errctx->error_ids->type_error,
+                          "len() of unsized object");
+        return;
+    }
+
+    *dst_length = (SliceIndex)ndarray->shape[0];
+}
+
+/**
+ * @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) {
+    __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);
+    }
+}
+
+/**
+ * @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;
+}
+}  // namespace basic
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+using namespace ndarray::basic;
+
+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);
+}
+
+void __nac3_ndarray_len(ErrorContext* errctx, NDArray<int32_t>* ndarray,
+                        SliceIndex* dst_len) {
+    return len(errctx, ndarray, dst_len);
+}
+
+void __nac3_ndarray_len64(ErrorContext* errctx, NDArray<int64_t>* ndarray,
+                          SliceIndex* dst_len) {
+    return len(errctx, ndarray, dst_len);
+}
+
+void __nac3_ndarray_util_assert_shape_no_negative(ErrorContext* errctx,
+                                                  int32_t ndims,
+                                                  int32_t* shape) {
+    util::assert_shape_no_negative(errctx, ndims, shape);
+}
+
+void __nac3_ndarray_util_assert_shape_no_negative64(ErrorContext* errctx,
+                                                    int64_t ndims,
+                                                    int64_t* shape) {
+    util::assert_shape_no_negative(errctx, ndims, shape);
+}
+
+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);
+}
+
+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);
+}
+
+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/def.hpp

@@ -0,0 +1,44 @@
+#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/fill.hpp

@@ -0,0 +1,38 @@
+#pragma once
+
+#include <irrt/ndarray/basic.hpp>
+#include <irrt/ndarray/def.hpp>
+
+namespace {
+namespace ndarray {
+namespace fill {
+
+/**
+ * Fill an ndarray with a value.
+ *
+ * @param pvalue Pointer to the fill value, and the fill value should be of `ndarray->itemsize` bytes.
+ */
+template <typename SizeT>
+void fill_generic(NDArray<SizeT>* ndarray, const uint8_t* pvalue) {
+    const SizeT size = ndarray::basic::size(ndarray);
+    for (SizeT i = 0; i < size; i++) {
+        uint8_t* pelement = ndarray::basic::get_nth_pelement(
+            ndarray, i);  // No need for checked_get_nth_pelement
+        ndarray::basic::set_pelement_value(ndarray, pelement, pvalue);
+    }
+}
+}  // namespace fill
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+using namespace ndarray::fill;
+
+void __nac3_ndarray_fill_generic(NDArray<int32_t>* ndarray, uint8_t* pvalue) {
+    fill_generic(ndarray, pvalue);
+}
+
+void __nac3_ndarray_fill_generic64(NDArray<int64_t>* ndarray, uint8_t* pvalue) {
+    fill_generic(ndarray, pvalue);
+}
+}

nac3core/irrt/irrt/ndarray/indexing.hpp

@@ -0,0 +1,200 @@
+#pragma once
+
+#include <irrt/error_context.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
+ *
+ * See https://numpy.org/doc/stable/user/basics.indexing.html#single-element-indexing
+ *
+ * `data` points to a `SliceIndex`.
+ */
+const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
+/**
+ * @brief A slice index
+ *
+ * See https://numpy.org/doc/stable/user/basics.indexing.html#slicing-and-striding
+ *
+ * `data` points to a `UserRange`.
+ */
+const NDIndexType ND_INDEX_TYPE_SLICE = 1;
+
+/**
+ * @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`.
+ */
+template <typename SizeT>
+void deduce_ndims_after_indexing(ErrorContext* errctx, SizeT* final_ndims,
+                                 SizeT ndims, SizeT num_indexes,
+                                 const NDIndex* indexes) {
+    if (num_indexes > ndims) {
+        errctx->set_error(errctx->error_ids->index_error,
+                          "too many indices for array: array is "
+                          "{0}-dimensional, but {1} were indexed",
+                          ndims, num_indexes);
+        return;
+    }
+
+    *final_ndims = ndims;
+    for (SizeT i = 0; i < num_indexes; i++) {
+        if (indexes[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
+            // An index demotes the rank by 1
+            (*final_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(ErrorContext* errctx, SizeT num_indexes, const NDIndex* indexes,
+           const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
+    // Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
+
+    dst_ndarray->data = src_ndarray->data;
+    dst_ndarray->itemsize = src_ndarray->itemsize;
+
+    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) {
+                errctx->set_error(errctx->error_ids->index_error,
+                                  "index {0} is out of bounds for axis {1} "
+                                  "with size {2}",
+                                  input, src_axis,
+                                  src_ndarray->shape[src_axis]);
+                return;
+            }
+
+            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(errctx, src_ndarray->shape[src_axis],
+                                   &slice);
+            if (errctx->has_error()) {
+                return;
+            }
+
+            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 {
+            __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];
+    }
+}
+}  // namespace indexing
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+using namespace ndarray::indexing;
+
+void __nac3_ndarray_indexing_deduce_ndims_after_indexing(
+    ErrorContext* errctx, int32_t* result, int32_t ndims, int32_t num_indexes,
+    const NDIndex* indexes) {
+    ndarray::indexing::util::deduce_ndims_after_indexing(errctx, result, ndims,
+                                                         num_indexes, indexes);
+}
+
+void __nac3_ndarray_indexing_deduce_ndims_after_indexing64(
+    ErrorContext* errctx, int64_t* result, int64_t ndims, int64_t num_indexes,
+    const NDIndex* indexes) {
+    ndarray::indexing::util::deduce_ndims_after_indexing(errctx, result, ndims,
+                                                         num_indexes, indexes);
+}
+
+void __nac3_ndarray_index(ErrorContext* errctx, int32_t num_indexes,
+                          NDIndex* indexes, NDArray<int32_t>* src_ndarray,
+                          NDArray<int32_t>* dst_ndarray) {
+    index(errctx, num_indexes, indexes, src_ndarray, dst_ndarray);
+}
+
+void __nac3_ndarray_index64(ErrorContext* errctx, int64_t num_indexes,
+                            NDIndex* indexes, NDArray<int64_t>* src_ndarray,
+                            NDArray<int64_t>* dst_ndarray) {
+    index(errctx, num_indexes, indexes, src_ndarray, dst_ndarray);
+}
+}

nac3core/irrt/irrt/ndarray/reshape.hpp

@@ -0,0 +1,116 @@
+#pragma once
+
+#include <irrt/error_context.hpp>
+#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(ErrorContext* errctx, 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.
+                    errctx->set_error(errctx->error_ids->value_error,
+                                      "can only specify one unknown dimension");
+                    return;
+                } 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...
+
+                errctx->set_error(errctx->error_ids->value_error,
+                                  "Found negative dimension {0} on axis {1}",
+                                  dim, axis_i);
+                return;
+            }
+        } 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) {
+        errctx->set_error(errctx->error_ids->value_error,
+                          "cannot reshape array of size {0} into given shape",
+                          size);
+        return;
+    }
+}
+}  // namespace util
+}  // namespace reshape
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+
+void __nac3_ndarray_resolve_and_check_new_shape(ErrorContext* errctx,
+                                                int32_t size, int32_t new_ndims,
+                                                int32_t* new_shape) {
+    ndarray::reshape::util::resolve_and_check_new_shape(errctx, size, new_ndims,
+                                                        new_shape);
+}
+
+void __nac3_ndarray_resolve_and_check_new_shape64(ErrorContext* errctx,
+                                                  int64_t size,
+                                                  int64_t new_ndims,
+                                                  int64_t* new_shape) {
+    ndarray::reshape::util::resolve_and_check_new_shape(errctx, size, new_ndims,
+                                                        new_shape);
+}
+}

nac3core/irrt/irrt/ndarray/transpose.hpp

@@ -0,0 +1,162 @@
+#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(ErrorContext* errctx, SizeT ndims, SizeT num_axes,
+                           const SizeT* axes) {
+    /*
+     * TODO: `axes` can actually contain negative indices, but it is not documented in numpy.
+     *
+     * Supporting it for now.
+     */
+
+    if (ndims != num_axes) {
+        errctx->set_error(errctx->error_ids->value_error,
+                          "axes don't match array");
+        return;
+    }
+
+    // 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`
+            errctx->set_error(
+                errctx->error_ids->value_error,
+                "axis {0} is out of bounds for array of dimension {1}", axis,
+                ndims);
+            return;
+        }
+
+        if (axe_specified[axis]) {
+            errctx->set_error(errctx->error_ids->value_error,
+                              "repeated axis in transpose");
+            return;
+        }
+
+        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, can be undefined if `axes` is nullptr.
+ * @param axes Axes permutation. Set it to `nullptr` if `<axes>` is supposed to be `None`.
+ */
+template <typename SizeT>
+void transpose(ErrorContext* errctx, 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(errctx, ndims, num_axes, axes);
+        if (errctx->has_error()) return;
+    }
+
+    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(ErrorContext* errctx,
+                              const NDArray<int32_t>* src_ndarray,
+                              NDArray<int32_t>* dst_ndarray, int32_t num_axes,
+                              const int32_t* axes) {
+    transpose(errctx, src_ndarray, dst_ndarray, num_axes, axes);
+}
+
+void __nac3_ndarray_transpose64(ErrorContext* errctx,
+                                const NDArray<int64_t>* src_ndarray,
+                                NDArray<int64_t>* dst_ndarray, int64_t num_axes,
+                                const int64_t* axes) {
+    transpose(errctx, src_ndarray, dst_ndarray, num_axes, axes);
+}
+}

nac3core/irrt/irrt/slice.hpp

@@ -0,0 +1,165 @@
+#pragma once
+
+#include <irrt/error_context.hpp>
+#include <irrt/int_defs.hpp>
+#include <irrt/slice.hpp>
+#include <irrt/utils.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.
+     */
+    void indices_checked(ErrorContext* errctx, SliceIndex length,
+                         Slice* result) {
+        if (length < 0) {
+            errctx->set_error(errctx->error_ids->value_error,
+                              "length should not be negative, got {0}", length);
+            return;
+        }
+
+        if (this->step_defined && this->step == 0) {
+            errctx->set_error(errctx->error_ids->value_error,
+                              "slice step cannot be zero");
+            return;
+        }
+
+        *result = this->indices(length);
+    }
+};
+}  // namespace

nac3core/irrt/irrt/utils.hpp

@@ -0,0 +1,104 @@
+#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;
+}
+
+/**
+ * @brief Compare contents of two arrays with the same length.
+ */
+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_basic.hpp

@@ -1,216 +0,0 @@
-#pragma once
-
-#include "irrt_utils.hpp"
-#include "irrt_typedefs.hpp"
-
-/*
-    This header contains IRRT implementations
-    that do not deserved to be categorized (e.g., into numpy, etc.)
-
-    Check out other *.hpp files before including them here!!
-*/
-
-// The type of an index or a value describing the length of a range/slice is
-// always `int32_t`.
-
-namespace {
-    // adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
-    // need to make sure `exp >= 0` before calling this function
-    template <typename T>
-    T __nac3_int_exp_impl(T base, T exp) {
-        T res = 1;
-        /* repeated squaring method */
-        do {
-            if (exp & 1) {
-                res *= base; /* for n odd */
-            }
-            exp >>= 1;
-            base *= base;
-        } while (exp);
-        return res;
-    }
-}
-
-extern "C" {
-    #define DEF_nac3_int_exp_(T) \
-        T __nac3_int_exp_##T(T base, T exp) {\
-            return __nac3_int_exp_impl(base, exp);\
-        }
-
-    DEF_nac3_int_exp_(int32_t)
-    DEF_nac3_int_exp_(int64_t)
-    DEF_nac3_int_exp_(uint32_t)
-    DEF_nac3_int_exp_(uint64_t)
-
-    SliceIndex __nac3_slice_index_bound(SliceIndex i, const SliceIndex len) {
-        if (i < 0) {
-            i = len + i;
-        }
-        if (i < 0) {
-            return 0;
-        } else if (i > len) {
-            return len;
-        }
-        return i;
-    }
-
-    SliceIndex __nac3_range_slice_len(
-        const SliceIndex start,
-        const SliceIndex end,
-        const SliceIndex step
-    ) {
-        SliceIndex diff = end - start;
-        if (diff > 0 && step > 0) {
-            return ((diff - 1) / step) + 1;
-        } else if (diff < 0 && step < 0) {
-            return ((diff + 1) / step) + 1;
-        } else {
-            return 0;
-        }
-    }
-
-    // Handle list assignment and dropping part of the list when
-    // both dest_step and src_step are +1.
-    // - All the index must *not* be out-of-bound or negative,
-    // - The end index is *inclusive*,
-    // - The length of src and dest slice size should already
-    // be checked: if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest)
-    SliceIndex __nac3_list_slice_assign_var_size(
-        SliceIndex dest_start,
-        SliceIndex dest_end,
-        SliceIndex dest_step,
-        uint8_t *dest_arr,
-        SliceIndex dest_arr_len,
-        SliceIndex src_start,
-        SliceIndex src_end,
-        SliceIndex src_step,
-        uint8_t *src_arr,
-        SliceIndex src_arr_len,
-        const SliceIndex size
-    ) {
-        /* if dest_arr_len == 0, do nothing since we do not support extending list */
-        if (dest_arr_len == 0) return dest_arr_len;
-        /* if both step is 1, memmove directly, handle the dropping of the list, and shrink size */
-        if (src_step == dest_step && dest_step == 1) {
-            const SliceIndex src_len = (src_end >= src_start) ? (src_end - src_start + 1) : 0;
-            const SliceIndex dest_len = (dest_end >= dest_start) ? (dest_end - dest_start + 1) : 0;
-            if (src_len > 0) {
-                __builtin_memmove(
-                    dest_arr + dest_start * size,
-                    src_arr + src_start * size,
-                    src_len * size
-                );
-            }
-            if (dest_len > 0) {
-                /* dropping */
-                __builtin_memmove(
-                    dest_arr + (dest_start + src_len) * size,
-                    dest_arr + (dest_end + 1) * size,
-                    (dest_arr_len - dest_end - 1) * size
-                );
-            }
-            /* shrink size */
-            return dest_arr_len - (dest_len - src_len);
-        }
-        /* if two range overlaps, need alloca */
-        uint8_t need_alloca =
-            (dest_arr == src_arr)
-            && !(
-                max(dest_start, dest_end) < min(src_start, src_end)
-                || max(src_start, src_end) < min(dest_start, dest_end)
-            );
-        if (need_alloca) {
-            uint8_t *tmp = reinterpret_cast<uint8_t *>(__builtin_alloca(src_arr_len * size));
-            __builtin_memcpy(tmp, src_arr, src_arr_len * size);
-            src_arr = tmp;
-        }
-        SliceIndex src_ind = src_start;
-        SliceIndex dest_ind = dest_start;
-        for (;
-            (src_step > 0) ? (src_ind <= src_end) : (src_ind >= src_end);
-            src_ind += src_step, dest_ind += dest_step
-        ) {
-            /* for constant optimization */
-            if (size == 1) {
-                __builtin_memcpy(dest_arr + dest_ind, src_arr + src_ind, 1);
-            } else if (size == 4) {
-                __builtin_memcpy(dest_arr + dest_ind * 4, src_arr + src_ind * 4, 4);
-            } else if (size == 8) {
-                __builtin_memcpy(dest_arr + dest_ind * 8, src_arr + src_ind * 8, 8);
-            } else {
-                /* memcpy for var size, cannot overlap after previous alloca */
-                __builtin_memcpy(dest_arr + dest_ind * size, src_arr + src_ind * size, size);
-            }
-        }
-        /* only dest_step == 1 can we shrink the dest list. */
-        /* size should be ensured prior to calling this function */
-        if (dest_step == 1 && dest_end >= dest_start) {
-            __builtin_memmove(
-                dest_arr + dest_ind * size,
-                dest_arr + (dest_end + 1) * size,
-                (dest_arr_len - dest_end - 1) * size
-            );
-            return dest_arr_len - (dest_end - dest_ind) - 1;
-        }
-        return dest_arr_len;
-    }
-
-    int32_t __nac3_isinf(double x) {
-        return __builtin_isinf(x);
-    }
-
-    int32_t __nac3_isnan(double x) {
-        return __builtin_isnan(x);
-    }
-
-    double tgamma(double arg);
-
-    double __nac3_gamma(double z) {
-        // Handling for denormals
-        //     | x                 | Python gamma(x) | C tgamma(x) |
-        // --- | ----------------- | --------------- | ----------- |
-        // (1) | nan               | nan             | nan         |
-        // (2) | -inf              | -inf            | inf         |
-        // (3) | inf               | inf             | inf         |
-        // (4) | 0.0               | inf             | inf         |
-        // (5) | {-1.0, -2.0, ...} | inf             | nan         |
-
-        // (1)-(3)
-        if (__builtin_isinf(z) || __builtin_isnan(z)) {
-            return z;
-        }
-
-        double v = tgamma(z);
-
-        // (4)-(5)
-        return __builtin_isinf(v) || __builtin_isnan(v) ? __builtin_inf() : v;
-    }
-
-    double lgamma(double arg);
-
-    double __nac3_gammaln(double x) {
-        // libm's handling of value overflows differs from scipy:
-        // - scipy: gammaln(-inf) -> -inf
-        // - libm : lgamma(-inf) -> inf
-
-        if (__builtin_isinf(x)) {
-            return x;
-        }
-
-        return lgamma(x);
-    }
-
-    double j0(double x);
-
-    double __nac3_j0(double x) {
-        // libm's handling of value overflows differs from scipy:
-        // - scipy: j0(inf) -> nan
-        // - libm : j0(inf) -> 0.0
-
-        if (__builtin_isinf(x)) {
-            return __builtin_nan("");
-        }
-
-        return j0(x);
-    }
-}

nac3core/irrt/irrt_everything.hpp

@@ -1,14 +1,14 @@
 #pragma once
 
-#include "irrt_utils.hpp"
-#include "irrt_typedefs.hpp"
-#include "irrt_basic.hpp"
-#include "irrt_slice.hpp"
-#include "irrt_numpy_ndarray.hpp"
-
-/*
-    All IRRT implementations.
-
-    We don't have any pre-compiled objects, so we are writing all implementations in headers and
-    concatenate them with `#include` into one massive source file that contains all the IRRT stuff.
-*/
+#include <irrt/artiq_defs.hpp>
+#include <irrt/core.hpp>
+#include <irrt/error_context.hpp>
+#include <irrt/int_defs.hpp>
+#include <irrt/ndarray/basic.hpp>
+#include <irrt/ndarray/def.hpp>
+#include <irrt/ndarray/fill.hpp>
+#include <irrt/ndarray/indexing.hpp>
+#include <irrt/ndarray/reshape.hpp>
+#include <irrt/ndarray/transpose.hpp>
+#include <irrt/slice.hpp>
+#include <irrt/utils.hpp>

nac3core/irrt/irrt_numpy_ndarray.hpp

@@ -1,466 +0,0 @@
-#pragma once
-
-#include "irrt_utils.hpp"
-#include "irrt_typedefs.hpp"
-#include "irrt_slice.hpp"
-
-/*
-    NDArray-related implementations.
-`*/
-
-// NDArray indices are always `uint32_t`.
-using NDIndex = uint32_t;
-
-namespace {
-    namespace ndarray_util {
-        template <typename SizeT>
-        static void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices, SizeT nth) {
-            for (int32_t i = 0; i < ndims; i++) {
-                int32_t dim_i = ndims - i - 1;
-                int32_t dim = shape[dim_i];
-
-                indices[dim_i] = nth % dim;
-                nth /= dim;
-            }
-        }
-
-        // Compute the strides of an ndarray given an ndarray `shape`
-        // and assuming that the ndarray is *fully C-contagious*.
-        //
-        // You might want to read up on https://ajcr.net/stride-guide-part-1/.
-        template <typename SizeT>
-        static void set_strides_by_shape(SizeT itemsize, SizeT ndims, SizeT* dst_strides, const SizeT* shape) {
-            SizeT stride_product = 1;
-            for (SizeT i = 0; i < ndims; i++) {
-                int dim_i = ndims - i - 1;
-                dst_strides[dim_i] = stride_product * itemsize;
-                stride_product *= shape[dim_i];
-            }
-        }
-
-        // Compute the size/# of elements of an ndarray given its shape
-        template <typename SizeT>
-        static SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
-            SizeT size = 1;
-            for (SizeT dim_i = 0; dim_i < ndims; dim_i++) size *= shape[dim_i];
-            return size;
-        }
-
-        template <typename SizeT>
-        static bool can_broadcast_shape_to(
-            const SizeT target_ndims,
-            const SizeT *target_shape,
-            const SizeT src_ndims,
-            const SizeT *src_shape
-        ) {
-            /*
-                // See https://numpy.org/doc/stable/user/basics.broadcasting.html
-
-                This function handles this example:
-                ```
-                Image  (3d array): 256 x 256 x 3
-                Scale  (1d array):             3
-                Result (3d array): 256 x 256 x 3
-                ```
-
-                Other interesting examples to consider:
-                - `can_broadcast_shape_to([3], [1, 1, 1, 1, 3]) == true`
-                - `can_broadcast_shape_to([3], [3, 1]) == false`
-                - `can_broadcast_shape_to([256, 256, 3], [256, 1, 3]) == true`
-
-                In cases when the shapes contain zero(es):
-                - `can_broadcast_shape_to([0], [1]) == true`
-                - `can_broadcast_shape_to([0], [2]) == false`
-                - `can_broadcast_shape_to([0, 4, 0, 0], [1]) == true`
-                - `can_broadcast_shape_to([0, 4, 0, 0], [1, 1, 1, 1]) == true`
-                - `can_broadcast_shape_to([0, 4, 0, 0], [1, 4, 1, 1]) == true`
-                - `can_broadcast_shape_to([4, 3], [0, 3]) == false`
-                - `can_broadcast_shape_to([4, 3], [0, 0]) == false`
-            */
-
-            // This is essentially doing the following in Python:
-            // `for target_dim, src_dim in itertools.zip_longest(target_shape[::-1], src_shape[::-1], fillvalue=1)`
-            for (SizeT i = 0; i < max(target_ndims, src_ndims); i++) {
-                SizeT target_dim_i = target_ndims - i - 1;
-                SizeT src_dim_i = src_ndims - i - 1;
-
-                bool target_dim_exists = target_dim_i >= 0;
-                bool src_dim_exists = src_dim_i >= 0;
-
-                SizeT target_dim = target_dim_exists ? target_shape[target_dim_i] : 1;
-                SizeT src_dim = src_dim_exists ? src_shape[src_dim_i] : 1;
-
-                bool ok = src_dim == 1 || target_dim == src_dim;
-                if (!ok) return false;
-            }
-
-            return true;
-        }
-    }
-
-    typedef uint8_t NDSliceType;
-    extern "C" {
-        const NDSliceType INPUT_SLICE_TYPE_INDEX = 0;
-        const NDSliceType INPUT_SLICE_TYPE_SLICE = 1;
-    }
-
-    struct NDSlice {
-        // A poor-man's `std::variant<int, UserRange>`
-        NDSliceType type;
-
-        /*
-            if type == INPUT_SLICE_TYPE_INDEX => `slice` points to a single `SizeT`
-            if type == INPUT_SLICE_TYPE_SLICE => `slice` points to a single `UserRange`
-        */
-        uint8_t *slice;
-    };
-
-    namespace ndarray_util {
-        template<typename SizeT>
-        SizeT deduce_ndims_after_slicing(SizeT ndims, SizeT num_slices, const NDSlice *slices) {
-            irrt_assert(num_slices <= ndims);
-
-            SizeT final_ndims = ndims;
-            for (SizeT i = 0; i < num_slices; i++) {
-                if (slices[i].type == INPUT_SLICE_TYPE_INDEX) {
-                    final_ndims--; // An integer slice demotes the rank by 1
-                }
-            }
-            return final_ndims;
-        }
-    }
-
-    template <typename SizeT>
-    struct NDArrayIndicesIter {
-        SizeT ndims;
-        const SizeT *shape;
-        SizeT *indices;
-
-        void set_indices_zero() {
-            __builtin_memset(indices, 0, sizeof(SizeT) * ndims);
-        }
-
-        void next() {
-            for (SizeT i = 0; i < ndims; i++) {
-                SizeT dim_i = ndims - i - 1;
-
-                indices[dim_i]++;
-                if (indices[dim_i] < shape[dim_i]) {
-                    break;
-                } else {
-                    indices[dim_i] = 0;
-                }
-            }
-        }
-    };
-
-    // The NDArray object. `SizeT` is the *signed* size type of this ndarray.
-    //
-    // NOTE: The order of fields is IMPORTANT. DON'T TOUCH IT
-    //
-    // Some resources you might find helpful:
-    // - The official numpy implementations:
-    //   - https://github.com/numpy/numpy/blob/735a477f0bc2b5b84d0e72d92f224bde78d4e069/doc/source/reference/c-api/types-and-structures.rst
-    // - On strides (about reshaping, slicing, C-contagiousness, etc)
-    //   - https://ajcr.net/stride-guide-part-1/.
-    //   - https://ajcr.net/stride-guide-part-2/.
-    //   - https://ajcr.net/stride-guide-part-3/.
-    template <typename SizeT>
-    struct NDArray {
-        // The underlying data this `ndarray` is pointing to.
-        //
-        // NOTE: Formally this should be of type `void *`, but clang
-        // translates `void *` to `i8 *` when run with `-S -emit-llvm`,
-        // so we will put `uint8_t *` here for clarity.
-        uint8_t *data;
-
-        // The number of bytes of a single element in `data`.
-        //
-        // The `SizeT` is treated as `unsigned`.
-        SizeT itemsize;
-
-        // The number of dimensions of this shape.
-        //
-        // The `SizeT` is treated as `unsigned`.
-        SizeT ndims;
-
-        // Array shape, with length equal to `ndims`.
-        //
-        // The `SizeT` is treated as `unsigned`.
-        //
-        // NOTE: `shape` can contain 0.
-        //   (those appear when the user makes an out of bounds slice into an ndarray, e.g., `np.zeros((3, 3))[400:].shape == (0, 3)`)
-        SizeT *shape;
-
-        // Array strides (stride value is in number of bytes, NOT number of elements), with length equal to `ndims`.
-        //
-        // The `SizeT` is treated as `signed`.
-        //
-        // NOTE: `strides` can have negative numbers.
-        //   (those appear when there is a slice with a negative step, e.g., `my_array[::-1]`)
-        SizeT *strides;
-
-        // Calculate the size/# of elements of an `ndarray`.
-        // This function corresponds to `np.size(<ndarray>)` or `ndarray.size`
-        SizeT size() {
-            return ndarray_util::calc_size_from_shape(ndims, shape);
-        }
-
-        // Calculate the number of bytes of its content of an `ndarray` *in its view*.
-        // This function corresponds to `ndarray.nbytes`
-        SizeT nbytes() {
-            return this->size() * itemsize;
-        }
-
-        void set_value_at_pelement(uint8_t* pelement, const uint8_t* pvalue) {
-            __builtin_memcpy(pelement, pvalue, itemsize);
-        }
-
-        uint8_t* get_pelement(const SizeT *indices) {
-            uint8_t* element = data;
-            for (SizeT dim_i = 0; dim_i < ndims; dim_i++)
-                element += indices[dim_i] * strides[dim_i];
-            return element;
-        }
-
-        uint8_t* get_nth_pelement(SizeT nth) {
-            irrt_assert(0 <= nth);
-            irrt_assert(nth < this->size());
-
-            SizeT* indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * this->ndims);
-            ndarray_util::set_indices_by_nth(this->ndims, this->shape, indices, nth);
-            return get_pelement(indices);
-        }
-
-        // Get pointer to the first element of this ndarray, assuming
-        // `this->size() > 0`, i.e., not "degenerate" due to zeroes in `this->shape`)
-        //
-        // This is particularly useful for when the ndarray is just containing a single scalar.
-        uint8_t* get_first_pelement() {
-            irrt_assert(this->size() > 0);
-            return this->data; // ...It is simply `this->data`
-        }
-
-        // Is the given `indices` valid/in-bounds?
-        bool in_bounds(const SizeT *indices) {
-            for (SizeT dim_i = 0; dim_i < ndims; dim_i++) {
-                bool dim_ok = indices[dim_i] < shape[dim_i];
-                if (!dim_ok) return false;
-            }
-            return true;
-        }
-
-        // Fill the ndarray with a value
-        void fill_generic(const uint8_t* pvalue) {
-            NDArrayIndicesIter<SizeT> iter;
-            iter.ndims = this->ndims;
-            iter.shape = this->shape;
-            iter.indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * ndims);
-            iter.set_indices_zero();
-
-            for (SizeT i = 0; i < this->size(); i++, iter.next()) {
-                uint8_t* pelement = get_pelement(iter.indices);
-                set_value_at_pelement(pelement, pvalue);
-            }
-        }
-
-        // Set the strides of the ndarray with `ndarray_util::set_strides_by_shape`
-        void set_strides_by_shape() {
-            ndarray_util::set_strides_by_shape(itemsize, ndims, strides, shape);
-        }
-
-        // https://numpy.org/doc/stable/reference/generated/numpy.eye.html
-        void set_to_eye(SizeT k, const uint8_t* zero_pvalue, const uint8_t* one_pvalue) {
-            __builtin_assume(ndims == 2);
-
-            // TODO: Better implementation
-
-            fill_generic(zero_pvalue);
-            for (SizeT i = 0; i < min(shape[0], shape[1]); i++) {
-                SizeT row = i;
-                SizeT col = i + k;
-                SizeT indices[2] = { row, col };
-
-                if (!in_bounds(indices)) continue;
-
-                uint8_t* pelement = get_pelement(indices);
-                set_value_at_pelement(pelement, one_pvalue);
-            }
-        }
-
-        // To support numpy complex slices (e.g., `my_array[:50:2,4,:2:-1]`)
-        //
-        // Things assumed by this function:
-        // - `dst_ndarray` is allocated by the caller
-        // - `dst_ndarray.ndims` has the correct value (according to `ndarray_util::deduce_ndims_after_slicing`).
-        //   - ... and `dst_ndarray.shape` and `dst_ndarray.strides` have been allocated by the caller as well
-        //
-        // Other notes:
-        // - `dst_ndarray->data` does not have to be set, it will be derived.
-        // - `dst_ndarray->itemsize` does not have to be set, it will be set to `this->itemsize`
-        // - `dst_ndarray->shape` and `dst_ndarray.strides` can contain empty values
-        void slice(SizeT num_ndslices, NDSlice* ndslices, NDArray<SizeT>* dst_ndarray) {
-            // REFERENCE CODE (check out `_index_helper` in `__getitem__`):
-            //   https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
-
-            irrt_assert(dst_ndarray->ndims == ndarray_util::deduce_ndims_after_slicing(this->ndims, num_ndslices, ndslices));
-
-            dst_ndarray->data = this->data;
-
-            SizeT this_axis = 0;
-            SizeT dst_axis = 0;
-
-            for (SizeT i = 0; i < num_ndslices; i++) {
-                NDSlice *ndslice = &ndslices[i];
-                if (ndslice->type == INPUT_SLICE_TYPE_INDEX) {
-                    // Handle when the ndslice is just a single (possibly negative) integer
-                    // e.g., `my_array[::2, -5, ::-1]`
-                    //                      ^^------ like this
-                    SizeT index_user = *((SizeT*) ndslice->slice);
-                    SizeT index = resolve_index_in_length(this->shape[this_axis], index_user);
-                    dst_ndarray->data += index * this->strides[this_axis]; // Add offset
-
-                    // Next
-                    this_axis++;
-                } else if (ndslice->type == INPUT_SLICE_TYPE_SLICE) {
-                    // Handle when the ndslice is a slice (represented by UserSlice in IRRT)
-                    // e.g., `my_array[::2, -5, ::-1]`
-                    //                 ^^^------^^^^----- like these
-                    UserSlice<SizeT>* user_slice = (UserSlice<SizeT>*) ndslice->slice;
-                    Slice<SizeT> slice = user_slice->indices(this->shape[this_axis]); // To resolve negative indices and other funny stuff written by the user
-
-                    // NOTE: There is no need to write special code to handle negative steps/strides.
-                    // This simple implementation meticulously handles both positive and negative steps/strides.
-                    // Check out the tinynumpy and IRRT's test cases if you are not convinced.
-                    dst_ndarray->data += slice.start * this->strides[this_axis]; // Add offset (NOTE: no need to `* itemsize`, strides count in # of bytes)
-                    dst_ndarray->strides[dst_axis] = slice.step * this->strides[this_axis]; // Determine stride
-                    dst_ndarray->shape[dst_axis] = slice.len(); // Determine shape dimension
-
-                    // Next
-                    dst_axis++;
-                    this_axis++;
-                } else {
-                    __builtin_unreachable();
-                }
-            }
-
-            irrt_assert(dst_axis == dst_ndarray->ndims); // Sanity check on the implementation
-        }
-
-        // Similar to `np.broadcast_to(<ndarray>, <target_shape>)`
-        // Assumptions:
-        //   - `this` has to be fully initialized.
-        //   - `dst_ndarray->ndims` has to be set.
-        //   - `dst_ndarray->shape` has to be set, this determines the shape `this` broadcasts to.
-        //
-        // Other notes:
-        //   - `dst_ndarray->data` does not have to be set, it will be set to `this->data`.
-        //   - `dst_ndarray->itemsize` does not have to be set, it will be set to `this->data`.
-        //   - `dst_ndarray->strides` does not have to be set, it will be overwritten.
-        //
-        // Cautions:
-        //   ```
-        //   xs = np.zeros((4,))
-        //   ys = np.zero((4, 1))
-        //   ys[:] = xs # ok
-        // 
-        //   xs = np.zeros((1, 4))
-        //   ys = np.zero((4,))
-        //   ys[:] = xs # allowed
-        //   # However `np.broadcast_to(xs, (4,))` would fails, as per numpy's broadcasting rule.
-        //   # and apparently numpy will "deprecate" this? SEE https://github.com/numpy/numpy/issues/21744
-        //   # This implementation will NOT support this assignment.
-        //   ```
-        void broadcast_to(NDArray<SizeT>* dst_ndarray) {
-            dst_ndarray->data = this->data;
-            dst_ndarray->itemsize = this->itemsize;
-
-            irrt_assert(
-                ndarray_util::can_broadcast_shape_to(
-                    dst_ndarray->ndims,
-                    dst_ndarray->shape,
-                    this->ndims,
-                    this->shape
-                )
-            );
-
-            SizeT stride_product = 1;
-            for (SizeT i = 0; i < max(this->ndims, dst_ndarray->ndims); i++) {
-                SizeT this_dim_i = this->ndims - i - 1;
-                SizeT dst_dim_i = dst_ndarray->ndims - i - 1;
-
-                bool this_dim_exists = this_dim_i >= 0;
-                bool dst_dim_exists = dst_dim_i >= 0;
-
-                // TODO: Explain how this works
-                bool c1 = this_dim_exists && this->shape[this_dim_i] == 1;
-                bool c2 = dst_dim_exists && dst_ndarray->shape[dst_dim_i] != 1;
-                if (!this_dim_exists || (c1 && c2)) {
-                    dst_ndarray->strides[dst_dim_i] = 0; // Freeze it in-place
-                } else {
-                    dst_ndarray->strides[dst_dim_i] = stride_product * this->itemsize;
-                    stride_product *= this->shape[this_dim_i]; // NOTE: this_dim_exist must be true here.
-                }
-            }
-        }
-
-        // Simulates `this_ndarray[:] = src_ndarray`, with automatic broadcasting.
-        // Caution on https://github.com/numpy/numpy/issues/21744
-        // Also see `NDArray::broadcast_to`
-        void assign_with(NDArray<SizeT>* src_ndarray) {
-            irrt_assert(
-                ndarray_util::can_broadcast_shape_to(
-                    this->ndims,
-                    this->shape,
-                    src_ndarray->ndims,
-                    src_ndarray->shape
-                )
-            );
-
-            // Broadcast the `src_ndarray` to make the reading process *much* easier
-            SizeT* broadcasted_src_ndarray_strides = __builtin_alloca(sizeof(SizeT) * this->ndims); // Remember to allocate strides beforehand
-            NDArray<SizeT> broadcasted_src_ndarray = {
-                .ndims = this->ndims,
-                .shape = this->shape,
-                .strides = broadcasted_src_ndarray_strides
-            };
-            src_ndarray->broadcast_to(&broadcasted_src_ndarray);
-
-            // Using iter instead of `get_nth_pelement` because it is slightly faster
-            SizeT* indices = __builtin_alloca(sizeof(SizeT) * this->ndims);
-            auto iter = NDArrayIndicesIter<SizeT> {
-                .ndims = this->ndims,
-                .shape = this->shape,
-                .indices = indices
-            };
-            const SizeT this_size = this->size();
-            for (SizeT i = 0; i < this_size; i++, iter.next()) {
-                uint8_t* src_pelement = broadcasted_src_ndarray_strides->get_pelement(indices);
-                uint8_t* this_pelement = this->get_pelement(indices);
-                this->set_value_at_pelement(src_pelement, src_pelement);
-            }
-        }
-    };
-}
-
-extern "C" {
-    uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
-        return ndarray->size();
-    }
-
-    uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
-        return ndarray->size();
-    }
-
-    void __nac3_ndarray_fill_generic(NDArray<int32_t>* ndarray, uint8_t* pvalue) {
-        ndarray->fill_generic(pvalue);
-    }
-
-    void __nac3_ndarray_fill_generic64(NDArray<int64_t>* ndarray, uint8_t* pvalue) {
-        ndarray->fill_generic(pvalue);
-    }
-
-    // void __nac3_ndarray_slice(NDArray<int32_t>* ndarray, int32_t num_slices, NDSlice<int32_t> *slices, NDArray<int32_t> *dst_ndarray) {
-    //     // ndarray->slice(num_slices, slices, dst_ndarray);
-    // }
-}

nac3core/irrt/irrt_slice.hpp

@@ -1,80 +0,0 @@
-#pragma once
-
-#include "irrt_utils.hpp"
-#include "irrt_typedefs.hpp"
-
-namespace {
-    // A proper slice in IRRT, all negative indices have be resolved to absolute values.
-    // Even though nac3core's slices are always `int32_t`, we will template slice anyway
-    // since this struct is used as a general utility.
-    template <typename T>
-    struct Slice {
-        T start;
-        T stop;
-        T step;
-
-        // The length/The number of elements of the slice if it were a range,
-        // i.e., the value of `len(range(this->start, this->stop, this->end))`
-        T len() {
-            T 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;
-            }
-        }
-    };
-
-    template<typename T>
-    T resolve_index_in_length(T length, T index) {
-        irrt_assert(length >= 0);
-        if (index < 0) {
-            // Remember that index is negative, so do a plus here
-            return max(length + index, 0);
-        } else {
-            return min(length, index);
-        }
-    }
-
-    // NOTE: using a bitfield for the `*_defined` is better, at the
-    // cost of a more annoying implementation in nac3core inkwell
-    template <typename T>
-    struct UserSlice {
-        uint8_t start_defined;
-        T start;
-
-        uint8_t stop_defined;
-        T stop;
-
-        uint8_t step_defined;
-        T step;
-
-        // Like Python's `slice(start, stop, step).indices(length)`
-        Slice<T> indices(T length) {
-            // NOTE: This function implements Python's `slice.indices` *FAITHFULLY*.
-            // SEE: https://github.com/python/cpython/blob/f62161837e68c1c77961435f1b954412dd5c2b65/Objects/sliceobject.c#L546
-            irrt_assert(length >= 0);
-            irrt_assert(!step_defined || step != 0); // step_defined -> step != 0; step cannot be zero if specified by user
-
-            Slice<T> result;
-            result.step = step_defined ? step : 1;
-            bool step_is_negative = result.step < 0;
-
-            if (start_defined) {
-                result.start = resolve_index_in_length(length, start);
-            } else {
-                result.start = step_is_negative ? length - 1 : 0;
-            }
-            
-            if (stop_defined) {
-                result.stop = resolve_index_in_length(length, stop);
-            } else {
-                result.stop = step_is_negative ? -1 : length;
-            }
-
-            return result;
-        }
-    };
-}

nac3core/irrt/irrt_test.cpp

@@ -4,655 +4,15 @@
 #include <cstdint>
 #include <cstdio>
 #include <cstdlib>
-
-// Set `IRRT_DONT_TYPEDEF_INTS` because `cstdint` defines them
-#define IRRT_DONT_TYPEDEF_INTS
-#include "irrt_everything.hpp"
-
-void test_fail() {
-    printf("[!] Test failed\n");
-    exit(1);
-}
-
-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__)
-
-template <typename T>
-void debug_print_array(const char* format, int len, T* as) {
-    printf("[");
-    for (int i = 0; i < len; i++) {
-        if (i != 0) printf(", ");
-        printf(format, as[i]);
-    }
-    printf("]");
-}
-
-template <typename T>
-void assert_arrays_match(const char* label, const char* format, int len, T* expected, T* got) {
-    if (!arrays_match(len, expected, got)) {
-        printf(">>>>>>> %s\n", label);
-        printf("    Expecting = ");
-        debug_print_array(format, len, expected);
-        printf("\n");
-        printf("          Got = ");
-        debug_print_array(format, len, got);
-        printf("\n");
-        test_fail();
-    }
-}
-
-template <typename T>
-void assert_values_match(const char* label, const char* format, T expected, T got) {
-    if (expected != got) {
-        printf(">>>>>>> %s\n", label);
-        printf("    Expecting = ");
-        printf(format, expected);
-        printf("\n");
-        printf("          Got = ");
-        printf(format, got);
-        printf("\n");
-        test_fail();
-    }
-}
-
-void print_repeated(const char *str, int count) {
-    for (int i = 0; i < count; i++) {
-        printf("%s", str);
-    }
-}
-
-template<typename SizeT, typename ElementT>
-void __print_ndarray_aux(const char *format, bool first, bool last, SizeT* cursor, SizeT depth, NDArray<SizeT>* ndarray) {
-    // A really lazy recursive implementation
-    
-    // Add left padding unless its the first entry (since there would be "[[[" before it)
-    if (!first) {
-        print_repeated(" ", depth);
-    }
-
-    const SizeT dim = ndarray->shape[depth];
-    if (depth + 1 == ndarray->ndims) {
-        // Recursed down to last dimension, print the values in a nice list
-        printf("[");
-
-        SizeT* indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * ndarray->ndims);
-        for (SizeT i = 0; i < dim; i++) {
-            ndarray_util::set_indices_by_nth(ndarray->ndims, ndarray->shape, indices, *cursor);
-            ElementT* pelement = (ElementT*) ndarray->get_pelement(indices);
-            ElementT element = *pelement;
-
-            if (i != 0) printf(", "); // List delimiter
-            printf(format, element);
-            printf("(@");
-            debug_print_array("%d", ndarray->ndims, indices);
-            printf(")");
-
-            (*cursor)++;
-        }
-        printf("]");
-    } else {
-        printf("[");
-        for (SizeT i = 0; i < ndarray->shape[depth]; i++) {
-            __print_ndarray_aux<SizeT, ElementT>(
-                format,
-                i == 0, // first?
-                i + 1 == dim, // last?
-                cursor,
-                depth + 1,
-                ndarray
-            );
-        }
-        printf("]");
-    }
-
-    // Add newline unless its the last entry (since there will be "]]]" after it)
-    if (!last) {
-        print_repeated("\n", depth);
-    }
-}
-
-template<typename SizeT, typename ElementT>
-void print_ndarray(const char *format, NDArray<SizeT>* ndarray) {
-    if (ndarray->ndims == 0) {
-        printf("<empty ndarray>");
-    } else {
-        SizeT cursor = 0;
-        __print_ndarray_aux<SizeT, ElementT>(format, true, true, &cursor, 0, ndarray);
-    }
-    printf("\n");
-}
-
-void test_calc_size_from_shape_normal() {
-    // Test shapes with normal values
-    BEGIN_TEST();
-
-    int32_t shape[4] = { 2, 3, 5, 7 };
-    assert_values_match("size", "%d", 210, ndarray_util::calc_size_from_shape<int32_t>(4, shape));
-}
-
-void test_calc_size_from_shape_has_zero() {
-    // Test shapes with 0 in them
-    BEGIN_TEST();
-
-    int32_t shape[4] = { 2, 0, 5, 7 };
-    assert_values_match("size", "%d", 0, ndarray_util::calc_size_from_shape<int32_t>(4, shape));
-}
-
-void test_set_strides_by_shape() {
-    // Test `set_strides_by_shape()`
-    BEGIN_TEST();
-
-    int32_t shape[4] = { 99, 3, 5, 7 };
-    int32_t strides[4] = { 0 };
-    ndarray_util::set_strides_by_shape((int32_t) sizeof(int32_t), 4, strides, shape);
-
-    int32_t expected_strides[4] = {
-        105 * sizeof(int32_t),
-        35 * sizeof(int32_t),
-        7 * sizeof(int32_t),
-        1 * sizeof(int32_t)
-    };
-    assert_arrays_match("strides", "%u", 4u, expected_strides, strides);
-}
-
-void test_ndarray_indices_iter_normal() {
-    // Test NDArrayIndicesIter normal behavior
-    BEGIN_TEST();
-
-    int32_t shape[3] = { 1, 2, 3 };
-    int32_t indices[3] = { 0, 0, 0 };
-    auto iter = NDArrayIndicesIter<int32_t> {
-        .ndims = 3,
-        .shape = shape,
-        .indices = indices
-    };
-
-    assert_arrays_match("indices #0", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 0 });
-    iter.next();
-    assert_arrays_match("indices #1", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 1 });
-    iter.next();
-    assert_arrays_match("indices #2", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 2 });
-    iter.next();
-    assert_arrays_match("indices #3", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 0 });
-    iter.next();
-    assert_arrays_match("indices #4", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 1 });
-    iter.next();
-    assert_arrays_match("indices #5", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 2 });
-    iter.next();
-    assert_arrays_match("indices #6", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 0 }); // Loops back
-    iter.next();
-    assert_arrays_match("indices #7", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 1 });
-}
-
-void test_ndarray_fill_generic() {
-    // Test ndarray fill_generic
-    BEGIN_TEST();
-
-    // Choose a type that's neither int32_t nor uint64_t (candidates of SizeT) to spice it up
-    // Also make all the octets non-zero, to see if `memcpy` in `fill_generic` is working perfectly.
-    uint16_t fill_value = 0xFACE;
-
-    uint16_t in_data[6] = { 100, 101, 102, 103, 104, 105 }; // Fill `data` with values that != `999`
-    int32_t in_itemsize = sizeof(uint16_t);
-    const int32_t in_ndims = 2;
-    int32_t in_shape[in_ndims] = { 2, 3 };
-    int32_t in_strides[in_ndims] = {};
-    NDArray<int32_t> ndarray = {
-        .data = (uint8_t*) in_data,
-        .itemsize = in_itemsize,
-        .ndims = in_ndims,
-        .shape = in_shape,
-        .strides = in_strides,
-    };
-    ndarray.set_strides_by_shape();
-    ndarray.fill_generic((uint8_t*) &fill_value); // `fill_generic` here
-
-    uint16_t expected_data[6] = { fill_value, fill_value, fill_value, fill_value, fill_value, fill_value };
-    assert_arrays_match("data", "0x%hX", 6, expected_data, in_data);
-}
-
-void test_ndarray_set_to_eye() {
-    // Test `set_to_eye` behavior (helper function to implement `np.eye()`)
-    BEGIN_TEST();
-
-    double in_data[9] = { 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0 };
-    int32_t in_itemsize = sizeof(double);
-    const int32_t in_ndims = 2;
-    int32_t in_shape[in_ndims] = { 3, 3 };
-    int32_t in_strides[in_ndims] = {};
-    NDArray<int32_t> ndarray = {
-        .data = (uint8_t*) in_data,
-        .itemsize = in_itemsize,
-        .ndims = in_ndims,
-        .shape = in_shape,
-        .strides = in_strides,
-    };
-    ndarray.set_strides_by_shape();
-
-    double zero = 0.0;
-    double one = 1.0;
-    ndarray.set_to_eye(1, (uint8_t*) &zero, (uint8_t*) &one);
-
-    assert_values_match("in_data[0]", "%f", 0.0, in_data[0]);
-    assert_values_match("in_data[1]", "%f", 1.0, in_data[1]);
-    assert_values_match("in_data[2]", "%f", 0.0, in_data[2]);
-    assert_values_match("in_data[3]", "%f", 0.0, in_data[3]);
-    assert_values_match("in_data[4]", "%f", 0.0, in_data[4]);
-    assert_values_match("in_data[5]", "%f", 1.0, in_data[5]);
-    assert_values_match("in_data[6]", "%f", 0.0, in_data[6]);
-    assert_values_match("in_data[7]", "%f", 0.0, in_data[7]);
-    assert_values_match("in_data[8]", "%f", 0.0, in_data[8]);
-}
-
-void test_slice_1() {
-    // Test `slice(5, None, None).indices(100) == slice(5, 100, 1)`
-    BEGIN_TEST();
-
-    UserSlice<int> user_slice = {
-        .start_defined = 1,
-        .start = 5,
-        .stop_defined = 0,
-        .step_defined = 0,
-    };
-
-    auto slice = user_slice.indices(100);
-    assert_values_match("start", "%d", 5, slice.start);
-    assert_values_match("stop", "%d", 100, slice.stop);
-    assert_values_match("step", "%d", 1, slice.step);
-}
-
-void test_slice_2() {
-    // Test `slice(400, 999, None).indices(100) == slice(100, 100, 1)`
-    BEGIN_TEST();
-
-    UserSlice<int> user_slice = {
-        .start_defined = 1,
-        .start = 400,
-        .stop_defined = 0,
-        .step_defined = 0,
-    };
-
-    auto slice = user_slice.indices(100);
-    assert_values_match("start", "%d", 100, slice.start);
-    assert_values_match("stop", "%d", 100, slice.stop);
-    assert_values_match("step", "%d", 1, slice.step);
-}
-
-void test_slice_3() {
-    // Test `slice(-10, -5, None).indices(100) == slice(90, 95, 1)`
-    BEGIN_TEST();
-
-    UserSlice<int> user_slice = {
-        .start_defined = 1,
-        .start = -10,
-        .stop_defined = 1,
-        .stop = -5,
-        .step_defined = 0,
-    };
-
-    auto slice = user_slice.indices(100);
-    assert_values_match("start", "%d", 90, slice.start);
-    assert_values_match("stop", "%d", 95, slice.stop);
-    assert_values_match("step", "%d", 1, slice.step);
-}
-
-void test_slice_4() {
-    // Test `slice(None, None, -5).indices(100) == (99, -1, -5)`
-    BEGIN_TEST();
-
-    UserSlice<int> user_slice = {
-        .start_defined = 0,
-        .stop_defined = 0,
-        .step_defined = 1,
-        .step = -5
-    };
-
-    auto slice = user_slice.indices(100);
-    assert_values_match("start", "%d", 99, slice.start);
-    assert_values_match("stop", "%d", -1, slice.stop);
-    assert_values_match("step", "%d", -5, slice.step);
-}
-
-void test_ndslice_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();
-
-    double in_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 };
-    int32_t in_itemsize = sizeof(double);
-    const int32_t in_ndims = 2;
-    int32_t in_shape[in_ndims] = { 3, 4 };
-    int32_t in_strides[in_ndims] = {};
-    NDArray<int32_t> ndarray = {
-        .data = (uint8_t*) in_data,
-        .itemsize = in_itemsize,
-        .ndims = in_ndims,
-        .shape = in_shape,
-        .strides = in_strides
-    };
-    ndarray.set_strides_by_shape();
-
-    // Destination ndarray
-    // As documented, ndims and shape & strides must be allocated and determined by the caller.
-    const int32_t dst_ndims = 2;
-    int32_t dst_shape[dst_ndims] = {999, 999}; // Empty values
-    int32_t dst_strides[dst_ndims] = {999, 999}; // Empty values
-    NDArray<int32_t> dst_ndarray = {
-        .data = nullptr,
-        .ndims = dst_ndims,
-        .shape = dst_shape,
-        .strides = dst_strides
-    };
-
-    // Create the slice in `ndarray[-2::, 1::2]`
-    UserSlice<int32_t> user_slice_1 = {
-        .start_defined = 1,
-        .start = -2,
-        .stop_defined = 0,
-        .step_defined = 0
-    };
-
-    UserSlice<int32_t> user_slice_2 = {
-        .start_defined = 1,
-        .start = 1,
-        .stop_defined = 0,
-        .step_defined = 1,
-        .step = 2
-    };
-
-    const int32_t num_ndslices = 2;
-    NDSlice ndslices[num_ndslices] = {
-        { .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_1 },
-        { .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_2 }
-    };
-
-    ndarray.slice(num_ndslices, ndslices, &dst_ndarray);
-
-    int32_t expected_shape[dst_ndims] = { 2, 2 };
-    int32_t expected_strides[dst_ndims] = { 32, 16 };
-    assert_arrays_match("shape", "%d", dst_ndims, expected_shape, dst_ndarray.shape);
-    assert_arrays_match("strides", "%d", dst_ndims, expected_strides, dst_ndarray.strides);
-
-    assert_values_match("dst_ndarray[0, 0]", "%f", 5.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 0, 0 })));
-    assert_values_match("dst_ndarray[0, 1]", "%f", 7.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 0, 1 })));
-    assert_values_match("dst_ndarray[1, 0]", "%f", 9.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 1, 0 })));
-    assert_values_match("dst_ndarray[1, 1]", "%f", 11.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 1, 1 })));
-}
-
-void test_ndslice_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
-
-        dst_ndarray[1, 0] == 99 # If you write to `dst_ndarray`
-        assert ndarray[1, 3] == 99 # `ndarray` also updates!!
-        ```
-    */
-    BEGIN_TEST();
-
-    double in_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 };
-    int32_t in_itemsize = sizeof(double);
-    const int32_t in_ndims = 2;
-    int32_t in_shape[in_ndims] = { 3, 4 };
-    int32_t in_strides[in_ndims] = {};
-    NDArray<int32_t> ndarray = {
-        .data = (uint8_t*) in_data,
-        .itemsize = in_itemsize,
-        .ndims = in_ndims,
-        .shape = in_shape,
-        .strides = in_strides
-    };
-    ndarray.set_strides_by_shape();
-
-    // Destination ndarray
-    // As documented, ndims and shape & strides must be allocated and determined by the caller.
-    const int32_t dst_ndims = 1;
-    int32_t dst_shape[dst_ndims] = {999}; // Empty values
-    int32_t dst_strides[dst_ndims] = {999}; // Empty values
-    NDArray<int32_t> dst_ndarray = {
-        .data = nullptr,
-        .ndims = dst_ndims,
-        .shape = dst_shape,
-        .strides = dst_strides
-    };
-
-    // Create the slice in `ndarray[2, ::-2]`
-    int32_t user_slice_1 = 2;
-    UserSlice<int32_t> user_slice_2 = {
-        .start_defined = 0,
-        .stop_defined = 0,
-        .step_defined = 1,
-        .step = -2
-    };
-
-    const int32_t num_ndslices = 2;
-    NDSlice ndslices[num_ndslices] = {
-        { .type = INPUT_SLICE_TYPE_INDEX, .slice = (uint8_t*) &user_slice_1 },
-        { .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_2 }
-    };
-
-    ndarray.slice(num_ndslices, ndslices, &dst_ndarray);
-
-    int32_t expected_shape[dst_ndims] = { 2 };
-    int32_t expected_strides[dst_ndims] = { -16 };
-    assert_arrays_match("shape", "%d", dst_ndims, expected_shape, dst_ndarray.shape);
-    assert_arrays_match("strides", "%d", dst_ndims, expected_strides, dst_ndarray.strides);
-
-    // [5.0, 3.0]
-    assert_values_match("dst_ndarray[0]", "%f", 11.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 0 })));
-    assert_values_match("dst_ndarray[1]", "%f", 9.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 1 })));
-}
-
-void test_can_broadcast_shape() {
-    BEGIN_TEST();
-
-    assert_values_match(
-        "can_broadcast_shape_to([3], [1, 1, 1, 1, 3]) == true",
-        "%d",
-        true,
-        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 5, (int32_t[]) { 1, 1, 1, 1, 3 })
-    );
-    assert_values_match(
-        "can_broadcast_shape_to([3], [3, 1]) == false",
-        "%d",
-        false,
-        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 2, (int32_t[]) { 3, 1 }));
-    assert_values_match(
-        "can_broadcast_shape_to([3], [3]) == true",
-        "%d",
-        true,
-        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 1, (int32_t[]) { 3 }));
-    assert_values_match(
-        "can_broadcast_shape_to([1], [3]) == false",
-        "%d",
-        false,
-        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 1 }, 1, (int32_t[]) { 3 }));
-    assert_values_match(
-        "can_broadcast_shape_to([1], [1]) == true",
-        "%d",
-        true,
-        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 1 }, 1, (int32_t[]) { 1 }));
-    assert_values_match(
-        "can_broadcast_shape_to([256, 256, 3], [256, 1, 3]) == true",
-        "%d",
-        true,
-        ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 3, (int32_t[]) { 256, 1, 3 })
-    );
-    assert_values_match(
-        "can_broadcast_shape_to([256, 256, 3], [3]) == true",
-        "%d",
-        true,
-        ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 1, (int32_t[]) { 3 })
-    );
-    assert_values_match(
-        "can_broadcast_shape_to([256, 256, 3], [2]) == false",
-        "%d",
-        false,
-        ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 1, (int32_t[]) { 2 })
-    );
-    assert_values_match(
-        "can_broadcast_shape_to([256, 256, 3], [1]) == true",
-        "%d",
-        true,
-        ndarray_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(
-        "can_broadcast_shape_to([0], [1]) == true",
-        "%d",
-        true,
-        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 0 }, 1, (int32_t[]) { 1 })
-    );
-    assert_values_match(
-        "can_broadcast_shape_to([0], [2]) == false",
-        "%d",
-        false,
-        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 0 }, 1, (int32_t[]) { 2 })
-    );
-    assert_values_match(
-        "can_broadcast_shape_to([0, 4, 0, 0], [1]) == true",
-        "%d",
-        true,
-        ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 1, (int32_t[]) { 1 })
-    );
-    assert_values_match(
-        "can_broadcast_shape_to([0, 4, 0, 0], [1, 1, 1, 1]) == true",
-        "%d",
-        true,
-        ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 4, (int32_t[]) { 1, 1, 1, 1 })
-    );
-    assert_values_match(
-        "can_broadcast_shape_to([0, 4, 0, 0], [1, 4, 1, 1]) == true",
-        "%d",
-        true,
-        ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 4, (int32_t[]) { 1, 4, 1, 1 })
-    );
-    assert_values_match(
-        "can_broadcast_shape_to([4, 3], [0, 3]) == false",
-        "%d",
-        false,
-        ndarray_util::can_broadcast_shape_to(2, (int32_t[]) { 4, 3 }, 2, (int32_t[]) { 0, 3 })
-    );
-    assert_values_match(
-        "can_broadcast_shape_to([4, 3], [0, 0]) == false",
-        "%d",
-        false,
-        ndarray_util::can_broadcast_shape_to(2, (int32_t[]) { 4, 3 }, 2, (int32_t[]) { 0, 0 })
-    );
-}
-
-void test_ndarray_broadcast_1() {
-    /*
-    # 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.set_strides_by_shape();
-
-    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_to(&dst_ndarray);
-
-    assert_arrays_match("dst_ndarray->strides", "%d", dst_ndims, (int32_t[]) { 0, 0, 8 }, dst_ndarray.strides);
-
-    assert_values_match("dst_ndarray[0, 0, 0]", "%f", 19.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 0})));
-    assert_values_match("dst_ndarray[0, 0, 1]", "%f", 29.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 1})));
-    assert_values_match("dst_ndarray[0, 0, 2]", "%f", 39.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 2})));
-    assert_values_match("dst_ndarray[0, 0, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 3})));
-    assert_values_match("dst_ndarray[0, 1, 0]", "%f", 19.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 0})));
-    assert_values_match("dst_ndarray[0, 1, 1]", "%f", 29.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 1})));
-    assert_values_match("dst_ndarray[0, 1, 2]", "%f", 39.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 2})));
-    assert_values_match("dst_ndarray[0, 1, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 3})));
-    assert_values_match("dst_ndarray[1, 2, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {1, 2, 3})));
-}
-
-void test_assign_with() {
-    /*
-        ```
-        xs = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], dtype=np.float64)
-        ys = xs.shape
-        ```
-    */
-}
+#include <test/test_core.hpp>
+#include <test/test_ndarray_basic.hpp>
+#include <test/test_ndarray_indexing.hpp>
+#include <test/test_slice.hpp>
 
 int main() {
-    test_calc_size_from_shape_normal();
-    test_calc_size_from_shape_has_zero();
-    test_set_strides_by_shape();
-    test_ndarray_indices_iter_normal();
-    test_ndarray_fill_generic();
-    test_ndarray_set_to_eye();
-    test_slice_1();
-    test_slice_2();
-    test_slice_3();
-    test_slice_4();
-    test_ndslice_1();
-    test_ndslice_2();
-    test_can_broadcast_shape();
-    test_ndarray_broadcast_1();
-    test_assign_with();
+    test::core::run();
+    test::slice::run();
+    test::ndarray_basic::run();
+    test::ndarray_indexing::run();
     return 0;
 }

nac3core/irrt/irrt_typedefs.hpp

@@ -1,14 +0,0 @@
-#pragma once
-
-// This is made toggleable since `irrt_test.cpp` itself would include
-// headers that define the `int_t` family.
-#ifndef IRRT_DONT_TYPEDEF_INTS
-typedef _BitInt(8) int8_t;
-typedef unsigned _BitInt(8) uint8_t;
-typedef _BitInt(32) int32_t;
-typedef unsigned _BitInt(32) uint32_t;
-typedef _BitInt(64) int64_t;
-typedef unsigned _BitInt(64) uint64_t;
-#endif
-
-typedef int32_t SliceIndex;

nac3core/irrt/irrt_utils.hpp

@@ -1,37 +0,0 @@
-#pragma once
-
-#include "irrt_typedefs.hpp"
-
-namespace {
-    template <typename T>
-    T max(T a, T b) {
-        return a > b ? a : b;
-    }
-
-    template <typename T>
-    T min(T a, 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;
-    }
-
-    void irrt_panic() {
-        // Crash the program for now.
-        // TODO: Don't crash the program
-        //       ... or at least produce a good message when doing testing IRRT
-            
-        uint8_t* death = nullptr;
-        *death = 0; // TODO: address 0 on hardware might be writable?
-    }
-
-    // TODO: Make this a macro and allow it to be toggled on/off (e.g., debug vs release)
-    void irrt_assert(bool condition) {
-        if (!condition) irrt_panic();
-    }
-}

nac3core/irrt/test/includes.hpp

@@ -0,0 +1,11 @@
+#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

@@ -0,0 +1,16 @@
+#pragma once
+
+#include <test/includes.hpp>
+
+namespace test {
+namespace core {
+void test_int_exp() {
+    BEGIN_TEST();
+
+    assert_values_match(125, __nac3_int_exp_impl<int32_t>(5, 3));
+    assert_values_match(3125, __nac3_int_exp_impl<int32_t>(5, 5));
+}
+
+void run() { test_int_exp(); }
+}  // namespace core
+}  // namespace test

nac3core/irrt/test/test_ndarray_basic.hpp

@@ -0,0 +1,30 @@
+#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();
+
+    int32_t shape[4] = {2, 3, 5, 7};
+    assert_values_match(
+        210, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
+}
+
+void test_calc_size_from_shape_has_zero() {
+    // Test shapes with 0 in them
+    BEGIN_TEST();
+
+    int32_t shape[4] = {2, 0, 5, 7};
+    assert_values_match(
+        0, ndarray::basic::util::calc_size_from_shape<int32_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_indexing.hpp

@@ -0,0 +1,220 @@
+#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};
+    int32_t src_itemsize = sizeof(double);
+    const int32_t src_ndims = 2;
+    int32_t src_shape[src_ndims] = {3, 4};
+    int32_t src_strides[src_ndims] = {};
+    NDArray<int32_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 int32_t dst_ndims = 2;
+    int32_t dst_shape[dst_ndims] = {999, 999};    // Empty values
+    int32_t dst_strides[dst_ndims] = {999, 999};  // Empty values
+    NDArray<int32_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 int32_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}};
+
+    ErrorContext errctx = create_testing_errctx();
+    ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
+                             &dst_ndarray);
+    assert_errctx_no_error(&errctx);
+
+    int32_t expected_shape[dst_ndims] = {2, 2};
+    int32_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, (int32_t[dst_ndims]){0, 0})));
+    // dst_ndarray[0, 1]
+    assert_values_match(7.0,
+                        *((double *)ndarray::basic::get_pelement_by_indices(
+                            &dst_ndarray, (int32_t[dst_ndims]){0, 1})));
+    // dst_ndarray[1, 0]
+    assert_values_match(9.0,
+                        *((double *)ndarray::basic::get_pelement_by_indices(
+                            &dst_ndarray, (int32_t[dst_ndims]){1, 0})));
+    // dst_ndarray[1, 1]
+    assert_values_match(11.0,
+                        *((double *)ndarray::basic::get_pelement_by_indices(
+                            &dst_ndarray, (int32_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};
+    int32_t src_itemsize = sizeof(double);
+    const int32_t src_ndims = 2;
+    int32_t src_shape[src_ndims] = {3, 4};
+    int32_t src_strides[src_ndims] = {};
+    NDArray<int32_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 int32_t dst_ndims = 1;
+    int32_t dst_shape[dst_ndims] = {999};    // Empty values
+    int32_t dst_strides[dst_ndims] = {999};  // Empty values
+    NDArray<int32_t> dst_ndarray = {.data = nullptr,
+                                    .ndims = dst_ndims,
+                                    .shape = dst_shape,
+                                    .strides = dst_strides};
+
+    // Create the subscripts in `ndarray[2, ::-2]`
+    int32_t subscript_1 = 2;
+
+    UserSlice subscript_2;
+    subscript_2.set_step(-2);
+
+    const int32_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}};
+
+    ErrorContext errctx = create_testing_errctx();
+    ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
+                             &dst_ndarray);
+    assert_errctx_no_error(&errctx);
+
+    int32_t expected_shape[dst_ndims] = {2};
+    int32_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, (int32_t[dst_ndims]){0})));
+    assert_values_match(9.0,
+                        *((double *)ndarray::basic::get_pelement_by_indices(
+                            &dst_ndarray, (int32_t[dst_ndims]){1})));
+}
+
+void test_index_subscript_out_of_bounds() {
+    /*
+        # Consider `my_array`
+
+        print(my_array.shape)
+        # (4, 5, 6)
+
+        my_array[2, 100] # error, index subscript at axis 1 is out of bounds
+    */
+    BEGIN_TEST();
+
+    // Prepare src_ndarray
+    const int32_t src_ndims = 2;
+    int32_t src_shape[src_ndims] = {3, 4};
+    int32_t src_strides[src_ndims] = {};
+    NDArray<int32_t> src_ndarray = {
+        .data = (uint8_t *)nullptr,  // placeholder, we wouldn't access it
+        .itemsize = sizeof(double),  // placeholder
+        .ndims = src_ndims,
+        .shape = src_shape,
+        .strides = src_strides};
+    ndarray::basic::set_strides_by_shape(&src_ndarray);
+
+    // Create the subscripts in `my_array[2, 100]`
+    int32_t subscript_1 = 2;
+    int32_t subscript_2 = 100;
+
+    const int32_t num_indexes = 2;
+    NDIndex indexes[num_indexes] = {
+        {.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
+        {.type = ND_INDEX_TYPE_SINGLE_ELEMENT,
+         .data = (uint8_t *)&subscript_2}};
+
+    // Prepare dst_ndarray
+    const int32_t dst_ndims = 0;
+    int32_t dst_shape[dst_ndims] = {};
+    int32_t dst_strides[dst_ndims] = {};
+    NDArray<int32_t> dst_ndarray = {.data = nullptr,  // placehloder
+                                    .ndims = dst_ndims,
+                                    .shape = dst_shape,
+                                    .strides = dst_strides};
+
+    ErrorContext errctx = create_testing_errctx();
+    ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
+                             &dst_ndarray);
+    assert_errctx_has_error(&errctx, errctx.error_ids->index_error);
+}
+
+void run() {
+    test_normal_1();
+    test_normal_2();
+    test_index_subscript_out_of_bounds();
+}
+}  // namespace ndarray_indexing
+}  // namespace test

nac3core/irrt/test/test_slice.hpp

@@ -0,0 +1,92 @@
+#pragma once
+
+#include <irrt_everything.hpp>
+#include <test/includes.hpp>
+
+namespace test {
+namespace slice {
+void test_slice_normal() {
+    // Normal situation
+    BEGIN_TEST();
+
+    UserSlice user_slice;
+    user_slice.set_stop(5);
+
+    Slice slice = user_slice.indices(100);
+
+    printf("%d, %d, %d\n", slice.start, slice.stop, slice.step);
+
+    assert_values_match(0, slice.start);
+    assert_values_match(5, slice.stop);
+    assert_values_match(1, slice.step);
+}
+
+void test_slice_start_too_large() {
+    // Start is too large and should be clamped to length
+    BEGIN_TEST();
+
+    UserSlice user_slice;
+    user_slice.set_start(400);
+
+    Slice slice = user_slice.indices(100);
+
+    assert_values_match(100, slice.start);
+    assert_values_match(100, slice.stop);
+    assert_values_match(1, slice.step);
+}
+
+void test_slice_negative_start_stop() {
+    // Negative start/stop should be resolved
+    BEGIN_TEST();
+
+    UserSlice user_slice;
+    user_slice.set_start(-10);
+    user_slice.set_stop(-5);
+
+    Slice slice = user_slice.indices(100);
+
+    assert_values_match(90, slice.start);
+    assert_values_match(95, slice.stop);
+    assert_values_match(1, slice.step);
+}
+
+void test_slice_only_negative_step() {
+    // Things like `[::-5]` should be handled correctly
+    BEGIN_TEST();
+
+    UserSlice user_slice;
+    user_slice.set_step(-5);
+
+    Slice slice = user_slice.indices(100);
+
+    assert_values_match(99, slice.start);
+    assert_values_match(-1, slice.stop);
+    assert_values_match(-5, slice.step);
+}
+
+void test_slice_step_zero() {
+    // Step = 0 is a value error
+    BEGIN_TEST();
+
+    ErrorContext errctx = create_testing_errctx();
+
+    UserSlice user_slice;
+    user_slice.set_start(2);
+    user_slice.set_stop(12);
+    user_slice.set_step(0);
+
+    Slice slice;
+    user_slice.indices_checked(&errctx, 100, &slice);
+
+    assert_errctx_has_error(&errctx, errctx.error_ids->value_error);
+}
+
+void run() {
+    test_slice_normal();
+    test_slice_start_too_large();
+    test_slice_negative_start_stop();
+    test_slice_only_negative_step();
+    test_slice_step_zero();
+}
+}  // namespace slice
+}  // namespace test

nac3core/irrt/test/util.hpp

@@ -0,0 +1,179 @@
+#pragma once
+
+#include <cstdio>
+#include <cstdlib>
+
+template <class T>
+void print_value(const T& value);
+
+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 uint8_t& value) {
+    printf("%u", value);
+}
+
+template <>
+void print_value(const uint32_t& value) {
+    printf("%u", 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)
+
+// A fake set of ErrorIds for testing only
+const ErrorIds TEST_ERROR_IDS = {
+    .index_error = 0,
+    .value_error = 1,
+    .assertion_error = 2,
+    .runtime_error = 3,
+    .type_error = 4,
+};
+
+ErrorContext create_testing_errctx() {
+    // Everything is global so it is fine to directly return a struct
+    // ErrorContext
+    ErrorContext errctx;
+    errctx.initialize(&TEST_ERROR_IDS);
+    return errctx;
+}
+
+void print_errctx_content(ErrorContext* errctx) {
+    if (errctx->has_error()) {
+        printf(
+            "(Error ID %d): %s ... where param1 = %ld, param2 = %ld, param3 = "
+            "%ld\n",
+            errctx->error_id, errctx->message_template, errctx->param1,
+            errctx->param2, errctx->param3);
+    } else {
+        printf("<no error>\n");
+    }
+}
+
+void __assert_errctx_no_error(const char* file, int line,
+                              ErrorContext* errctx) {
+    if (errctx->has_error()) {
+        print_assertion_failed(file, line);
+        printf("Expecting no error but caught the following:\n\n");
+        print_errctx_content(errctx);
+        test_fail();
+    }
+}
+
+#define assert_errctx_no_error(errctx) \
+    __assert_errctx_no_error(__FILE__, __LINE__, errctx)
+
+void __assert_errctx_has_error(const char* file, int line, ErrorContext* errctx,
+                               ExceptionId expected_error_id) {
+    if (errctx->has_error()) {
+        if (errctx->error_id != expected_error_id) {
+            print_assertion_failed(file, line);
+            printf(
+                "Expecting error id %d but got error id %d. Error caught:\n\n",
+                expected_error_id, errctx->error_id);
+            print_errctx_content(errctx);
+            test_fail();
+        }
+    } else {
+        print_assertion_failed(file, line);
+        printf("Expecting an error, but there is none.");
+        test_fail();
+    }
+}
+
+#define assert_errctx_has_error(errctx, expected_error_id) \
+    __assert_errctx_has_error(__FILE__, __LINE__, errctx, expected_error_id)

nac3core/src/codegen/classes.rs

@@ -1,6 +1,8 @@
 use crate::codegen::{
-    llvm_intrinsics::call_int_umin, stmt::gen_for_callback_incrementing, CodeGenContext,
-    CodeGenerator,
+    irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
+    llvm_intrinsics::call_int_umin,
+    stmt::gen_for_callback_incrementing,
+    CodeGenContext, CodeGenerator,
 };
 use inkwell::context::Context;
 use inkwell::types::{ArrayType, BasicType, StructType};
@@ -10,7 +12,6 @@ use inkwell::{
     values::{BasicValueEnum, IntValue, PointerValue},
     AddressSpace, IntPredicate,
 };
-use itertools::Itertools;
 
 /// A LLVM type that is used to represent a non-primitive type in NAC3.
 pub trait ProxyType<'ctx>: Into<Self::Base> {
@@ -1600,8 +1601,7 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
         ctx: &CodeGenContext<'ctx, '_>,
         generator: &G,
     ) -> IntValue<'ctx> {
-        todo!()
-        // call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
+        call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
     }
 }
 
@@ -1675,19 +1675,17 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
             indices_elem_ty.get_bit_width()
         );
 
-        todo!()
+        let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
 
-        // let index = call_ndarray_flatten_index(generator, ctx, *self.0, indices);
-
-        // unsafe {
-        //     ctx.builder
-        //         .build_in_bounds_gep(
-        //             self.base_ptr(ctx, generator),
-        //             &[index],
-        //             name.unwrap_or_default(),
-        //         )
-        //         .unwrap()
-        // }
+        unsafe {
+            ctx.builder
+                .build_in_bounds_gep(
+                    self.base_ptr(ctx, generator),
+                    &[index],
+                    name.unwrap_or_default(),
+                )
+                .unwrap()
+        }
     }
 
     fn ptr_offset<G: CodeGenerator + ?Sized>(
@@ -1763,307 +1761,3 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeMutator<'ctx,
     for NDArrayDataProxy<'ctx, '_>
 {
 }
-
-#[derive(Debug, Clone, Copy)]
-pub struct StructField<'ctx> {
-    /// The GEP index of this struct field.
-    pub gep_index: u32,
-    /// Name of this struct field.
-    ///
-    /// Used for generating names.
-    pub name: &'static str,
-    /// The type of this struct field.
-    pub ty: BasicTypeEnum<'ctx>,
-}
-
-pub struct StructFields<'ctx> {
-    /// Name of the struct.
-    ///
-    /// Used for generating names.
-    pub name: &'static str,
-
-    /// All the [`StructField`]s of this struct.
-    ///
-    /// **NOTE:** The index position of a [`StructField`]
-    /// matches the element's [`StructField::index`].
-    pub fields: Vec<StructField<'ctx>>,
-}
-
-struct StructFieldsBuilder<'ctx> {
-    gep_index_counter: u32,
-    /// Name of the struct to be built.
-    name: &'static str,
-    fields: Vec<StructField<'ctx>>,
-}
-
-impl<'ctx> StructField<'ctx> {
-    pub fn gep(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        ptr: PointerValue<'ctx>,
-    ) -> PointerValue<'ctx> {
-        ctx.builder.build_struct_gep(ptr, self.gep_index, self.name).unwrap()
-    }
-
-    pub fn load(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        ptr: PointerValue<'ctx>,
-    ) -> BasicValueEnum<'ctx> {
-        ctx.builder.build_load(self.gep(ctx, ptr), self.name).unwrap()
-    }
-
-    pub fn store<V>(&self, ctx: &CodeGenContext<'ctx, '_>, ptr: PointerValue<'ctx>, value: V)
-    where
-        V: BasicValue<'ctx>,
-    {
-        ctx.builder.build_store(ptr, value).unwrap();
-    }
-}
-
-type IsInstanceError = String;
-type IsInstanceResult = Result<(), IsInstanceError>;
-
-pub fn check_basic_types_match<'ctx, A, B>(expected: A, got: B) -> IsInstanceResult
-where
-    A: BasicType<'ctx>,
-    B: BasicType<'ctx>,
-{
-    let expected = expected.as_basic_type_enum();
-    let got = got.as_basic_type_enum();
-
-    // Put those logic into here,
-    // otherwise there is always a fallback reporting on any kind of mismatch
-    match (expected, got) {
-        (BasicTypeEnum::IntType(expected), BasicTypeEnum::IntType(got)) => {
-            if expected.get_bit_width() != got.get_bit_width() {
-                return Err(format!(
-                    "Expected IntType ({expected}-bit(s)), got IntType ({got}-bit(s))"
-                ));
-            }
-        }
-        (expected, got) => {
-            if expected != got {
-                return Err(format!("Expected {expected}, got {got}"));
-            }
-        }
-    }
-    Ok(())
-}
-
-impl<'ctx> StructFields<'ctx> {
-    pub fn num_fields(&self) -> u32 {
-        self.fields.len() as u32
-    }
-
-    pub fn as_struct_type(&self, ctx: &'ctx Context) -> StructType<'ctx> {
-        let llvm_fields = self.fields.iter().map(|field| field.ty).collect_vec();
-        ctx.struct_type(llvm_fields.as_slice(), false)
-    }
-
-    pub fn is_type(&self, scrutinee: StructType<'ctx>) -> IsInstanceResult {
-        // Check scrutinee's number of struct fields
-        if scrutinee.count_fields() != self.num_fields() {
-            return Err(format!(
-                "Expected {expected_count} field(s) in `{struct_name}` type, got {got_count}",
-                struct_name = self.name,
-                expected_count = self.num_fields(),
-                got_count = scrutinee.count_fields(),
-            ));
-        }
-
-        // Check the scrutinee's field types
-        for field in self.fields.iter() {
-            let expected_field_ty = field.ty;
-            let got_field_ty = scrutinee.get_field_type_at_index(field.gep_index).unwrap();
-
-            if let Err(field_err) = check_basic_types_match(expected_field_ty, got_field_ty) {
-                return Err(format!(
-                    "Field GEP index {gep_index} does not match the expected type of ({struct_name}::{field_name}): {field_err}",
-                    gep_index = field.gep_index,
-                    struct_name = self.name,
-                    field_name = field.name,
-                ));
-            }
-        }
-
-        // Done
-        Ok(())
-    }
-}
-
-impl<'ctx> StructFieldsBuilder<'ctx> {
-    fn start(name: &'static str) -> Self {
-        StructFieldsBuilder { gep_index_counter: 0, name, fields: Vec::new() }
-    }
-
-    fn add_field(&mut self, name: &'static str, ty: BasicTypeEnum<'ctx>) -> StructField<'ctx> {
-        let index = self.gep_index_counter;
-        self.gep_index_counter += 1;
-        StructField { gep_index: index, name, ty }
-    }
-
-    fn end(self) -> StructFields<'ctx> {
-        StructFields { name: self.name, fields: self.fields }
-    }
-}
-
-#[derive(Debug, Clone, Copy)]
-pub struct NpArrayType<'ctx> {
-    pub size_type: IntType<'ctx>,
-    pub elem_type: BasicTypeEnum<'ctx>,
-}
-
-pub struct NpArrayStructFields<'ctx> {
-    pub whole_struct: StructFields<'ctx>,
-    pub data: StructField<'ctx>,
-    pub itemsize: StructField<'ctx>,
-    pub ndims: StructField<'ctx>,
-    pub shape: StructField<'ctx>,
-    pub strides: StructField<'ctx>,
-}
-
-impl<'ctx> NpArrayType<'ctx> {
-    pub fn new_opaque_elem(
-        ctx: &CodeGenContext<'ctx, '_>,
-        size_type: IntType<'ctx>,
-    ) -> NpArrayType<'ctx> {
-        NpArrayType { size_type, elem_type: ctx.ctx.i8_type().as_basic_type_enum() }
-    }
-
-    pub fn struct_type(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructType<'ctx> {
-        self.fields().whole_struct.as_struct_type(ctx.ctx)
-    }
-
-    pub fn fields(&self) -> NpArrayStructFields<'ctx> {
-        let mut builder = StructFieldsBuilder::start("NpArray");
-
-        let addrspace = AddressSpace::default();
-
-        let byte_type = self.size_type.get_context().i8_type();
-
-        // Make sure the struct matches PERFECTLY with that defined in `nac3core/irrt`.
-        let data = builder.add_field("data", byte_type.ptr_type(addrspace).into());
-        let itemsize = builder.add_field("itemsize", self.size_type.into());
-        let ndims = builder.add_field("ndims", self.size_type.into());
-        let shape = builder.add_field("shape", self.size_type.ptr_type(addrspace).into());
-        let strides = builder.add_field("strides", self.size_type.ptr_type(addrspace).into());
-
-        NpArrayStructFields { whole_struct: builder.end(), data, itemsize, ndims, shape, strides }
-    }
-
-    /// Allocate an `ndarray` on stack, with the following notes:
-    ///
-    /// - `ndarray.ndims` will be initialized to `in_ndims`.
-    /// - `ndarray.itemsize` will be initialized to the size of `self.elem_type.size_of()`.
-    /// - `ndarray.shape` and `ndarray.strides` will be allocated on the stack with number of elements being `in_ndims`,
-    /// all with empty/uninitialized values.
-    pub fn alloca(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        in_ndims: IntValue<'ctx>,
-        name: &str,
-    ) -> NpArrayValue<'ctx> {
-        let fields = self.fields();
-        let ptr =
-            ctx.builder.build_alloca(fields.whole_struct.as_struct_type(ctx.ctx), name).unwrap();
-
-        // Allocate `in_dims` number of `size_type` on the stack for `shape` and `strides`
-        let allocated_shape =
-            ctx.builder.build_array_alloca(fields.shape.ty, in_ndims, "allocated_shape").unwrap();
-        let allocated_strides = ctx
-            .builder
-            .build_array_alloca(fields.strides.ty, in_ndims, "allocated_strides")
-            .unwrap();
-
-        let value = NpArrayValue { ty: *self, ptr };
-        value.store_ndims(ctx, in_ndims);
-        value.store_itemsize(ctx, self.elem_type.size_of().unwrap());
-        value.store_shape(ctx, allocated_shape);
-        value.store_strides(ctx, allocated_strides);
-
-        return value;
-    }
-}
-
-#[derive(Debug, Clone, Copy)]
-pub struct NpArrayValue<'ctx> {
-    pub ty: NpArrayType<'ctx>,
-    pub ptr: PointerValue<'ctx>,
-}
-
-impl<'ctx> NpArrayValue<'ctx> {
-    pub fn load_ndims(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
-        let field = self.ty.fields().ndims;
-        field.load(ctx, self.ptr).into_int_value()
-    }
-
-    pub fn store_ndims(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
-        let field = self.ty.fields().ndims;
-        field.store(ctx, self.ptr, value);
-    }
-
-    pub fn load_itemsize(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
-        let field = self.ty.fields().itemsize;
-        field.load(ctx, self.ptr).into_int_value()
-    }
-
-    pub fn store_itemsize(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
-        let field = self.ty.fields().itemsize;
-        field.store(ctx, self.ptr, value);
-    }
-
-    pub fn load_shape(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        let field = self.ty.fields().shape;
-        field.load(ctx, self.ptr).into_pointer_value()
-    }
-
-    pub fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
-        let field = self.ty.fields().shape;
-        field.store(ctx, self.ptr, value);
-    }
-
-    pub fn load_strides(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        let field = self.ty.fields().strides;
-        field.load(ctx, self.ptr).into_pointer_value()
-    }
-
-    pub fn store_strides(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
-        let field = self.ty.fields().strides;
-        field.store(ctx, self.ptr, value);
-    }
-
-    /// TODO: DOCUMENT ME -- NDIMS WOULD NEVER CHANGE!!!!!
-    pub fn shape_slice(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-    ) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
-        let field = self.ty.fields().shape;
-        field.gep(ctx, self.ptr);
-
-        let ndims = self.load_ndims(ctx);
-
-        TypedArrayLikeAdapter {
-            adapted: ArraySliceValue(self.ptr, ndims, Some(field.name)),
-            downcast_fn: Box::new(|_ctx, x| x.into_int_value()),
-            upcast_fn: Box::new(|_ctx, x| x.as_basic_value_enum()),
-        }
-    }
-
-    /// TODO: DOCUMENT ME -- NDIMS WOULD NEVER CHANGE!!!!!
-    pub fn strides_slice(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-    ) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
-        let field = self.ty.fields().strides;
-        field.gep(ctx, self.ptr);
-
-        let ndims = self.load_ndims(ctx);
-
-        TypedArrayLikeAdapter {
-            adapted: ArraySliceValue(self.ptr, ndims, Some(field.name)),
-            downcast_fn: Box::new(|_ctx, x| x.into_int_value()),
-            upcast_fn: Box::new(|_ctx, x| x.as_basic_value_enum()),
-        }
-    }
-}

nac3core/src/codegen/generator.rs

@@ -11,12 +11,18 @@ use inkwell::{
 };
 use nac3parser::ast::{Expr, Stmt, StrRef};
 
+use super::model::SizeTModel;
+
 pub trait CodeGenerator {
     /// Return the module name for the code generator.
     fn get_name(&self) -> &str;
 
     fn get_size_type<'ctx>(&self, ctx: &'ctx Context) -> IntType<'ctx>;
 
+    fn get_sizet<'ctx>(&self, ctx: &'ctx Context) -> SizeTModel<'ctx> {
+        SizeTModel(self.get_size_type(ctx))
+    }
+
     /// Generate function call and returns the function return value.
     /// - obj: Optional object for method call.
     /// - fun: Function signature and definition ID.

nac3core/src/codegen/irrt/error_context.rs

@@ -0,0 +1,195 @@
+use crate::codegen::{model::*, structs::cslice::CSlice, CodeGenContext, CodeGenerator};
+
+use super::util::get_sized_dependent_function_name;
+
+/// The [`IntModel`] of nac3core's error ID.
+///
+/// It is always [`Int32`].
+type ErrorId = Int32;
+
+#[allow(clippy::struct_field_names)]
+pub struct ErrorIdsFields {
+    pub index_error: Field<NIntModel<ErrorId>>,
+    pub value_error: Field<NIntModel<ErrorId>>,
+    pub assertion_error: Field<NIntModel<ErrorId>>,
+    pub runtime_error: Field<NIntModel<ErrorId>>,
+    pub type_error: Field<NIntModel<ErrorId>>,
+}
+
+/// Corresponds to IRRT's `struct ErrorIds`
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct ErrorIds;
+
+impl<'ctx> StructKind<'ctx> for ErrorIds {
+    type Fields = ErrorIdsFields;
+
+    fn struct_name(&self) -> &'static str {
+        "ErrorIds"
+    }
+
+    fn build_fields(&self, builder: &mut FieldBuilder) -> Self::Fields {
+        Self::Fields {
+            index_error: builder.add_field_auto("index_error"),
+            value_error: builder.add_field_auto("value_error"),
+            assertion_error: builder.add_field_auto("assertion_error"),
+            runtime_error: builder.add_field_auto("runtime_error"),
+            type_error: builder.add_field_auto("type_error"),
+        }
+    }
+}
+
+pub struct ErrorContextFields {
+    pub error_ids: Field<PointerModel<StructModel<ErrorIds>>>,
+    pub error_id: Field<NIntModel<ErrorId>>,
+    pub message_template: Field<PointerModel<NIntModel<Byte>>>,
+    pub param1: Field<NIntModel<Int64>>,
+    pub param2: Field<NIntModel<Int64>>,
+    pub param3: Field<NIntModel<Int64>>,
+}
+
+/// Corresponds to IRRT's `struct ErrorContext`
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct ErrorContext;
+
+impl<'ctx> StructKind<'ctx> for ErrorContext {
+    type Fields = ErrorContextFields;
+
+    fn struct_name(&self) -> &'static str {
+        "ErrorIds"
+    }
+
+    fn build_fields(&self, builder: &mut FieldBuilder) -> Self::Fields {
+        Self::Fields {
+            error_ids: builder.add_field_auto("error_ids"),
+            error_id: builder.add_field_auto("error_id"),
+            message_template: builder.add_field_auto("message_template"),
+            param1: builder.add_field_auto("param1"),
+            param2: builder.add_field_auto("param2"),
+            param3: builder.add_field_auto("param3"),
+        }
+    }
+}
+
+// Prepare ErrorIds
+fn build_error_ids<'ctx>(ctx: &CodeGenContext<'ctx, '_>) -> Pointer<'ctx, StructModel<ErrorIds>> {
+    // ErrorIdsLens.get_fields(ctx.ctx).assertion_error.
+    let error_ids = StructModel(ErrorIds).alloca(ctx, "error_ids");
+    let i32_model = NIntModel(Int32);
+    // i32_model.make_constant()
+
+    let get_string_id =
+        |string_id| i32_model.constant(ctx.ctx, ctx.resolver.get_string_id(string_id) as u64);
+
+    error_ids.gep(ctx, |f| f.index_error).store(ctx, get_string_id("0:IndexError"));
+    error_ids.gep(ctx, |f| f.value_error).store(ctx, get_string_id("0:ValueError"));
+    error_ids.gep(ctx, |f| f.assertion_error).store(ctx, get_string_id("0:AssertionError"));
+    error_ids.gep(ctx, |f| f.runtime_error).store(ctx, get_string_id("0:RuntimeError"));
+    error_ids.gep(ctx, |f| f.type_error).store(ctx, get_string_id("0:TypeError"));
+
+    error_ids
+}
+
+pub fn call_nac3_error_context_initialize<'ctx>(
+    ctx: &CodeGenContext<'ctx, '_>,
+    perrctx: Pointer<'ctx, StructModel<ErrorContext>>,
+    perror_ids: Pointer<'ctx, StructModel<ErrorIds>>,
+) {
+    FunctionBuilder::begin(ctx, "__nac3_error_context_initialize")
+        .arg("errctx", perrctx)
+        .arg("error_ids", perror_ids)
+        .returning_void();
+}
+
+pub fn call_nac3_error_context_has_error<'ctx>(
+    ctx: &CodeGenContext<'ctx, '_>,
+    errctx: Pointer<'ctx, StructModel<ErrorContext>>,
+) -> NInt<'ctx, Bool> {
+    FunctionBuilder::begin(ctx, "__nac3_error_context_has_error")
+        .arg("errctx", errctx)
+        .returning("has_error", NIntModel(Bool))
+}
+
+pub fn call_nac3_error_context_get_error_str<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    errctx: Pointer<'ctx, StructModel<ErrorContext>>,
+    dst_str: Pointer<'ctx, StructModel<CSlice<'ctx>>>,
+) {
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    FunctionBuilder::begin(
+        ctx,
+        &get_sized_dependent_function_name(sizet, "__nac3_error_context_get_error_str"),
+    )
+    .arg("errctx", errctx)
+    .arg("dst_str", dst_str)
+    .returning_void();
+}
+
+/// Setup a [`ErrorContext`] that could
+/// be passed to IRRT functions taking in a `ErrorContext* errctx`
+/// for error reporting purposes.
+///
+/// Also see: [`check_error_context`]
+pub fn setup_error_context<'ctx>(
+    ctx: &CodeGenContext<'ctx, '_>,
+) -> Pointer<'ctx, StructModel<ErrorContext>> {
+    let error_ids = build_error_ids(ctx);
+    let errctx_ptr = StructModel(ErrorContext).alloca(ctx, "errctx");
+    call_nac3_error_context_initialize(ctx, errctx_ptr, error_ids);
+    errctx_ptr
+}
+
+/// Check a [`ErrorContext`] to see
+/// if it contains error.
+///
+/// If there is an error, an LLVM exception will be raised at runtime.
+pub fn check_error_context<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    errctx_ptr: Pointer<'ctx, StructModel<ErrorContext>>,
+) {
+    let sizet = generator.get_sizet(ctx.ctx);
+    let cslice_model = StructModel(CSlice { sizet });
+
+    let current_bb = ctx.builder.get_insert_block().unwrap();
+    let irrt_has_error_bb = ctx.ctx.insert_basic_block_after(current_bb, "irrt_has_error");
+    let end_bb = ctx.ctx.insert_basic_block_after(irrt_has_error_bb, "end");
+
+    // Inserting into `current_bb`
+    let has_error = call_nac3_error_context_has_error(ctx, errctx_ptr);
+    ctx.builder.build_conditional_branch(has_error.value, irrt_has_error_bb, end_bb).unwrap();
+
+    // Inserting into `irrt_has_error_bb`
+    ctx.builder.position_at_end(irrt_has_error_bb);
+
+    // Load all the values for `ctx.make_assert_impl_by_id`
+    let pstr = cslice_model.alloca(ctx, "error_str");
+    call_nac3_error_context_get_error_str(generator, ctx, errctx_ptr, pstr);
+
+    let error_id = errctx_ptr.gep(ctx, |f| f.error_id).load(ctx, "error_id");
+    let msg = pstr.load(ctx, "msg");
+    let param1 = errctx_ptr.gep(ctx, |f| f.param1).load(ctx, "param1");
+    let param2 = errctx_ptr.gep(ctx, |f| f.param2).load(ctx, "param2");
+    let param3 = errctx_ptr.gep(ctx, |f| f.param3).load(ctx, "param3");
+
+    ctx.raise_exn_impl(
+        generator,
+        error_id,
+        msg,
+        [Some(param1), Some(param2), Some(param3)],
+        ctx.current_loc,
+    );
+
+    // Position to `end_bb` for continuation
+    ctx.builder.position_at_end(end_bb);
+}
+
+pub fn call_nac3_dummy_raise<G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext,
+) {
+    let errctx = setup_error_context(ctx);
+    FunctionBuilder::begin(ctx, "__nac3_error_dummy_raise").arg("errctx", errctx).returning_void();
+    check_error_context(generator, ctx, errctx);
+}

nac3core/src/codegen/irrt/mod.rs

@@ -1,13 +1,17 @@
-use crate::{typecheck::typedef::Type, util::SizeVariant};
+use crate::typecheck::typedef::Type;
 
+mod error_context;
+pub mod ndarray;
+pub mod slice;
 mod test;
+mod util;
 
 use super::{
     classes::{
-        ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue, NpArrayType,
-        NpArrayValue, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
+        ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
+        TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
     },
-    llvm_intrinsics, CodeGenContext, CodeGenerator,
+    llvm_intrinsics, CodeGenContext, CodeGenerator, Int, Int64, NIntModel,
 };
 use crate::codegen::classes::TypedArrayLikeAccessor;
 use crate::codegen::stmt::gen_for_callback_incrementing;
@@ -16,8 +20,8 @@ use inkwell::{
     context::Context,
     memory_buffer::MemoryBuffer,
     module::Module,
-    types::{BasicType, BasicTypeEnum, FunctionType, IntType, PointerType},
-    values::{BasicValueEnum, CallSiteValue, FloatValue, FunctionValue, IntValue},
+    types::{BasicTypeEnum, IntType},
+    values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
     AddressSpace, IntPredicate,
 };
 use itertools::Either;
@@ -416,14 +420,27 @@ 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();
+
+    // TODO: Temporary fix. Rewrite `list_slice_assignment` later
+    // Exception params should have been i64
+    {
+        let param_model = NIntModel(Int64);
+
+        let src_slice_len =
+            Int::from(src_slice_len).s_extend_or_bit_cast(ctx, param_model, "src_slice_len");
+        let dest_slice_len =
+            Int::from(dest_slice_len).s_extend_or_bit_cast(ctx, param_model, "dest_slice_len");
+        let dest_idx_2 = Int::from(dest_idx.2).s_extend_or_bit_cast(ctx, param_model, "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), Some(dest_slice_len), Some(dest_idx.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![
@@ -929,63 +946,3 @@ pub fn call_ndarray_calc_broadcast_index<
         Box::new(|_, v| v.into()),
     )
 }
-
-fn get_size_variant<'ctx>(ty: IntType<'ctx>) -> SizeVariant {
-    match ty.get_bit_width() {
-        32 => SizeVariant::Bits32,
-        64 => SizeVariant::Bits64,
-        _ => unreachable!("Unsupported int type bit width {}", ty.get_bit_width()),
-    }
-}
-
-fn get_size_type_dependent_function<'ctx, BuildFuncTypeFn>(
-    ctx: &CodeGenContext<'ctx, '_>,
-    size_type: IntType<'ctx>,
-    base_name: &str,
-    build_func_type: BuildFuncTypeFn,
-) -> FunctionValue<'ctx>
-where
-    BuildFuncTypeFn: Fn() -> FunctionType<'ctx>,
-{
-    let mut fn_name = base_name.to_owned();
-    match get_size_variant(size_type) {
-        SizeVariant::Bits32 => {
-            // The original fn_name is the correct function name
-        }
-        SizeVariant::Bits64 => {
-            // Append "64" at the end, this is the naming convention for 64-bit
-            fn_name.push_str("64");
-        }
-    }
-
-    // Get (or declare then get if does not exist) the corresponding function
-    ctx.module.get_function(&fn_name).unwrap_or_else(|| {
-        let fn_type = build_func_type();
-        ctx.module.add_function(&fn_name, fn_type, None)
-    })
-}
-
-fn get_ndarray_struct_ptr<'ctx>(ctx: &'ctx Context, size_type: IntType<'ctx>) -> PointerType<'ctx> {
-    let i8_type = ctx.i8_type();
-
-    let ndarray_ty = NpArrayType { size_type, elem_type: i8_type.as_basic_type_enum() };
-    let struct_ty = ndarray_ty.fields().whole_struct.as_struct_type(ctx);
-    struct_ty.ptr_type(AddressSpace::default())
-}
-
-pub fn call_nac3_ndarray_size<'ctx>(
-    ctx: &CodeGenContext<'ctx, '_>,
-    ndarray: NpArrayValue<'ctx>,
-) -> IntValue<'ctx> {
-    let size_type = ndarray.ty.size_type;
-    let function = get_size_type_dependent_function(ctx, size_type, "__nac3_ndarray_size", || {
-        size_type.fn_type(&[get_ndarray_struct_ptr(ctx.ctx, size_type).into()], false)
-    });
-
-    ctx.builder
-        .build_call(function, &[ndarray.ptr.into()], "size")
-        .unwrap()
-        .try_as_basic_value()
-        .unwrap_left()
-        .into_int_value()
-}

nac3core/src/codegen/irrt/ndarray/allocation.rs

@@ -0,0 +1,93 @@
+use crate::codegen::model::*;
+use crate::codegen::util::array_writer::ArrayWriter;
+use crate::codegen::{structs::ndarray::NpArray, CodeGenContext, CodeGenerator};
+
+use super::basic::{
+    call_nac3_ndarray_nbytes, call_nac3_ndarray_set_strides_by_shape,
+    call_nac3_ndarray_util_assert_shape_no_negative,
+};
+
+/**
+    Allocate an ndarray on the stack given its `ndims`.
+
+    `shape` and `strides` will be automatically allocated on the stack.
+
+    The returned ndarray's content will be:
+    - `data`: `nullptr`
+    - `itemsize`: **uninitialized** value
+    - `ndims`: initialized value, set to the input `ndims`
+    - `shape`: initialized pointer to an allocated stack with **uninitialized** values
+    - `strides`: initialized pointer to an allocated stack with **uninitialized** values
+*/
+pub fn alloca_ndarray<'ctx, G>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndims: SizeT<'ctx>,
+    name: &str,
+) -> Result<Pointer<'ctx, StructModel<NpArray<'ctx>>>, String>
+where
+    G: CodeGenerator + ?Sized,
+{
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    let ndarray_model = StructModel(NpArray { sizet });
+    let ndarray_data_model = PointerModel(NIntModel(Byte));
+
+    // Allocate ndarray
+    let ndarray_ptr = ndarray_model.alloca(ctx, name);
+
+    // Set data to nullptr
+    ndarray_ptr.gep(ctx, |f| f.data).store(ctx, ndarray_data_model.nullptr(ctx.ctx));
+
+    // Set ndims
+    ndarray_ptr.gep(ctx, |f| f.ndims).store(ctx, ndims);
+
+    // Allocate and set shape
+    let shape_array = sizet.array_alloca(ctx, ndims, "shape");
+    ndarray_ptr.gep(ctx, |f| f.shape).store(ctx, shape_array.pointer);
+
+    // Allocate and set strides
+    let strides_array = sizet.array_alloca(ctx, ndims, "strides");
+    ndarray_ptr.gep(ctx, |f| f.strides).store(ctx, strides_array.pointer);
+
+    Ok(ndarray_ptr)
+}
+
+/// Initialize an ndarray's `shape` and asserts on.
+/// `shape`'s values and prohibit illegal inputs like negative dimensions.
+pub fn init_ndarray_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray_ptr: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+    shape_writer: &ArrayWriter<'ctx, G, SizeTModel<'ctx>, SizeTModel<'ctx>>,
+) -> Result<(), String> {
+    (shape_writer.write)(generator, ctx, &ndarray_ptr.shape_slice(ctx))?;
+
+    call_nac3_ndarray_util_assert_shape_no_negative(
+        generator,
+        ctx,
+        shape_writer.count,
+        ndarray_ptr.gep(ctx, |f| f.shape).load(ctx, "shape"),
+    );
+
+    Ok(())
+}
+
+/// 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 init_ndarray_data_by_alloca<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray_ptr: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+) {
+    let ndarray_nbytes = call_nac3_ndarray_nbytes(generator, ctx, ndarray_ptr); // Needs `itemsize` initialized
+
+    let data_array = NIntModel(Byte).array_alloca(ctx, ndarray_nbytes, "data");
+    ndarray_ptr.gep(ctx, |f| f.data).store(ctx, data_array.pointer);
+
+    call_nac3_ndarray_set_strides_by_shape(generator, ctx, ndarray_ptr);
+}

nac3core/src/codegen/irrt/ndarray/basic.rs

@@ -0,0 +1,117 @@
+use crate::codegen::irrt::error_context::{check_error_context, setup_error_context};
+use crate::codegen::irrt::slice::SliceIndex;
+use crate::codegen::irrt::util::get_sized_dependent_function_name;
+use crate::codegen::model::*;
+use crate::codegen::{structs::ndarray::NpArray, CodeGenContext, CodeGenerator};
+
+pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray_ptr: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+) -> SizeT<'ctx> {
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    FunctionBuilder::begin(ctx, &get_sized_dependent_function_name(sizet, "__nac3_ndarray_size"))
+        .arg("ndarray", ndarray_ptr)
+        .returning("size", sizet)
+}
+
+pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray_ptr: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+) -> SizeT<'ctx> {
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    FunctionBuilder::begin(ctx, &get_sized_dependent_function_name(sizet, "__nac3_ndarray_nbytes"))
+        .arg("ndarray", ndarray_ptr)
+        .returning("nbytes", sizet)
+}
+
+pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray_ptr: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+) -> NInt<'ctx, SliceIndex> {
+    let sizet = generator.get_sizet(ctx.ctx);
+    let slice_index_model = NIntModel(SliceIndex::default());
+
+    let dst_len = slice_index_model.alloca(ctx, "dst_len");
+
+    let errctx = setup_error_context(ctx);
+    FunctionBuilder::begin(ctx, &get_sized_dependent_function_name(sizet, "__nac3_ndarray_len"))
+        .arg("errctx", errctx)
+        .arg("ndarray", ndarray_ptr)
+        .arg("dst_len", dst_len)
+        .returning_void();
+    check_error_context(generator, ctx, errctx);
+
+    dst_len.load(ctx, "len")
+}
+
+pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndims: SizeT<'ctx>,
+    shape_ptr: Pointer<'ctx, SizeTModel<'ctx>>,
+) {
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    let errctx = setup_error_context(ctx);
+    FunctionBuilder::begin(
+        ctx,
+        &get_sized_dependent_function_name(sizet, "__nac3_ndarray_util_assert_shape_no_negative"),
+    )
+    .arg("errctx", errctx)
+    .arg("ndims", ndims)
+    .arg("shape", shape_ptr)
+    .returning_void();
+    check_error_context(generator, ctx, errctx);
+}
+
+pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray_ptr: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+) {
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    FunctionBuilder::begin(
+        ctx,
+        &get_sized_dependent_function_name(sizet, "__nac3_ndarray_set_strides_by_shape"),
+    )
+    .arg("ndarray", ndarray_ptr)
+    .returning_void();
+}
+
+pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray_ptr: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+) -> NInt<'ctx, Bool> {
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    FunctionBuilder::begin(
+        ctx,
+        &get_sized_dependent_function_name(sizet, "__nac3_ndarray_is_c_contiguous"),
+    )
+    .arg("ndarray", ndarray_ptr)
+    .returning("is_c_contiguous", NIntModel(Bool))
+}
+
+pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    src_ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+    dst_ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+) -> NInt<'ctx, Bool> {
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    FunctionBuilder::begin(
+        ctx,
+        &get_sized_dependent_function_name(sizet, "__nac3_ndarray_copy_data"),
+    )
+    .arg("src_ndarray", src_ndarray)
+    .arg("dst_ndarray", dst_ndarray)
+    .returning("is_c_contiguous", NIntModel(Bool))
+}

nac3core/src/codegen/irrt/ndarray/fill.rs

@@ -0,0 +1,21 @@
+use crate::codegen::{
+    irrt::util::get_sized_dependent_function_name, model::*, structs::ndarray::NpArray,
+    CodeGenContext, CodeGenerator,
+};
+
+pub fn call_nac3_ndarray_fill_generic<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndarray_ptr: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+    fill_value_ptr: Pointer<'ctx, ByteModel>,
+) {
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    FunctionBuilder::begin(
+        ctx,
+        &get_sized_dependent_function_name(sizet, "__nac3_ndarray_fill_generic"),
+    )
+    .arg("ndarray", ndarray_ptr)
+    .arg("pvalue", fill_value_ptr)
+    .returning_void();
+}

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

@@ -0,0 +1,169 @@
+use crate::codegen::{
+    irrt::{
+        error_context::{check_error_context, setup_error_context},
+        slice::{RustUserSlice, SliceIndex, UserSlice},
+        util::get_sized_dependent_function_name,
+    },
+    model::*,
+    structs::ndarray::NpArray,
+    CodeGenContext, CodeGenerator,
+};
+
+#[derive(Debug, Clone, Copy)]
+pub struct NDIndexFields {
+    pub type_: Field<ByteModel>, // Defined to be uint8_t in IRRT
+    pub data: Field<PointerModel<ByteModel>>,
+}
+
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct NDIndex;
+
+impl<'ctx> StructKind<'ctx> for NDIndex {
+    type Fields = NDIndexFields;
+
+    fn struct_name(&self) -> &'static str {
+        "NDIndex"
+    }
+
+    fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
+        Self::Fields { type_: builder.add_field_auto("type"), data: builder.add_field_auto("data") }
+    }
+}
+
+// An enum variant to store the content
+// and type of an NDIndex in high level.
+#[derive(Debug, Clone)]
+pub enum RustNDIndex<'ctx> {
+    SingleElement(NInt<'ctx, SliceIndex>),
+    Slice(RustUserSlice<'ctx>),
+}
+
+impl<'ctx> RustNDIndex<'ctx> {
+    fn irrt_ndindex_id(&self) -> u64 {
+        // Defined in IRRT, must be in sync
+        match self {
+            RustNDIndex::SingleElement(_) => 0,
+            RustNDIndex::Slice(_) => 1,
+        }
+    }
+
+    fn write_to_ndindex(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        dst_ndindex_ptr: Pointer<'ctx, StructModel<NDIndex>>,
+    ) {
+        let byte_model = ByteModel::default();
+        let slice_index_model = NIntModel(SliceIndex::default());
+        let user_slice_model = StructModel(UserSlice);
+
+        // Set `dst_ndindex_ptr->type`
+        dst_ndindex_ptr
+            .gep(ctx, |f| f.type_)
+            .store(ctx, byte_model.constant(ctx.ctx, self.irrt_ndindex_id()));
+
+        // Set `dst_ndindex_ptr->data`
+        let data = match self {
+            RustNDIndex::SingleElement(in_index) => {
+                let index_ptr = slice_index_model.alloca(ctx, "index");
+                index_ptr.store(ctx, *in_index);
+                index_ptr.cast_to(ctx, NIntModel(Byte), "")
+            }
+            RustNDIndex::Slice(in_rust_slice) => {
+                let user_slice_ptr = user_slice_model.alloca(ctx, "user_slice");
+                in_rust_slice.write_to_user_slice(ctx, user_slice_ptr);
+                user_slice_ptr.cast_to(ctx, NIntModel(Byte), "")
+            }
+        };
+        dst_ndindex_ptr.gep(ctx, |f| f.data).store(ctx, data);
+    }
+
+    // Allocate an array of `NDIndex`es onto the stack and return its stack pointer
+    pub fn alloca_ndindexes<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        ndindexes: &[RustNDIndex<'ctx>],
+    ) -> ArraySlice<'ctx, SizeTModel<'ctx>, StructModel<NDIndex>> {
+        let sizet = generator.get_sizet(ctx.ctx);
+
+        let ndindex_model = StructModel(NDIndex);
+        let ndindex_array = ndindex_model.array_alloca(
+            ctx,
+            sizet.constant(ctx.ctx, ndindexes.len() as u64),
+            "ndindexs",
+        );
+
+        for (i, rust_ndindex) in ndindexes.iter().enumerate() {
+            let ndindex_ptr =
+                ndindex_array.ix_unchecked(ctx, sizet.constant(ctx.ctx, i as u64), "");
+            rust_ndindex.write_to_ndindex(ctx, ndindex_ptr);
+        }
+
+        ndindex_array
+    }
+
+    #[must_use]
+    pub fn deduce_ndims_after_slicing(slices: &[RustNDIndex], original_ndims: i32) -> i32 {
+        let mut final_ndims: i32 = original_ndims;
+        for slice in slices {
+            match slice {
+                RustNDIndex::SingleElement(_) => {
+                    final_ndims -= 1;
+                }
+                RustNDIndex::Slice(_) => {}
+            }
+        }
+        final_ndims
+    }
+}
+
+pub fn call_nac3_ndarray_indexing_deduce_ndims_after_indexing<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    ndims: Int<'ctx>,
+    num_ndindexs: SizeT<'ctx>,
+    ndindexs: Pointer<'ctx, StructModel<NDIndex>>,
+) -> SizeT<'ctx> {
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    let final_ndims = sizet.alloca(ctx, "result");
+
+    let errctx_ptr = setup_error_context(ctx);
+    FunctionBuilder::begin(
+        ctx,
+        &get_sized_dependent_function_name(
+            sizet,
+            "__nac3_ndarray_indexing_deduce_ndims_after_indexing",
+        ),
+    )
+    .arg("errctx", errctx_ptr)
+    .arg("result", final_ndims)
+    .arg("ndims", ndims)
+    .arg("num_ndindexs", num_ndindexs)
+    .arg("ndindexs", ndindexs)
+    .returning_void();
+    check_error_context(generator, ctx, errctx_ptr);
+
+    final_ndims.load(ctx, "final_ndims")
+}
+
+pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    num_indexes: SizeT<'ctx>,
+    indexes: Pointer<'ctx, StructModel<NDIndex>>,
+    src_ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+    dst_ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+) {
+    let sizet = generator.get_sizet(ctx.ctx);
+    let errctx_ptr = setup_error_context(ctx);
+
+    FunctionBuilder::begin(ctx, &get_sized_dependent_function_name(sizet, "__nac3_ndarray_index"))
+        .arg("errctx", errctx_ptr)
+        .arg("num_indexes", num_indexes)
+        .arg("indexes", indexes)
+        .arg("src_ndarray", src_ndarray)
+        .arg("dst_ndarray", dst_ndarray)
+        .returning_void();
+
+    check_error_context(generator, ctx, errctx_ptr);
+}

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

@@ -0,0 +1,6 @@
+pub mod allocation;
+pub mod basic;
+pub mod fill;
+pub mod indexing;
+pub mod reshape;
+pub mod transpose;

nac3core/src/codegen/irrt/ndarray/reshape.rs

@@ -0,0 +1,30 @@
+use crate::codegen::{
+    irrt::{
+        error_context::{check_error_context, setup_error_context},
+        util::get_sized_dependent_function_name,
+    },
+    model::*,
+    CodeGenContext, CodeGenerator,
+};
+
+pub fn call_nac3_ndarray_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    size: SizeT<'ctx>,
+    new_ndims: SizeT<'ctx>,
+    new_shape: Pointer<'ctx, SizeTModel<'ctx>>,
+) {
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    let perrctx = setup_error_context(ctx);
+    FunctionBuilder::begin(
+        ctx,
+        &get_sized_dependent_function_name(sizet, "__nac3_ndarray_resolve_and_check_new_shape"),
+    )
+    .arg("errctx", perrctx)
+    .arg("size", size)
+    .arg("new_ndims", new_ndims)
+    .arg("new_shape", new_shape)
+    .returning_void();
+    check_error_context(generator, ctx, perrctx);
+}

nac3core/src/codegen/irrt/ndarray/transpose.rs

@@ -0,0 +1,43 @@
+use crate::codegen::{
+    irrt::{
+        error_context::{check_error_context, setup_error_context},
+        util::get_sized_dependent_function_name,
+    },
+    model::*,
+    structs::ndarray::NpArray,
+    CodeGenContext, CodeGenerator,
+};
+
+pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    src_ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+    dst_ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+    axes_or_none: Option<ArraySlice<'ctx, SizeTModel<'ctx>, SizeTModel<'ctx>>>,
+) -> Pointer<'ctx, StructModel<NpArray<'ctx>>> {
+    let sizet = generator.get_sizet(ctx.ctx);
+    let axes_model = PointerModel(sizet);
+
+    let (num_axes, axes) = match axes_or_none {
+        Some(axes) => (axes.num_elements, axes.pointer),
+        None => {
+            // Please refer to the comment in the IRRT implementation
+            (sizet.constant(ctx.ctx, 0), axes_model.nullptr(ctx.ctx))
+        }
+    };
+
+    let perrctx = setup_error_context(ctx);
+    FunctionBuilder::begin(
+        ctx,
+        &get_sized_dependent_function_name(sizet, "__nac3_ndarray_transpose"),
+    )
+    .arg("errctx", perrctx)
+    .arg("src_ndarray", src_ndarray)
+    .arg("dst_ndarray", dst_ndarray)
+    .arg("num_axes", num_axes)
+    .arg("axes", axes)
+    .returning_void();
+    check_error_context(generator, ctx, perrctx);
+
+    dst_ndarray
+}

nac3core/src/codegen/irrt/slice.rs

@@ -0,0 +1,84 @@
+use crate::codegen::{model::*, CodeGenContext};
+
+// nac3core's slicing index/length values are always int32_t
+pub type SliceIndex = Int32;
+
+#[derive(Debug, Clone)]
+pub struct UserSliceFields {
+    pub start_defined: Field<BoolModel>,
+    pub start: Field<NIntModel<SliceIndex>>,
+    pub stop_defined: Field<BoolModel>,
+    pub stop: Field<NIntModel<SliceIndex>>,
+    pub step_defined: Field<BoolModel>,
+    pub step: Field<NIntModel<SliceIndex>>,
+}
+
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct UserSlice;
+
+impl<'ctx> StructKind<'ctx> for UserSlice {
+    type Fields = UserSliceFields;
+
+    fn struct_name(&self) -> &'static str {
+        "UserSlice"
+    }
+
+    fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
+        Self::Fields {
+            start_defined: builder.add_field_auto("start_defined"),
+            start: builder.add_field_auto("start"),
+            stop_defined: builder.add_field_auto("stop_defined"),
+            stop: builder.add_field_auto("stop"),
+            step_defined: builder.add_field_auto("step_defined"),
+            step: builder.add_field_auto("step"),
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct RustUserSlice<'ctx> {
+    pub start: Option<NInt<'ctx, SliceIndex>>,
+    pub stop: Option<NInt<'ctx, SliceIndex>>,
+    pub step: Option<NInt<'ctx, SliceIndex>>,
+}
+
+impl<'ctx> RustUserSlice<'ctx> {
+    // Set the values of an LLVM UserSlice
+    // in the format of Python's `slice()`
+    pub fn write_to_user_slice(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        dst_slice_ptr: Pointer<'ctx, StructModel<UserSlice>>,
+    ) {
+        // TODO: make this neater, with a helper lambda?
+
+        let bool_model = BoolModel::default();
+
+        let false_ = bool_model.constant(ctx.ctx, 0);
+        let true_ = bool_model.constant(ctx.ctx, 1);
+
+        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_),
+        }
+    }
+}

nac3core/src/codegen/irrt/util.rs

@@ -0,0 +1,16 @@
+use crate::codegen::model::*;
+
+#[must_use]
+pub fn get_sized_dependent_function_name(sizet: SizeTModel<'_>, fn_name: &str) -> String {
+    // When its 32-bits, the function name is "{fn_name}"
+    // When its 64-bits, the function name is "{fn_name}64"
+    let mut fn_name = fn_name.to_owned();
+    match sizet.0.get_bit_width() {
+        32 => {}
+        64 => fn_name.push_str("64"),
+        bit_width => {
+            panic!("Unsupported int type bit width {bit_width}, must be either 32-bits or 64-bits")
+        }
+    }
+    fn_name
+}

nac3core/src/codegen/mod.rs

@@ -1,7 +1,7 @@
 use crate::{
-    codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
+    codegen::classes::{ListType, ProxyType, RangeType},
     symbol_resolver::{StaticValue, SymbolResolver},
-    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
+    toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
     typecheck::{
         type_inferencer::{CodeLocation, PrimitiveStore},
         typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
@@ -24,6 +24,7 @@ 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};
@@ -32,6 +33,7 @@ use std::sync::{
     Arc,
 };
 use std::thread;
+use structs::{cslice::CSlice, exception::Exception, ndarray::NpArray};
 
 pub mod builtin_fns;
 pub mod classes;
@@ -41,11 +43,15 @@ 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 structs;
 
 #[cfg(test)]
 mod test;
+pub mod util;
 
 use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
 pub use generator::{CodeGenerator, DefaultCodeGenerator};
@@ -158,11 +164,11 @@ pub struct CodeGenContext<'ctx, 'a> {
     pub registry: &'a WorkerRegistry,
 
     /// Cache for constant strings.
-    pub const_strings: HashMap<String, BasicValueEnum<'ctx>>,
+    pub const_strings: HashMap<String, Struct<'ctx, CSlice<'ctx>>>,
 
     /// [`BasicBlock`] containing all `alloca` statements for the current function.
     pub init_bb: BasicBlock<'ctx>,
-    pub exception_val: Option<PointerValue<'ctx>>,
+    pub exception_val: Option<Pointer<'ctx, StructModel<Exception<'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.
@@ -471,12 +477,9 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                         }
 
                         TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                            let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
-                            let element_type = get_llvm_type(
-                                ctx, module, generator, unifier, top_level, type_cache, dtype,
-                            );
-
-                            NDArrayType::new(generator, ctx, element_type).as_base_type().into()
+                            let sizet = generator.get_sizet(ctx);
+                            let pndarray_model = PointerModel(StructModel(NpArray { sizet }));
+                            pndarray_model.get_type(ctx).into()
                         }
 
                         _ => unreachable!(
@@ -646,47 +649,24 @@ pub fn gen_func_impl<
         ..primitives
     };
 
-    let mut type_cache: HashMap<_, _> = [
+    let sizet = generator.get_sizet(context);
+    let cslice_type = StructModel(CSlice { sizet });
+    let pexception_type = PointerModel(StructModel(Exception { sizet }));
+
+    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, {
-            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.str, cslice_type.get_type(context).into()),
         (primitives.range, RangeType::new(context).as_base_type().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()
-            }
-        }),
+        (primitives.exception, pexception_type.get_type(context).into()),
     ]
-    .iter()
-    .copied()
+    .into_iter()
     .collect();
+
     // NOTE: special handling of option cannot use this type cache since it contains type var,
     // handled inside get_llvm_type instead
 

nac3core/src/codegen/model/core.rs

@@ -0,0 +1,204 @@
+use core::fmt;
+use std::marker::PhantomData;
+
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum, IntType},
+    values::{BasicValue, IntValue, PointerValue},
+};
+
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+use super::{ArraySlice, Pointer, PointerModel};
+
+/*
+TODO: UPDATE when the Model finally stablizes
+Explanation on the abstraction:
+
+    In LLVM, there are TYPES and VALUES.
+
+    Inkwell gives us TYPES [`BasicTypeEnum<'ctx>`] and VALUES [`BasicValueEnum<'ctx>`],
+        but by themselves, they lack a lot of Rust compile-time known info.
+
+        e.g., You did `let ptr = builder.build_alloca(my_llvm_ndarray_struct_ty)`,
+        but `ptr` is just a `PointerValue<'ctx>`, almost everything about the
+        underlying `my_llvm_ndarray_struct_ty` is gone.
+
+    The `Model` abstraction is a wrapper around inkwell TYPES and VALUES but with
+        a richer interface.
+
+    `Model<'ctx>` is a wrapper around for an inkwell TYPE:
+        - `NIntModel<Byte>` is a i8.
+        - `NIntModel<Int32>` is a i32.
+        - `NIntModel<Int64>` is a i64.
+        - `IntModel` is a carrier for an inkwell `IntType<'ctx>`,
+              used when the type is dynamic/cannot be specified in Rust compile-time.
+        - `PointerModel<'ctx, E>` is a wrapper for `PointerType<'ctx>`,
+              where `E` is another `Model<'ctx>` that describes the element type of the pointer.
+        - `StructModel<'ctx, NDArray>` is a wrapper for `StructType<'ctx>`,
+              with additional information encoded within `NDArray`. (See `IsStruct<'ctx>`)
+
+    `Model<'ctx>::Value`/`ModelValue<'ctx>` is a wrapper around for an inkwell VALUE:
+        - `NInt<'ctx, T>` is a value of `NIntModel<'ctx, T>`,
+              where `T` could be `Byte`, `Int32`, or `Int64`.
+        - `Pointer<'ctx, E>` is a value of `PointerModel<'ctx, E>`.
+
+    Other interesting utilities:
+        - Given a `Model<'ctx>`, say, `let ndarray_model = StructModel<'ctx, NDArray>`,
+              you are do `ndarray_model.alloca(ctx, "my_ndarray")` to get a `Pointer<'ctx, Struct<'ctx, NDArray>>`,
+              notice that all LLVM type information are preserved.
+        - For a `let my_ndarray = Pointer<'ctx, StructModel<NDArray>>`, you can access a field by doing
+              `my_ndarray.gep(ctx, |f| f.itemsize).load() // or .store()`, and you can chain them
+              together for nested structures.
+
+    A brief summary on the `Model<'ctx>` and `ModelValue<'ctx>` traits:
+        - Model<'ctx>
+            // The associated ModelValue of this Model
+            - type Value: ModelValue<'ctx>
+
+            // Get the LLVM type of this Model
+            - fn get_llvm_type(&self)
+
+            // Check if the input type is equal to the LLVM type of this Model
+            // NOTE: this function is provideed through `CanCheckLLVMType<'ctx>`
+            - fn check_llvm_type(&self, ty) -> Result<(), String>
+
+            // Check if the input value's type is equal to the LLVM type of this Model.
+            //
+            // If so, wrap it with `Self::Value`.
+            - fn review_value<V: BasicType<'ctx>>(&self, val: V) -> Result<Self::Value, String>
+
+        - ModelValue<'ctx>
+            // get the LLVM value of this ModelValue
+            - fn get_llvm_value(&self) -> BasicValueEnum<'ctx>
+*/
+
+#[derive(Debug, Clone)]
+pub struct ModelError(pub String);
+
+// NOTE: Should have been within [`Model<'ctx>`],
+// but rust object safety requirements made it necessary to
+// split the trait.
+pub trait CanCheckLLVMType<'ctx> {
+    /// See [`Model::check_llvm_type`]
+    fn check_llvm_type_impl(
+        &self,
+        ctx: &'ctx Context,
+        ty: BasicTypeEnum<'ctx>,
+    ) -> Result<(), ModelError>;
+}
+
+pub trait Model<'ctx>: fmt::Debug + Clone + Copy + CanCheckLLVMType<'ctx> + Sized + Eq {
+    /// The corresponding LLVM [`BasicValue<'ctx>`] of this Model.
+    type Value: BasicValue<'ctx>;
+    /// The corresponding LLVM [`BasicType<'ctx>`] of this Model.
+    type Type: BasicType<'ctx>;
+
+    /// Get the LLVM type of this [`Model<'ctx>`]
+    fn get_type(&self, ctx: &'ctx Context) -> Self::Type;
+
+    /// Check if the input type is equal to the LLVM type of this Model.
+    ///
+    /// If it doesn't match, an [`Err`] with a human-readable message is
+    /// thrown explaining *how* it was different. Meant for debugging.
+    fn check_type<T: BasicType<'ctx>>(&self, ctx: &'ctx Context, ty: T) -> Result<(), ModelError> {
+        self.check_llvm_type_impl(ctx, ty.as_basic_type_enum())
+    }
+
+    /// Check if an LLVM value's type is equal to the LLVM type of this [`Model`].
+    /// If so, wrap it with [`Instance`].
+    fn review_value<V: BasicValue<'ctx>>(
+        &self,
+        ctx: &'ctx Context,
+        value: V,
+    ) -> Result<Instance<'ctx, Self>, ModelError>;
+
+    /// Directly create an [`Instance`] of this [`Model`].
+    ///
+    /// It is assumed that the LLVM type of `value` has been checked.
+    ///
+    /// It is recommended that you use [`Model::review_value`] instead in order to
+    /// catch bugs.
+    fn believe_value(&self, value: Self::Value) -> Instance<'ctx, Self> {
+        Instance { model: *self, value, _phantom: PhantomData }
+    }
+
+    /// Build an instruction to allocate a value with the LLVM type of this [`Model<'ctx>`].
+    fn alloca(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> Pointer<'ctx, Self> {
+        let ptr_model = PointerModel(*self);
+        let ptr = ctx.builder.build_alloca(self.get_type(ctx.ctx), name).unwrap();
+        ptr_model.believe_value(ptr)
+    }
+
+    /// Build an instruction to allocate an array of the LLVM type of this [`Model<'ctx>`].
+    fn array_alloca<N>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        num_elements: Instance<'ctx, N>,
+        name: &str,
+    ) -> ArraySlice<'ctx, N, Self>
+    where
+        N: Model<'ctx, Value = IntValue<'ctx>, Type = IntType<'ctx>>,
+    {
+        let ptr_model = PointerModel(*self);
+        let ptr = ctx
+            .builder
+            .build_array_alloca(
+                self.get_type(ctx.ctx).as_basic_type_enum(),
+                num_elements.value,
+                name,
+            )
+            .unwrap();
+        let pointer = ptr_model.believe_value(ptr);
+
+        ArraySlice { pointer, num_elements }
+    }
+
+    /// Do [`CodeGenerator::gen_var_alloc`] with the LLVM type of this [`Model<'ctx>`].
+    fn var_alloc<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&str>,
+    ) -> Result<Pointer<'ctx, Self>, String> {
+        let ptr_model = PointerModel(*self);
+        let ptr =
+            generator.gen_var_alloc(ctx, self.get_type(ctx.ctx).as_basic_type_enum(), name)?;
+        Ok(ptr_model.believe_value(ptr))
+    }
+
+    /// Do [`CodeGenerator::gen_array_var_alloc`] with the LLVM type of this [`Model<'ctx>`].
+    fn array_var_alloc<G: CodeGenerator + ?Sized, N>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        num_elements: Instance<'ctx, N>,
+        name: Option<&'ctx str>,
+    ) -> Result<ArraySlice<'ctx, N, Self>, String>
+    where
+        N: Model<'ctx, Value = IntValue<'ctx>, Type = IntType<'ctx>>,
+    {
+        let ptr_model = PointerModel(*self);
+
+        // TODO: Remove ProxyType ArraySlice
+        let ptr = ptr_model.believe_value(PointerValue::from(generator.gen_array_var_alloc(
+            ctx,
+            self.get_type(ctx.ctx).as_basic_type_enum(),
+            num_elements.value,
+            name,
+        )?));
+
+        Ok(ArraySlice { num_elements, pointer: ptr })
+    }
+}
+
+/// An LLVM value of a type of a [`Model<'ctx>`].
+///
+/// It is guaranteed that [`Instance::value`]'s LLVM type
+/// has been *checked* to match [`Instance::model`].
+#[derive(Debug, Clone, Copy)]
+pub struct Instance<'ctx, M: Model<'ctx>> {
+    pub model: M,
+    pub value: M::Value,
+    _phantom: PhantomData<&'ctx ()>,
+}

nac3core/src/codegen/model/fixed_int.rs

@@ -0,0 +1,161 @@
+use core::fmt;
+
+use inkwell::{
+    context::Context,
+    types::{BasicTypeEnum, IntType},
+    values::{BasicValue, IntValue},
+};
+
+use super::{
+    core::*,
+    int_util::{check_int_llvm_type, int_constant, review_int_llvm_value},
+    Int, IntModel,
+};
+
+/// A marker trait to mark a singleton struct that describes a particular fixed integer type.
+/// See [`Bool`], [`Byte`], [`Int32`], etc.
+///
+/// The [`Default`] trait is to enable auto-instantiations.
+pub trait NIntKind: fmt::Debug + Clone + Copy + Default + PartialEq + Eq {
+    /// Get the [`IntType<'ctx>`] of this [`NIntKind`].
+    fn get_int_type(ctx: &Context) -> IntType<'_>;
+
+    /// Get the [`IntType<'ctx>`] of this [`NIntKind`].
+    ///
+    /// Compared to using [`NIntKind::get_int_type`], this
+    /// function does not require [`Context`].
+    fn get_bit_width() -> u32;
+}
+
+/// A [`Model`] representing an [`IntType<'ctx>`] of a specified bit width.
+///
+/// Also see [`IntModel`], which is less constrained than [`NIntModel`],
+/// but enables one to handle dynamic [`IntType<'ctx>`] at runtime.
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct NIntModel<T: NIntKind>(pub T);
+pub type NInt<'ctx, T> = Instance<'ctx, NIntModel<T>>;
+
+impl<'ctx, T: NIntKind> CanCheckLLVMType<'ctx> for NIntModel<T> {
+    fn check_llvm_type_impl(
+        &self,
+        ctx: &'ctx Context,
+        ty: BasicTypeEnum<'ctx>,
+    ) -> Result<(), ModelError> {
+        check_int_llvm_type(ty, T::get_int_type(ctx))
+    }
+}
+
+impl<'ctx, T: NIntKind> Model<'ctx> for NIntModel<T> {
+    type Type = IntType<'ctx>;
+    type Value = IntValue<'ctx>;
+
+    fn get_type(&self, ctx: &'ctx Context) -> Self::Type {
+        T::get_int_type(ctx)
+    }
+
+    fn review_value<V: BasicValue<'ctx>>(
+        &self,
+        ctx: &'ctx Context,
+        value: V,
+    ) -> Result<NInt<'ctx, T>, ModelError> {
+        let value = review_int_llvm_value(value.as_basic_value_enum(), T::get_int_type(ctx))?;
+        Ok(self.believe_value(value))
+    }
+}
+
+impl<T: NIntKind> NIntModel<T> {
+    /// "Demote" this [`NIntModel<T>`] to an [`IntModel`].
+    ///
+    /// Information about the [`NIntKind`] will be lost.
+    pub fn to_int_model(self, ctx: &Context) -> IntModel<'_> {
+        IntModel(T::get_int_type(ctx))
+    }
+
+    /// Create an unsigned constant of this [`NIntModel`].
+    pub fn constant<'ctx>(&self, ctx: &'ctx Context, value: u64) -> NInt<'ctx, T> {
+        int_constant(ctx, *self, value)
+    }
+}
+
+impl<'ctx, T: NIntKind> NInt<'ctx, T> {
+    /// "Demote" this [`NInt<T>`] to an [`Int`].
+    ///
+    /// Information about the [`NIntKind`] will be lost.
+    pub fn to_int(self, ctx: &'ctx Context) -> Int<'ctx> {
+        let int_model = self.model.to_int_model(ctx);
+        int_model.believe_value(self.value)
+    }
+}
+
+// Some pre-defined fixed integer types
+
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct Bool;
+pub type BoolModel = NIntModel<Bool>;
+
+impl NIntKind for Bool {
+    fn get_int_type(ctx: &Context) -> IntType<'_> {
+        ctx.bool_type()
+    }
+
+    fn get_bit_width() -> u32 {
+        1
+    }
+}
+
+// Extra utilities for [`Bool`]
+impl NIntModel<Bool> {
+    /// Create a constant `false`
+    #[must_use]
+    pub fn const_false<'ctx>(&self, ctx: &'ctx Context) -> NInt<'ctx, Bool> {
+        self.constant(ctx, 0)
+    }
+
+    /// Create a constant `true`
+    #[must_use]
+    pub fn const_true<'ctx>(&self, ctx: &'ctx Context) -> NInt<'ctx, Bool> {
+        self.constant(ctx, 1)
+    }
+}
+
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct Byte;
+pub type ByteModel = NIntModel<Byte>;
+
+impl NIntKind for Byte {
+    fn get_int_type(ctx: &Context) -> IntType<'_> {
+        ctx.i8_type()
+    }
+
+    fn get_bit_width() -> u32 {
+        8
+    }
+}
+
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct Int32;
+pub type Int32Model = NIntModel<Int32>;
+
+impl NIntKind for Int32 {
+    fn get_int_type(ctx: &Context) -> IntType<'_> {
+        ctx.i32_type()
+    }
+
+    fn get_bit_width() -> u32 {
+        32
+    }
+}
+
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct Int64;
+pub type Int64Model = NIntModel<Int64>;
+
+impl NIntKind for Int64 {
+    fn get_int_type(ctx: &Context) -> IntType<'_> {
+        ctx.i64_type()
+    }
+
+    fn get_bit_width() -> u32 {
+        64
+    }
+}

nac3core/src/codegen/model/function_builder.rs

@@ -0,0 +1,66 @@
+use inkwell::{
+    types::{BasicMetadataTypeEnum, BasicType},
+    values::{AnyValue, BasicMetadataValueEnum, BasicValue, BasicValueEnum},
+};
+
+use crate::codegen::{model::*, CodeGenContext};
+
+// TODO: Variadic argument?
+pub struct FunctionBuilder<'ctx, 'a> {
+    ctx: &'a CodeGenContext<'ctx, 'a>,
+    fn_name: &'a str,
+    arguments: Vec<(BasicMetadataTypeEnum<'ctx>, BasicMetadataValueEnum<'ctx>)>,
+}
+
+impl<'ctx, 'a> FunctionBuilder<'ctx, 'a> {
+    pub fn begin(ctx: &'a CodeGenContext<'ctx, 'a>, fn_name: &'a str) -> Self {
+        FunctionBuilder { ctx, fn_name, arguments: Vec::new() }
+    }
+
+    // NOTE: `_name` is for self-documentation
+    #[must_use]
+    #[allow(clippy::needless_pass_by_value)]
+    pub fn arg<M: Model<'ctx>>(mut self, _name: &'static str, arg: Instance<'ctx, M>) -> Self {
+        self.arguments.push((
+            arg.model.get_type(self.ctx.ctx).as_basic_type_enum().into(),
+            arg.value.as_basic_value_enum().into(),
+        ));
+        self
+    }
+
+    pub fn returning<M: Model<'ctx>>(
+        self,
+        name: &'static str,
+        return_model: M,
+    ) -> Instance<'ctx, M> {
+        let (param_tys, param_vals): (Vec<_>, Vec<_>) = self.arguments.into_iter().unzip();
+
+        // Get the LLVM function, create (by declaring) the function if it doesn't exist in `ctx.module`.
+        let function = self.ctx.module.get_function(self.fn_name).unwrap_or_else(|| {
+            let fn_type = return_model.get_type(self.ctx.ctx).fn_type(&param_tys, false);
+            self.ctx.module.add_function(self.fn_name, fn_type, None)
+        });
+
+        // Build call
+        let ret = self.ctx.builder.build_call(function, &param_vals, name).unwrap();
+
+        // Check the return value/type
+        let Ok(ret) = BasicValueEnum::try_from(ret.as_any_value_enum()) else {
+            panic!("Return type is not a BasicValue");
+        };
+        return_model.review_value(self.ctx.ctx, ret).unwrap()
+    }
+
+    // TODO: Code duplication, but otherwise returning<S: Optic<'ctx>> cannot resolve S if return_optic = None
+    pub fn returning_void(self) {
+        let (param_tys, param_vals): (Vec<_>, Vec<_>) = self.arguments.into_iter().unzip();
+
+        let function = self.ctx.module.get_function(self.fn_name).unwrap_or_else(|| {
+            let return_type = self.ctx.ctx.void_type();
+            let fn_type = return_type.fn_type(&param_tys, false);
+            self.ctx.module.add_function(self.fn_name, fn_type, None)
+        });
+
+        self.ctx.builder.build_call(function, &param_vals, "").unwrap();
+    }
+}

nac3core/src/codegen/model/int.rs

@@ -0,0 +1,107 @@
+use inkwell::{
+    context::Context,
+    types::{BasicTypeEnum, IntType},
+    values::{BasicValue, IntValue},
+};
+
+use super::{
+    core::*,
+    int_util::{check_int_llvm_type, int_constant, review_int_llvm_value},
+};
+
+/// A model representing an [`IntType<'ctx>`].
+///
+/// Also see [`NIntModel`][`super::NIntModel`], which is more constrained than [`IntModel`]
+/// but provides more type-safe mechanisms and even auto-derivation of [`BasicTypeEnum<'ctx>`]
+/// for creating LLVM structures.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub struct IntModel<'ctx>(pub IntType<'ctx>);
+
+pub type Int<'ctx> = Instance<'ctx, IntModel<'ctx>>;
+
+impl<'ctx> CanCheckLLVMType<'ctx> for IntModel<'ctx> {
+    fn check_llvm_type_impl(
+        &self,
+        _ctx: &'ctx Context,
+        ty: BasicTypeEnum<'ctx>,
+    ) -> Result<(), ModelError> {
+        check_int_llvm_type(ty, self.0)
+    }
+}
+
+impl<'ctx> Model<'ctx> for IntModel<'ctx> {
+    type Value = IntValue<'ctx>;
+    type Type = IntType<'ctx>;
+
+    fn get_type(&self, _ctx: &'ctx Context) -> Self::Type {
+        self.0
+    }
+
+    fn review_value<V: BasicValue<'ctx>>(
+        &self,
+        _ctx: &'ctx Context,
+        value: V,
+    ) -> Result<Int<'ctx>, ModelError> {
+        let value = review_int_llvm_value(value.as_basic_value_enum(), self.0)?;
+        Ok(self.believe_value(value))
+    }
+}
+
+impl<'ctx> IntModel<'ctx> {
+    /// Create a constant value that inhabits this [`IntModel<'ctx>`].
+    #[must_use]
+    pub fn constant(&self, ctx: &'ctx Context, value: u64) -> Int<'ctx> {
+        int_constant(ctx, *self, value)
+    }
+}
+
+impl<'ctx> From<IntValue<'ctx>> for Int<'ctx> {
+    fn from(value: IntValue<'ctx>) -> Self {
+        let model = IntModel(value.get_type());
+        model.believe_value(value)
+    }
+}
+
+/// A model representing an [`IntType<'ctx>`] that happens to be defined as `size_t`.
+///
+/// This is specifically created to guide developers to write `size_t`-dependent code.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub struct SizeTModel<'ctx>(pub IntType<'ctx>);
+
+pub type SizeT<'ctx> = Instance<'ctx, SizeTModel<'ctx>>;
+
+impl<'ctx> CanCheckLLVMType<'ctx> for SizeTModel<'ctx> {
+    fn check_llvm_type_impl(
+        &self,
+        _ctx: &'ctx Context,
+        ty: BasicTypeEnum<'ctx>,
+    ) -> Result<(), ModelError> {
+        check_int_llvm_type(ty, self.0)
+    }
+}
+
+impl<'ctx> Model<'ctx> for SizeTModel<'ctx> {
+    type Value = IntValue<'ctx>;
+    type Type = IntType<'ctx>;
+
+    fn get_type(&self, _ctx: &'ctx Context) -> Self::Type {
+        self.0
+    }
+
+    fn review_value<V: BasicValue<'ctx>>(
+        &self,
+        _ctx: &'ctx Context,
+        value: V,
+    ) -> Result<SizeT<'ctx>, ModelError> {
+        let value = review_int_llvm_value(value.as_basic_value_enum(), self.0)?;
+        Ok(self.believe_value(value))
+    }
+}
+
+impl<'ctx> SizeTModel<'ctx> {
+    /// Create a constant value that inhabits this [`SizeTModel<'ctx>`].
+    #[must_use]
+    pub fn constant(&self, ctx: &'ctx Context, value: u64) -> SizeT<'ctx> {
+        int_constant(ctx, *self, value)
+    }
+}

nac3core/src/codegen/model/int_util.rs

@@ -0,0 +1,87 @@
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum, IntType},
+    values::{BasicValueEnum, IntValue},
+};
+
+use crate::codegen::CodeGenContext;
+
+use super::{Instance, Model, ModelError};
+
+/// Helper function to check if `scrutinee` is the same as `expected_int_type`
+pub fn check_int_llvm_type<'ctx>(
+    ty: BasicTypeEnum<'ctx>,
+    expected_int_type: IntType<'ctx>,
+) -> Result<(), ModelError> {
+    // Check if llvm_type is int type
+    let BasicTypeEnum::IntType(ty) = ty else {
+        return Err(ModelError(format!("Expecting an int type but got {ty:?}")));
+    };
+
+    // Check bit width
+    if ty.get_bit_width() != expected_int_type.get_bit_width() {
+        return Err(ModelError(format!(
+            "Expecting an int type of {}-bit(s) but got int type {}-bit(s)",
+            expected_int_type.get_bit_width(),
+            ty.get_bit_width()
+        )));
+    }
+
+    Ok(())
+}
+
+/// Helper function to cast `scrutinee` is into an [`IntValue<'ctx>`].
+/// The LLVM type of `scrutinee` will be checked with [`check_int_llvm_type`].
+pub fn review_int_llvm_value<'ctx>(
+    value: BasicValueEnum<'ctx>,
+    expected_int_type: IntType<'ctx>,
+) -> Result<IntValue<'ctx>, ModelError> {
+    // Check if value is of int type, error if that is anything else
+    check_int_llvm_type(value.get_type().as_basic_type_enum(), expected_int_type)?;
+
+    // Ok, it is must be an int
+    Ok(value.into_int_value())
+}
+
+pub fn int_constant<'ctx, M>(ctx: &'ctx Context, model: M, value: u64) -> Instance<'ctx, M>
+where
+    M: Model<'ctx, Value = IntValue<'ctx>, Type = IntType<'ctx>>,
+{
+    let value = model.get_type(ctx).const_int(value, false);
+    model.believe_value(value)
+}
+
+impl<'ctx, M> Instance<'ctx, M>
+where
+    M: Model<'ctx, Value = IntValue<'ctx>, Type = IntType<'ctx>>,
+{
+    pub fn s_extend_or_bit_cast<N>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        to_model: N,
+        name: &str,
+    ) -> Instance<'ctx, N>
+    where
+        N: Model<'ctx, Value = IntValue<'ctx>, Type = IntType<'ctx>>,
+    {
+        let value = ctx
+            .builder
+            .build_int_s_extend_or_bit_cast(self.value, to_model.get_type(ctx.ctx), name)
+            .unwrap();
+        to_model.believe_value(value)
+    }
+
+    pub fn truncate<N>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        to_model: N,
+        name: &str,
+    ) -> Instance<'ctx, N>
+    where
+        N: Model<'ctx, Value = IntValue<'ctx>, Type = IntType<'ctx>>,
+    {
+        let value =
+            ctx.builder.build_int_truncate(self.value, to_model.get_type(ctx.ctx), name).unwrap();
+        to_model.believe_value(value)
+    }
+}

nac3core/src/codegen/model/mod.rs

@@ -0,0 +1,18 @@
+pub mod core;
+pub mod fixed_int;
+pub mod function_builder;
+pub mod int;
+mod int_util;
+pub mod opaque;
+pub mod pointer;
+pub mod slice;
+pub mod structure;
+
+pub use core::*;
+pub use fixed_int::*;
+pub use function_builder::*;
+pub use int::*;
+pub use opaque::*;
+pub use pointer::*;
+pub use slice::*;
+pub use structure::*;

nac3core/src/codegen/model/opaque.rs

@@ -0,0 +1,57 @@
+use inkwell::{
+    context::Context,
+    types::BasicTypeEnum,
+    values::{BasicValue, BasicValueEnum},
+};
+
+use super::*;
+
+/// A [`Model`] that holds an arbitrary [`BasicTypeEnum`].
+///
+/// Use this and [`Opaque`] when you are dealing with a [`BasicTypeEnum<'ctx>`]
+/// at runtime and there is no way to abstract your implementation
+/// with [`Model`].
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub struct OpaqueModel<'ctx>(pub BasicTypeEnum<'ctx>);
+
+impl<'ctx> CanCheckLLVMType<'ctx> for OpaqueModel<'ctx> {
+    fn check_llvm_type_impl(
+        &self,
+        _ctx: &'ctx Context,
+        ty: BasicTypeEnum<'ctx>,
+    ) -> Result<(), ModelError> {
+        if ty == self.0 {
+            Ok(())
+        } else {
+            Err(ModelError(format!("Expecting {}, but got {}", self.0, ty)))
+        }
+    }
+}
+
+impl<'ctx> Model<'ctx> for OpaqueModel<'ctx> {
+    type Value = BasicValueEnum<'ctx>;
+    type Type = BasicTypeEnum<'ctx>;
+
+    fn get_type(&self, _ctx: &'ctx Context) -> BasicTypeEnum<'ctx> {
+        self.0
+    }
+
+    fn review_value<V: BasicValue<'ctx>>(
+        &self,
+        ctx: &'ctx Context,
+        value: V,
+    ) -> Result<Opaque<'ctx>, ModelError> {
+        let value = value.as_basic_value_enum();
+        self.check_type(ctx, value.get_type())?;
+        Ok(self.believe_value(value))
+    }
+}
+
+pub type Opaque<'ctx> = Instance<'ctx, OpaqueModel<'ctx>>;
+
+impl<'ctx> From<BasicValueEnum<'ctx>> for Opaque<'ctx> {
+    fn from(value: BasicValueEnum<'ctx>) -> Self {
+        let model = OpaqueModel(value.get_type());
+        model.believe_value(value)
+    }
+}

nac3core/src/codegen/model/pointer.rs

@@ -0,0 +1,126 @@
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum, PointerType},
+    values::{BasicValue, PointerValue},
+    AddressSpace,
+};
+
+use crate::codegen::{model::*, CodeGenContext};
+
+use super::{core::*, OpaqueModel};
+
+/// A [`Model<'ctx>`] representing an LLVM [`PointerType<'ctx>`]
+/// with *full* information on the element u
+///
+/// [`self.0`] contains [`Model<'ctx>`] that represents the
+/// LLVM type of element of the [`PointerType<'ctx>`] is pointing at
+/// (like `PointerType<'ctx>::get_element_type()`, but abstracted as a [`Model<'ctx>`]).
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct PointerModel<E>(pub E);
+
+pub type Pointer<'ctx, E> = Instance<'ctx, PointerModel<E>>;
+
+impl<'ctx, E: Model<'ctx>> CanCheckLLVMType<'ctx> for PointerModel<E> {
+    fn check_llvm_type_impl(
+        &self,
+        ctx: &'ctx Context,
+        ty: BasicTypeEnum<'ctx>,
+    ) -> Result<(), ModelError> {
+        // Check if scrutinee is even a PointerValue
+        let BasicTypeEnum::PointerType(ty) = ty else {
+            return Err(ModelError(format!("Expecting a pointer value, but got {ty:?}")));
+        };
+
+        // Check the type of what the pointer is pointing at
+        // TODO: This will be deprecated by inkwell > llvm14 because `get_element_type()` will be gone
+        let Ok(element_ty) = BasicTypeEnum::try_from(ty.get_element_type()) else {
+            return Err(ModelError(format!(
+                "Expecting pointer to point to an inkwell BasicValue, but got {ty:?}"
+            )));
+        };
+
+        self.0.check_type(ctx, element_ty) // TODO: Include backtrace?
+    }
+}
+
+impl<'ctx, E: Model<'ctx>> Model<'ctx> for PointerModel<E> {
+    type Value = PointerValue<'ctx>;
+    type Type = PointerType<'ctx>;
+
+    fn get_type(&self, ctx: &'ctx Context) -> Self::Type {
+        self.0.get_type(ctx).ptr_type(AddressSpace::default())
+    }
+
+    fn review_value<V: BasicValue<'ctx>>(
+        &self,
+        ctx: &'ctx Context,
+        value: V,
+    ) -> Result<Pointer<'ctx, E>, ModelError> {
+        let value = value.as_basic_value_enum();
+        self.check_type(ctx, value.get_type())?;
+        Ok(self.believe_value(value.into_pointer_value()))
+    }
+}
+
+impl<'ctx, E: Model<'ctx>> PointerModel<E> {
+    /// Create a null [`Pointer`] of this [`PointerModel`]
+    pub fn nullptr(&self, ctx: &'ctx Context) -> Pointer<'ctx, E> {
+        let nullptr = self.get_type(ctx).const_null();
+        self.believe_value(nullptr)
+    }
+}
+
+impl<'ctx, E: Model<'ctx>> Pointer<'ctx, E> {
+    /// Build an instruction to store a value into this pointer
+    pub fn store(&self, ctx: &CodeGenContext<'ctx, '_>, instance: Instance<'ctx, E>) {
+        assert_eq!(
+            self.model.0, instance.model,
+            "Attempting to store an Instance of a different type"
+        );
+        ctx.builder.build_store(self.value, instance.value).unwrap();
+    }
+
+    /// Build an instruction to load a value from this pointer
+    pub fn load(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> Instance<'ctx, E> {
+        let value = ctx.builder.build_load(self.value, name).unwrap();
+        self.model.0.review_value(ctx.ctx, value).unwrap() // If unwrap() panics, there is a logic error in your code.
+    }
+
+    /// "Demote" the [`Model`] of the thing this pointer is pointing to.
+    pub fn cast_to_opaque(self, ctx: &'ctx Context) -> Pointer<'ctx, OpaqueModel<'ctx>> {
+        let ptr_model = PointerModel(OpaqueModel(self.model.get_type(ctx).as_basic_type_enum()));
+        ptr_model.believe_value(self.value)
+    }
+
+    /// Cast the [`Model`] of the thing this pointer is pointing to
+    /// and uses inkwell's [`Builder::build_pointer_cast`] to cast the LLVM pointer type.
+    pub fn cast_to<K: Model<'ctx>>(
+        self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        element: K,
+        name: &str,
+    ) -> Pointer<'ctx, K> {
+        let casted_ptr_model = PointerModel(element);
+        let casted_ptr = ctx
+            .builder
+            .build_pointer_cast(
+                self.value,
+                element.get_type(ctx.ctx).ptr_type(AddressSpace::default()),
+                name,
+            )
+            .unwrap();
+        casted_ptr_model.believe_value(casted_ptr)
+    }
+
+    pub fn is_null(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> NInt<'ctx, Bool> {
+        let model = NIntModel(Bool);
+        let value = ctx.builder.build_is_null(self.value, name).unwrap();
+        model.believe_value(value)
+    }
+
+    pub fn is_not_null(&self, ctx: &CodeGenContext<'ctx, '_>, name: &str) -> NInt<'ctx, Bool> {
+        let model = NIntModel(Bool);
+        let value = ctx.builder.build_is_not_null(self.value, name).unwrap();
+        model.believe_value(value)
+    }
+}

nac3core/src/codegen/model/slice.rs

@@ -0,0 +1,94 @@
+use inkwell::{types::IntType, values::IntValue};
+
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+use super::{int_util::int_constant, Instance, Model, Pointer};
+
+/// An LLVM "slice" - literally just a pointer and a length value.
+/// The pointer points to a location with `num_elements` **contiguously** placed
+/// values of [`E`][`Model<ctx>`] in memory.
+///
+/// NOTE: This is NOT a [`Model`]! This is simply a helper
+/// structure to aggregate a length value and a pointer together.
+pub struct ArraySlice<'ctx, N, E>
+where
+    N: Model<'ctx, Value = IntValue<'ctx>, Type = IntType<'ctx>>,
+    E: Model<'ctx>,
+{
+    pub pointer: Pointer<'ctx, E>,
+    pub num_elements: Instance<'ctx, N>,
+}
+
+impl<'ctx, N, E> ArraySlice<'ctx, N, E>
+where
+    N: Model<'ctx, Value = IntValue<'ctx>, Type = IntType<'ctx>>,
+    E: Model<'ctx>,
+{
+    /// Get the [Model][`super::Model`] of the element type of this [`ArraySlice`]
+    pub fn get_element_model(&self) -> E {
+        self.pointer.model.0
+    }
+
+    /// Get the `idx`-nth element of this [`ArraySlice`],
+    /// but doesn't do an assertion to see if `idx` is
+    /// out of bounds or not.
+    ///
+    /// Also see [`ArraySlice::ix`].
+    pub fn ix_unchecked(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        idx: Instance<'ctx, N>,
+        name: &str,
+    ) -> Pointer<'ctx, E> {
+        assert_eq!(idx.model, self.num_elements.model);
+        let element_ptr = unsafe {
+            ctx.builder.build_in_bounds_gep(self.pointer.value, &[idx.value], name).unwrap()
+        };
+        self.pointer.model.review_value(ctx.ctx, element_ptr).unwrap()
+    }
+
+    /// Call [`ArraySlice::ix_unchecked`], but
+    /// checks if `idx` is in bounds, otherwise
+    /// a runtime `IndexError` will be thrown.
+    pub fn ix<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        idx: Instance<'ctx, N>,
+        name: &str,
+    ) -> Pointer<'ctx, E> {
+        assert_eq!(idx.model, self.num_elements.model);
+        let int_type = self.num_elements.model;
+
+        // Assert `0 <= idx < length` and throw an Exception if `idx` is out of bounds
+        let lower_bounded = ctx
+            .builder
+            .build_int_compare(
+                inkwell::IntPredicate::SLE,
+                int_constant(ctx.ctx, int_type, 0).value,
+                idx.value,
+                "lower_bounded",
+            )
+            .unwrap();
+        let upper_bounded = ctx
+            .builder
+            .build_int_compare(
+                inkwell::IntPredicate::SLT,
+                idx.value,
+                self.num_elements.value,
+                "upper_bounded",
+            )
+            .unwrap();
+        let bounded = ctx.builder.build_and(lower_bounded, upper_bounded, "bounded").unwrap();
+        ctx.make_assert(
+            generator,
+            bounded,
+            "0:IndexError",
+            "nac3core LLVM codegen attempting to access out of bounds array index {0}. Must satisfy 0 <= index < {2}",
+            [ Some(idx.value), Some(self.num_elements.value), None],
+            ctx.current_loc
+        );
+
+        self.ix_unchecked(ctx, idx, name)
+    }
+}

nac3core/src/codegen/model/structure.rs

@@ -0,0 +1,384 @@
+use core::fmt;
+
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum, StructType},
+    values::{BasicValue, StructValue},
+};
+use itertools::{izip, Itertools};
+
+use crate::codegen::CodeGenContext;
+
+use super::{core::CanCheckLLVMType, Instance, Model, ModelError, Pointer, PointerModel};
+
+/// An LLVM struct's "field".
+#[derive(Debug, Clone, Copy)]
+pub struct Field<E> {
+    /// The GEP index of this field.
+    pub gep_index: u64,
+
+    /// The name of this field. Generally named
+    /// to how the field is named in ARTIQ or IRRT.
+    ///
+    /// NOTE: This is only used for debugging.
+    pub name: &'static str,
+
+    /// The [`Model`] of this field.
+    pub model: E,
+}
+
+// A helper struct for [`FieldBuilder`]
+struct FieldLLVM<'ctx> {
+    gep_index: u64,
+    name: &'ctx str,
+
+    // Only CanCheckLLVMType is needed, dont use `Model<'ctx>`
+    llvm_type_model: Box<dyn CanCheckLLVMType<'ctx> + 'ctx>,
+    llvm_type: BasicTypeEnum<'ctx>,
+}
+
+/// A helper struct to create [`Field`]-s in [`StructKind::build_fields`].
+///
+/// See [`StructKind`] for more details and see how [`FieldBuilder`] is put
+/// into action.
+pub struct FieldBuilder<'ctx> {
+    /// The [`Context`] this [`FieldBuilder`] is under.
+    ///
+    /// Can be used in [`StructKind::build_fields`].
+    /// See [`StructKind`] for more details and see how [`FieldBuilder`] is put
+    /// into action.
+    pub ctx: &'ctx Context,
+
+    /// An incrementing counter for GEP indices when
+    /// doing [`FieldBuilder::add_field`] or [`FieldBuilder::add_field_auto`].
+    gep_index_counter: u64,
+
+    /// Name of the `struct` this [`FieldBuilder`] is currently
+    /// building.
+    ///
+    /// NOTE: This is only used for debugging.
+    struct_name: &'ctx str,
+
+    /// The fields added so far.
+    fields: Vec<FieldLLVM<'ctx>>,
+}
+
+impl<'ctx> FieldBuilder<'ctx> {
+    #[must_use]
+    pub fn new(ctx: &'ctx Context, struct_name: &'ctx str) -> Self {
+        FieldBuilder { ctx, gep_index_counter: 0, struct_name, fields: Vec::new() }
+    }
+
+    fn next_gep_index(&mut self) -> u64 {
+        let index = self.gep_index_counter;
+        self.gep_index_counter += 1;
+        index
+    }
+
+    /// Add a new field.
+    ///
+    /// - `name`: The name of the field. See [`Field::name`].
+    /// - `element`: The [`Model`] of the type of the field. See [`Field::element`].
+    pub fn add_field<E: Model<'ctx> + 'ctx>(&mut self, name: &'static str, element: E) -> Field<E> {
+        let gep_index = self.next_gep_index();
+
+        self.fields.push(FieldLLVM {
+            gep_index,
+            name,
+            llvm_type: element.get_type(self.ctx).as_basic_type_enum(),
+            llvm_type_model: Box::new(element),
+        });
+
+        Field { gep_index, name, model: element }
+    }
+
+    /// Like [`FieldBuilder::add_field`] but `element` can be **automatically derived**
+    /// if it has the `Default` instance.
+    ///
+    /// Certain [`Model`] has a [`Default`] trait - [`Model`]s that are just singletons,
+    /// By deriving the [`Default`] trait on those [`Model`]s, Rust could automatically
+    /// construct the [`Model`] with [`Default::default`].
+    ///
+    /// This function is equivalent to
+    /// ```ignore
+    /// self.add_field(name, E::default())
+    /// ```
+    pub fn add_field_auto<E: Model<'ctx> + Default + 'ctx>(
+        &mut self,
+        name: &'static str,
+    ) -> Field<E> {
+        self.add_field(name, E::default())
+    }
+}
+
+/// A marker trait to mark singleton struct that
+/// describes a particular LLVM structure.
+///
+/// It is a powerful inkwell abstraction that can reduce
+/// a lot of inkwell boilerplate when dealing with LLVM structs,
+/// `getelementptr`, `load`-ing and `store`-ing fields.
+///
+/// ### Usage
+pub trait StructKind<'ctx>: fmt::Debug + Clone + Copy + PartialEq + Eq {
+    /// The type of the Rust `struct` that holds all the fields of this LLVM struct.
+    type Fields;
+
+    // TODO:
+    /// The name of this [`StructKind`].
+    ///
+    /// The name should be the name of in
+    /// IRRT's `struct` or ARTIQ's definition.
+    fn struct_name(&self) -> &'static str;
+
+    /// Define the [`Field`]s of this [`StructKind`]
+    ///
+    ///
+    /// ### Syntax
+    ///
+    /// Suppose you want to define the following C++ `struct`s in `nac3core`:
+    /// ```cpp
+    /// template <typename SizeT>
+    /// struct Str {
+    ///     uint8_t* content; // NOTE: could be `void *`
+    ///     SizeT length;
+    /// }
+    ///
+    /// template <typename SizeT>
+    /// struct Exception {
+    ///     uint32_t id;
+    ///     Str message;
+    ///     uint64_t param0;
+    ///     uint64_t param1;
+    ///     uint64_t param2;
+    /// }
+    /// ```
+    ///
+    /// You write this in nac3core:
+    /// ```ignore
+    /// struct Str<'ctx> {
+    ///     sizet: IntModel<'ctx>,
+    /// }
+    ///
+    /// struct StrFields<'ctx> {
+    ///     content: Field<PointerModel<ByteModel>>, // equivalent to `NIntModel<Byte>`.
+    ///     length: Field<IntModel<'ctx>>, // `SizeT` is only known in runtime - `CodeGenerator::get_size_type()`.  /// }
+    /// }
+    ///
+    /// impl StructKind<'ctx> for Str<'ctx> {
+    ///     fn struct_name() {
+    ///         "Str"
+    ///     }
+    ///
+    ///     fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
+    ///         // THE order of `builder.add_field*` is IMPORTANT!!!
+    ///         // so the GEP indices would be correct.
+    ///         StrFields {
+    ///             content: builder.add_field_auto("content"), // `PointerModel<ByteModel>` has `Default` trait.
+    ///             length: builder.add_field("length", IntModel(self.sizet)), // `PointerModel<ByteModel>` has `Default` trait.
+    ///         }
+    ///     }
+    /// }
+    ///
+    /// struct Exception<'ctx> {
+    ///     sizet: IntModel<'ctx>,
+    /// }
+    ///
+    /// struct ExceptionFields<'ctx> {
+    ///     id: Field<NIntModel<Int32>>,
+    ///     message: Field<StructModel<Str>>,
+    ///     param0: Field<NIntModel<Int64>>,
+    ///     param1: Field<NIntModel<Int64>>,
+    ///     param2: Field<NIntModel<Int64>>,
+    /// }
+    ///
+    /// impl StructKind<'ctx> for Exception<'ctx> {
+    ///     fn struct_name() {
+    ///         "Exception"
+    ///     }
+    ///
+    ///     fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
+    ///         // THE order of `builder.add_field*` is IMPORTANT!!!
+    ///         // so the GEP indices would be correct.
+    ///         ExceptionFields {
+    ///             id: builder.add_field_auto("content"), // `NIntModel<Int32>` has `Default` trait.
+    ///             message: builder.add_field("message", StructModel(Str { sizet: self.sizet })),
+    ///             param0: builder.add_field_auto("param0"), // has `Default` trait
+    ///             param1: builder.add_field_auto("param1"), // has `Default` trait
+    ///             param2: builder.add_field_auto("param2"), // has `Default` trait
+    ///         }
+    ///     }
+    /// }
+    /// ```
+    ///
+    /// Then to `alloca` an `Exception`, do this:
+    /// ```ignore
+    /// let generator: dyn CodeGenerator<'ctx>;
+    /// let ctx: &CodeGenContext<'ctx, '_>;
+    /// let sizet = generator.get_size_type();
+    /// let exn_model = StructModel(Exception { sizet });
+    /// let exn = exn_model.alloca(ctx, "my_exception"); // Every [`Model<'ctx>`] has an `.alloca()` function.
+    /// // exn: Pointer<'ctx, StructModel<Exception>>
+    /// ```
+    ///
+    /// NOTE: In fact, it is possible to define `Str` and `Exception` like this:
+    /// ```ignore
+    /// struct Str<SizeT: NIntModel> {
+    ///     _phantom: PhantomData<SizeT>,
+    /// }
+    ///
+    /// struct Exception<T: NIntModel> {
+    ///     _phantom: PhantomData<SizeT>,
+    /// }
+    /// ```
+    /// But issues arise by you don't know the nac3core
+    /// `CodeGenerator`'s `get_size_type()` before hand.
+    fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields;
+}
+
+/// A [`Model<'ctx>`] that represents an LLVM struct.
+///
+/// `self.0` contains a [`StructKind<'ctx>`] that gives the details of the LLVM struct.
+#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
+pub struct StructModel<S>(pub S);
+pub type Struct<'ctx, S> = Instance<'ctx, StructModel<S>>;
+
+impl<'ctx, S: StructKind<'ctx>> CanCheckLLVMType<'ctx> for StructModel<S> {
+    fn check_llvm_type_impl(
+        &self,
+        ctx: &'ctx Context,
+        ty: BasicTypeEnum<'ctx>,
+    ) -> Result<(), ModelError> {
+        // Check if scrutinee is even a struct type
+        let BasicTypeEnum::StructType(ty) = ty else {
+            return Err(ModelError(format!("Expecting a struct type, but got {ty:?}")));
+        };
+
+        // Ok. now check the struct type thoroughly
+        self.check_struct_type(ctx, ty)
+    }
+}
+
+impl<'ctx, S: StructKind<'ctx>> Model<'ctx> for StructModel<S> {
+    type Value = StructValue<'ctx>;
+    type Type = StructType<'ctx>;
+
+    fn get_type(&self, ctx: &'ctx Context) -> Self::Type {
+        self.get_struct_type(ctx)
+    }
+
+    fn review_value<V: BasicValue<'ctx>>(
+        &self,
+        ctx: &'ctx Context,
+        value: V,
+    ) -> Result<Struct<'ctx, S>, ModelError> {
+        let value = value.as_basic_value_enum();
+        self.check_type(ctx, value.get_type())?;
+        Ok(self.believe_value(value.into_struct_value()))
+    }
+}
+
+impl<'ctx, S: StructKind<'ctx>> StructModel<S> {
+    /// Get the [`S::Fields`] of this [`StructModel`].
+    pub fn get_fields(&self, ctx: &'ctx Context) -> S::Fields {
+        let mut builder = FieldBuilder::new(ctx, self.0.struct_name());
+        self.0.build_fields(&mut builder)
+    }
+
+    /// Get the LLVM struct type this [`IsStruct<'ctx>`] is representing.
+    pub fn get_struct_type(&self, ctx: &'ctx Context) -> StructType<'ctx> {
+        let mut builder = FieldBuilder::new(ctx, self.0.struct_name());
+        self.0.build_fields(&mut builder); // Self::Fields is discarded
+
+        let field_types = builder.fields.iter().map(|f| f.llvm_type).collect_vec();
+        ctx.struct_type(&field_types, false)
+    }
+
+    /// Check if `scrutinee` matches the [`StructType<'ctx>`] this [`IsStruct<'ctx>`] is representing.
+    pub fn check_struct_type(
+        &self,
+        ctx: &'ctx Context,
+        scrutinee: StructType<'ctx>,
+    ) -> Result<(), ModelError> {
+        // Details about scrutinee
+        let scrutinee_field_types = scrutinee.get_field_types();
+
+        // Details about the defined specifications of this struct
+        // We will access them through builder
+        let mut builder = FieldBuilder::new(ctx, self.0.struct_name());
+        self.0.build_fields(&mut builder);
+
+        // Check # of fields
+        if builder.fields.len() != scrutinee_field_types.len() {
+            return Err(ModelError(format!(
+                "Expecting struct to have {} field(s), but scrutinee has {} field(s)",
+                builder.fields.len(),
+                scrutinee_field_types.len()
+            )));
+        }
+
+        // Check the types of each field
+        // TODO: Traceback?
+        for (f, scrutinee_field_type) in izip!(builder.fields, scrutinee_field_types) {
+            f.llvm_type_model
+                .check_llvm_type_impl(ctx, scrutinee_field_type.as_basic_type_enum())?;
+        }
+
+        Ok(())
+    }
+}
+
+impl<'ctx, S: StructKind<'ctx>> Pointer<'ctx, StructModel<S>> {
+    /// Build an instruction that does `getelementptr` on an LLVM structure referenced by this pointer.
+    ///
+    /// This provides a nice syntax to chain up `getelementptr` in an intuitive and type-safe way:
+    ///
+    /// ```ignore
+    /// let ctx: &CodeGenContext<'ctx, '_>;
+    /// let ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>;
+    /// ndarray.gep(ctx, |f| f.ndims).store();
+    /// ```
+    ///
+    /// You might even write chains `gep`, i.e.,
+    /// ```ignore
+    /// let exn_ptr: Pointer<'ctx, StructModel<Exception>>;
+    /// let value: Int<'ctx>; // Suppose it has the correct inkwell `IntType<'ctx>`.
+    ///
+    /// // To do `exn.message.length = value`:
+    /// let exn_message_ptr = exn_ptr.gep(ctx, |f| f.message);
+    /// let exn_message_length_ptr = exn_message_ptr.gep(ctx, |f| f.length);
+    /// exn_message_length_ptr.store(ctx, my_value);
+    ///
+    /// // or simply:
+    /// exn_ptr
+    ///     .gep(ctx, |f| f.message)
+    ///     .gep(ctx, |f| f.length)
+    ///     .store(ctx, my_value) // Equivalent to `my_struct.thing1.value = my_value`
+    /// ```
+    pub fn gep<E, GetFieldFn>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        get_field: GetFieldFn,
+    ) -> Pointer<'ctx, E>
+    where
+        E: Model<'ctx>,
+        GetFieldFn: FnOnce(S::Fields) -> Field<E>,
+    {
+        let fields = self.model.0.get_fields(ctx.ctx);
+        let field = get_field(fields);
+
+        // TODO: I think I'm not supposed to *just* use i32 for GEP like that
+        let llvm_i32 = ctx.ctx.i32_type();
+
+        let ptr_model = PointerModel(field.model);
+        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()
+        };
+        ptr_model.believe_value(ptr)
+    }
+}

nac3core/src/codegen/numpy_new/factory.rs

@@ -0,0 +1,213 @@
+use inkwell::{
+    types::BasicType,
+    values::{BasicValue, BasicValueEnum, PointerValue},
+};
+use nac3parser::ast::StrRef;
+
+use crate::{
+    codegen::{
+        irrt::ndarray::{
+            allocation::{alloca_ndarray, init_ndarray_data_by_alloca, init_ndarray_shape},
+            fill::call_nac3_ndarray_fill_generic,
+        },
+        model::*,
+        structs::ndarray::NpArray,
+        util::shape::parse_input_shape_arg,
+        CodeGenContext, CodeGenerator,
+    },
+    symbol_resolver::ValueEnum,
+    toplevel::DefinitionId,
+    typecheck::typedef::{FunSignature, Type},
+};
+
+/// Helper function to create an ndarray with uninitialized values
+///
+/// * `elem_ty` - The [`Type`] of the ndarray elements
+/// * `shape` - The user input shape argument
+/// * `shape_ty` - The [`Type`] of the shape argument
+/// * `name` - LLVM IR name of the returned ndarray
+fn create_empty_ndarray<'ctx, G>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    shape: BasicValueEnum<'ctx>,
+    shape_ty: Type,
+    name: &str,
+) -> Result<Pointer<'ctx, StructModel<NpArray<'ctx>>>, String>
+where
+    G: CodeGenerator + ?Sized,
+{
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    let shape_writer = parse_input_shape_arg(generator, ctx, shape, shape_ty);
+    let ndims = shape_writer.count;
+
+    let ndarray = alloca_ndarray(generator, ctx, ndims, name)?;
+    init_ndarray_shape(generator, ctx, ndarray, &shape_writer)?;
+
+    let itemsize = sizet
+        .review_value(ctx.ctx, ctx.get_llvm_type(generator, elem_ty).size_of().unwrap())
+        .unwrap();
+    ndarray.gep(ctx, |f| f.itemsize).store(ctx, itemsize);
+
+    init_ndarray_data_by_alloca(generator, ctx, ndarray); // Needs `itemsize` and `shape` initialized first
+
+    Ok(ndarray)
+}
+
+/// Helper function to create an ndarray full of a value.
+///
+/// * `elem_ty` - The [`Type`] of the ndarray elements and the fill value
+/// * `shape` - The user input shape argument
+/// * `shape_ty` - The [`Type`] of the shape argument
+/// * `fill_value` - The user specified fill value
+/// * `name` - LLVM IR name of the returned ndarray
+fn create_full_ndarray<'ctx, G>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    shape: BasicValueEnum<'ctx>,
+    shape_ty: Type,
+    fill_value: BasicValueEnum<'ctx>,
+    name: &str,
+) -> Result<Pointer<'ctx, StructModel<NpArray<'ctx>>>, String>
+where
+    G: CodeGenerator + ?Sized,
+{
+    let byte_model = NIntModel(Byte);
+    let fill_value_model = OpaqueModel(fill_value.get_type());
+
+    // Caller has to put fill_value on the stack and pass its address
+    let fill_value_ptr = fill_value_model.alloca(ctx, "fill_value_ptr");
+    fill_value_ptr.store(ctx, fill_value_model.believe_value(fill_value));
+    let fill_value_ptr = fill_value_ptr.cast_to(ctx, byte_model, "fill_value_bytes_ptr");
+
+    let ndarray_ptr = create_empty_ndarray(generator, ctx, elem_ty, shape, shape_ty, name)?;
+    call_nac3_ndarray_fill_generic(generator, ctx, ndarray_ptr, fill_value_ptr);
+
+    Ok(ndarray_ptr)
+}
+
+/// Generates LLVM IR for `np.empty`.
+pub fn gen_ndarray_empty<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    // Parse arguments
+    let shape_ty = fun.0.args[0].ty;
+    let shape = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
+
+    // Implementation
+    let ndarray_ptr = create_empty_ndarray(
+        generator,
+        context,
+        context.primitives.float,
+        shape,
+        shape_ty,
+        "ndarray",
+    )?;
+    Ok(ndarray_ptr.value)
+}
+
+/// Generates LLVM IR for `np.zeros`.
+pub fn gen_ndarray_zeros<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    // Parse arguments
+    let shape_ty = fun.0.args[0].ty;
+    let shape = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
+
+    // Implementation
+    // NOTE: Currently nac3's `np.zeros` is always `float64`.
+    let float64_ty = context.primitives.float;
+    let float64_llvm_type = context.get_llvm_type(generator, float64_ty).into_float_type();
+
+    let ndarray_ptr = create_full_ndarray(
+        generator,
+        context,
+        float64_ty, // `elem_ty` is always `float64`
+        shape,
+        shape_ty,
+        float64_llvm_type.const_zero().as_basic_value_enum(),
+        "ndarray",
+    )?;
+    Ok(ndarray_ptr.value)
+}
+
+/// Generates LLVM IR for `np.ones`.
+pub fn gen_ndarray_ones<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 1);
+
+    // Parse arguments
+    let shape_ty = fun.0.args[0].ty;
+    let shape = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
+
+    // Implementation
+    // NOTE: Currently nac3's `np.ones` is always `float64`.
+    let float64_ty = context.primitives.float;
+    let float64_llvm_type = context.get_llvm_type(generator, float64_ty).into_float_type();
+
+    let ndarray_ptr = create_full_ndarray(
+        generator,
+        context,
+        float64_ty, // `elem_ty` is always `float64`
+        shape,
+        shape_ty,
+        float64_llvm_type.const_float(1.0).as_basic_value_enum(),
+        "ndarray",
+    )?;
+    Ok(ndarray_ptr.value)
+}
+
+/// Generates LLVM IR for `np.full`.
+pub fn gen_ndarray_full<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 2);
+
+    // Parse argument #1 shape
+    let shape_ty = fun.0.args[0].ty;
+    let shape_arg = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
+
+    // Parse argument #2 fill_value
+    let fill_value_ty = fun.0.args[1].ty;
+    let fill_value_arg =
+        args[1].1.clone().to_basic_value_enum(context, generator, fill_value_ty)?;
+
+    // Implementation
+    let ndarray_ptr = create_full_ndarray(
+        generator,
+        context,
+        fill_value_ty,
+        shape_arg,
+        shape_ty,
+        fill_value_arg,
+        "ndarray",
+    )?;
+    Ok(ndarray_ptr.value)
+}

nac3core/src/codegen/numpy_new/mod.rs

@@ -0,0 +1,2 @@
+pub mod factory;
+pub mod view;

nac3core/src/codegen/numpy_new/view.rs

@@ -0,0 +1,169 @@
+use inkwell::values::PointerValue;
+use nac3parser::ast::StrRef;
+
+use crate::{
+    codegen::{
+        irrt::ndarray::{
+            allocation::{alloca_ndarray, init_ndarray_shape},
+            basic::{
+                call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_nbytes,
+                call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
+            },
+            reshape::call_nac3_ndarray_resolve_and_check_new_shape,
+            transpose::call_nac3_ndarray_transpose,
+        },
+        model::*,
+        structs::{list::List, ndarray::NpArray},
+        util::{array_writer::ArrayWriter, shape::parse_input_shape_arg},
+        CodeGenContext, CodeGenerator,
+    },
+    symbol_resolver::ValueEnum,
+    toplevel::DefinitionId,
+    typecheck::typedef::{FunSignature, Type},
+};
+
+fn reshape_ndarray_or_copy<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    src_ndarray: Pointer<'ctx, StructModel<NpArray<'ctx>>>,
+    new_shape: &ArrayWriter<'ctx, G, SizeTModel<'ctx>, SizeTModel<'ctx>>,
+) -> Result<Pointer<'ctx, StructModel<NpArray<'ctx>>>, String> {
+    let byte_model = NIntModel(Byte);
+
+    /*
+        Reference pseudo-code:
+        ```c
+        NDArray<SizeT>* src_ndarray;
+
+        NDArray<SizeT>* dst_ndarray = __builtin_alloca(...);
+        dst_ndarray->ndims = ...
+        dst_ndarray->strides = __builtin_alloca(...);
+        dst_ndarray->shape = ... // Directly set by user, may contain -1, or even illegal values.
+        dst_ndarray->itemsize = src_ndarray->itemsize;
+        set_strides_by_shape(dst_ndarray);
+
+        // Do assertions on `dst_ndarray->shape` and resolve -1
+
+        resolve_and_check_new_shape(ndarray_size(src_ndarray), dst_ndarray->shape);
+
+        if (is_c_contiguous(src_ndarray)) {
+            dst_ndarray->data = src_ndarray->data;
+        } else {
+            dst_ndarray->data = __builtin_alloca( ndarray_nbytes(dst_ndarray) );
+            copy_data(src_ndarray, dst_ndarray);
+        }
+
+        return dst_ndarray;
+        ```
+    */
+
+    let current_bb = ctx.builder.get_insert_block().unwrap();
+    let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then");
+    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");
+
+    // current_bb
+    let dst_ndarray = alloca_ndarray(generator, ctx, new_shape.count, "ndarray").unwrap();
+
+    init_ndarray_shape(generator, ctx, dst_ndarray, new_shape)?;
+    dst_ndarray
+        .gep(ctx, |f| f.itemsize)
+        .store(ctx, src_ndarray.gep(ctx, |f| f.itemsize).load(ctx, "itemsize"));
+
+    call_nac3_ndarray_set_strides_by_shape(generator, ctx, dst_ndarray);
+
+    let src_ndarray_size = call_nac3_ndarray_size(generator, ctx, src_ndarray);
+    call_nac3_ndarray_resolve_and_check_new_shape(
+        generator,
+        ctx,
+        src_ndarray_size,
+        dst_ndarray.gep(ctx, |f| f.ndims).load(ctx, "ndims"),
+        dst_ndarray.gep(ctx, |f| f.shape).load(ctx, "shape"),
+    );
+
+    let is_c_contiguous = call_nac3_ndarray_is_c_contiguous(generator, ctx, src_ndarray);
+    ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
+
+    // then_bb: reshape is possible without copying
+    ctx.builder.position_at_end(then_bb);
+    dst_ndarray.gep(ctx, |f| f.data).store(ctx, src_ndarray.gep(ctx, |f| f.data).load(ctx, "data"));
+    ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+    // else_bb: reshape is impossible without copying
+    ctx.builder.position_at_end(else_bb);
+    let dst_ndarray_nbytes = call_nac3_ndarray_nbytes(generator, ctx, dst_ndarray);
+    let data = byte_model.array_alloca(ctx, dst_ndarray_nbytes, "new_data").pointer;
+    dst_ndarray.gep(ctx, |f| f.data).store(ctx, data);
+    ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+    // Reposition for continuation
+    ctx.builder.position_at_end(end_bb);
+
+    Ok(dst_ndarray)
+}
+
+/// Generates LLVM IR for `np.reshape`.
+pub fn gen_ndarray_reshape<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 2);
+
+    // Parse argument #1 ndarray
+    let ndarray_ty = fun.0.args[0].ty;
+    let ndarray_arg = args[0].1.clone().to_basic_value_enum(context, generator, ndarray_ty)?;
+
+    // Parse argument #2 shape
+    let shape_ty = fun.0.args[1].ty;
+    let shape_arg = args[1].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
+
+    let sizet = generator.get_sizet(context.ctx);
+    let pndarray_model = PointerModel(StructModel(NpArray { sizet }));
+
+    let src_ndarray = pndarray_model.review_value(context.ctx, ndarray_arg).unwrap();
+    let new_shape = parse_input_shape_arg(generator, context, shape_arg, shape_ty);
+
+    let reshaped_ndarray = reshape_ndarray_or_copy(generator, context, src_ndarray, &new_shape)?;
+    Ok(reshaped_ndarray.value)
+}
+
+pub fn gen_ndarray_transpose<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert!(matches!(args.len(), 1 | 2));
+
+    let sizet = generator.get_sizet(context.ctx);
+    let in_axes_model = PointerModel(StructModel(List { sizet, element: NIntModel(Int32) }));
+
+    // Parse argument #1 ndarray
+    let ndarray_ty = fun.0.args[0].ty;
+    let ndarray_arg = args[0].1.clone().to_basic_value_enum(context, generator, ndarray_ty)?;
+
+    // Parse argument #2 axes (optional)
+    let in_axes = if args.len() == 2 {
+        let in_shape_ty = fun.0.args[1].ty;
+        let in_shape_arg =
+            args[1].1.clone().to_basic_value_enum(context, generator, in_shape_ty)?;
+
+        let in_shape = in_axes_model.review_value(context.ctx, in_shape_arg).unwrap();
+
+        let num_axes = in_shape.gep(context, |f| f.size).load(context, "num_axes");
+        let axes = sizet.array_alloca(context, num_axes, "num_axes");
+
+        Some((in_shape_ty, in_shape_arg))
+    } else {
+        None
+    };
+    // call_nac3_ndarray_transpose(generator, ctx, src_ndarray, dst_ndarray, axes_or_none)
+
+    todo!()
+}

nac3core/src/codegen/stmt.rs

@@ -2,6 +2,8 @@ use super::{
     super::symbol_resolver::ValueEnum,
     expr::destructure_range,
     irrt::{handle_slice_indices, list_slice_assignment},
+    model::*,
+    structs::{cslice::CSlice, exception::Exception},
     CodeGenContext, CodeGenerator,
 };
 use crate::{
@@ -1113,47 +1115,37 @@ pub fn exn_constructor<'ctx>(
 pub fn gen_raise<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
-    exception: Option<&BasicValueEnum<'ctx>>,
+    exception: Option<Pointer<'ctx, StructModel<Exception<'ctx>>>>,
     loc: Location,
 ) {
-    if let Some(exception) = exception {
-        unsafe {
-            let int32 = ctx.ctx.i32_type();
-            let zero = int32.const_zero();
-            let exception = exception.into_pointer_value();
-            let file_ptr = ctx
-                .builder
-                .build_in_bounds_gep(exception, &[zero, int32.const_int(1, false)], "file_ptr")
-                .unwrap();
-            let filename = ctx.gen_string(generator, loc.file.0);
-            ctx.builder.build_store(file_ptr, filename).unwrap();
-            let row_ptr = ctx
-                .builder
-                .build_in_bounds_gep(exception, &[zero, int32.const_int(2, false)], "row_ptr")
-                .unwrap();
-            ctx.builder.build_store(row_ptr, int32.const_int(loc.row as u64, false)).unwrap();
-            let col_ptr = ctx
-                .builder
-                .build_in_bounds_gep(exception, &[zero, int32.const_int(3, false)], "col_ptr")
-                .unwrap();
-            ctx.builder.build_store(col_ptr, int32.const_int(loc.column as u64, false)).unwrap();
+    if let Some(pexn) = exception {
+        let sizet = generator.get_sizet(ctx.ctx);
+        let i32_model = NIntModel(Int32);
+        let cslice_model = StructModel(CSlice { sizet });
 
-            let current_fun = ctx.builder.get_insert_block().unwrap().get_parent().unwrap();
-            let fun_name = ctx.gen_string(generator, current_fun.get_name().to_str().unwrap());
-            let name_ptr = ctx
-                .builder
-                .build_in_bounds_gep(exception, &[zero, int32.const_int(4, false)], "name_ptr")
-                .unwrap();
-            ctx.builder.build_store(name_ptr, fun_name).unwrap();
-        }
+        // Get and store filename
+        let filename = loc.file.0;
+        let filename = ctx.gen_string(generator, &String::from(filename)).value;
+        let filename = cslice_model.review_value(ctx.ctx, filename).unwrap();
+        pexn.gep(ctx, |f| f.filename).store(ctx, filename);
+
+        let row = i32_model.constant(ctx.ctx, loc.row as u64);
+        pexn.gep(ctx, |f| f.line).store(ctx, row);
+
+        let column = i32_model.constant(ctx.ctx, loc.column as u64);
+        pexn.gep(ctx, |f| f.column).store(ctx, 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.gep(ctx, |f| f.function_name).store(ctx, fn_name);
 
         let raise = get_builtins(generator, ctx, "__nac3_raise");
-        let exception = *exception;
-        ctx.build_call_or_invoke(raise, &[exception], "raise");
+        ctx.build_call_or_invoke(raise, &[pexn.value.into()], "raise");
     } else {
         let resume = get_builtins(generator, ctx, "__nac3_resume");
         ctx.build_call_or_invoke(resume, &[], "resume");
     }
+
     ctx.builder.build_unreachable().unwrap();
 }
 
@@ -1609,35 +1601,50 @@ pub fn gen_stmt<G: CodeGenerator>(
         StmtKind::Try { .. } => gen_try(generator, ctx, stmt)?,
         StmtKind::Raise { exc, .. } => {
             if let Some(exc) = exc {
-                let exc = if let Some(v) = generator.gen_expr(ctx, exc)? {
-                    v.to_basic_value_enum(ctx, generator, exc.custom.unwrap())?
-                } else {
+                // Define all used models
+                let sizet = generator.get_sizet(ctx.ctx);
+                let pexn_model = PointerModel(StructModel(Exception { sizet }));
+
+                let Some(exn) = generator.gen_expr(ctx, exc)? else {
                     return Ok(());
                 };
-                gen_raise(generator, ctx, Some(&exc), stmt.location);
+                let pexn = exn.to_basic_value_enum(ctx, generator, ctx.primitives.exception)?;
+                let pexn = pexn_model.review_value(ctx.ctx, pexn).unwrap();
+
+                gen_raise(generator, ctx, Some(pexn), stmt.location);
             } else {
                 gen_raise(generator, ctx, None, stmt.location);
             }
         }
         StmtKind::Assert { test, msg, .. } => {
-            let test = if let Some(v) = generator.gen_expr(ctx, test)? {
-                v.to_basic_value_enum(ctx, generator, test.custom.unwrap())?
-            } else {
+            // Define all used models
+            let sizet = generator.get_sizet(ctx.ctx);
+            let byte_model = NIntModel(Byte);
+            let cslice_model = StructModel(CSlice { sizet });
+
+            // Check `test`
+            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.review_value(ctx.ctx, test).unwrap(); // Python `bool`s are represented as `i8` in nac3core
+
+            // Check `msg`
             let err_msg = match msg {
                 Some(msg) => {
-                    if let Some(v) = generator.gen_expr(ctx, msg)? {
-                        v.to_basic_value_enum(ctx, generator, msg.custom.unwrap())?
-                    } else {
+                    let Some(msg) = generator.gen_expr(ctx, msg)? else {
                         return Ok(());
-                    }
+                    };
+
+                    let msg = msg.to_basic_value_enum(ctx, generator, ctx.primitives.str)?;
+                    cslice_model.review_value(ctx.ctx, msg).unwrap()
                 }
                 None => ctx.gen_string(generator, ""),
             };
+
             ctx.make_assert_impl(
                 generator,
-                test.into_int_value(),
+                test.value,
                 "0:AssertionError",
                 err_msg,
                 [None, None, None],

nac3core/src/codegen/structs/cslice.rs

@@ -0,0 +1,50 @@
+use crate::codegen::{model::*, CodeGenContext};
+
+/// Fields of [`CSlice<'ctx>`].
+pub struct CSliceFields<'ctx> {
+    /// Pointer to the data.
+    pub base: Field<PointerModel<ByteModel>>,
+    /// Number of bytes of the data.
+    pub len: Field<SizeTModel<'ctx>>,
+}
+
+/// 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, PartialEq, Eq)]
+pub struct CSlice<'ctx> {
+    pub sizet: SizeTModel<'ctx>,
+}
+
+impl<'ctx> StructKind<'ctx> for CSlice<'ctx> {
+    type Fields = CSliceFields<'ctx>;
+
+    fn struct_name(&self) -> &'static str {
+        "CSlice"
+    }
+
+    fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
+        Self::Fields {
+            base: builder.add_field_auto("content"),
+            len: builder.add_field("length", self.sizet),
+        }
+    }
+}
+
+impl<'ctx> StructModel<CSlice<'ctx>> {
+    /// Create a [`CSlice`].
+    ///
+    /// `base` and `len` must be LLVM global constants.
+    pub fn create_const(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        base: Pointer<'ctx, ByteModel>,
+        len: SizeT<'ctx>,
+    ) -> Struct<'ctx, CSlice<'ctx>> {
+        let value = self
+            .get_struct_type(ctx.ctx)
+            .const_named_struct(&[base.value.into(), len.value.into()]);
+        self.believe_value(value)
+    }
+}

nac3core/src/codegen/structs/exception.rs

@@ -0,0 +1,78 @@
+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> {
+    /// nac3core's ID of the exception
+    pub exception_id: Field<NIntModel<ExceptionId>>,
+    /// The name of the file this `Exception` was raised in.
+    pub filename: Field<StructModel<CSlice<'ctx>>>,
+    /// The line number in the file this `Exception` was raised in.
+    pub line: Field<NIntModel<Int32>>,
+    /// The column number in the file this `Exception` was raised in.
+    pub column: Field<NIntModel<Int32>>,
+    /// The name of the Python function this `Exception` was raised in.
+    pub function_name: Field<StructModel<CSlice<'ctx>>>,
+    /// 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 message: Field<StructModel<CSlice<'ctx>>>,
+    pub params: [Field<NIntModel<Int64>>; 3],
+}
+
+/// nac3core & ARTIQ's Exception
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub struct Exception<'ctx> {
+    /// The `SizeT` type of this string.
+    pub sizet: SizeTModel<'ctx>,
+}
+
+impl<'ctx> StructKind<'ctx> for Exception<'ctx> {
+    type Fields = ExceptionFields<'ctx>;
+
+    fn struct_name(&self) -> &'static str {
+        "Exception"
+    }
+
+    fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
+        let str = StructModel(CSlice { sizet: self.sizet });
+
+        let exception_id = builder.add_field_auto("exception_id");
+        let file_name = builder.add_field("file_name", str);
+        let line = builder.add_field_auto("line");
+        let column = builder.add_field_auto("column");
+        let function_name = builder.add_field("function_name", str);
+        let message = builder.add_field("message", str);
+        let params = [
+            builder.add_field_auto("param0"),
+            builder.add_field_auto("param1"),
+            builder.add_field_auto("param2"),
+        ];
+
+        Self::Fields {
+            exception_id,
+            filename: file_name,
+            line,
+            column,
+            function_name,
+            message,
+            params,
+        }
+    }
+}

nac3core/src/codegen/structs/list.rs

@@ -0,0 +1,42 @@
+use crate::codegen::{model::*, CodeGenContext};
+
+/// Fields of [`List`]
+pub struct ListFields<'ctx, T: Model<'ctx>> {
+    /// Length of the list
+    pub size: Field<SizeTModel<'ctx>>,
+    /// Base pointer of the list
+    pub data: Field<PointerModel<T>>,
+}
+
+/// nac3core's `List` definition
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub struct List<'ctx, T: Model<'ctx>> {
+    pub sizet: SizeTModel<'ctx>,
+    pub element: T,
+}
+
+impl<'ctx, T: Model<'ctx> + 'ctx> StructKind<'ctx> for List<'ctx, T> {
+    type Fields = ListFields<'ctx, T>;
+
+    fn struct_name(&self) -> &'static str {
+        "List"
+    }
+
+    fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
+        Self::Fields {
+            size: builder.add_field("size", self.sizet),
+            data: builder.add_field("data", PointerModel(self.element)),
+        }
+    }
+}
+
+impl<'ctx, T: Model<'ctx> + 'ctx> Pointer<'ctx, StructModel<List<'ctx, T>>> {
+    pub fn as_slice(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> ArraySlice<'ctx, SizeTModel<'ctx>, T> {
+        let num_elements = self.gep(ctx, |f| f.size).load(ctx, "num_elements");
+        let pointer = self.gep(ctx, |f| f.data).load(ctx, "base");
+        ArraySlice { num_elements, pointer }
+    }
+}

nac3core/src/codegen/structs/mod.rs

@@ -0,0 +1,4 @@
+pub mod cslice;
+pub mod exception;
+pub mod list;
+pub mod ndarray;

nac3core/src/codegen/structs/ndarray.rs

@@ -0,0 +1,54 @@
+use crate::codegen::*;
+
+pub struct NpArrayFields<'ctx> {
+    pub data: Field<PointerModel<ByteModel>>,
+    pub itemsize: Field<SizeTModel<'ctx>>,
+    pub ndims: Field<SizeTModel<'ctx>>,
+    pub shape: Field<PointerModel<SizeTModel<'ctx>>>,
+    pub strides: Field<PointerModel<SizeTModel<'ctx>>>,
+}
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub struct NpArray<'ctx> {
+    pub sizet: SizeTModel<'ctx>,
+}
+
+impl<'ctx> StructKind<'ctx> for NpArray<'ctx> {
+    type Fields = NpArrayFields<'ctx>;
+
+    fn struct_name(&self) -> &'static str {
+        "NDArray"
+    }
+
+    fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
+        NpArrayFields {
+            data: builder.add_field_auto("data"),
+            itemsize: builder.add_field("itemsize", self.sizet),
+            ndims: builder.add_field("ndims", self.sizet),
+            shape: builder.add_field("shape", PointerModel(self.sizet)),
+            strides: builder.add_field("strides", PointerModel(self.sizet)),
+        }
+    }
+}
+
+impl<'ctx> Pointer<'ctx, StructModel<NpArray<'ctx>>> {
+    /// Get an [`ArraySlice`] of [`NpArrayFields::shape`] with [`NpArrayFields::ndims`] as its length.
+    pub fn shape_slice(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> ArraySlice<'ctx, SizeTModel<'ctx>, SizeTModel<'ctx>> {
+        let ndims = self.gep(ctx, |f| f.ndims).load(ctx, "ndims");
+        let shape_base_ptr = self.gep(ctx, |f| f.shape).load(ctx, "shape");
+        ArraySlice { num_elements: ndims, pointer: shape_base_ptr }
+    }
+
+    /// Get an [`ArraySlice`] of [`NpArrayFields::strides`] with [`NpArrayFields::ndims`] as its length.
+    pub fn strides_slice(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> ArraySlice<'ctx, SizeTModel<'ctx>, SizeTModel<'ctx>> {
+        let ndims = self.gep(ctx, |f| f.ndims).load(ctx, "ndims");
+        let strides_base_ptr = self.gep(ctx, |f| f.strides).load(ctx, "strides");
+        ArraySlice { num_elements: ndims, pointer: strides_base_ptr }
+    }
+}

nac3core/src/codegen/util/array_writer.rs

@@ -0,0 +1,17 @@
+use inkwell::{types::IntType, values::IntValue};
+
+use crate::codegen::{model::*, CodeGenContext, CodeGenerator};
+
+pub type ArrayWriterWrite<'ctx, G, N, E> = Box<
+    dyn Fn(&mut G, &mut CodeGenContext<'ctx, '_>, &ArraySlice<'ctx, N, E>) -> Result<(), String>
+        + 'ctx,
+>;
+
+// TODO: Document
+pub struct ArrayWriter<'ctx, G: CodeGenerator + ?Sized, N, E: Model<'ctx>>
+where
+    N: Model<'ctx, Value = IntValue<'ctx>, Type = IntType<'ctx>>,
+{
+    pub count: Instance<'ctx, N>,
+    pub write: ArrayWriterWrite<'ctx, G, N, E>,
+}

nac3core/src/codegen/util/control_flow.rs

@@ -0,0 +1,28 @@
+use inkwell::{types::IntType, values::IntValue};
+
+use crate::codegen::{model::*, CodeGenContext, CodeGenerator};
+
+/// Convenient structure that looks like
+/// Python `range`, with a dependent int type.
+pub struct ForRange<T> {
+    pub start: T,
+    pub stop: T,
+    pub step: T,
+}
+
+impl<'ctx, T> ForRange<T>
+where
+    T: Model<'ctx, Value = IntValue<'ctx>, Type = IntType<'ctx>>,
+{
+    pub fn end(ctx: &'ctx Context, end: T) -> Self {
+        todo!()
+    }
+}
+
+pub fn for_range<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, 'a>,
+    range: ForRange,
+    body: BodyFn,
+) {
+}

nac3core/src/codegen/util/mod.rs

@@ -0,0 +1,3 @@
+pub mod array_writer;
+pub mod control_flow;
+pub mod shape;

nac3core/src/codegen/util/shape.rs

@@ -0,0 +1,144 @@
+use inkwell::values::BasicValueEnum;
+
+use crate::{
+    codegen::{
+        classes::{ListValue, UntypedArrayLikeAccessor},
+        model::*,
+        stmt::gen_for_callback_incrementing,
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+use super::array_writer::ArrayWriter;
+
+/// TODO: UPDATE DOCUMENTATION
+/// LLVM-typed implementation for generating a [`ArrayWriter`] that sets a list of ints.
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+/// * `shape` - The `shape` parameter used to construct the `NDArray`.
+///
+/// ### Notes on `shape`
+///
+/// Just like numpy, the `shape` argument can be:
+///   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])`
+///
+/// See also [`typecheck::type_inferencer::fold_numpy_function_call_shape_argument`] to
+/// learn how `shape` gets from being a Python user expression to here.
+pub fn parse_input_shape_arg<'ctx, G>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    shape: BasicValueEnum<'ctx>,
+    shape_ty: Type,
+) -> ArrayWriter<'ctx, G, SizeTModel<'ctx>, SizeTModel<'ctx>>
+where
+    G: CodeGenerator + ?Sized,
+{
+    let sizet = generator.get_sizet(ctx.ctx);
+
+    match &*ctx.unifier.get_ty(shape_ty) {
+        TypeEnum::TObj { obj_id, .. }
+            if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+        {
+            // 1. A list of ints; e.g., `np.empty([600, 800, 3])`
+
+            // A list has to be a PointerValue
+            let shape_list = ListValue::from_ptr_val(shape.into_pointer_value(), sizet.0, None);
+
+            // Create `ArrayWriter`
+            let ndims =
+                sizet.review_value(ctx.ctx, shape_list.load_size(ctx, Some("count"))).unwrap();
+            ArrayWriter {
+                count: ndims,
+                write: Box::new(move |generator, ctx, dst_array| {
+                    // Basically iterate through the list and write to `dst_slice` accordingly
+                    let init_val = sizet.constant(ctx.ctx, 0).value;
+                    let max_val = (ndims.value, false);
+                    let incr_val = sizet.constant(ctx.ctx, 1).value;
+                    gen_for_callback_incrementing(
+                        generator,
+                        ctx,
+                        init_val,
+                        max_val,
+                        |generator, ctx, _hooks, axis| {
+                            let axis = sizet.review_value(ctx.ctx, axis).unwrap();
+
+                            // TODO: Remove ProxyValue ListValue
+
+                            // Get the dimension at `axis`
+                            let dim: Int<'ctx> = shape_list
+                                .data()
+                                .get(ctx, generator, &axis.value, None)
+                                .into_int_value()
+                                .into();
+
+                            let dim = dim.s_extend_or_bit_cast(ctx, sizet, "dim_casted");
+                            dst_array.ix(generator, ctx, axis, "dim").store(ctx, dim);
+                            Ok(())
+                        },
+                        incr_val,
+                    )
+                }),
+            }
+        }
+        TypeEnum::TTuple { ty: tuple_types } => {
+            // 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
+
+            // Get the length/size of the tuple, which also happens to be the value of `ndims`.
+            let ndims = tuple_types.len();
+
+            // A tuple has to be a StructValue
+            // Read [`codegen::expr::gen_expr`] to see how `nac3core` translates a Python tuple into LLVM.
+            let shape_tuple = shape.into_struct_value();
+
+            ArrayWriter {
+                count: sizet.constant(ctx.ctx, ndims as u64),
+                write: Box::new(move |generator, ctx, dst_array| {
+                    for axis in 0..ndims {
+                        // Get the dimension at `axis`
+                        let dim: Int<'ctx> = ctx
+                            .builder
+                            .build_extract_value(
+                                shape_tuple,
+                                axis as u32,
+                                format!("dim{axis}").as_str(),
+                            )
+                            .unwrap()
+                            .into_int_value()
+                            .into();
+
+                        let dim = dim.s_extend_or_bit_cast(ctx, sizet, "dim_casted");
+                        dst_array
+                            .ix(generator, ctx, sizet.constant(ctx.ctx, axis as u64), "dim")
+                            .store(ctx, dim);
+                    }
+                    Ok(())
+                }),
+            }
+        }
+        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])`
+
+            // The value has to be an integer
+            let shape_int: Int<'ctx> = shape.into_int_value().into();
+
+            ArrayWriter {
+                count: sizet.constant(ctx.ctx, 1),
+                write: Box::new(move |generator, ctx, dst_array| {
+                    // Cast `shape_int` to SizeT
+                    let dim = shape_int.s_extend_or_bit_cast(ctx, sizet, "dim_casted");
+
+                    // Set shape[0] = shape_int
+                    dst_array.ix(generator, ctx, sizet.constant(ctx.ctx, 0), "dim").store(ctx, dim);
+
+                    Ok(())
+                }),
+            }
+        }
+        _ => panic!("parse_input_shape_arg encountered unknown type"),
+    }
+}

nac3core/src/lib.rs

@@ -23,4 +23,3 @@ pub mod codegen;
 pub mod symbol_resolver;
 pub mod toplevel;
 pub mod typecheck;
-pub mod util;

nac3core/src/toplevel/builtins.rs

@@ -1,6 +1,5 @@
 use std::iter::once;
 
-use crate::util::SizeVariant;
 use helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDefDetails};
 use indexmap::IndexMap;
 use inkwell::{
@@ -10,16 +9,20 @@ use inkwell::{
     IntPredicate,
 };
 use itertools::Either;
+use ndarray::basic::call_nac3_ndarray_len;
 use strum::IntoEnumIterator;
 
 use crate::{
     codegen::{
         builtin_fns,
-        classes::{ArrayLikeValue, NDArrayValue, ProxyValue, RangeValue, TypedArrayLikeAccessor},
+        classes::{ProxyValue, RangeValue},
         expr::destructure_range,
         irrt::*,
+        model::*,
         numpy::*,
+        numpy_new,
         stmt::exn_constructor,
+        structs::ndarray::NpArray,
     },
     symbol_resolver::SymbolValue,
     toplevel::{helper::PrimDef, numpy::make_ndarray_ty},
@@ -279,11 +282,20 @@ pub fn get_builtins(unifier: &mut Unifier, primitives: &PrimitiveStore) -> Built
         .collect()
 }
 
-fn size_variant_to_int_type(variant: SizeVariant, primitives: &PrimitiveStore) -> Type {
-    match variant {
+/// A helper enum used by [`BuiltinBuilder`]
+#[derive(Clone, Copy)]
+enum SizeVariant {
+    Bits32,
+    Bits64,
+}
+
+impl SizeVariant {
+    fn of_int(self, primitives: &PrimitiveStore) -> Type {
+        match self {
             SizeVariant::Bits32 => primitives.int32,
             SizeVariant::Bits64 => primitives.int64,
         }
+    }
 }
 
 struct BuiltinBuilder<'a> {
@@ -484,6 +496,10 @@ impl<'a> BuiltinBuilder<'a> {
             | PrimDef::FunNpEye
             | PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
 
+            PrimDef::FunNpReshape | PrimDef::FunNpTranspose => {
+                self.build_ndarray_view_functions(prim)
+            }
+
             PrimDef::FunStr => self.build_str_function(),
 
             PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
@@ -502,7 +518,9 @@ impl<'a> BuiltinBuilder<'a> {
 
             PrimDef::FunMin | PrimDef::FunMax => self.build_min_max_function(prim),
 
-            PrimDef::FunNpMin | PrimDef::FunNpMax => self.build_np_min_max_function(prim),
+            PrimDef::FunNpArgmin | PrimDef::FunNpArgmax | PrimDef::FunNpMin | PrimDef::FunNpMax => {
+                self.build_np_max_min_function(prim)
+            }
 
             PrimDef::FunNpMinimum | PrimDef::FunNpMaximum => {
                 self.build_np_minimum_maximum_function(prim)
@@ -554,7 +572,7 @@ impl<'a> BuiltinBuilder<'a> {
             match (&tld, prim.details()) {
                 (
                     TopLevelDef::Class { name, object_id, .. },
-                    PrimDefDetails::PrimClass { name: exp_name },
+                    PrimDefDetails::PrimClass { name: exp_name, .. },
                 ) => {
                     let exp_object_id = prim.id();
                     assert_eq!(name, &exp_name.into());
@@ -953,9 +971,8 @@ impl<'a> BuiltinBuilder<'a> {
                 resolver: None,
                 codegen_callback: Some(Arc::new(GenCall::new(Box::new(
                     |ctx, obj, fun, args, generator| {
-                        todo!()
-                        // gen_ndarray_copy(ctx, &obj, fun, &args, generator)
-                        //     .map(|val| Some(val.as_basic_value_enum()))
+                        gen_ndarray_copy(ctx, &obj, fun, &args, generator)
+                            .map(|val| Some(val.as_basic_value_enum()))
                     },
                 )))),
                 loc: None,
@@ -971,9 +988,8 @@ impl<'a> BuiltinBuilder<'a> {
                 resolver: None,
                 codegen_callback: Some(Arc::new(GenCall::new(Box::new(
                     |ctx, obj, fun, args, generator| {
-                        todo!()
-                        // gen_ndarray_fill(ctx, &obj, fun, &args, generator)?;
-                        // Ok(None)
+                        gen_ndarray_fill(ctx, &obj, fun, &args, generator)?;
+                        Ok(None)
                     },
                 )))),
                 loc: None,
@@ -1053,7 +1069,7 @@ impl<'a> BuiltinBuilder<'a> {
         );
 
         // The size variant of the function determines the size of the returned int.
-        let int_sized = size_variant_to_int_type(size_variant, self.primitives);
+        let int_sized = size_variant.of_int(self.primitives);
 
         let ndarray_int_sized =
             make_ndarray_ty(self.unifier, self.primitives, Some(int_sized), Some(common_ndim.ty));
@@ -1078,7 +1094,7 @@ 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_elem_ty = size_variant_to_int_type(size_variant, &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)?))
             }),
         )
@@ -1119,7 +1135,7 @@ impl<'a> BuiltinBuilder<'a> {
             make_ndarray_ty(self.unifier, self.primitives, Some(float), Some(common_ndim.ty));
 
         // The size variant of the function determines the type of int returned
-        let int_sized = size_variant_to_int_type(size_variant, self.primitives);
+        let int_sized = size_variant.of_int(self.primitives);
         let ndarray_int_sized =
             make_ndarray_ty(self.unifier, self.primitives, Some(int_sized), Some(common_ndim.ty));
 
@@ -1142,7 +1158,7 @@ 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_elem_ty = size_variant_to_int_type(size_variant, &ctx.primitives);
+                let ret_elem_ty = size_variant.of_int(&ctx.primitives);
                 let func = match kind {
                     Kind::Ceil => builtin_fns::call_ceil,
                     Kind::Floor => builtin_fns::call_floor,
@@ -1193,14 +1209,15 @@ impl<'a> BuiltinBuilder<'a> {
             self.ndarray_float,
             &[(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
             Box::new(move |ctx, obj, fun, args, generator| {
-                todo!()
-                // let func = match prim {
-                //     PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => gen_ndarray_empty,
-                //     PrimDef::FunNpZeros => gen_ndarray_zeros,
-                //     PrimDef::FunNpOnes => gen_ndarray_ones,
-                //     _ => unreachable!(),
-                // };
-                // func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
+                let func = match prim {
+                    PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => {
+                        numpy_new::factory::gen_ndarray_empty
+                    }
+                    PrimDef::FunNpZeros => numpy_new::factory::gen_ndarray_zeros,
+                    PrimDef::FunNpOnes => numpy_new::factory::gen_ndarray_ones,
+                    _ => unreachable!(),
+                };
+                func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
             }),
         )
     }
@@ -1246,9 +1263,8 @@ impl<'a> BuiltinBuilder<'a> {
                     resolver: None,
                     codegen_callback: Some(Arc::new(GenCall::new(Box::new(
                         |ctx, obj, fun, args, generator| {
-                            todo!()
-                            // gen_ndarray_array(ctx, &obj, fun, &args, generator)
-                            //     .map(|val| Some(val.as_basic_value_enum()))
+                            gen_ndarray_array(ctx, &obj, fun, &args, generator)
+                                .map(|val| Some(val.as_basic_value_enum()))
                         },
                     )))),
                     loc: None,
@@ -1266,9 +1282,8 @@ impl<'a> BuiltinBuilder<'a> {
                     // type variable
                     &[(self.list_int32, "shape"), (tv.ty, "fill_value")],
                     Box::new(move |ctx, obj, fun, args, generator| {
-                        todo!()
-                        // gen_ndarray_full(ctx, &obj, fun, &args, generator)
-                        //     .map(|val| Some(val.as_basic_value_enum()))
+                        numpy_new::factory::gen_ndarray_full(ctx, &obj, fun, &args, generator)
+                            .map(|val| Some(val.as_basic_value_enum()))
                     }),
                 )
             }
@@ -1300,9 +1315,8 @@ impl<'a> BuiltinBuilder<'a> {
                     resolver: None,
                     codegen_callback: Some(Arc::new(GenCall::new(Box::new(
                         |ctx, obj, fun, args, generator| {
-                            todo!()
-                            // gen_ndarray_eye(ctx, &obj, fun, &args, generator)
-                            //     .map(|val| Some(val.as_basic_value_enum()))
+                            gen_ndarray_eye(ctx, &obj, fun, &args, generator)
+                                .map(|val| Some(val.as_basic_value_enum()))
                         },
                     )))),
                     loc: None,
@@ -1315,15 +1329,97 @@ impl<'a> BuiltinBuilder<'a> {
                 self.ndarray_float_2d,
                 &[(int32, "n")],
                 Box::new(|ctx, obj, fun, args, generator| {
-                    todo!()
-                    // gen_ndarray_identity(ctx, &obj, fun, &args, generator)
-                    //     .map(|val| Some(val.as_basic_value_enum()))
+                    gen_ndarray_identity(ctx, &obj, fun, &args, generator)
+                        .map(|val| Some(val.as_basic_value_enum()))
                 }),
             ),
             _ => unreachable!(),
         }
     }
 
+    // Build functions related to NDArray views
+    fn build_ndarray_view_functions(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpReshape, PrimDef::FunNpTranspose]);
+
+        match prim {
+            PrimDef::FunNpReshape => {
+                let new_ndim_ty = self.unifier.get_fresh_var(Some("NewNDim".into()), None);
+                let returned_ndarray_ty = make_ndarray_ty(
+                    self.unifier,
+                    self.primitives,
+                    Some(self.ndarray_dtype_tvar.ty),
+                    Some(new_ndim_ty.ty),
+                );
+
+                create_fn_by_codegen(
+                    self.unifier,
+                    &into_var_map([self.ndarray_dtype_tvar, self.ndarray_ndims_tvar, new_ndim_ty]),
+                    prim.name(),
+                    returned_ndarray_ty,
+                    &[
+                        (self.primitives.ndarray, "array"),
+                        (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"),
+                    ],
+                    Box::new(|ctx, obj, fun, args, generator| {
+                        numpy_new::view::gen_ndarray_reshape(ctx, &obj, fun, &args, generator)
+                            .map(|val| Some(val.as_basic_value_enum()))
+                    }),
+                )
+            }
+            PrimDef::FunNpTranspose => {
+                /*
+                    # NDim has to be known (for checking axes's len)
+                    def np_transpose(
+                      array: NDArray[DType, NDim],
+                      axes: Optional[List[int32]] = None,
+                    ) -> NDArray[DType, NDim]
+                */
+
+                // TODO: Allow tuples (or even general iterables in the very far future) on `axes`
+                let optional_axes_ty = self
+                    .unifier
+                    .subst(
+                        self.primitives.option,
+                        &VarMap::from([(self.option_tvar.id, self.list_int32)]),
+                    )
+                    .unwrap();
+
+                TopLevelDef::Function {
+                    name: prim.name().into(),
+                    simple_name: prim.simple_name().into(),
+                    signature: self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
+                        args: vec![
+                            FuncArg {
+                                name: "array".into(),
+                                ty: self.primitives.ndarray,
+                                default_value: None,
+                            },
+                            FuncArg {
+                                name: "axes".into(),
+                                ty: optional_axes_ty,
+                                default_value: Some(SymbolValue::OptionNone),
+                            },
+                        ],
+                        ret: self.primitives.ndarray,
+                        vars: VarMap::default(),
+                    })),
+                    var_id: vec![self.ndarray_ndims_tvar.id],
+                    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| {
+                            numpy_new::view::gen_ndarray_transpose(ctx, &obj, fun, &args, generator)
+                                .map(|val| Some(val.as_basic_value_enum()))
+                        },
+                    )))),
+                    loc: None,
+                }
+            }
+            _ => unreachable!(),
+        }
+    }
+
     /// Build the `str()` function.
     fn build_str_function(&mut self) -> TopLevelDef {
         let prim = PrimDef::FunStr;
@@ -1461,51 +1557,12 @@ impl<'a> BuiltinBuilder<'a> {
                                 }
                             }
                             TypeEnum::TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                                let llvm_i32 = ctx.ctx.i32_type();
-                                let llvm_usize = generator.get_size_type(ctx.ctx);
+                                let sizet = generator.get_sizet(ctx.ctx);
+                                let pndarray_model = PointerModel(StructModel(NpArray { sizet }));
 
-                                let arg = NDArrayValue::from_ptr_val(
-                                    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(),
-                                    )
-                                }
+                                let ndarray = pndarray_model.review_value(ctx.ctx, arg).unwrap();
+                                let len = call_nac3_ndarray_len(generator, ctx, ndarray);
+                                Some(len.value.as_basic_value_enum())
                             }
                             _ => unreachable!(),
                         }
@@ -1554,10 +1611,19 @@ impl<'a> BuiltinBuilder<'a> {
         }
     }
 
-    /// Build the functions `np_min()` and `np_max()`.
-    fn build_np_min_max_function(&mut self, prim: PrimDef) -> TopLevelDef {
-        debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpMin, PrimDef::FunNpMax]);
+    /// Build the functions `np_max()`, `np_min()`, `np_argmax()` and `np_argmin()`
+    /// Calls `call_numpy_max_min` with the function name
+    fn build_np_max_min_function(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(
+            prim,
+            &[PrimDef::FunNpArgmin, PrimDef::FunNpArgmax, PrimDef::FunNpMin, PrimDef::FunNpMax],
+        );
 
+        let (var_map, ret_ty) = match prim {
+            PrimDef::FunNpArgmax | PrimDef::FunNpArgmin => {
+                (self.num_or_ndarray_var_map.clone(), self.primitives.int64)
+            }
+            PrimDef::FunNpMax | PrimDef::FunNpMin => {
                 let ret_ty = self.unifier.get_fresh_var(Some("R".into()), None);
                 let var_map = self
                     .num_or_ndarray_var_map
@@ -1565,28 +1631,25 @@ impl<'a> BuiltinBuilder<'a> {
                     .into_iter()
                     .chain(once((ret_ty.id, ret_ty.ty)))
                     .collect::<IndexMap<_, _>>();
+                (var_map, ret_ty.ty)
+            }
+            _ => unreachable!(),
+        };
 
         create_fn_by_codegen(
             self.unifier,
             &var_map,
             prim.name(),
-            ret_ty.ty,
-            &[(self.float_or_ndarray_ty.ty, "a")],
+            ret_ty,
+            &[(self.num_or_ndarray_ty.ty, "a")],
             Box::new(move |ctx, _, fun, args, generator| {
                 let a_ty = fun.0.args[0].ty;
                 let a = args[0].1.clone().to_basic_value_enum(ctx, generator, a_ty)?;
 
-                let func = match prim {
-                    PrimDef::FunNpMin => builtin_fns::call_numpy_min,
-                    PrimDef::FunNpMax => builtin_fns::call_numpy_max,
-                    _ => unreachable!(),
-                };
-
-                Ok(Some(func(generator, ctx, (a_ty, a))?))
+                Ok(Some(builtin_fns::call_numpy_max_min(generator, ctx, (a_ty, a), prim.name())?))
             }),
         )
     }
-
     /// Build the functions `np_minimum()` and `np_maximum()`.
     fn build_np_minimum_maximum_function(&mut self, prim: PrimDef) -> TopLevelDef {
         debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpMinimum, PrimDef::FunNpMaximum]);

nac3core/src/toplevel/composer.rs

@@ -766,6 +766,7 @@ impl TopLevelComposer {
             let target_ty = get_type_from_type_annotation_kinds(
                 &temp_def_list,
                 unifier,
+                primitives,
                 &def,
                 &mut subst_list,
             )?;
@@ -936,6 +937,7 @@ impl TopLevelComposer {
                         let ty = get_type_from_type_annotation_kinds(
                             temp_def_list.as_ref(),
                             unifier,
+                            primitives_store,
                             &type_annotation,
                             &mut None,
                         )?;
@@ -1002,6 +1004,7 @@ impl TopLevelComposer {
                     get_type_from_type_annotation_kinds(
                         &temp_def_list,
                         unifier,
+                        primitives_store,
                         &return_ty_annotation,
                         &mut None,
                     )?
@@ -1622,6 +1625,7 @@ impl TopLevelComposer {
                 let self_type = get_type_from_type_annotation_kinds(
                     &def_list,
                     unifier,
+                    primitives_ty,
                     &make_self_type_annotation(type_vars, *object_id),
                     &mut None,
                 )?;
@@ -1803,7 +1807,11 @@ impl TopLevelComposer {
 
                         let ty_ann = make_self_type_annotation(type_vars, *class_id);
                         let self_ty = get_type_from_type_annotation_kinds(
-                            &def_list, unifier, &ty_ann, &mut None,
+                            &def_list,
+                            unifier,
+                            primitives_ty,
+                            &ty_ann,
+                            &mut None,
                         )?;
                         vars.extend(type_vars.iter().map(|ty| {
                             let TypeEnum::TVar { id, .. } = &*unifier.get_ty(*ty) else {

nac3core/src/toplevel/helper.rs

@@ -46,6 +46,8 @@ pub enum PrimDef {
     FunNpArray,
     FunNpEye,
     FunNpIdentity,
+    FunNpReshape,
+    FunNpTranspose,
     FunRound,
     FunRound64,
     FunNpRound,
@@ -62,9 +64,11 @@ pub enum PrimDef {
     FunMin,
     FunNpMin,
     FunNpMinimum,
+    FunNpArgmin,
     FunMax,
     FunNpMax,
     FunNpMaximum,
+    FunNpArgmax,
     FunAbs,
     FunNpIsNan,
     FunNpIsInf,
@@ -111,7 +115,7 @@ pub enum PrimDef {
 /// Associated details of a [`PrimDef`]
 pub enum PrimDefDetails {
     PrimFunction { name: &'static str, simple_name: &'static str },
-    PrimClass { name: &'static str },
+    PrimClass { name: &'static str, get_ty_fn: fn(&PrimitiveStore) -> Type },
 }
 
 impl PrimDef {
@@ -153,15 +157,17 @@ impl PrimDef {
     #[must_use]
     pub fn name(&self) -> &'static str {
         match self.details() {
-            PrimDefDetails::PrimFunction { name, .. } | PrimDefDetails::PrimClass { name } => name,
+            PrimDefDetails::PrimFunction { name, .. } | PrimDefDetails::PrimClass { name, .. } => {
+                name
+            }
         }
     }
 
     /// Get the associated details of this [`PrimDef`]
     #[must_use]
     pub fn details(self) -> PrimDefDetails {
-        fn class(name: &'static str) -> PrimDefDetails {
-            PrimDefDetails::PrimClass { name }
+        fn class(name: &'static str, get_ty_fn: fn(&PrimitiveStore) -> Type) -> PrimDefDetails {
+            PrimDefDetails::PrimClass { name, get_ty_fn }
         }
 
         fn fun(name: &'static str, simple_name: Option<&'static str>) -> PrimDefDetails {
@@ -169,22 +175,22 @@ impl PrimDef {
         }
 
         match self {
-            PrimDef::Int32 => class("int32"),
-            PrimDef::Int64 => class("int64"),
-            PrimDef::Float => class("float"),
-            PrimDef::Bool => class("bool"),
-            PrimDef::None => class("none"),
-            PrimDef::Range => class("range"),
-            PrimDef::Str => class("str"),
-            PrimDef::Exception => class("Exception"),
-            PrimDef::UInt32 => class("uint32"),
-            PrimDef::UInt64 => class("uint64"),
-            PrimDef::Option => class("Option"),
+            PrimDef::Int32 => class("int32", |primitives| primitives.int32),
+            PrimDef::Int64 => class("int64", |primitives| primitives.int64),
+            PrimDef::Float => class("float", |primitives| primitives.float),
+            PrimDef::Bool => class("bool", |primitives| primitives.bool),
+            PrimDef::None => class("none", |primitives| primitives.none),
+            PrimDef::Range => class("range", |primitives| primitives.range),
+            PrimDef::Str => class("str", |primitives| primitives.str),
+            PrimDef::Exception => class("Exception", |primitives| primitives.exception),
+            PrimDef::UInt32 => class("uint32", |primitives| primitives.uint32),
+            PrimDef::UInt64 => class("uint64", |primitives| primitives.uint64),
+            PrimDef::Option => class("Option", |primitives| primitives.option),
             PrimDef::OptionIsSome => fun("Option.is_some", Some("is_some")),
             PrimDef::OptionIsNone => fun("Option.is_none", Some("is_none")),
             PrimDef::OptionUnwrap => fun("Option.unwrap", Some("unwrap")),
-            PrimDef::List => class("list"),
-            PrimDef::NDArray => class("ndarray"),
+            PrimDef::List => class("list", |primitives| primitives.list),
+            PrimDef::NDArray => class("ndarray", |primitives| primitives.ndarray),
             PrimDef::NDArrayCopy => fun("ndarray.copy", Some("copy")),
             PrimDef::NDArrayFill => fun("ndarray.fill", Some("fill")),
             PrimDef::FunInt32 => fun("int32", None),
@@ -200,6 +206,8 @@ impl PrimDef {
             PrimDef::FunNpArray => fun("np_array", None),
             PrimDef::FunNpEye => fun("np_eye", None),
             PrimDef::FunNpIdentity => fun("np_identity", None),
+            PrimDef::FunNpReshape => fun("np_reshape", None),
+            PrimDef::FunNpTranspose => fun("np_transpose", None),
             PrimDef::FunRound => fun("round", None),
             PrimDef::FunRound64 => fun("round64", None),
             PrimDef::FunNpRound => fun("np_round", None),
@@ -216,9 +224,11 @@ impl PrimDef {
             PrimDef::FunMin => fun("min", None),
             PrimDef::FunNpMin => fun("np_min", None),
             PrimDef::FunNpMinimum => fun("np_minimum", None),
+            PrimDef::FunNpArgmin => fun("np_argmin", None),
             PrimDef::FunMax => fun("max", None),
             PrimDef::FunNpMax => fun("np_max", None),
             PrimDef::FunNpMaximum => fun("np_maximum", None),
+            PrimDef::FunNpArgmax => fun("np_argmax", None),
             PrimDef::FunAbs => fun("abs", None),
             PrimDef::FunNpIsNan => fun("np_isnan", None),
             PrimDef::FunNpIsInf => fun("np_isinf", None),

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap

@@ -5,7 +5,7 @@ expression: res_vec
 [
     "Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
     "Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(245)]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [\"aa\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap

@@ -7,7 +7,7 @@ expression: res_vec
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
-    "Class {\nname: \"B\",\nancestors: [\"B[typevar234]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar234\"]\n}\n",
+    "Class {\nname: \"B\",\nancestors: [\"B[typevar237]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar237\"]\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap

@@ -5,8 +5,8 @@ expression: res_vec
 [
     "Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
-    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(247)]\n}\n",
-    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(252)]\n}\n",
+    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(250)]\n}\n",
+    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(255)]\n}\n",
     "Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap

@@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
 expression: res_vec
 ---
 [
-    "Class {\nname: \"A\",\nancestors: [\"A[typevar233, typevar234]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar233\", \"typevar234\"]\n}\n",
+    "Class {\nname: \"A\",\nancestors: [\"A[typevar236, typevar237]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar236\", \"typevar237\"]\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap

@@ -6,12 +6,12 @@ expression: res_vec
     "Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(253)]\n}\n",
+    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(256)]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(261)]\n}\n",
+    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(264)]\n}\n",
 ]

nac3core/src/toplevel/type_annotation.rs

@@ -1,8 +1,9 @@
 use super::*;
 use crate::symbol_resolver::SymbolValue;
-use crate::toplevel::helper::PrimDef;
+use crate::toplevel::helper::{PrimDef, PrimDefDetails};
 use crate::typecheck::typedef::VarMap;
 use nac3parser::ast::Constant;
+use strum::IntoEnumIterator;
 
 #[derive(Clone, Debug)]
 pub enum TypeAnnotation {
@@ -357,6 +358,7 @@ pub fn parse_ast_to_type_annotation_kinds<T, S: std::hash::BuildHasher + Clone>(
 pub fn get_type_from_type_annotation_kinds(
     top_level_defs: &[Arc<RwLock<TopLevelDef>>],
     unifier: &mut Unifier,
+    primitives: &PrimitiveStore,
     ann: &TypeAnnotation,
     subst_list: &mut Option<Vec<Type>>,
 ) -> Result<Type, HashSet<String>> {
@@ -379,10 +381,43 @@ pub fn get_type_from_type_annotation_kinds(
             let param_ty = params
                 .iter()
                 .map(|x| {
-                    get_type_from_type_annotation_kinds(top_level_defs, unifier, x, subst_list)
+                    get_type_from_type_annotation_kinds(
+                        top_level_defs,
+                        unifier,
+                        primitives,
+                        x,
+                        subst_list,
+                    )
                 })
                 .collect::<Result<Vec<_>, _>>()?;
 
+            let ty = if let Some(prim_def) = PrimDef::iter().find(|prim| prim.id() == *obj_id) {
+                // Primitive TopLevelDefs do not contain all fields that are present in their Type
+                // counterparts, so directly perform subst on the Type instead.
+
+                let PrimDefDetails::PrimClass { get_ty_fn, .. } = prim_def.details() else {
+                    unreachable!()
+                };
+
+                let base_ty = get_ty_fn(primitives);
+                let params =
+                    if let TypeEnum::TObj { params, .. } = &*unifier.get_ty_immutable(base_ty) {
+                        params.clone()
+                    } else {
+                        unreachable!()
+                    };
+
+                unifier
+                    .subst(
+                        get_ty_fn(primitives),
+                        &params
+                            .iter()
+                            .zip(param_ty)
+                            .map(|(obj_tv, param)| (*obj_tv.0, param))
+                            .collect(),
+                    )
+                    .unwrap_or(base_ty)
+            } else {
                 let subst = {
                     // check for compatible range
                     // TODO: if allow type var to be applied(now this disallowed in the parse_to_type_annotation), need more check
@@ -424,12 +459,15 @@ pub fn get_type_from_type_annotation_kinds(
                                 }
                             }
 
-                        TypeEnum::TVar { id, range, name, loc, is_const_generic: true, .. } => {
+                            TypeEnum::TVar {
+                                id, range, name, loc, is_const_generic: true, ..
+                            } => {
                                 let ty = range[0];
                                 let ok: bool = {
                                     // create a temp type var and unify to check compatibility
                                     p == *tvar || {
-                                    let temp = unifier.get_fresh_const_generic_var(ty, *name, *loc);
+                                        let temp =
+                                            unifier.get_fresh_const_generic_var(ty, *name, *loc);
                                         unifier.unify(temp.ty, p).is_ok()
                                     }
                                 };
@@ -468,11 +506,16 @@ pub fn get_type_from_type_annotation_kinds(
                     fields: tobj_fields,
                     params: subst,
                 });
+
                 if need_subst {
                     if let Some(wl) = subst_list.as_mut() {
                         wl.push(ty);
                     }
                 }
+
+                ty
+            };
+
             Ok(ty)
         }
         TypeAnnotation::Primitive(ty) | TypeAnnotation::TypeVar(ty) => Ok(*ty),
@@ -490,6 +533,7 @@ pub fn get_type_from_type_annotation_kinds(
             let ty = get_type_from_type_annotation_kinds(
                 top_level_defs,
                 unifier,
+                primitives,
                 ty.as_ref(),
                 subst_list,
             )?;
@@ -499,7 +543,13 @@ pub fn get_type_from_type_annotation_kinds(
             let tys = tys
                 .iter()
                 .map(|x| {
-                    get_type_from_type_annotation_kinds(top_level_defs, unifier, x, subst_list)
+                    get_type_from_type_annotation_kinds(
+                        top_level_defs,
+                        unifier,
+                        primitives,
+                        x,
+                        subst_list,
+                    )
                 })
                 .collect::<Result<Vec<_>, _>>()?;
             Ok(unifier.add_ty(TypeEnum::TTuple { ty: tys }))

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -398,7 +398,10 @@ impl<'a> Fold<()> for Inferencer<'a> {
                 }
                 if let Some(exc) = exc {
                     self.virtual_checks.push((
-                        exc.custom.unwrap(),
+                        match &*self.unifier.get_ty(exc.custom.unwrap()) {
+                            TypeEnum::TFunc(sign) => sign.ret,
+                            _ => exc.custom.unwrap(),
+                        },
                         self.primitives.exception,
                         exc.location,
                     ));
@@ -1387,6 +1390,55 @@ impl<'a> Inferencer<'a> {
             }));
         }
 
+        // Handle `np.reshape(<array>, <shape>)`
+        if ["np_reshape".into()].contains(id) && args.len() == 2 {
+            // Extract arguments
+            let array_expr = args.remove(0);
+            let shape_expr = args.remove(0);
+
+            // Fold `<array>`
+            let array = self.fold_expr(array_expr)?;
+            let array_ty = array.custom.unwrap();
+            let (array_dtype, _) = unpack_ndarray_var_tys(self.unifier, array_ty);
+
+            // Fold `<shape>`
+            let (target_ndims, target_shape) =
+                self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?;
+            let target_shape_ty = target_shape.custom.unwrap();
+            // ... and deduce the return type of the call
+            let target_ndims_ty =
+                self.unifier.get_fresh_literal(vec![SymbolValue::U64(target_ndims)], None);
+            let ret = make_ndarray_ty(
+                self.unifier,
+                self.primitives,
+                Some(array_dtype),
+                Some(target_ndims_ty),
+            );
+
+            let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
+                args: vec![
+                    FuncArg { name: "array".into(), ty: array_ty, default_value: None },
+                    FuncArg { name: "shape".into(), ty: target_shape_ty, default_value: None },
+                ],
+                ret,
+                vars: VarMap::new(),
+            }));
+
+            return Ok(Some(Located {
+                location,
+                custom: Some(ret),
+                node: ExprKind::Call {
+                    func: Box::new(Located {
+                        custom: Some(custom),
+                        location: func.location,
+                        node: ExprKind::Name { id: *id, ctx: *ctx },
+                    }),
+                    args: vec![array, target_shape],
+                    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 {

nac3standalone/demo/demo.c

@@ -107,8 +107,25 @@ uint32_t __nac3_personality(uint32_t state, uint32_t exception_object, uint32_t
     __builtin_unreachable();
 }
 
-uint32_t __nac3_raise(uint32_t state, uint32_t exception_object, uint32_t context) {
-    printf("__nac3_raise(state: %u, exception_object: %u, context: %u)\n", state, exception_object, context);
+// See `struct Exception<'a>` in
+// https://github.com/m-labs/artiq/blob/master/artiq/firmware/libeh/eh_artiq.rs
+struct Exception {
+    uint32_t id;
+    struct cslice file;
+    uint32_t line;
+    uint32_t column;
+    struct cslice function;
+    struct cslice message;
+    int64_t param[3];
+};
+
+uint32_t __nac3_raise(struct Exception* e) {
+    printf("__nac3_raise called. Exception details:\n");
+    printf("          ID: %lld\n", e->id);
+    printf("    Location: %*s:%lld:%lld\n" , e->file.len, (const char*) e->file.data, e->line, e->column);
+    printf("    Function: %*s\n" , e->function.len, (const char*) e->function.data);
+    printf("     Message: \"%*s\"\n" , e->message.len, (const char*) e->message.data);
+    printf("      Params: {0}=%lld, {1}=%lld, {2}=%lld\n", e->param[0], e->param[1], e->param[2]);
     exit(101);
     __builtin_unreachable();
 }

nac3standalone/demo/interpret_demo.py

@@ -167,7 +167,7 @@ def patch(module):
     module.ceil64 = _ceil
     module.np_ceil = np.ceil
 
-    # NumPy ndarray functions
+    # NumPy NDArray factory functions
     module.ndarray = NDArray
     module.np_ndarray = np.ndarray
     module.np_empty = np.empty
@@ -178,13 +178,19 @@ def patch(module):
     module.np_identity = np.identity
     module.np_array = np.array
 
+    # NumPy view functions
+    module.np_reshape = np.reshape
+    module.np_transpose = np.transpose
+
     # NumPy Math functions
     module.np_isnan = np.isnan
     module.np_isinf = np.isinf
     module.np_min = np.min
     module.np_minimum = np.minimum
+    module.np_argmin = np.argmin
     module.np_max = np.max
     module.np_maximum = np.maximum
+    module.np_argmax = np.argmax
     module.np_sin = np.sin
     module.np_cos = np.cos
     module.np_exp = np.exp
@@ -216,7 +222,7 @@ def patch(module):
     module.np_hypot = np.hypot
     module.np_nextafter = np.nextafter
 
-    # SciPy Math Functions
+    # SciPy Math functions
     module.sp_spec_erf = special.erf
     module.sp_spec_erfc = special.erfc
     module.sp_spec_gamma = special.gamma
@@ -224,15 +230,6 @@ def patch(module):
     module.sp_spec_j0 = special.j0
     module.sp_spec_j1 = special.j1
 
-    # NumPy NDArray Functions
-    module.np_ndarray = np.ndarray
-    module.np_empty = np.empty
-    module.np_zeros = np.zeros
-    module.np_ones = np.ones
-    module.np_full = np.full
-    module.np_eye = np.eye
-    module.np_identity = np.identity
-
 def file_import(filename, prefix="file_import_"):
     filename = pathlib.Path(filename)
     modname = prefix + filename.stem

nac3standalone/demo/src/ndarray.py

@@ -867,6 +867,13 @@ def test_ndarray_minimum_broadcast_rhs_scalar():
     output_ndarray_float_2(min_x_zeros)
     output_ndarray_float_2(min_x_ones)
 
+def test_ndarray_argmin():
+    x = np_array([[1., 2.], [3., 4.]])
+    y = np_argmin(x)
+
+    output_ndarray_float_2(x)
+    output_int64(y)
+
 def test_ndarray_max():
     x = np_identity(2)
     y = np_max(x)
@@ -910,6 +917,13 @@ def test_ndarray_maximum_broadcast_rhs_scalar():
     output_ndarray_float_2(max_x_zeros)
     output_ndarray_float_2(max_x_ones)
 
+def test_ndarray_argmax():
+    x = np_array([[1., 2.], [3., 4.]])
+    y = np_argmax(x)
+
+    output_ndarray_float_2(x)
+    output_int64(y)
+
 def test_ndarray_abs():
     x = np_identity(2)
     y = abs(x)
@@ -1524,11 +1538,13 @@ def run() -> int32:
     test_ndarray_minimum_broadcast()
     test_ndarray_minimum_broadcast_lhs_scalar()
     test_ndarray_minimum_broadcast_rhs_scalar()
+    test_ndarray_argmin()
     test_ndarray_max()
     test_ndarray_maximum()
     test_ndarray_maximum_broadcast()
     test_ndarray_maximum_broadcast_lhs_scalar()
     test_ndarray_maximum_broadcast_rhs_scalar()
+    test_ndarray_argmax()
     test_ndarray_abs()
     test_ndarray_isnan()
     test_ndarray_isinf()

nac3standalone/src/main.rs

@@ -113,7 +113,9 @@ fn handle_typevar_definition(
                         x,
                         HashMap::new(),
                     )?;
-                    get_type_from_type_annotation_kinds(def_list, unifier, &ty, &mut None)
+                    get_type_from_type_annotation_kinds(
+                        def_list, unifier, primitives, &ty, &mut None,
+                    )
                 })
                 .collect::<Result<Vec<_>, _>>()?;
             let loc = func.location;
@@ -152,7 +154,7 @@ fn handle_typevar_definition(
                 HashMap::new(),
             )?;
             let constraint =
-                get_type_from_type_annotation_kinds(def_list, unifier, &ty, &mut None)?;
+                get_type_from_type_annotation_kinds(def_list, unifier, primitives, &ty, &mut None)?;
             let loc = func.location;
 
             Ok(unifier.get_fresh_const_generic_var(constraint, Some(generic_name), Some(loc)).ty)

nix/windows/default.nix

@@ -81,7 +81,6 @@ in rec {
     ''
     mkdir -p $out/bin
     ln -s ${llvm-nac3}/bin/clang.exe $out/bin/clang-irrt.exe
-    ln -s ${llvm-nac3}/bin/clang.exe $out/bin/clang-irrt-test.exe
     ln -s ${llvm-nac3}/bin/llvm-as.exe $out/bin/llvm-as-irrt.exe
     '';
   nac3artiq = pkgs.rustPlatform.buildRustPackage {