WIP

.clang-format

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

Cargo.lock

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

flake.nix

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

nac3artiq/demo/list_cmp.py

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

nac3artiq/src/symbol_resolver.rs

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

nac3core/build.rs

@@ -3,34 +3,20 @@ use std::{
     env,
     fs::File,
     io::Write,
-    path::{Path, PathBuf},
+    path::Path,
     process::{Command, Stdio},
 };
 
-const CMD_IRRT_CLANG: &str = "clang-irrt";
+fn compile_irrt(irrt_dir: &Path, out_dir: &Path) {
-const CMD_IRRT_CLANG_TEST: &str = "clang-irrt-test";
+    let irrt_cpp_path = irrt_dir.join("irrt.cpp");
-const CMD_IRRT_LLVM_AS: &str = "llvm-as-irrt";
-
-fn get_out_dir() -> PathBuf {
-    PathBuf::from(env::var("OUT_DIR").unwrap())
-}
-
-fn get_irrt_dir() -> &'static Path {
-    Path::new("irrt")
-}
-
-/// Compile `irrt.cpp` for use in `src/codegen`
-fn compile_irrt_cpp() {
-    let out_dir = get_out_dir();
-    let irrt_dir = get_irrt_dir();
 
     /*
      * HACK: Sadly, clang doesn't let us emit generic LLVM bitcode.
      * Compiling for WASM32 and filtering the output with regex is the closest we can get.
      */
-    let irrt_cpp_path = irrt_dir.join("irrt.cpp");
     let flags: &[&str] = &[
         "--target=wasm32",
+        irrt_cpp_path.to_str().unwrap(),
         "-x",
         "c++",
         "-fno-discard-value-names",
@@ -45,18 +31,16 @@ fn compile_irrt_cpp() {
         "-S",
         "-Wall",
         "-Wextra",
-        "-o",
+        "-Werror=return-type",
-        "-",
         "-I",
         irrt_dir.to_str().unwrap(),
-        irrt_cpp_path.to_str().unwrap(),
+        "-o",
+        "-",
     ];
 
-    // Tell Cargo to rerun if any file under `irrt_dir` (recursive) changes
+    println!("cargo:rerun-if-changed={}", out_dir.to_str().unwrap());
-    println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
 
-    // Compile IRRT and capture the LLVM IR output
+    let output = Command::new("clang-irrt")
-    let output = Command::new(CMD_IRRT_CLANG)
         .args(flags)
         .output()
         .map(|o| {
@@ -69,17 +53,11 @@ fn compile_irrt_cpp() {
     let output = std::str::from_utf8(&output.stdout).unwrap().replace("\r\n", "\n");
     let mut filtered_output = String::with_capacity(output.len());
 
-    // Filter out irrelevant IR
+    // (?ms:^define.*?\}$) to capture `define` blocks
-    //
+    // (?m:^declare.*?$) to capture `declare` blocks
-    // Regex:
+    // (?m:^%.+?=\s*type\s*\{.+?\}$) to capture `type` declarations
-    // - `(?ms:^define.*?\}$)` captures LLVM `define` blocks
+    let regex_filter =
-    // - `(?m:^declare.*?$)` captures LLVM `declare` lines
+        Regex::new(r"(?ms:^define.*?\}$)|(?m:^declare.*?$)|(?m:^%.+?=\s*type\s*\{.+?\}$)").unwrap();
-    // - `(?m:^%.+?=\s*type\s*\{.+?\}$)` captures LLVM `type` declarations
-    // - `(?m:^@.+?=.+$)` captures global constants
-    let regex_filter = Regex::new(
-        r"(?ms:^define.*?\}$)|(?m:^declare.*?$)|(?m:^%.+?=\s*type\s*\{.+?\}$)|(?m:^@.+?=.+$)",
-    )
-    .unwrap();
     for f in regex_filter.captures_iter(&output) {
         assert_eq!(f.len(), 1);
         filtered_output.push_str(&f[0]);
@@ -90,21 +68,15 @@ fn compile_irrt_cpp() {
         .unwrap()
         .replace_all(&filtered_output, "");
 
-    // For debugging
+    println!("cargo:rerun-if-env-changed=DEBUG_DUMP_IRRT");
-    // Doing `DEBUG_DUMP_IRRT=1 cargo build -p nac3core` dumps the LLVM IR generated
+    if env::var("DEBUG_DUMP_IRRT").is_ok() {
-    const DEBUG_DUMP_IRRT: &str = "DEBUG_DUMP_IRRT";
-    println!("cargo:rerun-if-env-changed={DEBUG_DUMP_IRRT}");
-    if env::var(DEBUG_DUMP_IRRT).is_ok() {
         let mut file = File::create(out_dir.join("irrt.ll")).unwrap();
         file.write_all(output.as_bytes()).unwrap();
-
         let mut file = File::create(out_dir.join("irrt-filtered.ll")).unwrap();
         file.write_all(filtered_output.as_bytes()).unwrap();
     }
 
-    // Assemble the emitted and filtered IR to .bc
+    let mut llvm_as = Command::new("llvm-as-irrt")
-    // That .bc will be integrated into nac3core's codegen
-    let mut llvm_as = Command::new(CMD_IRRT_LLVM_AS)
         .stdin(Stdio::piped())
         .arg("-o")
         .arg(out_dir.join("irrt.bc"))
@@ -114,13 +86,10 @@ fn compile_irrt_cpp() {
     assert!(llvm_as.wait().unwrap().success());
 }
 
-/// Compile `irrt_test.cpp` for testing
+fn compile_irrt_test(irrt_dir: &Path, out_dir: &Path) {
-fn compile_irrt_test_cpp() {
-    let out_dir = get_out_dir();
-    let irrt_dir = get_irrt_dir();
-
-    let exe_path = out_dir.join("irrt_test.out"); // Output path of the compiled test executable
     let irrt_test_cpp_path = irrt_dir.join("irrt_test.cpp");
+    let exe_path = out_dir.join("irrt_test.out");
+
     let flags: &[&str] = &[
         irrt_test_cpp_path.to_str().unwrap(),
         "-x",
@@ -138,7 +107,7 @@ fn compile_irrt_test_cpp() {
         exe_path.to_str().unwrap(),
     ];
 
-    Command::new(CMD_IRRT_CLANG_TEST)
+    Command::new("clang-irrt-test")
         .args(flags)
         .output()
         .map(|o| {
@@ -146,15 +115,20 @@ fn compile_irrt_test_cpp() {
             o
         })
         .unwrap();
-    println!("cargo:rerun-if-changed={}", irrt_dir.to_str().unwrap());
+    println!("cargo:rerun-if-changed={}", out_dir.to_str().unwrap());
 }
 
 fn main() {
-    compile_irrt_cpp();
+    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_cpp();
+        compile_irrt_test(irrt_dir, out_dir);
     }
 }

nac3core/irrt/irrt.cpp

@@ -1,10 +1,5 @@
-#define IRRT_DEFINE_TYPEDEF_INTS
+#include "irrt_everything.hpp"
-#include <irrt_everything.hpp>
 
 /*
- * All IRRT implementations.
+    This file will be read by `clang-irrt` to conveniently produce LLVM IR for `nac3core/codegen`.
- *
+*/
- * We don't have pre-compiled objects, so we are writing all implementations in
- * headers and concatenate them with `#include` into one massive source file that
- * contains all the IRRT stuff.
- */

nac3core/irrt/irrt.hpp

@@ -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/artiq_defs.hpp

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

nac3core/irrt/irrt/core.hpp

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

nac3core/irrt/irrt/error_context.hpp

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

nac3core/irrt/irrt/int_defs.hpp

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

nac3core/irrt/irrt/ndarray/basic.hpp

@@ -1,305 +0,0 @@
-#pragma once
-
-#include <irrt/error_context.hpp>
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/def.hpp>
-
-namespace {
-namespace ndarray {
-namespace basic {
-namespace util {
-/**
- * @brief Asserts that `shape` does not contain negative dimensions.
- *
- * @param ndims Number of dimensions in `shape`
- * @param shape The shape to check on
- */
-template <typename SizeT>
-void assert_shape_no_negative(ErrorContext* errctx, SizeT ndims,
-                              const SizeT* shape) {
-    for (SizeT axis = 0; axis < ndims; axis++) {
-        if (shape[axis] < 0) {
-            errctx->set_error(errctx->error_ids->value_error,
-                              "negative dimensions are not allowed; axis {0} "
-                              "has dimension {1}",
-                              axis, shape[axis]);
-            return;
-        }
-    }
-}
-
-/**
- * @brief Returns the number of elements of an ndarray given its shape.
- *
- * @param ndims Number of dimensions in `shape`
- * @param shape The shape of the ndarray
- */
-template <typename SizeT>
-SizeT calc_size_from_shape(SizeT ndims, const SizeT* shape) {
-    SizeT size = 1;
-    for (SizeT axis = 0; axis < ndims; axis++) size *= shape[axis];
-    return size;
-}
-
-/**
- * @brief Compute the array indices of the `nth` (0-based) element of an ndarray given only its shape.
- *
- * @param ndims Number of elements in `shape` and `indices`
- * @param shape The shape of the ndarray
- * @param indices The returned indices indexing the ndarray with shape `shape`.
- * @param nth The index of the element of interest.
- */
-template <typename SizeT>
-void set_indices_by_nth(SizeT ndims, const SizeT* shape, SizeT* indices,
-                        SizeT nth) {
-    for (int32_t i = 0; i < ndims; i++) {
-        int32_t axis = ndims - i - 1;
-        int32_t dim = shape[axis];
-
-        indices[axis] = nth % dim;
-        nth /= dim;
-    }
-}
-}  // namespace util
-
-/**
- * @brief Return the number of elements of an `ndarray`
- *
- * This function corresponds to `<an_ndarray>.size`
- */
-template <typename SizeT>
-SizeT size(const NDArray<SizeT>* ndarray) {
-    return util::calc_size_from_shape(ndarray->ndims, ndarray->shape);
-}
-
-/**
- * @brief Return of the number of its content of an `ndarray`.
- *
- * This function corresponds to `<an_ndarray>.nbytes`.
- */
-template <typename SizeT>
-SizeT nbytes(const NDArray<SizeT>* ndarray) {
-    return size(ndarray) * ndarray->itemsize;
-}
-
-/**
- * @brief Update the strides of an ndarray given an ndarray `shape`
- * and assuming that the ndarray is fully c-contagious.
- *
- * You might want to read https://ajcr.net/stride-guide-part-1/.
- */
-template <typename SizeT>
-void set_strides_by_shape(NDArray<SizeT>* ndarray) {
-    SizeT stride_product = 1;
-    for (SizeT i = 0; i < ndarray->ndims; i++) {
-        int axis = ndarray->ndims - i - 1;
-        ndarray->strides[axis] = stride_product * ndarray->itemsize;
-        stride_product *= ndarray->shape[axis];
-    }
-}
-
-/**
- * @brief Return the pointer to the element indexed by `indices`.
- */
-template <typename SizeT>
-uint8_t* get_pelement_by_indices(const NDArray<SizeT>* ndarray,
-                                 const SizeT* indices) {
-    uint8_t* element = ndarray->data;
-    for (SizeT dim_i = 0; dim_i < ndarray->ndims; dim_i++)
-        element += indices[dim_i] * ndarray->strides[dim_i];
-    return element;
-}
-
-/**
- * @brief Return the pointer to the nth (0-based) element in a flattened view of `ndarray`.
- */
-template <typename SizeT>
-uint8_t* get_nth_pelement(const NDArray<SizeT>* ndarray, SizeT nth) {
-    SizeT* indices = (SizeT*)__builtin_alloca(sizeof(SizeT) * ndarray->ndims);
-    util::set_indices_by_nth(ndarray->ndims, ndarray->shape, indices, nth);
-    return get_pelement_by_indices(ndarray, indices);
-}
-
-/**
- * @brief Like `get_nth_pelement` but asserts that `nth` is in bounds.
- */
-template <typename SizeT>
-uint8_t* checked_get_nth_pelement(ErrorContext* errctx,
-                                  const NDArray<SizeT>* ndarray, SizeT nth) {
-    SizeT arr_size = ndarray->size();
-    if (!(0 <= nth && nth < arr_size)) {
-        errctx->set_error(
-            errctx->error_ids->index_error,
-            "index {0} is out of bounds, valid range is {1} <= index < {2}",
-            nth, 0, arr_size);
-        return 0;
-    }
-    return get_nth_pelement(ndarray, nth);
-}
-
-/**
- * @brief Set an element in `ndarray`.
- *
- * @param pelement Pointer to the element in `ndarray` to be set.
- * @param pvalue Pointer to the value `pelement` will be set to.
- */
-template <typename SizeT>
-void set_pelement_value(NDArray<SizeT>* ndarray, uint8_t* pelement,
-                        const uint8_t* pvalue) {
-    __builtin_memcpy(pelement, pvalue, ndarray->itemsize);
-}
-
-/**
- * @brief Get the `len()` of an ndarray, and asserts that `ndarray` is a sized object.
- *
- * This function corresponds to `<an_ndarray>.__len__`.
- *
- * @param dst_length The returned result
- */
-template <typename SizeT>
-void len(ErrorContext* errctx, const NDArray<SizeT>* ndarray,
-         SliceIndex* dst_length) {
-    // numpy prohibits `__len__` on unsized objects
-    if (ndarray->ndims == 0) {
-        errctx->set_error(errctx->error_ids->type_error,
-                          "len() of unsized object");
-        return;
-    }
-
-    *dst_length = (SliceIndex)ndarray->shape[0];
-}
-
-/**
- * @brief Copy data from one ndarray to another of the exact same size and itemsize.
- *
- * Both ndarrays will be viewed in their flatten views when copying the elements.
- */
-template <typename SizeT>
-void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
-    __builtin_assume(src_ndarray->itemsize == dst_ndarray->itemsize);
-
-    for (SizeT i = 0; i < size(src_ndarray); i++) {
-        auto src_element = ndarray::basic::get_nth_pelement(src_ndarray, i);
-        auto dst_element = ndarray::basic::get_nth_pelement(dst_ndarray, i);
-        ndarray::basic::set_pelement_value(dst_ndarray, dst_element,
-                                           src_element);
-    }
-}
-
-/**
- * @brief Return a boolean indicating if `ndarray` is (C-)contiguous.
- *
- * You may want to see: ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
- */
-template <typename SizeT>
-bool is_c_contiguous(const NDArray<SizeT>* ndarray) {
-    // Other references:
-    // - tinynumpy's implementation: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L102
-    // - ndarray's flags["C_CONTIGUOUS"]: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags
-    // - ndarray's rules for C-contiguity: https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45
-
-    // From https://github.com/numpy/numpy/blob/df256d0d2f3bc6833699529824781c58f9c6e697/numpy/core/src/multiarray/flagsobject.c#L95C1-L99C45:
-    //
-    // The traditional rule is that for an array to be flagged as C contiguous,
-    // the following must hold:
-    //
-    // strides[-1] == itemsize
-    // strides[i] == shape[i+1] * strides[i + 1]
-    // [...]
-    // According to these rules, a 0- or 1-dimensional array is either both
-    // C- and F-contiguous, or neither; and an array with 2+ dimensions
-    // can be C- or F- contiguous, or neither, but not both. Though there
-    // there are exceptions for arrays with zero or one item, in the first
-    // case the check is relaxed up to and including the first dimension
-    // with shape[i] == 0. In the second case `strides == itemsize` will
-    // can be true for all dimensions and both flags are set.
-
-    if (ndarray->ndims == 0) {
-        return true;
-    }
-
-    if (ndarray->strides[ndarray->ndims - 1] != ndarray->itemsize) {
-        return false;
-    }
-
-    for (SizeT i = 1; i < ndarray->ndims; i++) {
-        SizeT axis_i = ndarray->ndims - i - 1;
-        if (ndarray->strides[axis_i] !=
-            ndarray->shape[axis_i + 1] + ndarray->strides[axis_i + 1]) {
-            return false;
-        }
-    }
-
-    return true;
-}
-}  // namespace basic
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-using namespace ndarray::basic;
-
-uint32_t __nac3_ndarray_size(NDArray<int32_t>* ndarray) {
-    return size(ndarray);
-}
-
-uint64_t __nac3_ndarray_size64(NDArray<int64_t>* ndarray) {
-    return size(ndarray);
-}
-
-uint32_t __nac3_ndarray_nbytes(NDArray<int32_t>* ndarray) {
-    return nbytes(ndarray);
-}
-
-uint64_t __nac3_ndarray_nbytes64(NDArray<int64_t>* ndarray) {
-    return nbytes(ndarray);
-}
-
-void __nac3_ndarray_len(ErrorContext* errctx, NDArray<int32_t>* ndarray,
-                        SliceIndex* dst_len) {
-    return len(errctx, ndarray, dst_len);
-}
-
-void __nac3_ndarray_len64(ErrorContext* errctx, NDArray<int64_t>* ndarray,
-                          SliceIndex* dst_len) {
-    return len(errctx, ndarray, dst_len);
-}
-
-void __nac3_ndarray_util_assert_shape_no_negative(ErrorContext* errctx,
-                                                  int32_t ndims,
-                                                  int32_t* shape) {
-    util::assert_shape_no_negative(errctx, ndims, shape);
-}
-
-void __nac3_ndarray_util_assert_shape_no_negative64(ErrorContext* errctx,
-                                                    int64_t ndims,
-                                                    int64_t* shape) {
-    util::assert_shape_no_negative(errctx, ndims, shape);
-}
-
-void __nac3_ndarray_set_strides_by_shape(NDArray<int32_t>* ndarray) {
-    set_strides_by_shape(ndarray);
-}
-
-void __nac3_ndarray_set_strides_by_shape64(NDArray<int64_t>* ndarray) {
-    set_strides_by_shape(ndarray);
-}
-
-bool __nac3_ndarray_is_c_contiguous(NDArray<int32_t>* ndarray) {
-    return is_c_contiguous(ndarray);
-}
-
-bool __nac3_ndarray_is_c_contiguous64(NDArray<int64_t>* ndarray) {
-    return is_c_contiguous(ndarray);
-}
-
-void __nac3_ndarray_copy_data(NDArray<int32_t>* src_ndarray,
-                              NDArray<int32_t>* dst_ndarray) {
-    copy_data(src_ndarray, dst_ndarray);
-}
-
-void __nac3_ndarray_copy_data64(NDArray<int64_t>* src_ndarray,
-                                NDArray<int64_t>* dst_ndarray) {
-    copy_data(src_ndarray, dst_ndarray);
-}
-}

nac3core/irrt/irrt/ndarray/def.hpp

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

nac3core/irrt/irrt/ndarray/fill.hpp

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

nac3core/irrt/irrt/ndarray/indexing.hpp

@@ -1,200 +0,0 @@
-#pragma once
-
-#include <irrt/error_context.hpp>
-#include <irrt/ndarray/basic.hpp>
-#include <irrt/ndarray/def.hpp>
-#include <irrt/slice.hpp>
-
-namespace {
-typedef uint8_t NDIndexType;
-
-/**
- * @brief A single element index
- *
- * See https://numpy.org/doc/stable/user/basics.indexing.html#single-element-indexing
- *
- * `data` points to a `SliceIndex`.
- */
-const NDIndexType ND_INDEX_TYPE_SINGLE_ELEMENT = 0;
-/**
- * @brief A slice index
- *
- * See https://numpy.org/doc/stable/user/basics.indexing.html#slicing-and-striding
- *
- * `data` points to a `UserRange`.
- */
-const NDIndexType ND_INDEX_TYPE_SLICE = 1;
-
-/**
- * @brief An index used in ndarray indexing
- */
-struct NDIndex {
-    /**
-     * @brief Enum tag to specify the type of index.
-     *
-     * Please see comments of each enum constant.
-     */
-    NDIndexType type;
-
-    /**
-     * @brief The accompanying data associated with `type`.
-     *
-     * Please see comments of each enum constant.
-     */
-    uint8_t* data;
-};
-}  // namespace
-
-namespace {
-namespace ndarray {
-namespace indexing {
-namespace util {
-
-/**
- * @brief Return the expected rank of the resulting ndarray
- * created by indexing an ndarray of rank `ndims` using `indexes`.
- */
-template <typename SizeT>
-void deduce_ndims_after_indexing(ErrorContext* errctx, SizeT* final_ndims,
-                                 SizeT ndims, SizeT num_indexes,
-                                 const NDIndex* indexes) {
-    if (num_indexes > ndims) {
-        errctx->set_error(errctx->error_ids->index_error,
-                          "too many indices for array: array is "
-                          "{0}-dimensional, but {1} were indexed",
-                          ndims, num_indexes);
-        return;
-    }
-
-    *final_ndims = ndims;
-    for (SizeT i = 0; i < num_indexes; i++) {
-        if (indexes[i].type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
-            // An index demotes the rank by 1
-            (*final_ndims)--;
-        }
-    }
-}
-}  // namespace util
-
-/**
- * @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
- *
- * This is function very similar to performing `dst_ndarray = src_ndarray[indexes]` in Python (where the variables
- * can all be found in the parameter of this function).
- *
- * In other words, this function takes in an ndarray (`src_ndarray`), index it with `indexes`, and return the
- * indexed array (by writing the result to `dst_ndarray`).
- *
- * This function also does proper assertions on `indexes`.
- *
- * # Notes on `dst_ndarray`
- * The caller is responsible for allocating space for the resulting ndarray.
- * Here is what this function expects from `dst_ndarray` when called:
- *   - `dst_ndarray->data` does not have to be initialized.
- *   - `dst_ndarray->itemsize` does not have to be initialized.
- *   - `dst_ndarray->ndims` must be initialized, and it must be equal to the expected `ndims` of the `dst_ndarray` after
- *       indexing `src_ndarray` with `indexes`.
- *   - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
- *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
- * When this function call ends:
- *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
- *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
- *   - `dst_ndarray->ndims` is unchanged.
- *   - `dst_ndarray->shape` is updated according to how `src_ndarray` is indexed.
- *   - `dst_ndarray->strides` is updated accordingly by how ndarray indexing works.
- *
- * @param indexes Indexes to index `src_ndarray`, ordered in the same way you would write them in Python.
- * @param src_ndarray The NDArray to be indexed.
- * @param dst_ndarray The resulting NDArray after indexing. Further details in the comments above,
- */
-template <typename SizeT>
-void index(ErrorContext* errctx, SizeT num_indexes, const NDIndex* indexes,
-           const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
-    // Reference code: https://github.com/wadetb/tinynumpy/blob/0d23d22e07062ffab2afa287374c7b366eebdda1/tinynumpy/tinynumpy.py#L652
-
-    dst_ndarray->data = src_ndarray->data;
-    dst_ndarray->itemsize = src_ndarray->itemsize;
-
-    SizeT src_axis = 0;
-    SizeT dst_axis = 0;
-
-    for (SliceIndex i = 0; i < num_indexes; i++) {
-        const NDIndex* index = &indexes[i];
-        if (index->type == ND_INDEX_TYPE_SINGLE_ELEMENT) {
-            SliceIndex input = *((SliceIndex*)index->data);
-            SliceIndex k = slice::resolve_index_in_length(
-                src_ndarray->shape[src_axis], input);
-
-            if (k == slice::OUT_OF_BOUNDS) {
-                errctx->set_error(errctx->error_ids->index_error,
-                                  "index {0} is out of bounds for axis {1} "
-                                  "with size {2}",
-                                  input, src_axis,
-                                  src_ndarray->shape[src_axis]);
-                return;
-            }
-
-            dst_ndarray->data += k * src_ndarray->strides[src_axis];
-
-            src_axis++;
-        } else if (index->type == ND_INDEX_TYPE_SLICE) {
-            UserSlice* input = (UserSlice*)index->data;
-
-            Slice slice;
-            input->indices_checked(errctx, src_ndarray->shape[src_axis],
-                                   &slice);
-            if (errctx->has_error()) {
-                return;
-            }
-
-            dst_ndarray->data +=
-                (SizeT)slice.start * src_ndarray->strides[src_axis];
-            dst_ndarray->strides[dst_axis] =
-                ((SizeT)slice.step) * src_ndarray->strides[src_axis];
-            dst_ndarray->shape[dst_axis] = (SizeT)slice.len();
-
-            dst_axis++;
-            src_axis++;
-        } else {
-            __builtin_unreachable();
-        }
-    }
-
-    for (; dst_axis < dst_ndarray->ndims; dst_axis++, src_axis++) {
-        dst_ndarray->shape[dst_axis] = src_ndarray->shape[src_axis];
-        dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
-    }
-}
-}  // namespace indexing
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-using namespace ndarray::indexing;
-
-void __nac3_ndarray_indexing_deduce_ndims_after_indexing(
-    ErrorContext* errctx, int32_t* result, int32_t ndims, int32_t num_indexes,
-    const NDIndex* indexes) {
-    ndarray::indexing::util::deduce_ndims_after_indexing(errctx, result, ndims,
-                                                         num_indexes, indexes);
-}
-
-void __nac3_ndarray_indexing_deduce_ndims_after_indexing64(
-    ErrorContext* errctx, int64_t* result, int64_t ndims, int64_t num_indexes,
-    const NDIndex* indexes) {
-    ndarray::indexing::util::deduce_ndims_after_indexing(errctx, result, ndims,
-                                                         num_indexes, indexes);
-}
-
-void __nac3_ndarray_index(ErrorContext* errctx, int32_t num_indexes,
-                          NDIndex* indexes, NDArray<int32_t>* src_ndarray,
-                          NDArray<int32_t>* dst_ndarray) {
-    index(errctx, num_indexes, indexes, src_ndarray, dst_ndarray);
-}
-
-void __nac3_ndarray_index64(ErrorContext* errctx, int64_t num_indexes,
-                            NDIndex* indexes, NDArray<int64_t>* src_ndarray,
-                            NDArray<int64_t>* dst_ndarray) {
-    index(errctx, num_indexes, indexes, src_ndarray, dst_ndarray);
-}
-}

nac3core/irrt/irrt/ndarray/reshape.hpp

@@ -1,116 +0,0 @@
-#pragma once
-
-#include <irrt/error_context.hpp>
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/def.hpp>
-
-namespace {
-namespace ndarray {
-namespace reshape {
-namespace util {
-
-/**
- * @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
- *
- * If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
- * modified to contain the resolved dimension.
- *
- * To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
- * `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
- *
- * @param size The `.size` of `<ndarray>`
- * @param new_ndims Number of elements in `new_shape`
- * @param new_shape Target shape to reshape to
- */
-template <typename SizeT>
-void resolve_and_check_new_shape(ErrorContext* errctx, SizeT size,
-                                 SizeT new_ndims, SizeT* new_shape) {
-    // Is there a -1 in `new_shape`?
-    bool neg1_exists = false;
-    // Location of -1, only initialized if `neg1_exists` is true
-    SizeT neg1_axis_i;
-    // The computed ndarray size of `new_shape`
-    SizeT new_size = 1;
-
-    for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
-        SizeT dim = new_shape[axis_i];
-        if (dim < 0) {
-            if (dim == -1) {
-                if (neg1_exists) {
-                    // Multiple `-1` found. Throw an error.
-                    errctx->set_error(errctx->error_ids->value_error,
-                                      "can only specify one unknown dimension");
-                    return;
-                } else {
-                    neg1_exists = true;
-                    neg1_axis_i = axis_i;
-                }
-            } else {
-                // TODO: What? In `np.reshape` any negative dimensions is
-                // treated like its `-1`.
-                //
-                // Try running `np.zeros((3, 4)).reshape((-999, 2))`
-                //
-                // It is not documented by numpy.
-                // Throw an error for now...
-
-                errctx->set_error(errctx->error_ids->value_error,
-                                  "Found negative dimension {0} on axis {1}",
-                                  dim, axis_i);
-                return;
-            }
-        } else {
-            new_size *= dim;
-        }
-    }
-
-    bool can_reshape;
-    if (neg1_exists) {
-        // Let `x` be the unknown dimension
-        // solve `x * <new_size> = <size>`
-        if (new_size == 0 && size == 0) {
-            // `x` has infinitely many solutions
-            can_reshape = false;
-        } else if (new_size == 0 && size != 0) {
-            // `x` has no solutions
-            can_reshape = false;
-        } else if (size % new_size != 0) {
-            // `x` has no integer solutions
-            can_reshape = false;
-        } else {
-            can_reshape = true;
-            new_shape[neg1_axis_i] = size / new_size;  // Resolve dimension
-        }
-    } else {
-        can_reshape = (new_size == size);
-    }
-
-    if (!can_reshape) {
-        errctx->set_error(errctx->error_ids->value_error,
-                          "cannot reshape array of size {0} into given shape",
-                          size);
-        return;
-    }
-}
-}  // namespace util
-}  // namespace reshape
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-
-void __nac3_ndarray_resolve_and_check_new_shape(ErrorContext* errctx,
-                                                int32_t size, int32_t new_ndims,
-                                                int32_t* new_shape) {
-    ndarray::reshape::util::resolve_and_check_new_shape(errctx, size, new_ndims,
-                                                        new_shape);
-}
-
-void __nac3_ndarray_resolve_and_check_new_shape64(ErrorContext* errctx,
-                                                  int64_t size,
-                                                  int64_t new_ndims,
-                                                  int64_t* new_shape) {
-    ndarray::reshape::util::resolve_and_check_new_shape(errctx, size, new_ndims,
-                                                        new_shape);
-}
-}

nac3core/irrt/irrt/ndarray/transpose.hpp

@@ -1,162 +0,0 @@
-#pragma once
-
-#include <irrt/int_defs.hpp>
-#include <irrt/ndarray/def.hpp>
-#include <irrt/slice.hpp>
-
-/*
- * Notes on `np.transpose(<array>, <axes>)`
- *
- * TODO: `axes`, if specified, can actually contain negative indices,
- * but it is not documented in numpy.
- *
- * Supporting it for now.
- */
-
-namespace {
-namespace ndarray {
-namespace transpose {
-namespace util {
-
-/**
- * @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
- *
- * Note that `np.transpose`'s `<axe>` argument is optional. If the argument
- * is specified but the user, use this function to do assertions on it.
- *
- * @param ndims The number of dimensions of `<array>`
- * @param num_axes Number of elements in `<axes>` as specified by the user.
- *   This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
- * @param axes The user specified `<axes>`.
- */
-template <typename SizeT>
-void assert_transpose_axes(ErrorContext* errctx, SizeT ndims, SizeT num_axes,
-                           const SizeT* axes) {
-    /*
-     * TODO: `axes` can actually contain negative indices, but it is not documented in numpy.
-     *
-     * Supporting it for now.
-     */
-
-    if (ndims != num_axes) {
-        errctx->set_error(errctx->error_ids->value_error,
-                          "axes don't match array");
-        return;
-    }
-
-    // TODO: Optimize this
-    bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
-    for (SizeT i = 0; i < ndims; i++) axe_specified[i] = false;
-
-    for (SizeT i = 0; i < ndims; i++) {
-        SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
-        if (axis == slice::OUT_OF_BOUNDS) {
-            // TODO: numpy actually throws a `numpy.exceptions.AxisError`
-            errctx->set_error(
-                errctx->error_ids->value_error,
-                "axis {0} is out of bounds for array of dimension {1}", axis,
-                ndims);
-            return;
-        }
-
-        if (axe_specified[axis]) {
-            errctx->set_error(errctx->error_ids->value_error,
-                              "repeated axis in transpose");
-            return;
-        }
-
-        axe_specified[axis] = true;
-    }
-}
-}  // namespace util
-
-/**
- * @brief Create a transpose view of `src_ndarray` and perform proper assertions.
- *
- * This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
- * If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
- *
- * The transpose view created is returned by modifying `dst_ndarray`.
- *
- * The caller is responsible for setting up `dst_ndarray` before calling this function.
- * Here is what this function expects from `dst_ndarray` when called:
- *   - `dst_ndarray->data` does not have to be initialized.
- *   - `dst_ndarray->itemsize` does not have to be initialized.
- *   - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
- *   - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
- *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
- * When this function call ends:
- *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
- *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
- *   - `dst_ndarray->ndims` is unchanged
- *   - `dst_ndarray->shape` is updated according to how `np.transpose` works
- *   - `dst_ndarray->strides` is updated according to how `np.transpose` works
- *
- * @param src_ndarray The NDArray to build a transpose view on
- * @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
- * @param num_axes Number of elements in axes, can be undefined if `axes` is nullptr.
- * @param axes Axes permutation. Set it to `nullptr` if `<axes>` is supposed to be `None`.
- */
-template <typename SizeT>
-void transpose(ErrorContext* errctx, const NDArray<SizeT>* src_ndarray,
-               NDArray<SizeT>* dst_ndarray, SizeT num_axes, const SizeT* axes) {
-    __builtin_assume(src_ndarray->ndims == dst_ndarray->ndims);
-    const auto ndims = src_ndarray->ndims;
-
-    if (axes != nullptr) {
-        util::assert_transpose_axes(errctx, ndims, num_axes, axes);
-        if (errctx->has_error()) return;
-    }
-
-    dst_ndarray->data = src_ndarray->data;
-    dst_ndarray->itemsize = src_ndarray->itemsize;
-
-    // Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
-    if (axes == nullptr) {
-        // `np.transpose(<array>, axes=None)`
-
-        /*
-         * Minor note: `np.transpose(<array>, axes=None)` is equivalent to
-         * `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
-         * is reversing the order of strides and shape.
-         *
-         * This is a fast implementation to handle this special (but very common) case.
-         */
-
-        for (SizeT axis = 0; axis < ndims; axis++) {
-            dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
-            dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
-        }
-    } else {
-        // `np.transpose(<array>, <axes>)`
-
-        // Permute strides and shape according to `axes`, while resolving negative indices in `axes`
-        for (SizeT axis = 0; axis < ndims; axis++) {
-            // `i` cannot be OUT_OF_BOUNDS because of assertions
-            SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
-
-            dst_ndarray->shape[axis] = src_ndarray->shape[i];
-            dst_ndarray->strides[axis] = src_ndarray->strides[i];
-        }
-    }
-}
-}  // namespace transpose
-}  // namespace ndarray
-}  // namespace
-
-extern "C" {
-using namespace ndarray::transpose;
-void __nac3_ndarray_transpose(ErrorContext* errctx,
-                              const NDArray<int32_t>* src_ndarray,
-                              NDArray<int32_t>* dst_ndarray, int32_t num_axes,
-                              const int32_t* axes) {
-    transpose(errctx, src_ndarray, dst_ndarray, num_axes, axes);
-}
-
-void __nac3_ndarray_transpose64(ErrorContext* errctx,
-                                const NDArray<int64_t>* src_ndarray,
-                                NDArray<int64_t>* dst_ndarray, int64_t num_axes,
-                                const int64_t* axes) {
-    transpose(errctx, src_ndarray, dst_ndarray, num_axes, axes);
-}
-}

nac3core/irrt/irrt/slice.hpp

@@ -1,165 +0,0 @@
-#pragma once
-
-#include <irrt/error_context.hpp>
-#include <irrt/int_defs.hpp>
-#include <irrt/slice.hpp>
-#include <irrt/utils.hpp>
-
-// The type of an index or a value describing the length of a
-// range/slice is always `int32_t`.
-using SliceIndex = int32_t;
-
-namespace {
-
-/**
- * @brief A Python-like slice with resolved indices.
- *
- * "Resolved indices" means that `start` and `stop` must be positive and are
- * bound to a known length.
- */
-struct Slice {
-    SliceIndex start;
-    SliceIndex stop;
-    SliceIndex step;
-
-    /**
-     * @brief Calculate and return the length / the number of the slice.
-     * 
-     * If this were a Python range, this function would be `len(range(start, stop, step))`.
-     */
-    SliceIndex len() {
-        SliceIndex diff = stop - start;
-        if (diff > 0 && step > 0) {
-            return ((diff - 1) / step) + 1;
-        } else if (diff < 0 && step < 0) {
-            return ((diff + 1) / step) + 1;
-        } else {
-            return 0;
-        }
-    }
-};
-
-namespace slice {
-/**
- * @brief Resolve a slice index under a given length like Python indexing.
- *
- * In Python, if you have a `list` of length 100, `list[-1]` resolves to
- * `list[99]`, so `resolve_index_in_length_clamped(100, -1)` returns `99`.
- *
- * If `length` is 0, 0 is returned for any value of `index`.
- *
- * If `index` is out of bounds, clamps the returned value between `0` and
- * `length - 1` (inclusive).
- *
- */
-SliceIndex resolve_index_in_length_clamped(SliceIndex length,
-                                           SliceIndex index) {
-    if (index < 0) {
-        return max<SliceIndex>(length + index, 0);
-    } else {
-        return min<SliceIndex>(length, index);
-    }
-}
-
-const SliceIndex OUT_OF_BOUNDS = -1;
-
-/**
- * @brief Like `resolve_index_in_length_clamped`, but returns `OUT_OF_BOUNDS`
- * if `index` is out of bounds.
- */
-SliceIndex resolve_index_in_length(SliceIndex length, SliceIndex index) {
-    SliceIndex resolved = index < 0 ? length + index : index;
-    if (0 <= resolved && resolved < length) {
-        return resolved;
-    } else {
-        return OUT_OF_BOUNDS;
-    }
-}
-}  // namespace slice
-
-/**
- * @brief A Python-like slice with **unresolved** indices.
- */
-struct UserSlice {
-    bool start_defined;
-    SliceIndex start;
-
-    bool stop_defined;
-    SliceIndex stop;
-
-    bool step_defined;
-    SliceIndex step;
-
-    UserSlice() { this->reset(); }
-
-    void reset() {
-        this->start_defined = false;
-        this->stop_defined = false;
-        this->step_defined = false;
-    }
-
-    void set_start(SliceIndex start) {
-        this->start_defined = true;
-        this->start = start;
-    }
-
-    void set_stop(SliceIndex stop) {
-        this->stop_defined = true;
-        this->stop = stop;
-    }
-
-    void set_step(SliceIndex step) {
-        this->step_defined = true;
-        this->step = step;
-    }
-
-    /**
-     * @brief Resolve this slice.
-     *
-     * In Python, this would be `slice(start, stop, step).indices(length)`.
-     *
-     * @return A `Slice` with the resolved indices.
-     */
-    Slice indices(SliceIndex length) {
-        Slice result;
-
-        result.step = step_defined ? step : 1;
-        bool step_is_negative = result.step < 0;
-
-        if (start_defined) {
-            result.start =
-                slice::resolve_index_in_length_clamped(length, start);
-        } else {
-            result.start = step_is_negative ? length - 1 : 0;
-        }
-
-        if (stop_defined) {
-            result.stop = slice::resolve_index_in_length_clamped(length, stop);
-        } else {
-            result.stop = step_is_negative ? -1 : length;
-        }
-
-        return result;
-    }
-
-    /**
-     * @brief Like `.indices()` but with assertions.
-     */
-    void indices_checked(ErrorContext* errctx, SliceIndex length,
-                         Slice* result) {
-        if (length < 0) {
-            errctx->set_error(errctx->error_ids->value_error,
-                              "length should not be negative, got {0}", length);
-            return;
-        }
-
-        if (this->step_defined && this->step == 0) {
-            errctx->set_error(errctx->error_ids->value_error,
-                              "slice step cannot be zero");
-            return;
-        }
-
-        *result = this->indices(length);
-    }
-};
-}  // namespace

nac3core/irrt/irrt/utils.hpp

@@ -1,104 +0,0 @@
-#pragma once
-
-namespace {
-template <typename T>
-const T& max(const T& a, const T& b) {
-    return a > b ? a : b;
-}
-
-template <typename T>
-const T& min(const T& a, const T& b) {
-    return a > b ? b : a;
-}
-
-/**
- * @brief Compare contents of two arrays with the same length.
- */
-template <typename T>
-bool arrays_match(int len, T* as, T* bs) {
-    for (int i = 0; i < len; i++) {
-        if (as[i] != bs[i]) return false;
-    }
-    return true;
-}
-
-namespace cstr_utils {
-/**
- * @brief Return true if `str` is empty.
- */
-bool is_empty(const char* str) { return str[0] == '\0'; }
-
-/**
- * @brief Implementation of `strcmp()`
- */
-int8_t compare(const char* a, const char* b) {
-    uint32_t i = 0;
-    while (true) {
-        if (a[i] < b[i]) {
-            return -1;
-        } else if (a[i] > b[i]) {
-            return 1;
-        } else {
-            if (a[i] == '\0') {
-                return 0;
-            } else {
-                i++;
-            }
-        }
-    }
-}
-
-/**
- * @brief Return true two strings have the same content.
- */
-int8_t equal(const char* a, const char* b) { return compare(a, b) == 0; }
-
-/**
- * @brief Implementation of `strlen()`.
- */
-uint32_t length(const char* str) {
-    uint32_t length = 0;
-    while (*str != '\0') {
-        length++;
-        str++;
-    }
-    return length;
-}
-
-/**
- * @brief Copy a null-terminated string to a buffer with limited size and guaranteed null-termination.
- *
- * `dst_max_size` must be greater than 0, otherwise this function has undefined behavior.
- *
- * This function attempts to copy everything from `src` from `dst`, and *always* null-terminates `dst`.
- *
- * If the size of `dst` is too small, the final byte (`dst[dst_max_size - 1]`) of `dst` will be set to
- * the null terminator.
- *
- * @param src String to copy from.
- * @param dst Buffer to copy string to.
- * @param dst_max_size
- *   Number of bytes of this buffer, including the space needed for the null terminator.
- *   Must be greater than 0.
- * @return If `dst` is too small to contain everything in `src`.
- */
-bool copy(const char* src, char* dst, uint32_t dst_max_size) {
-    for (uint32_t i = 0; i < dst_max_size; i++) {
-        bool is_last = i + 1 == dst_max_size;
-        if (is_last && src[i] != '\0') {
-            dst[i] = '\0';
-            return false;
-        }
-
-        if (src[i] == '\0') {
-            dst[i] = '\0';
-            return true;
-        }
-
-        dst[i] = src[i];
-    }
-
-    __builtin_unreachable();
-}
-}  // namespace cstr_utils
-}  // namespace

nac3core/irrt/irrt_basic.hpp

@@ -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_everything.hpp

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

nac3core/irrt/irrt_numpy_ndarray.hpp

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

nac3core/irrt/irrt_slice.hpp

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

nac3core/irrt/irrt_test.cpp

@@ -4,15 +4,655 @@
 #include <cstdint>
 #include <cstdio>
 #include <cstdlib>
-#include <test/test_core.hpp>
+
-#include <test/test_ndarray_basic.hpp>
+// Set `IRRT_DONT_TYPEDEF_INTS` because `cstdint` defines them
-#include <test/test_ndarray_indexing.hpp>
+#define IRRT_DONT_TYPEDEF_INTS
-#include <test/test_slice.hpp>
+#include "irrt_everything.hpp"
+
+void test_fail() {
+    printf("[!] Test failed\n");
+    exit(1);
+}
+
+void __begin_test(const char* function_name, const char* file, int line) {
+    printf("######### Running %s @ %s:%d\n", function_name, file, line);
+}
+
+#define BEGIN_TEST() __begin_test(__FUNCTION__, __FILE__, __LINE__)
+
+template <typename T>
+void debug_print_array(const char* format, int len, T* as) {
+    printf("[");
+    for (int i = 0; i < len; i++) {
+        if (i != 0) printf(", ");
+        printf(format, as[i]);
+    }
+    printf("]");
+}
+
+template <typename T>
+void assert_arrays_match(const char* label, const char* format, int len, T* expected, T* got) {
+    if (!arrays_match(len, expected, got)) {
+        printf(">>>>>>> %s\n", label);
+        printf("    Expecting = ");
+        debug_print_array(format, len, expected);
+        printf("\n");
+        printf("          Got = ");
+        debug_print_array(format, len, got);
+        printf("\n");
+        test_fail();
+    }
+}
+
+template <typename T>
+void assert_values_match(const char* label, const char* format, T expected, T got) {
+    if (expected != got) {
+        printf(">>>>>>> %s\n", label);
+        printf("    Expecting = ");
+        printf(format, expected);
+        printf("\n");
+        printf("          Got = ");
+        printf(format, got);
+        printf("\n");
+        test_fail();
+    }
+}
+
+void print_repeated(const char *str, int count) {
+    for (int i = 0; i < count; i++) {
+        printf("%s", str);
+    }
+}
+
+template<typename SizeT, typename ElementT>
+void __print_ndarray_aux(const char *format, bool first, bool last, SizeT* cursor, SizeT depth, NDArray<SizeT>* ndarray) {
+    // A really lazy recursive implementation
+    
+    // Add left padding unless its the first entry (since there would be "[[[" before it)
+    if (!first) {
+        print_repeated(" ", depth);
+    }
+
+    const SizeT dim = ndarray->shape[depth];
+    if (depth + 1 == ndarray->ndims) {
+        // Recursed down to last dimension, print the values in a nice list
+        printf("[");
+
+        SizeT* indices = (SizeT*) __builtin_alloca(sizeof(SizeT) * ndarray->ndims);
+        for (SizeT i = 0; i < dim; i++) {
+            ndarray_util::set_indices_by_nth(ndarray->ndims, ndarray->shape, indices, *cursor);
+            ElementT* pelement = (ElementT*) ndarray->get_pelement(indices);
+            ElementT element = *pelement;
+
+            if (i != 0) printf(", "); // List delimiter
+            printf(format, element);
+            printf("(@");
+            debug_print_array("%d", ndarray->ndims, indices);
+            printf(")");
+
+            (*cursor)++;
+        }
+        printf("]");
+    } else {
+        printf("[");
+        for (SizeT i = 0; i < ndarray->shape[depth]; i++) {
+            __print_ndarray_aux<SizeT, ElementT>(
+                format,
+                i == 0, // first?
+                i + 1 == dim, // last?
+                cursor,
+                depth + 1,
+                ndarray
+            );
+        }
+        printf("]");
+    }
+
+    // Add newline unless its the last entry (since there will be "]]]" after it)
+    if (!last) {
+        print_repeated("\n", depth);
+    }
+}
+
+template<typename SizeT, typename ElementT>
+void print_ndarray(const char *format, NDArray<SizeT>* ndarray) {
+    if (ndarray->ndims == 0) {
+        printf("<empty ndarray>");
+    } else {
+        SizeT cursor = 0;
+        __print_ndarray_aux<SizeT, ElementT>(format, true, true, &cursor, 0, ndarray);
+    }
+    printf("\n");
+}
+
+void test_calc_size_from_shape_normal() {
+    // Test shapes with normal values
+    BEGIN_TEST();
+
+    int32_t shape[4] = { 2, 3, 5, 7 };
+    assert_values_match("size", "%d", 210, ndarray_util::calc_size_from_shape<int32_t>(4, shape));
+}
+
+void test_calc_size_from_shape_has_zero() {
+    // Test shapes with 0 in them
+    BEGIN_TEST();
+
+    int32_t shape[4] = { 2, 0, 5, 7 };
+    assert_values_match("size", "%d", 0, ndarray_util::calc_size_from_shape<int32_t>(4, shape));
+}
+
+void test_set_strides_by_shape() {
+    // Test `set_strides_by_shape()`
+    BEGIN_TEST();
+
+    int32_t shape[4] = { 99, 3, 5, 7 };
+    int32_t strides[4] = { 0 };
+    ndarray_util::set_strides_by_shape((int32_t) sizeof(int32_t), 4, strides, shape);
+
+    int32_t expected_strides[4] = {
+        105 * sizeof(int32_t),
+        35 * sizeof(int32_t),
+        7 * sizeof(int32_t),
+        1 * sizeof(int32_t)
+    };
+    assert_arrays_match("strides", "%u", 4u, expected_strides, strides);
+}
+
+void test_ndarray_indices_iter_normal() {
+    // Test NDArrayIndicesIter normal behavior
+    BEGIN_TEST();
+
+    int32_t shape[3] = { 1, 2, 3 };
+    int32_t indices[3] = { 0, 0, 0 };
+    auto iter = NDArrayIndicesIter<int32_t> {
+        .ndims = 3,
+        .shape = shape,
+        .indices = indices
+    };
+
+    assert_arrays_match("indices #0", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 0 });
+    iter.next();
+    assert_arrays_match("indices #1", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 1 });
+    iter.next();
+    assert_arrays_match("indices #2", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 2 });
+    iter.next();
+    assert_arrays_match("indices #3", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 0 });
+    iter.next();
+    assert_arrays_match("indices #4", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 1 });
+    iter.next();
+    assert_arrays_match("indices #5", "%u", 3u, iter.indices, (int32_t[3]) { 0, 1, 2 });
+    iter.next();
+    assert_arrays_match("indices #6", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 0 }); // Loops back
+    iter.next();
+    assert_arrays_match("indices #7", "%u", 3u, iter.indices, (int32_t[3]) { 0, 0, 1 });
+}
+
+void test_ndarray_fill_generic() {
+    // Test ndarray fill_generic
+    BEGIN_TEST();
+
+    // Choose a type that's neither int32_t nor uint64_t (candidates of SizeT) to spice it up
+    // Also make all the octets non-zero, to see if `memcpy` in `fill_generic` is working perfectly.
+    uint16_t fill_value = 0xFACE;
+
+    uint16_t in_data[6] = { 100, 101, 102, 103, 104, 105 }; // Fill `data` with values that != `999`
+    int32_t in_itemsize = sizeof(uint16_t);
+    const int32_t in_ndims = 2;
+    int32_t in_shape[in_ndims] = { 2, 3 };
+    int32_t in_strides[in_ndims] = {};
+    NDArray<int32_t> ndarray = {
+        .data = (uint8_t*) in_data,
+        .itemsize = in_itemsize,
+        .ndims = in_ndims,
+        .shape = in_shape,
+        .strides = in_strides,
+    };
+    ndarray.set_strides_by_shape();
+    ndarray.fill_generic((uint8_t*) &fill_value); // `fill_generic` here
+
+    uint16_t expected_data[6] = { fill_value, fill_value, fill_value, fill_value, fill_value, fill_value };
+    assert_arrays_match("data", "0x%hX", 6, expected_data, in_data);
+}
+
+void test_ndarray_set_to_eye() {
+    // Test `set_to_eye` behavior (helper function to implement `np.eye()`)
+    BEGIN_TEST();
+
+    double in_data[9] = { 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0, 99.0 };
+    int32_t in_itemsize = sizeof(double);
+    const int32_t in_ndims = 2;
+    int32_t in_shape[in_ndims] = { 3, 3 };
+    int32_t in_strides[in_ndims] = {};
+    NDArray<int32_t> ndarray = {
+        .data = (uint8_t*) in_data,
+        .itemsize = in_itemsize,
+        .ndims = in_ndims,
+        .shape = in_shape,
+        .strides = in_strides,
+    };
+    ndarray.set_strides_by_shape();
+
+    double zero = 0.0;
+    double one = 1.0;
+    ndarray.set_to_eye(1, (uint8_t*) &zero, (uint8_t*) &one);
+
+    assert_values_match("in_data[0]", "%f", 0.0, in_data[0]);
+    assert_values_match("in_data[1]", "%f", 1.0, in_data[1]);
+    assert_values_match("in_data[2]", "%f", 0.0, in_data[2]);
+    assert_values_match("in_data[3]", "%f", 0.0, in_data[3]);
+    assert_values_match("in_data[4]", "%f", 0.0, in_data[4]);
+    assert_values_match("in_data[5]", "%f", 1.0, in_data[5]);
+    assert_values_match("in_data[6]", "%f", 0.0, in_data[6]);
+    assert_values_match("in_data[7]", "%f", 0.0, in_data[7]);
+    assert_values_match("in_data[8]", "%f", 0.0, in_data[8]);
+}
+
+void test_slice_1() {
+    // Test `slice(5, None, None).indices(100) == slice(5, 100, 1)`
+    BEGIN_TEST();
+
+    UserSlice<int> user_slice = {
+        .start_defined = 1,
+        .start = 5,
+        .stop_defined = 0,
+        .step_defined = 0,
+    };
+
+    auto slice = user_slice.indices(100);
+    assert_values_match("start", "%d", 5, slice.start);
+    assert_values_match("stop", "%d", 100, slice.stop);
+    assert_values_match("step", "%d", 1, slice.step);
+}
+
+void test_slice_2() {
+    // Test `slice(400, 999, None).indices(100) == slice(100, 100, 1)`
+    BEGIN_TEST();
+
+    UserSlice<int> user_slice = {
+        .start_defined = 1,
+        .start = 400,
+        .stop_defined = 0,
+        .step_defined = 0,
+    };
+
+    auto slice = user_slice.indices(100);
+    assert_values_match("start", "%d", 100, slice.start);
+    assert_values_match("stop", "%d", 100, slice.stop);
+    assert_values_match("step", "%d", 1, slice.step);
+}
+
+void test_slice_3() {
+    // Test `slice(-10, -5, None).indices(100) == slice(90, 95, 1)`
+    BEGIN_TEST();
+
+    UserSlice<int> user_slice = {
+        .start_defined = 1,
+        .start = -10,
+        .stop_defined = 1,
+        .stop = -5,
+        .step_defined = 0,
+    };
+
+    auto slice = user_slice.indices(100);
+    assert_values_match("start", "%d", 90, slice.start);
+    assert_values_match("stop", "%d", 95, slice.stop);
+    assert_values_match("step", "%d", 1, slice.step);
+}
+
+void test_slice_4() {
+    // Test `slice(None, None, -5).indices(100) == (99, -1, -5)`
+    BEGIN_TEST();
+
+    UserSlice<int> user_slice = {
+        .start_defined = 0,
+        .stop_defined = 0,
+        .step_defined = 1,
+        .step = -5
+    };
+
+    auto slice = user_slice.indices(100);
+    assert_values_match("start", "%d", 99, slice.start);
+    assert_values_match("stop", "%d", -1, slice.stop);
+    assert_values_match("step", "%d", -5, slice.step);
+}
+
+void test_ndslice_1() {
+    /*
+        Reference Python code:
+        ```python
+        ndarray = np.arange(12, dtype=np.float64).reshape((3, 4));
+        # array([[ 0.,  1.,  2.,  3.],
+        #        [ 4.,  5.,  6.,  7.],
+        #        [ 8.,  9., 10., 11.]])
+
+        dst_ndarray = ndarray[-2:, 1::2]
+        # array([[ 5.,  7.],
+        #        [ 9., 11.]])
+
+        assert dst_ndarray.shape == (2, 2)
+        assert dst_ndarray.strides == (32, 16)
+        assert dst_ndarray[0, 0] == 5.0
+        assert dst_ndarray[0, 1] == 7.0
+        assert dst_ndarray[1, 0] == 9.0
+        assert dst_ndarray[1, 1] == 11.0
+        ```
+    */
+    BEGIN_TEST();
+
+    double in_data[12] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
+    int32_t in_itemsize = sizeof(double);
+    const int32_t in_ndims = 2;
+    int32_t in_shape[in_ndims] = { 3, 4 };
+    int32_t in_strides[in_ndims] = {};
+    NDArray<int32_t> ndarray = {
+        .data = (uint8_t*) in_data,
+        .itemsize = in_itemsize,
+        .ndims = in_ndims,
+        .shape = in_shape,
+        .strides = in_strides
+    };
+    ndarray.set_strides_by_shape();
+
+    // Destination ndarray
+    // As documented, ndims and shape & strides must be allocated and determined by the caller.
+    const int32_t dst_ndims = 2;
+    int32_t dst_shape[dst_ndims] = {999, 999}; // Empty values
+    int32_t dst_strides[dst_ndims] = {999, 999}; // Empty values
+    NDArray<int32_t> dst_ndarray = {
+        .data = nullptr,
+        .ndims = dst_ndims,
+        .shape = dst_shape,
+        .strides = dst_strides
+    };
+
+    // Create the slice in `ndarray[-2::, 1::2]`
+    UserSlice<int32_t> user_slice_1 = {
+        .start_defined = 1,
+        .start = -2,
+        .stop_defined = 0,
+        .step_defined = 0
+    };
+
+    UserSlice<int32_t> user_slice_2 = {
+        .start_defined = 1,
+        .start = 1,
+        .stop_defined = 0,
+        .step_defined = 1,
+        .step = 2
+    };
+
+    const int32_t num_ndslices = 2;
+    NDSlice ndslices[num_ndslices] = {
+        { .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_1 },
+        { .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_2 }
+    };
+
+    ndarray.slice(num_ndslices, ndslices, &dst_ndarray);
+
+    int32_t expected_shape[dst_ndims] = { 2, 2 };
+    int32_t expected_strides[dst_ndims] = { 32, 16 };
+    assert_arrays_match("shape", "%d", dst_ndims, expected_shape, dst_ndarray.shape);
+    assert_arrays_match("strides", "%d", dst_ndims, expected_strides, dst_ndarray.strides);
+
+    assert_values_match("dst_ndarray[0, 0]", "%f", 5.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 0, 0 })));
+    assert_values_match("dst_ndarray[0, 1]", "%f", 7.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 0, 1 })));
+    assert_values_match("dst_ndarray[1, 0]", "%f", 9.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 1, 0 })));
+    assert_values_match("dst_ndarray[1, 1]", "%f", 11.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 1, 1 })));
+}
+
+void test_ndslice_2() {
+    /*
+        ```python
+        ndarray = np.arange(12, dtype=np.float64).reshape((3, 4))
+        # array([[ 0.,  1.,  2.,  3.],
+        #        [ 4.,  5.,  6.,  7.],
+        #        [ 8.,  9., 10., 11.]])
+
+        dst_ndarray = ndarray[2, ::-2]
+        # array([11.,  9.])
+
+        assert dst_ndarray.shape == (2,)
+        assert dst_ndarray.strides == (-16,)
+        assert dst_ndarray[0] == 11.0
+        assert dst_ndarray[1] == 9.0
+
+        dst_ndarray[1, 0] == 99 # If you write to `dst_ndarray`
+        assert ndarray[1, 3] == 99 # `ndarray` also updates!!
+        ```
+    */
+    BEGIN_TEST();
+
+    double in_data[12] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0 };
+    int32_t in_itemsize = sizeof(double);
+    const int32_t in_ndims = 2;
+    int32_t in_shape[in_ndims] = { 3, 4 };
+    int32_t in_strides[in_ndims] = {};
+    NDArray<int32_t> ndarray = {
+        .data = (uint8_t*) in_data,
+        .itemsize = in_itemsize,
+        .ndims = in_ndims,
+        .shape = in_shape,
+        .strides = in_strides
+    };
+    ndarray.set_strides_by_shape();
+
+    // Destination ndarray
+    // As documented, ndims and shape & strides must be allocated and determined by the caller.
+    const int32_t dst_ndims = 1;
+    int32_t dst_shape[dst_ndims] = {999}; // Empty values
+    int32_t dst_strides[dst_ndims] = {999}; // Empty values
+    NDArray<int32_t> dst_ndarray = {
+        .data = nullptr,
+        .ndims = dst_ndims,
+        .shape = dst_shape,
+        .strides = dst_strides
+    };
+
+    // Create the slice in `ndarray[2, ::-2]`
+    int32_t user_slice_1 = 2;
+    UserSlice<int32_t> user_slice_2 = {
+        .start_defined = 0,
+        .stop_defined = 0,
+        .step_defined = 1,
+        .step = -2
+    };
+
+    const int32_t num_ndslices = 2;
+    NDSlice ndslices[num_ndslices] = {
+        { .type = INPUT_SLICE_TYPE_INDEX, .slice = (uint8_t*) &user_slice_1 },
+        { .type = INPUT_SLICE_TYPE_SLICE, .slice = (uint8_t*) &user_slice_2 }
+    };
+
+    ndarray.slice(num_ndslices, ndslices, &dst_ndarray);
+
+    int32_t expected_shape[dst_ndims] = { 2 };
+    int32_t expected_strides[dst_ndims] = { -16 };
+    assert_arrays_match("shape", "%d", dst_ndims, expected_shape, dst_ndarray.shape);
+    assert_arrays_match("strides", "%d", dst_ndims, expected_strides, dst_ndarray.strides);
+
+    // [5.0, 3.0]
+    assert_values_match("dst_ndarray[0]", "%f", 11.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 0 })));
+    assert_values_match("dst_ndarray[1]", "%f", 9.0, *((double *) dst_ndarray.get_pelement((int32_t[dst_ndims]) { 1 })));
+}
+
+void test_can_broadcast_shape() {
+    BEGIN_TEST();
+
+    assert_values_match(
+        "can_broadcast_shape_to([3], [1, 1, 1, 1, 3]) == true",
+        "%d",
+        true,
+        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 5, (int32_t[]) { 1, 1, 1, 1, 3 })
+    );
+    assert_values_match(
+        "can_broadcast_shape_to([3], [3, 1]) == false",
+        "%d",
+        false,
+        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 2, (int32_t[]) { 3, 1 }));
+    assert_values_match(
+        "can_broadcast_shape_to([3], [3]) == true",
+        "%d",
+        true,
+        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 3 }, 1, (int32_t[]) { 3 }));
+    assert_values_match(
+        "can_broadcast_shape_to([1], [3]) == false",
+        "%d",
+        false,
+        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 1 }, 1, (int32_t[]) { 3 }));
+    assert_values_match(
+        "can_broadcast_shape_to([1], [1]) == true",
+        "%d",
+        true,
+        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 1 }, 1, (int32_t[]) { 1 }));
+    assert_values_match(
+        "can_broadcast_shape_to([256, 256, 3], [256, 1, 3]) == true",
+        "%d",
+        true,
+        ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 3, (int32_t[]) { 256, 1, 3 })
+    );
+    assert_values_match(
+        "can_broadcast_shape_to([256, 256, 3], [3]) == true",
+        "%d",
+        true,
+        ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 1, (int32_t[]) { 3 })
+    );
+    assert_values_match(
+        "can_broadcast_shape_to([256, 256, 3], [2]) == false",
+        "%d",
+        false,
+        ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 1, (int32_t[]) { 2 })
+    );
+    assert_values_match(
+        "can_broadcast_shape_to([256, 256, 3], [1]) == true",
+        "%d",
+        true,
+        ndarray_util::can_broadcast_shape_to(3, (int32_t[]) { 256, 256, 3 }, 1, (int32_t[]) { 1 })
+    );
+
+    // In cases when the shapes contain zero(es)
+    assert_values_match(
+        "can_broadcast_shape_to([0], [1]) == true",
+        "%d",
+        true,
+        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 0 }, 1, (int32_t[]) { 1 })
+    );
+    assert_values_match(
+        "can_broadcast_shape_to([0], [2]) == false",
+        "%d",
+        false,
+        ndarray_util::can_broadcast_shape_to(1, (int32_t[]) { 0 }, 1, (int32_t[]) { 2 })
+    );
+    assert_values_match(
+        "can_broadcast_shape_to([0, 4, 0, 0], [1]) == true",
+        "%d",
+        true,
+        ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 1, (int32_t[]) { 1 })
+    );
+    assert_values_match(
+        "can_broadcast_shape_to([0, 4, 0, 0], [1, 1, 1, 1]) == true",
+        "%d",
+        true,
+        ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 4, (int32_t[]) { 1, 1, 1, 1 })
+    );
+    assert_values_match(
+        "can_broadcast_shape_to([0, 4, 0, 0], [1, 4, 1, 1]) == true",
+        "%d",
+        true,
+        ndarray_util::can_broadcast_shape_to(4, (int32_t[]) { 0, 4, 0, 0 }, 4, (int32_t[]) { 1, 4, 1, 1 })
+    );
+    assert_values_match(
+        "can_broadcast_shape_to([4, 3], [0, 3]) == false",
+        "%d",
+        false,
+        ndarray_util::can_broadcast_shape_to(2, (int32_t[]) { 4, 3 }, 2, (int32_t[]) { 0, 3 })
+    );
+    assert_values_match(
+        "can_broadcast_shape_to([4, 3], [0, 0]) == false",
+        "%d",
+        false,
+        ndarray_util::can_broadcast_shape_to(2, (int32_t[]) { 4, 3 }, 2, (int32_t[]) { 0, 0 })
+    );
+}
+
+void test_ndarray_broadcast_1() {
+    /*
+    # array = np.array([[19.9, 29.9, 39.9, 49.9]], dtype=np.float64)
+    # >>> [[19.9 29.9 39.9 49.9]]
+    #
+    # array = np.broadcast_to(array, (2, 3, 4))
+    # >>> [[[19.9 29.9 39.9 49.9]
+    # >>>   [19.9 29.9 39.9 49.9]
+    # >>>   [19.9 29.9 39.9 49.9]]
+    # >>>  [[19.9 29.9 39.9 49.9]
+    # >>>   [19.9 29.9 39.9 49.9]
+    # >>>   [19.9 29.9 39.9 49.9]]]
+    #
+    # assery array.strides == (0, 0, 8)
+
+    */
+    BEGIN_TEST();
+
+    double in_data[4] = { 19.9, 29.9, 39.9, 49.9 };
+    const int32_t in_ndims = 2;
+    int32_t in_shape[in_ndims] = {1, 4};
+    int32_t in_strides[in_ndims] = {};
+    NDArray<int32_t> ndarray = {
+        .data = (uint8_t*) in_data,
+        .itemsize = sizeof(double),
+        .ndims = in_ndims,
+        .shape = in_shape,
+        .strides = in_strides
+    };
+    ndarray.set_strides_by_shape();
+
+    const int32_t dst_ndims = 3;
+    int32_t dst_shape[dst_ndims] = {2, 3, 4};
+    int32_t dst_strides[dst_ndims] = {};
+    NDArray<int32_t> dst_ndarray = {
+        .ndims = dst_ndims,
+        .shape = dst_shape,
+        .strides = dst_strides
+    };
+
+    ndarray.broadcast_to(&dst_ndarray);
+
+    assert_arrays_match("dst_ndarray->strides", "%d", dst_ndims, (int32_t[]) { 0, 0, 8 }, dst_ndarray.strides);
+
+    assert_values_match("dst_ndarray[0, 0, 0]", "%f", 19.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 0})));
+    assert_values_match("dst_ndarray[0, 0, 1]", "%f", 29.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 1})));
+    assert_values_match("dst_ndarray[0, 0, 2]", "%f", 39.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 2})));
+    assert_values_match("dst_ndarray[0, 0, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 0, 3})));
+    assert_values_match("dst_ndarray[0, 1, 0]", "%f", 19.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 0})));
+    assert_values_match("dst_ndarray[0, 1, 1]", "%f", 29.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 1})));
+    assert_values_match("dst_ndarray[0, 1, 2]", "%f", 39.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 2})));
+    assert_values_match("dst_ndarray[0, 1, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {0, 1, 3})));
+    assert_values_match("dst_ndarray[1, 2, 3]", "%f", 49.9, *((double*) dst_ndarray.get_pelement((int32_t[]) {1, 2, 3})));
+}
+
+void test_assign_with() {
+    /*
+        ```
+        xs = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], dtype=np.float64)
+        ys = xs.shape
+        ```
+    */
+}
 
 int main() {
-    test::core::run();
+    test_calc_size_from_shape_normal();
-    test::slice::run();
+    test_calc_size_from_shape_has_zero();
-    test::ndarray_basic::run();
+    test_set_strides_by_shape();
-    test::ndarray_indexing::run();
+    test_ndarray_indices_iter_normal();
+    test_ndarray_fill_generic();
+    test_ndarray_set_to_eye();
+    test_slice_1();
+    test_slice_2();
+    test_slice_3();
+    test_slice_4();
+    test_ndslice_1();
+    test_ndslice_2();
+    test_can_broadcast_shape();
+    test_ndarray_broadcast_1();
+    test_assign_with();
     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 +1,37 @@
+#pragma once
+
+#include "irrt_typedefs.hpp"
+
+namespace {
+    template <typename T>
+    T max(T a, T b) {
+        return a > b ? a : b;
+    }
+
+    template <typename T>
+    T min(T a, T b) {
+        return a > b ? b : a;
+    }
+
+    template <typename T>
+    bool arrays_match(int len, T *as, T *bs) {
+        for (int i = 0; i < len; i++) {
+            if (as[i] != bs[i]) return false;
+        }
+        return true;
+    }
+
+    void irrt_panic() {
+        // Crash the program for now.
+        // TODO: Don't crash the program
+        //       ... or at least produce a good message when doing testing IRRT
+            
+        uint8_t* death = nullptr;
+        *death = 0; // TODO: address 0 on hardware might be writable?
+    }
+
+    // TODO: Make this a macro and allow it to be toggled on/off (e.g., debug vs release)
+    void irrt_assert(bool condition) {
+        if (!condition) irrt_panic();
+    }
+}

nac3core/irrt/test/includes.hpp

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

nac3core/irrt/test/test_core.hpp

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

nac3core/irrt/test/test_ndarray_basic.hpp

@@ -1,30 +0,0 @@
-#pragma once
-
-#include <test/includes.hpp>
-
-namespace test {
-namespace ndarray_basic {
-void test_calc_size_from_shape_normal() {
-    // Test shapes with normal values
-    BEGIN_TEST();
-
-    int32_t shape[4] = {2, 3, 5, 7};
-    assert_values_match(
-        210, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
-}
-
-void test_calc_size_from_shape_has_zero() {
-    // Test shapes with 0 in them
-    BEGIN_TEST();
-
-    int32_t shape[4] = {2, 0, 5, 7};
-    assert_values_match(
-        0, ndarray::basic::util::calc_size_from_shape<int32_t>(4, shape));
-}
-
-void run() {
-    test_calc_size_from_shape_normal();
-    test_calc_size_from_shape_has_zero();
-}
-}  // namespace ndarray_basic
-}  // namespace test

nac3core/irrt/test/test_ndarray_indexing.hpp

@@ -1,220 +0,0 @@
-#pragma once
-
-#include <test/includes.hpp>
-
-namespace test {
-namespace ndarray_indexing {
-void test_normal_1() {
-    /*
-        Reference Python code:
-        ```python
-        ndarray = np.arange(12, dtype=np.float64).reshape((3, 4));
-        # array([[ 0.,  1.,  2.,  3.],
-        #        [ 4.,  5.,  6.,  7.],
-        #        [ 8.,  9., 10., 11.]])
-
-        dst_ndarray = ndarray[-2:, 1::2]
-        # array([[ 5.,  7.],
-        #        [ 9., 11.]])
-
-        assert dst_ndarray.shape == (2, 2)
-        assert dst_ndarray.strides == (32, 16)
-        assert dst_ndarray[0, 0] == 5.0
-        assert dst_ndarray[0, 1] == 7.0
-        assert dst_ndarray[1, 0] == 9.0
-        assert dst_ndarray[1, 1] == 11.0
-        ```
-    */
-    BEGIN_TEST();
-
-    // Prepare src_ndarray
-    double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0,  5.0,
-                           6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
-    int32_t src_itemsize = sizeof(double);
-    const int32_t src_ndims = 2;
-    int32_t src_shape[src_ndims] = {3, 4};
-    int32_t src_strides[src_ndims] = {};
-    NDArray<int32_t> src_ndarray = {.data = (uint8_t *)src_data,
-                                    .itemsize = src_itemsize,
-                                    .ndims = src_ndims,
-                                    .shape = src_shape,
-                                    .strides = src_strides};
-    ndarray::basic::set_strides_by_shape(&src_ndarray);
-
-    // Prepare dst_ndarray
-    const int32_t dst_ndims = 2;
-    int32_t dst_shape[dst_ndims] = {999, 999};    // Empty values
-    int32_t dst_strides[dst_ndims] = {999, 999};  // Empty values
-    NDArray<int32_t> dst_ndarray = {.data = nullptr,
-                                    .ndims = dst_ndims,
-                                    .shape = dst_shape,
-                                    .strides = dst_strides};
-
-    // Create the subscripts in `ndarray[-2::, 1::2]`
-    UserSlice subscript_1;
-    subscript_1.set_start(-2);
-
-    UserSlice subscript_2;
-    subscript_2.set_start(1);
-    subscript_2.set_step(2);
-
-    const int32_t num_indexes = 2;
-    NDIndex indexes[num_indexes] = {
-        {.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_1},
-        {.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
-
-    ErrorContext errctx = create_testing_errctx();
-    ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
-                             &dst_ndarray);
-    assert_errctx_no_error(&errctx);
-
-    int32_t expected_shape[dst_ndims] = {2, 2};
-    int32_t expected_strides[dst_ndims] = {32, 16};
-
-    assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
-    assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
-
-    // dst_ndarray[0, 0]
-    assert_values_match(5.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int32_t[dst_ndims]){0, 0})));
-    // dst_ndarray[0, 1]
-    assert_values_match(7.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int32_t[dst_ndims]){0, 1})));
-    // dst_ndarray[1, 0]
-    assert_values_match(9.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int32_t[dst_ndims]){1, 0})));
-    // dst_ndarray[1, 1]
-    assert_values_match(11.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int32_t[dst_ndims]){1, 1})));
-}
-
-void test_normal_2() {
-    /*
-        ```python
-        ndarray = np.arange(12, dtype=np.float64).reshape((3, 4))
-        # array([[ 0.,  1.,  2.,  3.],
-        #        [ 4.,  5.,  6.,  7.],
-        #        [ 8.,  9., 10., 11.]])
-
-        dst_ndarray = ndarray[2, ::-2]
-        # array([11.,  9.])
-
-        assert dst_ndarray.shape == (2,)
-        assert dst_ndarray.strides == (-16,)
-        assert dst_ndarray[0] == 11.0
-        assert dst_ndarray[1] == 9.0
-        ```
-    */
-    BEGIN_TEST();
-
-    // Prepare src_ndarray
-    double src_data[12] = {0.0, 1.0, 2.0, 3.0, 4.0,  5.0,
-                           6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
-    int32_t src_itemsize = sizeof(double);
-    const int32_t src_ndims = 2;
-    int32_t src_shape[src_ndims] = {3, 4};
-    int32_t src_strides[src_ndims] = {};
-    NDArray<int32_t> src_ndarray = {.data = (uint8_t *)src_data,
-                                    .itemsize = src_itemsize,
-                                    .ndims = src_ndims,
-                                    .shape = src_shape,
-                                    .strides = src_strides};
-    ndarray::basic::set_strides_by_shape(&src_ndarray);
-
-    // Prepare dst_ndarray
-    const int32_t dst_ndims = 1;
-    int32_t dst_shape[dst_ndims] = {999};    // Empty values
-    int32_t dst_strides[dst_ndims] = {999};  // Empty values
-    NDArray<int32_t> dst_ndarray = {.data = nullptr,
-                                    .ndims = dst_ndims,
-                                    .shape = dst_shape,
-                                    .strides = dst_strides};
-
-    // Create the subscripts in `ndarray[2, ::-2]`
-    int32_t subscript_1 = 2;
-
-    UserSlice subscript_2;
-    subscript_2.set_step(-2);
-
-    const int32_t num_indexes = 2;
-    NDIndex indexes[num_indexes] = {
-        {.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
-        {.type = ND_INDEX_TYPE_SLICE, .data = (uint8_t *)&subscript_2}};
-
-    ErrorContext errctx = create_testing_errctx();
-    ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
-                             &dst_ndarray);
-    assert_errctx_no_error(&errctx);
-
-    int32_t expected_shape[dst_ndims] = {2};
-    int32_t expected_strides[dst_ndims] = {-16};
-    assert_arrays_match(dst_ndims, expected_shape, dst_ndarray.shape);
-    assert_arrays_match(dst_ndims, expected_strides, dst_ndarray.strides);
-
-    assert_values_match(11.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int32_t[dst_ndims]){0})));
-    assert_values_match(9.0,
-                        *((double *)ndarray::basic::get_pelement_by_indices(
-                            &dst_ndarray, (int32_t[dst_ndims]){1})));
-}
-
-void test_index_subscript_out_of_bounds() {
-    /*
-        # Consider `my_array`
-
-        print(my_array.shape)
-        # (4, 5, 6)
-
-        my_array[2, 100] # error, index subscript at axis 1 is out of bounds
-    */
-    BEGIN_TEST();
-
-    // Prepare src_ndarray
-    const int32_t src_ndims = 2;
-    int32_t src_shape[src_ndims] = {3, 4};
-    int32_t src_strides[src_ndims] = {};
-    NDArray<int32_t> src_ndarray = {
-        .data = (uint8_t *)nullptr,  // placeholder, we wouldn't access it
-        .itemsize = sizeof(double),  // placeholder
-        .ndims = src_ndims,
-        .shape = src_shape,
-        .strides = src_strides};
-    ndarray::basic::set_strides_by_shape(&src_ndarray);
-
-    // Create the subscripts in `my_array[2, 100]`
-    int32_t subscript_1 = 2;
-    int32_t subscript_2 = 100;
-
-    const int32_t num_indexes = 2;
-    NDIndex indexes[num_indexes] = {
-        {.type = ND_INDEX_TYPE_SINGLE_ELEMENT, .data = (uint8_t *)&subscript_1},
-        {.type = ND_INDEX_TYPE_SINGLE_ELEMENT,
-         .data = (uint8_t *)&subscript_2}};
-
-    // Prepare dst_ndarray
-    const int32_t dst_ndims = 0;
-    int32_t dst_shape[dst_ndims] = {};
-    int32_t dst_strides[dst_ndims] = {};
-    NDArray<int32_t> dst_ndarray = {.data = nullptr,  // placehloder
-                                    .ndims = dst_ndims,
-                                    .shape = dst_shape,
-                                    .strides = dst_strides};
-
-    ErrorContext errctx = create_testing_errctx();
-    ndarray::indexing::index(&errctx, num_indexes, indexes, &src_ndarray,
-                             &dst_ndarray);
-    assert_errctx_has_error(&errctx, errctx.error_ids->index_error);
-}
-
-void run() {
-    test_normal_1();
-    test_normal_2();
-    test_index_subscript_out_of_bounds();
-}
-}  // namespace ndarray_indexing
-}  // namespace test

nac3core/irrt/test/test_slice.hpp

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

nac3core/irrt/test/util.hpp

@@ -1,179 +0,0 @@
-#pragma once
-
-#include <cstdio>
-#include <cstdlib>
-
-template <class T>
-void print_value(const T& value);
-
-template <>
-void print_value(const int8_t& value) {
-    printf("%d", value);
-}
-
-template <>
-void print_value(const int32_t& value) {
-    printf("%d", value);
-}
-
-template <>
-void print_value(const uint8_t& value) {
-    printf("%u", value);
-}
-
-template <>
-void print_value(const uint32_t& value) {
-    printf("%u", value);
-}
-
-template <>
-void print_value(const float& value) {
-    printf("%f", value);
-}
-
-template <>
-void print_value(const double& value) {
-    printf("%f", value);
-}
-
-void __begin_test(const char* function_name, const char* file, int line) {
-    printf("######### Running %s @ %s:%d\n", function_name, file, line);
-}
-
-#define BEGIN_TEST() __begin_test(__FUNCTION__, __FILE__, __LINE__)
-
-void test_fail() {
-    printf("[!] Test failed. Exiting with status code 1.\n");
-    exit(1);
-}
-
-template <typename T>
-void debug_print_array(int len, const T* as) {
-    printf("[");
-    for (int i = 0; i < len; i++) {
-        if (i != 0) printf(", ");
-        print_value(as[i]);
-    }
-    printf("]");
-}
-
-void print_assertion_passed(const char* file, int line) {
-    printf("[*] Assertion passed on %s:%d\n", file, line);
-}
-
-void print_assertion_failed(const char* file, int line) {
-    printf("[!] Assertion failed on %s:%d\n", file, line);
-}
-
-void __assert_true(const char* file, int line, bool cond) {
-    if (cond) {
-        print_assertion_passed(file, line);
-    } else {
-        print_assertion_failed(file, line);
-        test_fail();
-    }
-}
-
-#define assert_true(cond) __assert_true(__FILE__, __LINE__, cond)
-
-template <typename T>
-void __assert_arrays_match(const char* file, int line, int len,
-                           const T* expected, const T* got) {
-    if (arrays_match(len, expected, got)) {
-        print_assertion_passed(file, line);
-    } else {
-        print_assertion_failed(file, line);
-        printf("Expect = ");
-        debug_print_array(len, expected);
-        printf("\n");
-        printf("   Got = ");
-        debug_print_array(len, got);
-        printf("\n");
-        test_fail();
-    }
-}
-
-#define assert_arrays_match(len, expected, got) \
-    __assert_arrays_match(__FILE__, __LINE__, len, expected, got)
-
-template <typename T>
-void __assert_values_match(const char* file, int line, T expected, T got) {
-    if (expected == got) {
-        print_assertion_passed(file, line);
-    } else {
-        print_assertion_failed(file, line);
-        printf("Expect = ");
-        print_value(expected);
-        printf("\n");
-        printf("   Got = ");
-        print_value(got);
-        printf("\n");
-        test_fail();
-    }
-}
-
-#define assert_values_match(expected, got) \
-    __assert_values_match(__FILE__, __LINE__, expected, got)
-
-// A fake set of ErrorIds for testing only
-const ErrorIds TEST_ERROR_IDS = {
-    .index_error = 0,
-    .value_error = 1,
-    .assertion_error = 2,
-    .runtime_error = 3,
-    .type_error = 4,
-};
-
-ErrorContext create_testing_errctx() {
-    // Everything is global so it is fine to directly return a struct
-    // ErrorContext
-    ErrorContext errctx;
-    errctx.initialize(&TEST_ERROR_IDS);
-    return errctx;
-}
-
-void print_errctx_content(ErrorContext* errctx) {
-    if (errctx->has_error()) {
-        printf(
-            "(Error ID %d): %s ... where param1 = %ld, param2 = %ld, param3 = "
-            "%ld\n",
-            errctx->error_id, errctx->message_template, errctx->param1,
-            errctx->param2, errctx->param3);
-    } else {
-        printf("<no error>\n");
-    }
-}
-
-void __assert_errctx_no_error(const char* file, int line,
-                              ErrorContext* errctx) {
-    if (errctx->has_error()) {
-        print_assertion_failed(file, line);
-        printf("Expecting no error but caught the following:\n\n");
-        print_errctx_content(errctx);
-        test_fail();
-    }
-}
-
-#define assert_errctx_no_error(errctx) \
-    __assert_errctx_no_error(__FILE__, __LINE__, errctx)
-
-void __assert_errctx_has_error(const char* file, int line, ErrorContext* errctx,
-                               ExceptionId expected_error_id) {
-    if (errctx->has_error()) {
-        if (errctx->error_id != expected_error_id) {
-            print_assertion_failed(file, line);
-            printf(
-                "Expecting error id %d but got error id %d. Error caught:\n\n",
-                expected_error_id, errctx->error_id);
-            print_errctx_content(errctx);
-            test_fail();
-        }
-    } else {
-        print_assertion_failed(file, line);
-        printf("Expecting an error, but there is none.");
-        test_fail();
-    }
-}
-
-#define assert_errctx_has_error(errctx, expected_error_id) \
-    __assert_errctx_has_error(__FILE__, __LINE__, errctx, expected_error_id)

nac3core/src/codegen/classes.rs

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

nac3core/src/codegen/generator.rs

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

nac3core/src/codegen/irrt/error_context.rs

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

nac3core/src/codegen/irrt/mod.rs

@@ -1,17 +1,13 @@
-use crate::typecheck::typedef::Type;
+use crate::{typecheck::typedef::Type, util::SizeVariant};
 
-mod error_context;
-pub mod ndarray;
-pub mod slice;
 mod test;
-mod util;
 
 use super::{
     classes::{
-        ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue,
+        ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, NDArrayValue, NpArrayType,
-        TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
+        NpArrayValue, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
     },
-    llvm_intrinsics, CodeGenContext, CodeGenerator, Int, Int64, NIntModel,
+    llvm_intrinsics, CodeGenContext, CodeGenerator,
 };
 use crate::codegen::classes::TypedArrayLikeAccessor;
 use crate::codegen::stmt::gen_for_callback_incrementing;
@@ -20,8 +16,8 @@ use inkwell::{
     context::Context,
     memory_buffer::MemoryBuffer,
     module::Module,
-    types::{BasicTypeEnum, IntType},
+    types::{BasicType, BasicTypeEnum, FunctionType, IntType, PointerType},
-    values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
+    values::{BasicValueEnum, CallSiteValue, FloatValue, FunctionValue, IntValue},
     AddressSpace, IntPredicate,
 };
 use itertools::Either;
@@ -420,27 +416,14 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
         .unwrap();
     let cond_1 = ctx.builder.build_and(dest_step_eq_one, src_slt_dest, "slice_cond_1").unwrap();
     let cond = ctx.builder.build_or(src_eq_dest, cond_1, "slice_cond").unwrap();
-
-    // TODO: Temporary fix. Rewrite `list_slice_assignment` later
-    // Exception params should have been i64
-    {
-        let param_model = NIntModel(Int64);
-
-        let src_slice_len =
-            Int::from(src_slice_len).s_extend_or_bit_cast(ctx, param_model, "src_slice_len");
-        let dest_slice_len =
-            Int::from(dest_slice_len).s_extend_or_bit_cast(ctx, param_model, "dest_slice_len");
-        let dest_idx_2 = Int::from(dest_idx.2).s_extend_or_bit_cast(ctx, param_model, "dest_idx_2");
-
     ctx.make_assert(
         generator,
         cond,
         "0:ValueError",
         "attempt to assign sequence of size {0} to slice of size {1} with step size {2}",
-            [Some(src_slice_len.value), Some(dest_slice_len.value), Some(dest_idx_2.value)],
+        [Some(src_slice_len), Some(dest_slice_len), Some(dest_idx.2)],
         ctx.current_loc,
     );
-    }
 
     let new_len = {
         let args = vec![
@@ -946,3 +929,63 @@ pub fn call_ndarray_calc_broadcast_index<
         Box::new(|_, v| v.into()),
     )
 }
+
+fn get_size_variant<'ctx>(ty: IntType<'ctx>) -> SizeVariant {
+    match ty.get_bit_width() {
+        32 => SizeVariant::Bits32,
+        64 => SizeVariant::Bits64,
+        _ => unreachable!("Unsupported int type bit width {}", ty.get_bit_width()),
+    }
+}
+
+fn get_size_type_dependent_function<'ctx, BuildFuncTypeFn>(
+    ctx: &CodeGenContext<'ctx, '_>,
+    size_type: IntType<'ctx>,
+    base_name: &str,
+    build_func_type: BuildFuncTypeFn,
+) -> FunctionValue<'ctx>
+where
+    BuildFuncTypeFn: Fn() -> FunctionType<'ctx>,
+{
+    let mut fn_name = base_name.to_owned();
+    match get_size_variant(size_type) {
+        SizeVariant::Bits32 => {
+            // The original fn_name is the correct function name
+        }
+        SizeVariant::Bits64 => {
+            // Append "64" at the end, this is the naming convention for 64-bit
+            fn_name.push_str("64");
+        }
+    }
+
+    // Get (or declare then get if does not exist) the corresponding function
+    ctx.module.get_function(&fn_name).unwrap_or_else(|| {
+        let fn_type = build_func_type();
+        ctx.module.add_function(&fn_name, fn_type, None)
+    })
+}
+
+fn get_ndarray_struct_ptr<'ctx>(ctx: &'ctx Context, size_type: IntType<'ctx>) -> PointerType<'ctx> {
+    let i8_type = ctx.i8_type();
+
+    let ndarray_ty = NpArrayType { size_type, elem_type: i8_type.as_basic_type_enum() };
+    let struct_ty = ndarray_ty.fields().whole_struct.as_struct_type(ctx);
+    struct_ty.ptr_type(AddressSpace::default())
+}
+
+pub fn call_nac3_ndarray_size<'ctx>(
+    ctx: &CodeGenContext<'ctx, '_>,
+    ndarray: NpArrayValue<'ctx>,
+) -> IntValue<'ctx> {
+    let size_type = ndarray.ty.size_type;
+    let function = get_size_type_dependent_function(ctx, size_type, "__nac3_ndarray_size", || {
+        size_type.fn_type(&[get_ndarray_struct_ptr(ctx.ctx, size_type).into()], false)
+    });
+
+    ctx.builder
+        .build_call(function, &[ndarray.ptr.into()], "size")
+        .unwrap()
+        .try_as_basic_value()
+        .unwrap_left()
+        .into_int_value()
+}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

nac3core/src/codegen/irrt/slice.rs

@@ -1,84 +0,0 @@
-use crate::codegen::{model::*, CodeGenContext};
-
-// nac3core's slicing index/length values are always int32_t
-pub type SliceIndex = Int32;
-
-#[derive(Debug, Clone)]
-pub struct UserSliceFields {
-    pub start_defined: Field<BoolModel>,
-    pub start: Field<NIntModel<SliceIndex>>,
-    pub stop_defined: Field<BoolModel>,
-    pub stop: Field<NIntModel<SliceIndex>>,
-    pub step_defined: Field<BoolModel>,
-    pub step: Field<NIntModel<SliceIndex>>,
-}
-
-#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
-pub struct UserSlice;
-
-impl<'ctx> StructKind<'ctx> for UserSlice {
-    type Fields = UserSliceFields;
-
-    fn struct_name(&self) -> &'static str {
-        "UserSlice"
-    }
-
-    fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
-        Self::Fields {
-            start_defined: builder.add_field_auto("start_defined"),
-            start: builder.add_field_auto("start"),
-            stop_defined: builder.add_field_auto("stop_defined"),
-            stop: builder.add_field_auto("stop"),
-            step_defined: builder.add_field_auto("step_defined"),
-            step: builder.add_field_auto("step"),
-        }
-    }
-}
-
-#[derive(Debug, Clone)]
-pub struct RustUserSlice<'ctx> {
-    pub start: Option<NInt<'ctx, SliceIndex>>,
-    pub stop: Option<NInt<'ctx, SliceIndex>>,
-    pub step: Option<NInt<'ctx, SliceIndex>>,
-}
-
-impl<'ctx> RustUserSlice<'ctx> {
-    // Set the values of an LLVM UserSlice
-    // in the format of Python's `slice()`
-    pub fn write_to_user_slice(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        dst_slice_ptr: Pointer<'ctx, StructModel<UserSlice>>,
-    ) {
-        // TODO: make this neater, with a helper lambda?
-
-        let bool_model = BoolModel::default();
-
-        let false_ = bool_model.constant(ctx.ctx, 0);
-        let true_ = bool_model.constant(ctx.ctx, 1);
-
-        match self.start {
-            Some(start) => {
-                dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, true_);
-                dst_slice_ptr.gep(ctx, |f| f.start).store(ctx, start);
-            }
-            None => dst_slice_ptr.gep(ctx, |f| f.start_defined).store(ctx, false_),
-        }
-
-        match self.stop {
-            Some(stop) => {
-                dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, true_);
-                dst_slice_ptr.gep(ctx, |f| f.stop).store(ctx, stop);
-            }
-            None => dst_slice_ptr.gep(ctx, |f| f.stop_defined).store(ctx, false_),
-        }
-
-        match self.step {
-            Some(step) => {
-                dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, true_);
-                dst_slice_ptr.gep(ctx, |f| f.step).store(ctx, step);
-            }
-            None => dst_slice_ptr.gep(ctx, |f| f.step_defined).store(ctx, false_),
-        }
-    }
-}

nac3core/src/codegen/irrt/util.rs

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

nac3core/src/codegen/mod.rs

@@ -1,7 +1,7 @@
 use crate::{
-    codegen::classes::{ListType, ProxyType, RangeType},
+    codegen::classes::{ListType, NDArrayType, ProxyType, RangeType},
     symbol_resolver::{StaticValue, SymbolResolver},
-    toplevel::{helper::PrimDef, TopLevelContext, TopLevelDef},
+    toplevel::{helper::PrimDef, numpy::unpack_ndarray_var_tys, TopLevelContext, TopLevelDef},
     typecheck::{
         type_inferencer::{CodeLocation, PrimitiveStore},
         typedef::{CallId, FuncArg, Type, TypeEnum, Unifier},
@@ -24,7 +24,6 @@ use inkwell::{
     AddressSpace, IntPredicate, OptimizationLevel,
 };
 use itertools::Itertools;
-use model::*;
 use nac3parser::ast::{Location, Stmt, StrRef};
 use parking_lot::{Condvar, Mutex};
 use std::collections::{HashMap, HashSet};
@@ -33,7 +32,6 @@ use std::sync::{
     Arc,
 };
 use std::thread;
-use structs::{cslice::CSlice, exception::Exception, ndarray::NpArray};
 
 pub mod builtin_fns;
 pub mod classes;
@@ -43,15 +41,11 @@ pub mod extern_fns;
 mod generator;
 pub mod irrt;
 pub mod llvm_intrinsics;
-pub mod model;
 pub mod numpy;
-pub mod numpy_new;
 pub mod stmt;
-pub mod structs;
 
 #[cfg(test)]
 mod test;
-pub mod util;
 
 use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
 pub use generator::{CodeGenerator, DefaultCodeGenerator};
@@ -164,11 +158,11 @@ pub struct CodeGenContext<'ctx, 'a> {
     pub registry: &'a WorkerRegistry,
 
     /// Cache for constant strings.
-    pub const_strings: HashMap<String, Struct<'ctx, CSlice<'ctx>>>,
+    pub const_strings: HashMap<String, BasicValueEnum<'ctx>>,
 
     /// [`BasicBlock`] containing all `alloca` statements for the current function.
     pub init_bb: BasicBlock<'ctx>,
-    pub exception_val: Option<Pointer<'ctx, StructModel<Exception<'ctx>>>>,
+    pub exception_val: Option<PointerValue<'ctx>>,
 
     /// The header and exit basic blocks of a loop in this context. See
     /// <https://llvm.org/docs/LoopTerminology.html> for explanation of these terminology.
@@ -477,9 +471,12 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                         }
 
                         TObj { obj_id, .. } if *obj_id == PrimDef::NDArray.id() => {
-                            let sizet = generator.get_sizet(ctx);
+                            let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
-                            let pndarray_model = PointerModel(StructModel(NpArray { sizet }));
+                            let element_type = get_llvm_type(
-                            pndarray_model.get_type(ctx).into()
+                                ctx, module, generator, unifier, top_level, type_cache, dtype,
+                            );
+
+                            NDArrayType::new(generator, ctx, element_type).as_base_type().into()
                         }
 
                         _ => unreachable!(
@@ -649,24 +646,47 @@ pub fn gen_func_impl<
         ..primitives
     };
 
-    let sizet = generator.get_sizet(context);
+    let mut type_cache: HashMap<_, _> = [
-    let cslice_type = StructModel(CSlice { sizet });
-    let pexception_type = PointerModel(StructModel(Exception { sizet }));
-
-    let mut type_cache: HashMap<_, BasicTypeEnum<'ctx>> = [
         (primitives.int32, context.i32_type().into()),
         (primitives.int64, context.i64_type().into()),
         (primitives.uint32, context.i32_type().into()),
         (primitives.uint64, context.i64_type().into()),
         (primitives.float, context.f64_type().into()),
         (primitives.bool, context.i8_type().into()),
-        (primitives.str, cslice_type.get_type(context).into()),
+        (primitives.str, {
+            let name = "str";
+            match module.get_struct_type(name) {
+                None => {
+                    let str_type = context.opaque_struct_type("str");
+                    let fields = [
+                        context.i8_type().ptr_type(AddressSpace::default()).into(),
+                        generator.get_size_type(context).into(),
+                    ];
+                    str_type.set_body(&fields, false);
+                    str_type.into()
+                }
+                Some(t) => t.as_basic_type_enum(),
+            }
+        }),
         (primitives.range, RangeType::new(context).as_base_type().into()),
-        (primitives.exception, pexception_type.get_type(context).into()),
+        (primitives.exception, {
+            let name = "Exception";
+            if let Some(t) = module.get_struct_type(name) {
+                t.ptr_type(AddressSpace::default()).as_basic_type_enum()
+            } else {
+                let exception = context.opaque_struct_type("Exception");
+                let int32 = context.i32_type().into();
+                let int64 = context.i64_type().into();
+                let str_ty = module.get_struct_type("str").unwrap().as_basic_type_enum();
+                let fields = [int32, str_ty, int32, int32, str_ty, str_ty, int64, int64, int64];
+                exception.set_body(&fields, false);
+                exception.ptr_type(AddressSpace::default()).as_basic_type_enum()
+            }
+        }),
     ]
-    .into_iter()
+    .iter()
+    .copied()
     .collect();
-
     // NOTE: special handling of option cannot use this type cache since it contains type var,
     // handled inside get_llvm_type instead
 

nac3core/src/codegen/model/core.rs

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

nac3core/src/codegen/model/fixed_int.rs

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

nac3core/src/codegen/model/function_builder.rs

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

nac3core/src/codegen/model/int.rs

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

nac3core/src/codegen/model/int_util.rs

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

nac3core/src/codegen/model/mod.rs

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

nac3core/src/codegen/model/opaque.rs

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

nac3core/src/codegen/model/pointer.rs

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

nac3core/src/codegen/model/slice.rs

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

nac3core/src/codegen/model/structure.rs

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

nac3core/src/codegen/numpy_new/factory.rs

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

nac3core/src/codegen/numpy_new/mod.rs

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

nac3core/src/codegen/numpy_new/view.rs

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

nac3core/src/codegen/stmt.rs

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

nac3core/src/codegen/structs/cslice.rs

@@ -1,50 +0,0 @@
-use crate::codegen::{model::*, CodeGenContext};
-
-/// Fields of [`CSlice<'ctx>`].
-pub struct CSliceFields<'ctx> {
-    /// Pointer to the data.
-    pub base: Field<PointerModel<ByteModel>>,
-    /// Number of bytes of the data.
-    pub len: Field<SizeTModel<'ctx>>,
-}
-
-/// See <https://crates.io/crates/cslice>.
-///
-/// Additionally, see <https://github.com/m-labs/artiq/blob/b0d2705c385f64b6e6711c1726cd9178f40b598e/artiq/firmware/libeh/eh_artiq.rs>)
-/// for ARTIQ-specific notes.
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub struct CSlice<'ctx> {
-    pub sizet: SizeTModel<'ctx>,
-}
-
-impl<'ctx> StructKind<'ctx> for CSlice<'ctx> {
-    type Fields = CSliceFields<'ctx>;
-
-    fn struct_name(&self) -> &'static str {
-        "CSlice"
-    }
-
-    fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
-        Self::Fields {
-            base: builder.add_field_auto("content"),
-            len: builder.add_field("length", self.sizet),
-        }
-    }
-}
-
-impl<'ctx> StructModel<CSlice<'ctx>> {
-    /// Create a [`CSlice`].
-    ///
-    /// `base` and `len` must be LLVM global constants.
-    pub fn create_const(
-        &self,
-        ctx: &CodeGenContext<'ctx, '_>,
-        base: Pointer<'ctx, ByteModel>,
-        len: SizeT<'ctx>,
-    ) -> Struct<'ctx, CSlice<'ctx>> {
-        let value = self
-            .get_struct_type(ctx.ctx)
-            .const_named_struct(&[base.value.into(), len.value.into()]);
-        self.believe_value(value)
-    }
-}

nac3core/src/codegen/structs/exception.rs

@@ -1,78 +0,0 @@
-use crate::codegen::model::*;
-
-use super::cslice::CSlice;
-
-/// The LLVM int type of an Exception ID.
-pub type ExceptionId = Int32;
-
-/// Fields of [`Exception<'ctx>`]
-///
-/// The definition came from `pub struct Exception<'a>` in
-/// <https://github.com/m-labs/artiq/blob/master/artiq/firmware/libeh/eh_artiq.rs>.
-pub struct ExceptionFields<'ctx> {
-    /// nac3core's ID of the exception
-    pub exception_id: Field<NIntModel<ExceptionId>>,
-    /// The name of the file this `Exception` was raised in.
-    pub filename: Field<StructModel<CSlice<'ctx>>>,
-    /// The line number in the file this `Exception` was raised in.
-    pub line: Field<NIntModel<Int32>>,
-    /// The column number in the file this `Exception` was raised in.
-    pub column: Field<NIntModel<Int32>>,
-    /// The name of the Python function this `Exception` was raised in.
-    pub function_name: Field<StructModel<CSlice<'ctx>>>,
-    /// The message of this Exception.
-    ///
-    /// The message can optionally contain integer parameters `{0}`, `{1}`, and `{2}` in its string,
-    /// where they will be substituted by `params[0]`, `params[1]`, and `params[2]` respectively (as `int64_t`s).
-    /// Here is an example:
-    ///
-    /// ```ignore
-    /// "Index {0} is out of bounds! List only has {1} element(s)."
-    /// ```
-    ///
-    /// In this case, `params[0]` and `params[1]` must be specified, and `params[2]` is ***unused***.
-    /// Having only 3 parameters is a constraint in ARTIQ.
-    pub message: Field<StructModel<CSlice<'ctx>>>,
-    pub params: [Field<NIntModel<Int64>>; 3],
-}
-
-/// nac3core & ARTIQ's Exception
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
-pub struct Exception<'ctx> {
-    /// The `SizeT` type of this string.
-    pub sizet: SizeTModel<'ctx>,
-}
-
-impl<'ctx> StructKind<'ctx> for Exception<'ctx> {
-    type Fields = ExceptionFields<'ctx>;
-
-    fn struct_name(&self) -> &'static str {
-        "Exception"
-    }
-
-    fn build_fields(&self, builder: &mut FieldBuilder<'ctx>) -> Self::Fields {
-        let str = StructModel(CSlice { sizet: self.sizet });
-
-        let exception_id = builder.add_field_auto("exception_id");
-        let file_name = builder.add_field("file_name", str);
-        let line = builder.add_field_auto("line");
-        let column = builder.add_field_auto("column");
-        let function_name = builder.add_field("function_name", str);
-        let message = builder.add_field("message", str);
-        let params = [
-            builder.add_field_auto("param0"),
-            builder.add_field_auto("param1"),
-            builder.add_field_auto("param2"),
-        ];
-
-        Self::Fields {
-            exception_id,
-            filename: file_name,
-            line,
-            column,
-            function_name,
-            message,
-            params,
-        }
-    }
-}

nac3core/src/codegen/structs/list.rs

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

nac3core/src/codegen/structs/mod.rs

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

nac3core/src/codegen/structs/ndarray.rs

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

nac3core/src/codegen/util/array_writer.rs

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

nac3core/src/codegen/util/control_flow.rs

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

nac3core/src/codegen/util/mod.rs

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

nac3core/src/codegen/util/shape.rs

@@ -1,144 +0,0 @@
-use inkwell::values::BasicValueEnum;
-
-use crate::{
-    codegen::{
-        classes::{ListValue, UntypedArrayLikeAccessor},
-        model::*,
-        stmt::gen_for_callback_incrementing,
-        CodeGenContext, CodeGenerator,
-    },
-    typecheck::typedef::{Type, TypeEnum},
-};
-
-use super::array_writer::ArrayWriter;
-
-/// TODO: UPDATE DOCUMENTATION
-/// LLVM-typed implementation for generating a [`ArrayWriter`] that sets a list of ints.
-///
-/// * `elem_ty` - The element type of the `NDArray`.
-/// * `shape` - The `shape` parameter used to construct the `NDArray`.
-///
-/// ### Notes on `shape`
-///
-/// Just like numpy, the `shape` argument can be:
-///   1. A list of `int32`;   e.g., `np.empty([600, 800, 3])`
-///   2. A tuple of `int32`;  e.g., `np.empty((600, 800, 3))`
-///   3. A scalar `int32`;     e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
-///
-/// See also [`typecheck::type_inferencer::fold_numpy_function_call_shape_argument`] to
-/// learn how `shape` gets from being a Python user expression to here.
-pub fn parse_input_shape_arg<'ctx, G>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    shape: BasicValueEnum<'ctx>,
-    shape_ty: Type,
-) -> ArrayWriter<'ctx, G, SizeTModel<'ctx>, SizeTModel<'ctx>>
-where
-    G: CodeGenerator + ?Sized,
-{
-    let sizet = generator.get_sizet(ctx.ctx);
-
-    match &*ctx.unifier.get_ty(shape_ty) {
-        TypeEnum::TObj { obj_id, .. }
-            if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
-        {
-            // 1. A list of ints; e.g., `np.empty([600, 800, 3])`
-
-            // A list has to be a PointerValue
-            let shape_list = ListValue::from_ptr_val(shape.into_pointer_value(), sizet.0, None);
-
-            // Create `ArrayWriter`
-            let ndims =
-                sizet.review_value(ctx.ctx, shape_list.load_size(ctx, Some("count"))).unwrap();
-            ArrayWriter {
-                count: ndims,
-                write: Box::new(move |generator, ctx, dst_array| {
-                    // Basically iterate through the list and write to `dst_slice` accordingly
-                    let init_val = sizet.constant(ctx.ctx, 0).value;
-                    let max_val = (ndims.value, false);
-                    let incr_val = sizet.constant(ctx.ctx, 1).value;
-                    gen_for_callback_incrementing(
-                        generator,
-                        ctx,
-                        init_val,
-                        max_val,
-                        |generator, ctx, _hooks, axis| {
-                            let axis = sizet.review_value(ctx.ctx, axis).unwrap();
-
-                            // TODO: Remove ProxyValue ListValue
-
-                            // Get the dimension at `axis`
-                            let dim: Int<'ctx> = shape_list
-                                .data()
-                                .get(ctx, generator, &axis.value, None)
-                                .into_int_value()
-                                .into();
-
-                            let dim = dim.s_extend_or_bit_cast(ctx, sizet, "dim_casted");
-                            dst_array.ix(generator, ctx, axis, "dim").store(ctx, dim);
-                            Ok(())
-                        },
-                        incr_val,
-                    )
-                }),
-            }
-        }
-        TypeEnum::TTuple { ty: tuple_types } => {
-            // 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
-
-            // Get the length/size of the tuple, which also happens to be the value of `ndims`.
-            let ndims = tuple_types.len();
-
-            // A tuple has to be a StructValue
-            // Read [`codegen::expr::gen_expr`] to see how `nac3core` translates a Python tuple into LLVM.
-            let shape_tuple = shape.into_struct_value();
-
-            ArrayWriter {
-                count: sizet.constant(ctx.ctx, ndims as u64),
-                write: Box::new(move |generator, ctx, dst_array| {
-                    for axis in 0..ndims {
-                        // Get the dimension at `axis`
-                        let dim: Int<'ctx> = ctx
-                            .builder
-                            .build_extract_value(
-                                shape_tuple,
-                                axis as u32,
-                                format!("dim{axis}").as_str(),
-                            )
-                            .unwrap()
-                            .into_int_value()
-                            .into();
-
-                        let dim = dim.s_extend_or_bit_cast(ctx, sizet, "dim_casted");
-                        dst_array
-                            .ix(generator, ctx, sizet.constant(ctx.ctx, axis as u64), "dim")
-                            .store(ctx, dim);
-                    }
-                    Ok(())
-                }),
-            }
-        }
-        TypeEnum::TObj { obj_id, .. }
-            if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
-        {
-            // 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
-
-            // The value has to be an integer
-            let shape_int: Int<'ctx> = shape.into_int_value().into();
-
-            ArrayWriter {
-                count: sizet.constant(ctx.ctx, 1),
-                write: Box::new(move |generator, ctx, dst_array| {
-                    // Cast `shape_int` to SizeT
-                    let dim = shape_int.s_extend_or_bit_cast(ctx, sizet, "dim_casted");
-
-                    // Set shape[0] = shape_int
-                    dst_array.ix(generator, ctx, sizet.constant(ctx.ctx, 0), "dim").store(ctx, dim);
-
-                    Ok(())
-                }),
-            }
-        }
-        _ => panic!("parse_input_shape_arg encountered unknown type"),
-    }
-}

nac3core/src/lib.rs

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

nac3core/src/toplevel/builtins.rs

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

nac3core/src/toplevel/composer.rs

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

nac3core/src/toplevel/helper.rs

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

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap

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

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap

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

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap

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

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap

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

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap

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

nac3core/src/toplevel/type_annotation.rs

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

nac3core/src/typecheck/type_inferencer/mod.rs

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

nac3standalone/demo/demo.c

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

nac3standalone/demo/interpret_demo.py

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

nac3standalone/demo/src/ndarray.py

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

nac3standalone/src/main.rs

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

nix/windows/default.nix

@@ -81,6 +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 {