WIP: core: save progress

flake.nix

@@ -13,6 +13,7 @@
           ''
           mkdir -p $out/bin
           ln -s ${pkgs.llvmPackages_14.clang-unwrapped}/bin/clang $out/bin/clang-irrt
+          ln -s ${pkgs.llvmPackages_14.clang}/bin/clang $out/bin/clang-irrt-test
           ln -s ${pkgs.llvmPackages_14.llvm.out}/bin/llvm-as $out/bin/llvm-as-irrt
           '';
         nac3artiq = pkgs.python3Packages.toPythonModule (
@@ -23,6 +24,7 @@
             cargoLock = {
               lockFile = ./Cargo.lock;
             };
+            cargoTestFlags = [ "--features" "test" ];
             passthru.cargoLock = cargoLock;
             nativeBuildInputs = [ pkgs.python3 pkgs.llvmPackages_14.clang llvm-tools-irrt pkgs.llvmPackages_14.llvm.out llvm-nac3 ];
             buildInputs = [ pkgs.python3 llvm-nac3 ];
@@ -161,7 +163,10 @@
           clippy
           pre-commit
           rustfmt
+          rust-analyzer
         ];
+        # https://nixos.wiki/wiki/Rust#Shell.nix_example
+        RUST_SRC_PATH = "${pkgs.rust.packages.stable.rustPlatform.rustLibSrc}";
       };
       devShells.x86_64-linux.msys2 = pkgs.mkShell {
         name = "nac3-dev-shell-msys2";

nac3core/Cargo.toml

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

nac3core/build.rs

@@ -7,8 +7,8 @@ use std::{
     process::{Command, Stdio},
 };
 
-fn main() {
-    const FILE: &str = "src/codegen/irrt/irrt.cpp";
+fn compile_irrt(irrt_dir: &Path, out_dir: &Path) {
+    let irrt_cpp_path = irrt_dir.join("irrt.cpp");
 
     /*
      * HACK: Sadly, clang doesn't let us emit generic LLVM bitcode.
@@ -16,7 +16,7 @@ fn main() {
      */
     let flags: &[&str] = &[
         "--target=wasm32",
-        FILE,
+        irrt_cpp_path.to_str().unwrap(),
         "-x",
         "c++",
         "-fno-discard-value-names",
@@ -31,13 +31,14 @@ fn main() {
         "-S",
         "-Wall",
         "-Wextra",
+        "-Werror=return-type",
+        "-I",
+        irrt_dir.to_str().unwrap(),
         "-o",
         "-",
     ];
 
-    println!("cargo:rerun-if-changed={FILE}");
-    let out_dir = env::var("OUT_DIR").unwrap();
-    let out_path = Path::new(&out_dir);
+    println!("cargo:rerun-if-changed={}", out_dir.to_str().unwrap());
 
     let output = Command::new("clang-irrt")
         .args(flags)
@@ -52,7 +53,11 @@ fn main() {
     let output = std::str::from_utf8(&output.stdout).unwrap().replace("\r\n", "\n");
     let mut filtered_output = String::with_capacity(output.len());
 
-    let regex_filter = Regex::new(r"(?ms:^define.*?\}$)|(?m:^declare.*?$)").unwrap();
+    // (?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();
     for f in regex_filter.captures_iter(&output) {
         assert_eq!(f.len(), 1);
         filtered_output.push_str(&f[0]);
@@ -65,18 +70,65 @@ fn main() {
 
     println!("cargo:rerun-if-env-changed=DEBUG_DUMP_IRRT");
     if env::var("DEBUG_DUMP_IRRT").is_ok() {
-        let mut file = File::create(out_path.join("irrt.ll")).unwrap();
+        let mut file = File::create(out_dir.join("irrt.ll")).unwrap();
         file.write_all(output.as_bytes()).unwrap();
-        let mut file = File::create(out_path.join("irrt-filtered.ll")).unwrap();
+        let mut file = File::create(out_dir.join("irrt-filtered.ll")).unwrap();
         file.write_all(filtered_output.as_bytes()).unwrap();
     }
 
     let mut llvm_as = Command::new("llvm-as-irrt")
         .stdin(Stdio::piped())
         .arg("-o")
-        .arg(out_path.join("irrt.bc"))
+        .arg(out_dir.join("irrt.bc"))
         .spawn()
         .unwrap();
     llvm_as.stdin.as_mut().unwrap().write_all(filtered_output.as_bytes()).unwrap();
     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");
+
+    let flags: &[&str] = &[
+        irrt_test_cpp_path.to_str().unwrap(),
+        "-x",
+        "c++",
+        "-I",
+        irrt_dir.to_str().unwrap(),
+        "-g",
+        "-fno-discard-value-names",
+        "-O0",
+        "-Wall",
+        "-Wextra",
+        "-Werror=return-type",
+        "-lm", // for `tgamma()`, `lgamma()`
+        "-o",
+        exe_path.to_str().unwrap(),
+    ];
+
+    Command::new("clang-irrt-test")
+        .args(flags)
+        .output()
+        .map(|o| {
+            assert!(o.status.success(), "{}", std::str::from_utf8(&o.stderr).unwrap());
+            o
+        })
+        .unwrap();
+    println!("cargo:rerun-if-changed={}", out_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);
+
+    // 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);
+    }
+}

nac3core/irrt/irrt.cpp

@@ -0,0 +1,5 @@
+#include "irrt_everything.hpp"
+
+/*
+    This file will be read by `clang-irrt` to conveniently produce LLVM IR for `nac3core/codegen`.
+*/

nac3core/irrt/irrt.hpp

@@ -0,0 +1,437 @@
+#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_basic.hpp

@@ -0,0 +1,216 @@
+#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_error_context.hpp

@@ -0,0 +1,31 @@
+#pragma once
+
+#include "irrt_printer.hpp"
+
+namespace {
+    #define MAX_ERROR_NAME_LEN 32
+
+    // TODO: right now just to report some messages for now
+    struct ErrorContext {
+        Printer error;
+        // TODO: add error_class_name??
+
+        void initialize(char* string_base_ptr, uint32_t max_length) {
+            error.initialize(string_base_ptr, max_length);
+        }
+
+        bool has_error() {
+            return error.length > 0;
+        }
+    };
+}
+
+extern "C" {
+    void __nac3_error_context_init(ErrorContext* ctx, char* string_base_ptr, uint32_t max_length) {
+        ctx->initialize(string_base_ptr, max_length);
+    }
+
+    uint8_t __nac3_error_context_has_error(ErrorContext* ctx) {
+        return (uint8_t) ctx->has_error();
+    }
+}

nac3core/irrt/irrt_everything.hpp

@@ -0,0 +1,16 @@
+#pragma once
+
+#include "irrt_basic.hpp"
+#include "irrt_error_context.hpp"
+#include "irrt_numpy_ndarray.hpp"
+#include "irrt_printer.hpp"
+#include "irrt_slice.hpp"
+#include "irrt_typedefs.hpp"
+#include "irrt_utils.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.
+*/

nac3core/irrt/irrt_numpy_ndarray.hpp

@@ -0,0 +1,520 @@
+#pragma once
+
+#include "irrt_utils.hpp"
+#include "irrt_typedefs.hpp"
+#include "irrt_slice.hpp"
+
+/*
+    NDArray-related implementations.
+`*/
+
+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 enum variant type
+        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<SizeT>`
+
+            `SizeT` is controlled by the caller: `NDSlice` only cares about where that
+            slice is (the pointer), `NDSlice` does not care/know about the actual `sizeof()`
+            of the slice value.
+        */
+        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 index 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.
+        //
+        // This pointer should point to the first element of the ndarray directly
+        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_pelement_value(uint8_t* pelement, const uint8_t* pvalue) {
+            __builtin_memcpy(pelement, pvalue, itemsize);
+        }
+
+        uint8_t* get_pelement_by_indices(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_by_indices(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) {
+            const SizeT size = this->size();
+            for (SizeT i = 0; i < size; i++) {
+                uint8_t* pelement = get_nth_pelement(i);
+                set_pelement_value(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_by_indices(indices);
+                set_pelement_value(pelement, one_pvalue);
+            }
+        }
+
+        // To support numpy "basic indexing" https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing
+        // "Advanced indexing" https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing is not supported
+        //
+        // This function supports:
+        //   - "scalar indexing",
+        //   - "slicing and strides",
+        //   - and "dimensional indexing tools" (TODO, but this is really easy to implement).
+        //
+        // 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 subscript(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;
+            dst_ndarray->itemsize = this->itemsize;
+
+            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* user_slice = (UserSlice*) ndslice->slice;
+                    Slice 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 += (SizeT) slice.start * this->strides[this_axis]; // Add offset (NOTE: no need to `* itemsize`, strides count in # of bytes)
+                    dst_ndarray->strides[dst_axis] = ((SizeT) slice.step) * this->strides[this_axis]; // Determine stride
+                    dst_ndarray->shape[dst_axis] = (SizeT) slice.len(); // Determine shape dimension
+
+                    // Next
+                    dst_axis++;
+                    this_axis++;
+                } else {
+                    __builtin_unreachable();
+                }
+            }
+
+            /*
+                Reference python code:
+                ```python
+                dst_ndarray.shape.extend(this.shape[this_axis:])
+                dst_ndarray.strides.extend(this.strides[this_axis:])
+                ```
+            */
+
+            for (; dst_axis < dst_ndarray->ndims; dst_axis++, this_axis++) {
+                dst_ndarray->shape[dst_axis] = this->shape[this_axis];
+                dst_ndarray->strides[dst_axis] = this->strides[this_axis];
+            }
+        }
+
+        // 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);
+
+            const SizeT size = this->size();
+            for (SizeT i = 0; i < size; i++) {
+                uint8_t* src_pelement = broadcasted_src_ndarray_strides->get_nth_pelement(i);
+                uint8_t* this_pelement = this->get_nth_pelement(i);
+                this->set_pelement_value(this_pelement, src_pelement);
+            }
+        }
+
+        // TODO: DOCUMENT ME
+        bool is_unsized() {
+            return this->ndims == 0;
+        }
+
+        // Simulate `len(<ndarray>)`
+        //   See (it doesn't help): https://numpy.org/doc/stable/reference/generated/numpy.ndarray.__len__.html#numpy.ndarray.__len__
+        SliceIndex len() {
+            // If you do `len(np.asarray(42))` (note that its `.shape` is just `()` - an empty tuple),
+            // numpy throws a `TypeError: len() of unsized object`
+            irrt_assert(!this->is_unsized());
+
+            // Apparently `len(<ndarray>)` is defined to be the first dimension
+            // REFERENCE: https://stackoverflow.com/questions/43081809/len-of-a-numpy-array-in-python
+            return (SliceIndex) this->shape[0];
+        }
+    };
+}
+
+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_set_strides_by_shape(NDArray<int32_t>* ndarray) {
+        ndarray->set_strides_by_shape();
+    }
+
+    void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
+        ndarray->set_strides_by_shape();
+    }
+
+    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);
+    }
+
+    int32_t __nac3_ndarray_deduce_ndims_after_slicing(int32_t ndims, int32_t num_slices, const NDSlice* slices) {
+        return ndarray_util::deduce_ndims_after_slicing(ndims, num_slices, slices);
+    }
+
+    int64_t __nac3_ndarray_deduce_ndims_after_slicing64(int64_t ndims, int64_t num_slices, const NDSlice* slices) {
+        return ndarray_util::deduce_ndims_after_slicing(ndims, num_slices, slices);
+    }
+
+    void __nac3_ndarray_subscript(NDArray<int32_t>* ndarray, int32_t num_slices, NDSlice* slices, NDArray<int32_t> *dst_ndarray) {
+        ndarray->subscript(num_slices, slices, dst_ndarray);
+    }
+
+    void __nac3_ndarray_subscript64(NDArray<int64_t>* ndarray, int32_t num_slices, NDSlice* slices, NDArray<int64_t> *dst_ndarray) {
+        ndarray->subscript(num_slices, slices, dst_ndarray);
+    }
+
+    SliceIndex __nac3_ndarray_len(NDArray<int32_t>* ndarray) {
+        return ndarray->len();
+    }
+
+    SliceIndex __nac3_ndarray_len64(NDArray<int64_t>* ndarray) {
+        return ndarray->len();
+    }
+}

nac3core/irrt/irrt_printer.hpp

@@ -0,0 +1,82 @@
+#pragma once
+
+#include "irrt_typedefs.hpp"
+
+// TODO: obviously implementing printf from scratch is bad,
+// is there a header only, no-cstdlib library for this?
+
+namespace {
+    struct Printer {
+        char* string_base_ptr;
+        uint32_t max_length;
+        uint32_t length; // NOTE: this could be incremented past max_length, which indicates
+
+        void initialize(char *string_base_ptr, uint32_t max_length) {
+            this->string_base_ptr = string_base_ptr;
+            this->max_length = max_length;
+            this->length = 0;
+        }
+
+        void put_space() {
+            put_char(' ');
+        }
+
+        void put_char(char ch) {
+            push_char(ch);
+        }
+
+        void put_string(const char* string) {
+            // TODO: optimize?
+            while (*string != '\0') {
+                push_char(*string);
+                string++; // Move to next char
+            }
+        }
+
+        template<typename T>
+        void put_int(T value) {
+            // NOTE: Try not to use recursion to print the digits
+
+            // value == 0 is a special case
+            if (value == 0) {
+                push_char('0');
+            } else {
+                // Add a '-' if the value is negative
+                if (value < 0) {
+                    push_char('-');
+                    value = -value; // Negate then continue to print the digits
+                }
+
+                // TODO: Recursion is a bad idea on embedded systems?
+                uint32_t num_digits = int_log_floor(value, 10) + 1;
+                put_int_helper(num_digits, value);
+            }
+        }
+
+        // TODO: implement put_float() and more would be useful
+    private:
+        void push_char(char ch) {
+            if (length < max_length) {
+                string_base_ptr[length] = ch;
+            }
+
+            // NOTE: this could increment past max_length,
+            // to indicate the true length of the message even if it gets cut off
+            length++;
+        }
+
+        template <typename T>
+        void put_int_helper(uint32_t num_digits, T value) {
+            // Print the digits recursively
+            __builtin_assume(0 <= value);
+
+            if (num_digits > 0) {
+                put_int_helper(num_digits - 1, value / 10);
+
+                uint32_t digit = value % 10;
+                char digit_char = '0' + (char) digit;
+                put_char(digit_char);
+            }
+        }
+    };
+}

nac3core/irrt/irrt_slice.hpp

@@ -0,0 +1,83 @@
+#pragma once
+
+#include "irrt_utils.hpp"
+#include "irrt_typedefs.hpp"
+
+namespace {
+    struct Slice {
+        SliceIndex start;
+        SliceIndex stop;
+        SliceIndex 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))`
+        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;
+            }
+        }
+    };
+
+    SliceIndex resolve_index_in_length(SliceIndex length, SliceIndex index) {
+        irrt_assert(length >= 0);
+        if (index < 0) {
+            // Remember that index is negative, so do a plus here
+            return max<SliceIndex>(length + index, 0);
+        } else {
+            return min<SliceIndex>(length, index);
+        }
+    }
+
+    // A user-written Python-like slice.
+    //
+    // i.e., this slice is a triple of either an int or nothing. (e.g., `my_array[:10:2]`, `start` is None)
+    //
+    // You can "resolve" a `UserSlice` by using `UserSlice::indices(<length>)`
+    //
+    // NOTE: using a bitfield for the `*_defined` is better, at the
+    // cost of a more annoying implementation in nac3core inkwell
+    struct UserSlice {
+        // Did the user specify `start`? If 0, `start` is undefined (and contains an empty value)
+        uint8_t start_defined;
+        SliceIndex start;
+
+        // Similar to `start_defined`
+        uint8_t stop_defined;
+        SliceIndex stop;
+
+        // Similar to `start_defined`
+        uint8_t step_defined;
+        SliceIndex step;
+
+        // Like Python's `slice(start, stop, step).indices(length)`
+        Slice indices(SliceIndex 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 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

@@ -0,0 +1,707 @@
+// This file will be compiled like a real C++ program,
+// and we do have the luxury to use the standard libraries.
+// That is if the nix flakes do not have issues... especially on msys2...
+#include <cstdint>
+#include <cstdio>
+#include <cstdlib>
+
+// 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_by_indices(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 `subscript(5, None, None).indices(100) == subscript(5, 100, 1)`
+    BEGIN_TEST();
+
+    UserSlice 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 `subscript(400, 999, None).indices(100) == subscript(100, 100, 1)`
+    BEGIN_TEST();
+
+    UserSlice 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 `subscript(-10, -5, None).indices(100) == subscript(90, 95, 1)`
+    BEGIN_TEST();
+
+    UserSlice 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 `subscript(None, None, -5).indices(100) == (99, -1, -5)`
+    BEGIN_TEST();
+
+    UserSlice 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 user_slice_1 = {
+        .start_defined = 1,
+        .start = -2,
+        .stop_defined = 0,
+        .step_defined = 0
+    };
+
+    UserSlice 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.subscript(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_by_indices((int32_t[dst_ndims]) { 0, 0 })));
+    assert_values_match("dst_ndarray[0, 1]", "%f", 7.0, *((double *) dst_ndarray.get_pelement_by_indices((int32_t[dst_ndims]) { 0, 1 })));
+    assert_values_match("dst_ndarray[1, 0]", "%f", 9.0, *((double *) dst_ndarray.get_pelement_by_indices((int32_t[dst_ndims]) { 1, 0 })));
+    assert_values_match("dst_ndarray[1, 1]", "%f", 11.0, *((double *) dst_ndarray.get_pelement_by_indices((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 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.subscript(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_by_indices((int32_t[dst_ndims]) { 0 })));
+    assert_values_match("dst_ndarray[1]", "%f", 9.0, *((double *) dst_ndarray.get_pelement_by_indices((int32_t[dst_ndims]) { 1 })));
+}
+
+void test_ndslice_3() {
+    BEGIN_TEST();
+
+    double in_data[12] = { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 };
+    const 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();
+
+    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[2:3]`
+    UserSlice user_slice_1 = {
+        .start_defined = 1,
+        .start = 2,
+        .stop_defined = 1,
+        .stop = 3,
+        .step_defined = 0,
+    };
+
+    const int32_t num_ndslices = 1;
+    NDSlice ndslices[num_ndslices] = {
+        { .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_1 },
+    };
+
+    ndarray.subscript(num_ndslices, ndslices, &dst_ndarray);
+}
+
+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() {
+    /*
+        ```python
+        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]]]
+        
+        assert array.strides == (0, 0, 8)
+        # and then pick some values in `array` and check them...
+        ```
+    */
+    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_by_indices((int32_t[]) {0, 0, 0})));
+    assert_values_match("dst_ndarray[0, 0, 1]", "%f", 29.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 0, 1})));
+    assert_values_match("dst_ndarray[0, 0, 2]", "%f", 39.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 0, 2})));
+    assert_values_match("dst_ndarray[0, 0, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 0, 3})));
+    assert_values_match("dst_ndarray[0, 1, 0]", "%f", 19.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 1, 0})));
+    assert_values_match("dst_ndarray[0, 1, 1]", "%f", 29.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 1, 1})));
+    assert_values_match("dst_ndarray[0, 1, 2]", "%f", 39.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 1, 2})));
+    assert_values_match("dst_ndarray[0, 1, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {0, 1, 3})));
+    assert_values_match("dst_ndarray[1, 2, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement_by_indices((int32_t[]) {1, 2, 3})));
+}
+
+void test_printer() {
+    const uint32_t buffer_len = 256;
+    char buffer[buffer_len];
+    Printer printer = {
+        .string_base_ptr = buffer,
+        .max_length = buffer_len,
+        .length = 0
+    };
+
+}
+
+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_ndslice_3();
+    test_can_broadcast_shape();
+    test_ndarray_broadcast_1();
+    test_printer();
+    return 0;
+}

nac3core/irrt/irrt_typedefs.hpp

@@ -0,0 +1,14 @@
+#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

@@ -0,0 +1,74 @@
+#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;
+    }
+
+    template<typename T>
+    uint32_t int_log_floor(T value, T base) {
+        uint32_t result = 0;
+        while (value < base) {
+            result++;
+            value /= base;
+        }
+        return result;
+    }
+
+    bool string_is_empty(const char *str) {
+        return str[0] == '\0';
+    }
+
+    // TODO: DOCUMENT ME!!!!!
+    // returns false if `src_str` could not be fully copied over to `dst_str`
+    bool string_copy(uint32_t dst_max_size, char* dst_str, const char* src_str) {
+        // This function guarantess that `dst_str` will be null-terminated,
+
+        for (uint32_t i = 0; i < dst_max_size; i++) {
+            bool is_last = i + 1 == dst_max_size;
+            if (is_last && src_str[i] != '\0') {
+                dst_str[i] = '\0';
+                return false;
+            }
+
+            if (src_str[i] == '\0') {
+                dst_str[i] = '\0';
+                return true;
+            }
+
+            dst_str[i] = src_str[i];
+        }
+
+        __builtin_unreachable();
+    }
+
+    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/src/codegen/classes.rs

@@ -1,8 +1,9 @@
 use crate::codegen::{
-    irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
+    // irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
     llvm_intrinsics::call_int_umin,
     stmt::gen_for_callback_incrementing,
-    CodeGenContext, CodeGenerator,
+    CodeGenContext,
+    CodeGenerator,
 };
 use inkwell::context::Context;
 use inkwell::types::{ArrayType, BasicType, StructType};
@@ -12,6 +13,7 @@ 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> {
@@ -1208,25 +1210,27 @@ impl<'ctx> NDArrayType<'ctx> {
         ctx: &'ctx Context,
         dtype: BasicTypeEnum<'ctx>,
     ) -> Self {
-        let llvm_usize = generator.get_size_type(ctx);
+        todo!()
 
-        // struct NDArray { num_dims: size_t, dims: size_t*, data: T* }
-        //
-        // * num_dims: Number of dimensions in the array
-        // * dims: Pointer to an array containing the size of each dimension
-        // * data: Pointer to an array containing the array data
-        let llvm_ndarray = ctx
-            .struct_type(
-                &[
-                    llvm_usize.into(),
-                    llvm_usize.ptr_type(AddressSpace::default()).into(),
-                    dtype.ptr_type(AddressSpace::default()).into(),
-                ],
-                false,
-            )
-            .ptr_type(AddressSpace::default());
+        // let llvm_usize = generator.get_size_type(ctx);
 
-        NDArrayType::from_type(llvm_ndarray, llvm_usize)
+        // // struct NDArray { num_dims: size_t, dims: size_t*, data: T* }
+        // //
+        // // * num_dims: Number of dimensions in the array
+        // // * dims: Pointer to an array containing the size of each dimension
+        // // * data: Pointer to an array containing the array data
+        // let llvm_ndarray = ctx
+        //     .struct_type(
+        //         &[
+        //             llvm_usize.into(),
+        //             llvm_usize.ptr_type(AddressSpace::default()).into(),
+        //             dtype.ptr_type(AddressSpace::default()).into(),
+        //         ],
+        //         false,
+        //     )
+        //     .ptr_type(AddressSpace::default());
+
+        // NDArrayType::from_type(llvm_ndarray, llvm_usize)
     }
 
     /// Creates an [`NDArrayType`] from a [`PointerType`].
@@ -1601,7 +1605,8 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
         ctx: &CodeGenContext<'ctx, '_>,
         generator: &G,
     ) -> IntValue<'ctx> {
-        call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
+        todo!()
+        // call_ndarray_calc_size(generator, ctx, &self.as_slice_value(ctx, generator), (None, None))
     }
 }
 
@@ -1659,33 +1664,34 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
         indices: &Index,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
-        let llvm_usize = generator.get_size_type(ctx.ctx);
+        todo!()
+        // let llvm_usize = generator.get_size_type(ctx.ctx);
 
-        let indices_elem_ty = indices
-            .ptr_offset(ctx, generator, &llvm_usize.const_zero(), None)
-            .get_type()
-            .get_element_type();
-        let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
-            panic!("Expected list[int32] but got {indices_elem_ty}")
-        };
-        assert_eq!(
-            indices_elem_ty.get_bit_width(),
-            32,
-            "Expected list[int32] but got list[int{}]",
-            indices_elem_ty.get_bit_width()
-        );
+        // let indices_elem_ty = indices
+        //     .ptr_offset(ctx, generator, &llvm_usize.const_zero(), None)
+        //     .get_type()
+        //     .get_element_type();
+        // let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
+        //     panic!("Expected list[int32] but got {indices_elem_ty}")
+        // };
+        // assert_eq!(
+        //     indices_elem_ty.get_bit_width(),
+        //     32,
+        //     "Expected list[int32] but got list[int{}]",
+        //     indices_elem_ty.get_bit_width()
+        // );
 
-        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>(
@@ -1761,3 +1767,164 @@ 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>>,
+// }
+// 
+// pub 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> {
+//     /// TODO: DOCUMENT ME
+//     pub fn gep(
+//         &self,
+//         ctx: &CodeGenContext<'ctx, '_>,
+//         struct_ptr: PointerValue<'ctx>,
+//     ) -> PointerValue<'ctx> {
+//         let index_type = ctx.ctx.i32_type(); // TODO: I think I'm not supposed to use i32 for GEP like that
+//         unsafe {
+//             ctx.builder
+//                 .build_in_bounds_gep(
+//                     struct_ptr,
+//                     &[index_type.const_zero(), index_type.const_int(self.gep_index as u64, false)],
+//                     self.name,
+//                 )
+//                 .unwrap()
+//         }
+//     }
+// 
+//     /// TODO: DOCUMENT ME
+//     pub fn load(
+//         &self,
+//         ctx: &CodeGenContext<'ctx, '_>,
+//         struct_ptr: PointerValue<'ctx>,
+//     ) -> BasicValueEnum<'ctx> {
+//         ctx.builder.build_load(self.gep(ctx, struct_ptr), self.name).unwrap()
+//     }
+// 
+//     /// TODO: DOCUMENT ME
+//     pub fn store<V>(&self, ctx: &CodeGenContext<'ctx, '_>, struct_ptr: PointerValue<'ctx>, value: V)
+//     where
+//         V: BasicValue<'ctx>,
+//     {
+//         ctx.builder.build_store(self.gep(ctx, struct_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 get_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> {
+//     pub fn start(name: &'static str) -> Self {
+//         StructFieldsBuilder { gep_index_counter: 0, name, fields: Vec::new() }
+//     }
+// 
+//     pub fn add_field(&mut self, name: &'static str, ty: BasicTypeEnum<'ctx>) -> StructField<'ctx> {
+//         let index = self.gep_index_counter;
+//         self.gep_index_counter += 1;
+// 
+//         let field = StructField { gep_index: index, name, ty };
+//         self.fields.push(field); // Register into self.fields
+// 
+//         field // Return to the caller to conveniently let them do whatever they want
+//     }
+// 
+//     pub fn end(self) -> StructFields<'ctx> {
+//         StructFields { name: self.name, fields: self.fields }
+//     }
+// }
+// 

nac3core/src/codegen/irrt/classes.rs

@@ -0,0 +1,87 @@
+// TODO: Use derppening's abstraction
+
+use std::marker::PhantomData;
+
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum, IntType},
+    values::BasicValueEnum,
+    AddressSpace,
+};
+
+use crate::codegen::structure::{
+    CustomStructType, CustomType, Field, FieldCreator, IntType2, Object, PointerType2,
+    PointingArrayType,
+};
+
+#[derive(Debug, Clone, Copy)]
+pub struct NpArrayType<'ctx> {
+    pub size_type: IntType<'ctx>,
+    pub elem_type: BasicTypeEnum<'ctx>,
+}
+
+pub struct NpArrayFields<'ctx> {
+    pub data: Field<'ctx, PointerType2<'ctx>>,
+    pub itemsize: Field<'ctx, IntType2<'ctx>>,
+    pub ndims: Field<'ctx, IntType2<'ctx>>,
+    pub shape: Field<'ctx, PointingArrayType<'ctx, IntType2<'ctx>>>,
+    pub strides: Field<'ctx, PointingArrayType<'ctx, IntType2<'ctx>>>,
+}
+
+pub type NpArrayValue<'ctx> = Object<'ctx, NpArrayType<'ctx>>;
+
+// impl<'ctx> CustomType<'ctx> for NpArrayType<'ctx> {
+//     type Value = NpArrayValue<'ctx>;
+//
+//     fn llvm_basic_type_enum(
+//         &self,
+//         ctx: &'ctx inkwell::context::Context,
+//     ) -> inkwell::types::BasicTypeEnum<'ctx> {
+//         self.llvm_struct_type(ctx).as_basic_type_enum()
+//     }
+//
+//     fn llvm_field_load(
+//         &self,
+//         ctx: &crate::codegen::CodeGenContext<'ctx, '_>,
+//         field: crate::codegen::structure::FieldInfo,
+//         struct_ptr: inkwell::values::PointerValue<'ctx>,
+//     ) -> Self::Value {
+//         let ok = field.llvm_load(ctx, struct_ptr);
+//         todo!()
+//     }
+//
+//     fn llvm_field_store(
+//         &self,
+//         ctx: &crate::codegen::CodeGenContext<'ctx, '_>,
+//         field: crate::codegen::structure::FieldInfo,
+//         struct_ptr: inkwell::values::PointerValue<'ctx>,
+//         value: &Self::Value,
+//     ) {
+//         todo!()
+//     }
+// }
+
+impl<'ctx> CustomStructType<'ctx> for NpArrayType<'ctx> {
+    type Fields = NpArrayFields<'ctx>;
+
+    fn llvm_struct_name() -> &'static str {
+        "NDArray"
+    }
+
+    fn add_fields_to(&self, creator: &mut FieldCreator<'ctx>) -> Self::Fields {
+        let pi8 = creator.ctx.i8_type().ptr_type(AddressSpace::default());
+        NpArrayFields {
+            data: creator.add_field("data", PointerType2(pi8)),
+            itemsize: creator.add_field("itemsize", IntType2(self.size_type)),
+            ndims: creator.add_field("ndims", IntType2(self.size_type)),
+            shape: creator.add_field("shape", PointingArrayType::new(IntType2(self.size_type))),
+            strides: creator.add_field("strides", PointingArrayType::new(IntType2(self.size_type))),
+        }
+    }
+}
+
+impl<'ctx> NpArrayType<'ctx> {
+    pub fn new_opaque_elem(ctx: &'ctx Context, size_type: IntType<'ctx>) -> Self {
+        NpArrayType { elem_type: ctx.i8_type().into(), size_type }
+    }
+}

nac3core/src/codegen/irrt/irrt.cpp

@@ -1,414 +0,0 @@
-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);
-
-// NDArray indices are always `uint32_t`.
-using NDIndex = uint32_t;
-// The type of an index or a value describing the length of a range/slice is always `int32_t`.
-using SliceIndex = int32_t;
-
-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;
-}
-
-// 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,
-    NDIndex* idxs
-) {
-    SizeT stride = 1;
-    for (SizeT dim = 0; dim < num_dims; dim++) {
-        SizeT i = num_dims - dim - 1;
-        __builtin_assume(dims[i] > 0);
-        idxs[i] = (index / stride) % dims[i];
-        stride *= dims[i];
-    }
-}
-
-template <typename SizeT>
-SizeT __nac3_ndarray_flatten_index_impl(
-    const SizeT* dims,
-    SizeT num_dims,
-    const NDIndex* indices,
-    SizeT num_indices
-) {
-    SizeT idx = 0;
-    SizeT stride = 1;
-    for (SizeT i = 0; i < num_dims; ++i) {
-        SizeT ri = num_dims - i - 1;
-        if (ri < num_indices) {
-            idx += stride * indices[ri];
-        }
-
-        __builtin_assume(dims[i] > 0);
-        stride *= dims[ri];
-    }
-    return idx;
-}
-
-template <typename SizeT>
-void __nac3_ndarray_calc_broadcast_impl(
-    const SizeT* lhs_dims,
-    SizeT lhs_ndims,
-    const SizeT* rhs_dims,
-    SizeT rhs_ndims,
-    SizeT* out_dims
-) {
-    SizeT max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
-
-    for (SizeT i = 0; i < max_ndims; ++i) {
-        const SizeT* lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : nullptr;
-        const SizeT* rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : nullptr;
-        SizeT* out_dim = &out_dims[max_ndims - i - 1];
-
-        if (lhs_dim_sz == nullptr) {
-            *out_dim = *rhs_dim_sz;
-        } else if (rhs_dim_sz == nullptr) {
-            *out_dim = *lhs_dim_sz;
-        } else if (*lhs_dim_sz == 1) {
-            *out_dim = *rhs_dim_sz;
-        } else if (*rhs_dim_sz == 1) {
-            *out_dim = *lhs_dim_sz;
-        } else if (*lhs_dim_sz == *rhs_dim_sz) {
-            *out_dim = *lhs_dim_sz;
-        } else {
-            __builtin_unreachable();
-        }
-    }
-}
-
-template <typename SizeT>
-void __nac3_ndarray_calc_broadcast_idx_impl(
-    const SizeT* src_dims,
-    SizeT src_ndims,
-    const NDIndex* in_idx,
-    NDIndex* out_idx
-) {
-    for (SizeT i = 0; i < src_ndims; ++i) {
-        SizeT src_i = src_ndims - i - 1;
-        out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
-    }
-}
-}  // namespace
-
-extern "C" {
-#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);
-}
-}  // extern "C"

nac3core/src/codegen/irrt/test.rs

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

nac3core/src/codegen/llvm_intrinsics.rs

@@ -35,6 +35,54 @@ fn get_float_intrinsic_repr(ctx: &Context, ft: FloatType) -> &'static str {
     unreachable!()
 }
 
+/// Invokes the [`llvm.lifetime.start`](https://releases.llvm.org/14.0.0/docs/LangRef.html#llvm-lifetime-start-intrinsic)
+/// intrinsic.
+pub fn call_lifetime_start<'ctx>(
+    ctx: &CodeGenContext<'ctx, '_>,
+    size: IntValue<'ctx>,
+    ptr: PointerValue<'ctx>,
+) {
+    const FN_NAME: &str = "llvm.lifetime.start";
+    // NOTE: inkwell temporary workaround, see [`call_stackrestore`] for details
+    let intrinsic_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
+        let llvm_void = ctx.ctx.void_type();
+        let llvm_i64 = ctx.ctx.i64_type();
+        let llvm_p0i8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
+        let fn_type = llvm_void.fn_type(&[llvm_i64.into(), llvm_p0i8.into()], false);
+
+        ctx.module.add_function(FN_NAME, fn_type, None)
+    });
+
+    ctx.builder
+        .build_call(intrinsic_fn, &[size.into(), ptr.into()], "")
+        .map(CallSiteValue::try_as_basic_value)
+        .unwrap();
+}
+
+/// Invokes the [`llvm.lifetime.end`](https://releases.llvm.org/14.0.0/docs/LangRef.html#llvm-lifetime-end-intrinsic)
+/// intrinsic.
+pub fn call_lifetime_end<'ctx>(
+    ctx: &CodeGenContext<'ctx, '_>,
+    size: IntValue<'ctx>,
+    ptr: PointerValue<'ctx>,
+) {
+    const FN_NAME: &str = "llvm.lifetime.end";
+    // NOTE: inkwell temporary workaround, see [`call_stackrestore`] for details
+    let intrinsic_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
+        let llvm_void = ctx.ctx.void_type();
+        let llvm_i64 = ctx.ctx.i64_type();
+        let llvm_p0i8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
+        let fn_type = llvm_void.fn_type(&[llvm_i64.into(), llvm_p0i8.into()], false);
+
+        ctx.module.add_function(FN_NAME, fn_type, None)
+    });
+
+    ctx.builder
+        .build_call(intrinsic_fn, &[size.into(), ptr.into()], "")
+        .map(CallSiteValue::try_as_basic_value)
+        .unwrap();
+}
+
 /// Invokes the [`llvm.stacksave`](https://llvm.org/docs/LangRef.html#llvm-stacksave-intrinsic)
 /// intrinsic.
 pub fn call_stacksave<'ctx>(

nac3core/src/codegen/mod.rs

@@ -7,6 +7,7 @@ use crate::{
         typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
     },
 };
+use classes::NpArrayType;
 use crossbeam::channel::{unbounded, Receiver, Sender};
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
@@ -43,6 +44,7 @@ pub mod irrt;
 pub mod llvm_intrinsics;
 pub mod numpy;
 pub mod stmt;
+pub mod structure;
 
 #[cfg(test)]
 mod test;
@@ -476,7 +478,14 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                                 ctx, module, generator, unifier, top_level, type_cache, dtype,
                             );
 
-                            NDArrayType::new(generator, ctx, element_type).as_base_type().into()
+                            let ndarray_ty = NpArrayType {
+                                size_type: generator.get_size_type(ctx),
+                                elem_type: element_type,
+                            };
+                            ndarray_ty
+                                .get_struct_type(ctx)
+                                .ptr_type(AddressSpace::default())
+                                .as_basic_type_enum()
                         }
 
                         _ => unreachable!(

nac3core/src/codegen/structure.rs

@@ -0,0 +1,318 @@
+use std::marker::PhantomData;
+
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum, IntType, PointerType, StructType},
+    values::{BasicValue, BasicValueEnum, IntValue, PointerValue},
+    AddressSpace,
+};
+
+use super::CodeGenContext;
+
+#[derive(Debug, Clone, Copy)]
+pub struct FieldInfo {
+    gep_index: u32,
+    name: &'static str,
+}
+
+impl FieldInfo {
+    pub fn llvm_gep<'ctx>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        struct_ptr: PointerValue<'ctx>,
+    ) -> PointerValue<'ctx> {
+        let index_type = ctx.ctx.i32_type(); // TODO: I think I'm not supposed to *just* use i32 for GEP like that
+        unsafe {
+            ctx.builder
+                .build_in_bounds_gep(
+                    struct_ptr,
+                    &[index_type.const_zero(), index_type.const_int(self.gep_index as u64, false)],
+                    self.name,
+                )
+                .unwrap()
+        }
+    }
+
+    pub fn llvm_load<'ctx>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        struct_ptr: PointerValue<'ctx>,
+    ) -> BasicValueEnum<'ctx> {
+        // We will use `self.name` as the LLVM label for debugging purposes
+        ctx.builder.build_load(self.llvm_gep(ctx, struct_ptr), self.name).unwrap()
+    }
+
+    pub fn llvm_store<'ctx>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        struct_ptr: PointerValue<'ctx>,
+        value: BasicValueEnum<'ctx>,
+    ) {
+        ctx.builder.build_store(self.llvm_gep(ctx, struct_ptr), value).unwrap();
+    }
+}
+
+pub struct Object<'ctx, T> {
+    pub ty: T,
+    pub ptr: PointerValue<'ctx>,
+}
+
+pub struct Field<'ctx, T: CustomType<'ctx>> {
+    pub info: FieldInfo,
+    pub ty: T,
+    _phantom: PhantomData<&'ctx ()>,
+}
+
+pub struct FieldCreator<'ctx> {
+    pub ctx: &'ctx Context,
+    struct_name: &'ctx str,
+    gep_index_counter: u32,
+    fields: Vec<(FieldInfo, BasicTypeEnum<'ctx>)>,
+}
+
+impl<'ctx> FieldCreator<'ctx> {
+    pub fn new(ctx: &'ctx Context, struct_name: &'ctx str) -> Self {
+        FieldCreator { ctx, struct_name, gep_index_counter: 0, fields: Vec::new() }
+    }
+
+    fn next_gep_index(&mut self) -> u32 {
+        let index = self.gep_index_counter;
+        self.gep_index_counter += 1;
+        index
+    }
+
+    fn get_struct_field_types(&self) -> Vec<BasicTypeEnum<'ctx>> {
+        self.fields.iter().map(|x| x.1.clone()).collect()
+    }
+
+    pub fn add_field<T: CustomType<'ctx>>(&mut self, name: &'static str, ty: T) -> Field<'ctx, T> {
+        let gep_index = self.next_gep_index();
+
+        let field_type = ty.llvm_basic_type_enum(self.ctx);
+        let field_info = FieldInfo { gep_index, name };
+        let field = Field { info: field_info, ty, _phantom: PhantomData };
+
+        self.fields.push((field_info.clone(), field_type));
+
+        field
+    }
+
+    fn num_fields(&self) -> u32 {
+        self.fields.len() as u32 // casted to u32 because that is what inkwell returns
+    }
+}
+
+pub trait CustomType<'ctx>: Clone {
+    type Value;
+
+    fn llvm_basic_type_enum(&self, ctx: &'ctx Context) -> BasicTypeEnum<'ctx>;
+
+    fn llvm_field_load(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        field: FieldInfo,
+        struct_ptr: PointerValue<'ctx>,
+    ) -> Self::Value;
+
+    fn llvm_field_store(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        field: FieldInfo,
+        struct_ptr: PointerValue<'ctx>,
+        value: &Self::Value,
+    );
+}
+
+#[derive(Debug, Clone, Copy)]
+pub struct IntType2<'ctx>(pub IntType<'ctx>);
+
+impl<'ctx> CustomType<'ctx> for IntType2<'ctx> {
+    type Value = IntValue<'ctx>;
+
+    fn llvm_basic_type_enum(&self, ctx: &'ctx Context) -> BasicTypeEnum<'ctx> {
+        self.0.as_basic_type_enum()
+    }
+
+    fn llvm_field_load(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        field: FieldInfo,
+        struct_ptr: PointerValue<'ctx>,
+    ) -> Self::Value {
+        let int_value = field.llvm_load(ctx, struct_ptr).into_int_value();
+        assert_eq!(int_value.get_type().get_bit_width(), self.0.get_bit_width());
+        int_value
+    }
+
+    fn llvm_field_store(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        field: FieldInfo,
+        struct_ptr: PointerValue<'ctx>,
+        int_value: &Self::Value,
+    ) {
+        assert_eq!(int_value.get_type().get_bit_width(), self.0.get_bit_width());
+        field.llvm_store(ctx, struct_ptr, int_value.as_basic_value_enum());
+    }
+}
+
+#[derive(Debug, Clone, Copy)]
+pub struct PointerType2<'ctx>(pub PointerType<'ctx>);
+
+impl<'ctx> CustomType<'ctx> for PointerType2<'ctx> {
+    type Value = PointerValue<'ctx>;
+
+    fn llvm_basic_type_enum(&self, ctx: &'ctx Context) -> BasicTypeEnum<'ctx> {
+        self.0.as_basic_type_enum()
+    }
+
+    fn llvm_field_load(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        field: FieldInfo,
+        struct_ptr: PointerValue<'ctx>,
+    ) -> Self::Value {
+        field.llvm_load(ctx, struct_ptr).into_pointer_value()
+    }
+
+    fn llvm_field_store(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        field: FieldInfo,
+        struct_ptr: PointerValue<'ctx>,
+        pointer_value: &Self::Value,
+    ) {
+        field.llvm_store(ctx, struct_ptr, pointer_value.as_basic_value_enum());
+    }
+}
+
+#[derive(Debug, Clone, Copy)]
+pub struct PointingArrayType<'ctx, ElementType: CustomType<'ctx>> {
+    pub element_type: ElementType,
+    _phantom: PhantomData<&'ctx ()>,
+}
+
+impl<'ctx, ElementType: CustomType<'ctx>> PointingArrayType<'ctx, ElementType> {
+    pub fn new(element_type: ElementType) -> Self {
+        PointingArrayType { element_type, _phantom: PhantomData }
+    }
+}
+
+impl<'ctx, Element: CustomType<'ctx>> CustomType<'ctx> for PointingArrayType<'ctx, Element> {
+    type Value = Object<'ctx, Self>;
+
+    fn llvm_basic_type_enum(&self, ctx: &'ctx Context) -> BasicTypeEnum<'ctx> {
+        // Element*
+        self.element_type
+            .llvm_basic_type_enum(ctx)
+            .ptr_type(AddressSpace::default())
+            .as_basic_type_enum()
+    }
+
+    fn llvm_field_load(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        field: FieldInfo,
+        struct_ptr: PointerValue<'ctx>,
+    ) -> Self::Value {
+        // Remember that it is just a pointer
+        Object { ty: self.clone(), ptr: field.llvm_load(ctx, struct_ptr).into_pointer_value() }
+    }
+
+    fn llvm_field_store(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        field: FieldInfo,
+        struct_ptr: PointerValue<'ctx>,
+        value: &Self::Value,
+    ) {
+        // Remember that it is just a pointer
+        todo!()
+    }
+}
+
+pub fn check_basic_types_match<'ctx, A, B>(expected: A, got: B) -> Result<(), String>
+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(())
+}
+
+pub trait CustomStructType<'ctx> {
+    type Fields;
+
+    fn llvm_struct_name() -> &'static str;
+
+    fn add_fields_to(&self, creator: &mut FieldCreator<'ctx>) -> Self::Fields;
+
+    fn fields(&self, ctx: &'ctx Context) -> Self::Fields {
+        let mut creator = FieldCreator::new(ctx, Self::llvm_struct_name());
+        let fields = self.add_fields_to(&mut creator);
+        fields
+    }
+
+    fn llvm_struct_type(&self, ctx: &'ctx Context) -> StructType<'ctx> {
+        let mut creator = FieldCreator::new(ctx, Self::llvm_struct_name());
+        self.add_fields_to(&mut creator);
+
+        ctx.struct_type(&creator.get_struct_field_types(), false)
+    }
+
+    fn check_struct_type(
+        &self,
+        ctx: &'ctx Context,
+        scrutinee: StructType<'ctx>,
+    ) -> Result<(), String> {
+        let mut creator = FieldCreator::new(ctx, Self::llvm_struct_name());
+        self.add_fields_to(&mut creator);
+
+        // Check scrutinee's number of struct fields
+        let expected_field_count = creator.num_fields();
+        let got_field_count = scrutinee.count_fields();
+        if got_field_count != expected_field_count {
+            return Err(format!(
+                "Expected {expected_count} field(s) in `{struct_name}` type, got {got_count}",
+                struct_name = Self::llvm_struct_name(),
+                expected_count = expected_field_count,
+                got_count = got_field_count,
+            ));
+        }
+
+        // Check the scrutinee's field types
+        for (field_info, expected_field_ty) in creator.fields {
+            let got_field_ty = scrutinee.get_field_type_at_index(field_info.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_info.gep_index,
+                    struct_name = Self::llvm_struct_name(),
+                    field_name = field_info.name,
+                ));
+            }
+        }
+
+        // Done
+        Ok(())
+    }
+}

nac3core/src/codegen/test.rs

@@ -438,14 +438,15 @@ fn test_classes_range_type_new() {
     assert!(RangeType::is_type(llvm_range.as_base_type()).is_ok());
 }
 
-#[test]
-fn test_classes_ndarray_type_new() {
-    let ctx = inkwell::context::Context::create();
-    let generator = DefaultCodeGenerator::new(String::new(), 64);
-
-    let llvm_i32 = ctx.i32_type();
-    let llvm_usize = generator.get_size_type(&ctx);
-
-    let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into());
-    assert!(NDArrayType::is_type(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
-}
+// #[test]
+// fn test_classes_ndarray_type_new() {
+//     let ctx = inkwell::context::Context::create();
+//     let generator = DefaultCodeGenerator::new(String::new(), 64);
+//
+//     let llvm_i32 = ctx.i32_type();
+//     let llvm_usize = generator.get_size_type(&ctx);
+//
+//     let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into());
+//     assert!(NDArrayType::is_type(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
+// }
+//

nac3core/src/lib.rs

@@ -7,6 +7,7 @@
 )]
 #![warn(clippy::pedantic)]
 #![allow(
+    unused,
     dead_code,
     clippy::cast_possible_truncation,
     clippy::cast_sign_loss,
@@ -23,3 +24,4 @@ pub mod codegen;
 pub mod symbol_resolver;
 pub mod toplevel;
 pub mod typecheck;
+pub mod util;

nac3core/src/toplevel/builtins.rs

@@ -1,5 +1,7 @@
 use std::iter::once;
 
+use crate::util::SizeVariant;
+use classes::NpArrayType;
 use helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDefDetails};
 use indexmap::IndexMap;
 use inkwell::{
@@ -278,19 +280,10 @@ pub fn get_builtins(unifier: &mut Unifier, primitives: &PrimitiveStore) -> Built
         .collect()
 }
 
-/// 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,
-        }
+fn size_variant_to_int_type(variant: SizeVariant, primitives: &PrimitiveStore) -> Type {
+    match variant {
+        SizeVariant::Bits32 => primitives.int32,
+        SizeVariant::Bits64 => primitives.int64,
     }
 }
 
@@ -961,8 +954,9 @@ impl<'a> BuiltinBuilder<'a> {
                 resolver: None,
                 codegen_callback: Some(Arc::new(GenCall::new(Box::new(
                     |ctx, obj, fun, args, generator| {
-                        gen_ndarray_copy(ctx, &obj, fun, &args, generator)
-                            .map(|val| Some(val.as_basic_value_enum()))
+                        todo!()
+                        // gen_ndarray_copy(ctx, &obj, fun, &args, generator)
+                        //     .map(|val| Some(val.as_basic_value_enum()))
                     },
                 )))),
                 loc: None,
@@ -978,8 +972,9 @@ impl<'a> BuiltinBuilder<'a> {
                 resolver: None,
                 codegen_callback: Some(Arc::new(GenCall::new(Box::new(
                     |ctx, obj, fun, args, generator| {
-                        gen_ndarray_fill(ctx, &obj, fun, &args, generator)?;
-                        Ok(None)
+                        todo!()
+                        // gen_ndarray_fill(ctx, &obj, fun, &args, generator)?;
+                        // Ok(None)
                     },
                 )))),
                 loc: None,
@@ -1059,7 +1054,7 @@ impl<'a> BuiltinBuilder<'a> {
         );
 
         // The size variant of the function determines the size of the returned int.
-        let int_sized = size_variant.of_int(self.primitives);
+        let int_sized = size_variant_to_int_type(size_variant, self.primitives);
 
         let ndarray_int_sized =
             make_ndarray_ty(self.unifier, self.primitives, Some(int_sized), Some(common_ndim.ty));
@@ -1084,7 +1079,7 @@ 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.of_int(&ctx.primitives);
+                let ret_elem_ty = size_variant_to_int_type(size_variant, &ctx.primitives);
                 Ok(Some(builtin_fns::call_round(generator, ctx, (arg_ty, arg), ret_elem_ty)?))
             }),
         )
@@ -1125,7 +1120,7 @@ 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.of_int(self.primitives);
+        let int_sized = size_variant_to_int_type(size_variant, self.primitives);
         let ndarray_int_sized =
             make_ndarray_ty(self.unifier, self.primitives, Some(int_sized), Some(common_ndim.ty));
 
@@ -1148,7 +1143,7 @@ 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.of_int(&ctx.primitives);
+                let ret_elem_ty = size_variant_to_int_type(size_variant, &ctx.primitives);
                 let func = match kind {
                     Kind::Ceil => builtin_fns::call_ceil,
                     Kind::Floor => builtin_fns::call_floor,
@@ -1202,7 +1197,7 @@ impl<'a> BuiltinBuilder<'a> {
                 let func = match prim {
                     PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => gen_ndarray_empty,
                     PrimDef::FunNpZeros => gen_ndarray_zeros,
-                    PrimDef::FunNpOnes => gen_ndarray_ones,
+                    PrimDef::FunNpOnes => gen_ndarray_ones, // gen_ndarray_ones,
                     _ => unreachable!(),
                 };
                 func(ctx, &obj, fun, &args, generator).map(|val| Some(val.as_basic_value_enum()))
@@ -1251,8 +1246,9 @@ impl<'a> BuiltinBuilder<'a> {
                     resolver: None,
                     codegen_callback: Some(Arc::new(GenCall::new(Box::new(
                         |ctx, obj, fun, args, generator| {
-                            gen_ndarray_array(ctx, &obj, fun, &args, generator)
-                                .map(|val| Some(val.as_basic_value_enum()))
+                            todo!()
+                            // gen_ndarray_array(ctx, &obj, fun, &args, generator)
+                            //     .map(|val| Some(val.as_basic_value_enum()))
                         },
                     )))),
                     loc: None,
@@ -1270,8 +1266,9 @@ impl<'a> BuiltinBuilder<'a> {
                     // type variable
                     &[(self.list_int32, "shape"), (tv.ty, "fill_value")],
                     Box::new(move |ctx, obj, fun, args, generator| {
-                        gen_ndarray_full(ctx, &obj, fun, &args, generator)
-                            .map(|val| Some(val.as_basic_value_enum()))
+                        todo!()
+                        // gen_ndarray_full(ctx, &obj, fun, &args, generator)
+                        //     .map(|val| Some(val.as_basic_value_enum()))
                     }),
                 )
             }
@@ -1303,8 +1300,9 @@ impl<'a> BuiltinBuilder<'a> {
                     resolver: None,
                     codegen_callback: Some(Arc::new(GenCall::new(Box::new(
                         |ctx, obj, fun, args, generator| {
-                            gen_ndarray_eye(ctx, &obj, fun, &args, generator)
-                                .map(|val| Some(val.as_basic_value_enum()))
+                            todo!()
+                            // gen_ndarray_eye(ctx, &obj, fun, &args, generator)
+                            //     .map(|val| Some(val.as_basic_value_enum()))
                         },
                     )))),
                     loc: None,
@@ -1317,8 +1315,9 @@ impl<'a> BuiltinBuilder<'a> {
                 self.ndarray_float_2d,
                 &[(int32, "n")],
                 Box::new(|ctx, obj, fun, args, generator| {
-                    gen_ndarray_identity(ctx, &obj, fun, &args, generator)
-                        .map(|val| Some(val.as_basic_value_enum()))
+                    todo!()
+                    // gen_ndarray_identity(ctx, &obj, fun, &args, generator)
+                    //     .map(|val| Some(val.as_basic_value_enum()))
                 }),
             ),
             _ => unreachable!(),
@@ -1462,51 +1461,65 @@ 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);
+                                // TODO: Check is unsized and throw error if so
 
-                                let arg = NDArrayValue::from_ptr_val(
-                                    arg.into_pointer_value(),
-                                    llvm_usize,
-                                    None,
-                                );
+                                // Parse `arg`
+                                let ndarray_ptr = arg.into_pointer_value(); // It has to be an ndarray
 
-                                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 size_type = generator.get_size_type(ctx.ctx);
+                                let ndarray_ty = NpArrayType::new_opaque_elem(ctx.ctx, size_type); // We don't need to care about the element type - we only want the shape
+                                let ndarray = ndarray_ty.value_from_ptr(ctx.ctx, ndarray_ptr);
 
-                                let len = unsafe {
-                                    arg.dim_sizes().get_typed_unchecked(
-                                        ctx,
-                                        generator,
-                                        &llvm_usize.const_zero(),
-                                        None,
-                                    )
-                                };
+                                let result = call_nac3_len(ctx, ndarray).as_basic_value_enum();
+                                Some(result)
 
-                                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(),
-                                    )
-                                }
+                                // Some(.as_basic_value_enum())
+
+                                // let llvm_i32 = ctx.ctx.i32_type();
+                                // let llvm_usize = generator.get_size_type(ctx.ctx);
+
+                                // 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(),
+                                //     )
+                                // }
                             }
                             _ => unreachable!(),
                         }

nac3core/src/util.rs

@@ -0,0 +1,5 @@
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub(crate) enum SizeVariant {
+    Bits32,
+    Bits64,
+}

nac3standalone/demo/src/my_ndarray.py

@@ -0,0 +1,18 @@
+@extern
+def output_float64(x: float):
+    ...
+
+def output_ndarray_float_1(n: ndarray[float, Literal[1]]):
+    for i in range(len(n)):
+        output_float64(n[i])
+
+def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
+    for r in range(len(n)):
+        for c in range(len(n[r])):
+            output_float64(n[r][c])
+
+def run() -> int32:
+    hello = np_ones((3, 4))
+    # output_float64(hello[2, 3])
+    output_ndarray_float_1(hello[::-2, 2])
+    return 0

nac3standalone/src/main.rs

@@ -449,6 +449,11 @@ fn main() {
         .create_target_machine(llvm_options.opt_level)
         .expect("couldn't create target machine");
 
+    // Debug print if DEBUG_STANDALONE_DUMP_IR is defined
+    if std::env::var("DEBUG_STANDALONE_DUMP_IR").is_ok() {
+        main.print_to_file("standalone.ll").unwrap();
+    }
+
     let pass_options = PassBuilderOptions::create();
     pass_options.set_merge_functions(true);
     let passes = format!("default<O{}>", opt_level as u32);

nix/windows/default.nix

@@ -81,6 +81,7 @@ 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 {