[meta] Apply clippy suggestions

This is needed as ndarray.{shapes,strides} are ArrayValues, and so a GEP
operation is required to convert them into pointers to their first
elements.

Cargo.lock

@@ -126,9 +126,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
 
 [[package]]
 name = "cc"
-version = "1.2.4"
+version = "1.2.7"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9157bbaa6b165880c27a4293a474c91cdcf265cc68cc829bf10be0964a391caf"
+checksum = "a012a0df96dd6d06ba9a1b29d6402d1a5d77c6befd2566afdc26e10603dc93d7"
 dependencies = [
  "shlex",
 ]
@@ -170,7 +170,7 @@ dependencies = [
  "heck 0.5.0",
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -187,14 +187,14 @@ checksum = "5b63caa9aa9397e2d9480a9b13673856c78d8ac123288526c37d7839f2a86990"
 
 [[package]]
 name = "console"
-version = "0.15.8"
+version = "0.15.10"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0e1f83fc076bd6dd27517eacdf25fef6c4dfe5f1d7448bafaaf3a26f13b5e4eb"
+checksum = "ea3c6ecd8059b57859df5c69830340ed3c41d30e3da0c1cbed90a96ac853041b"
 dependencies = [
  "encode_unicode",
- "lazy_static",
  "libc",
- "windows-sys 0.52.0",
+ "once_cell",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -221,18 +221,18 @@ dependencies = [
 
 [[package]]
 name = "crossbeam-channel"
-version = "0.5.13"
+version = "0.5.14"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "33480d6946193aa8033910124896ca395333cae7e2d1113d1fef6c3272217df2"
+checksum = "06ba6d68e24814cb8de6bb986db8222d3a027d15872cabc0d18817bc3c0e4471"
 dependencies = [
  "crossbeam-utils",
 ]
 
 [[package]]
 name = "crossbeam-deque"
-version = "0.8.5"
+version = "0.8.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "613f8cc01fe9cf1a3eb3d7f488fd2fa8388403e97039e2f73692932e291a770d"
+checksum = "9dd111b7b7f7d55b72c0a6ae361660ee5853c9af73f70c3c2ef6858b950e2e51"
 dependencies = [
  "crossbeam-epoch",
  "crossbeam-utils",
@@ -249,18 +249,18 @@ dependencies = [
 
 [[package]]
 name = "crossbeam-queue"
-version = "0.3.11"
+version = "0.3.12"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "df0346b5d5e76ac2fe4e327c5fd1118d6be7c51dfb18f9b7922923f287471e35"
+checksum = "0f58bbc28f91df819d0aa2a2c00cd19754769c2fad90579b3592b1c9ba7a3115"
 dependencies = [
  "crossbeam-utils",
 ]
 
 [[package]]
 name = "crossbeam-utils"
-version = "0.8.20"
+version = "0.8.21"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "22ec99545bb0ed0ea7bb9b8e1e9122ea386ff8a48c0922e43f36d45ab09e0e80"
+checksum = "d0a5c400df2834b80a4c3327b3aad3a4c4cd4de0629063962b03235697506a28"
 
 [[package]]
 name = "crypto-common"
@@ -305,9 +305,9 @@ dependencies = [
 
 [[package]]
 name = "encode_unicode"
-version = "0.3.6"
+version = "1.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a357d28ed41a50f9c765dbfe56cbc04a64e53e5fc58ba79fbc34c10ef3df831f"
+checksum = "34aa73646ffb006b8f5147f3dc182bd4bcb190227ce861fc4a4844bf8e3cb2c0"
 
 [[package]]
 name = "equivalent"
@@ -378,9 +378,9 @@ dependencies = [
 
 [[package]]
 name = "glob"
-version = "0.3.1"
+version = "0.3.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d2fabcfbdc87f4758337ca535fb41a6d701b65693ce38287d856d1674551ec9b"
+checksum = "a8d1add55171497b4705a648c6b583acafb01d58050a51727785f0b2c8e0a2b2"
 
 [[package]]
 name = "hashbrown"
@@ -417,11 +417,11 @@ checksum = "2304e00983f87ffb38b55b444b5e3b60a884b5d30c0fca7d82fe33449bbe55ea"
 
 [[package]]
 name = "home"
-version = "0.5.9"
+version = "0.5.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e3d1354bf6b7235cb4a0576c2619fd4ed18183f689b12b006a0ee7329eeff9a5"
+checksum = "589533453244b0995c858700322199b2becb13b627df2851f64a2775d024abcf"
 dependencies = [
- "windows-sys 0.52.0",
+ "windows-sys 0.59.0",
 ]
 
 [[package]]
@@ -472,7 +472,7 @@ checksum = "9dd28cfd4cfba665d47d31c08a6ba637eed16770abca2eccbbc3ca831fef1e44"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -559,9 +559,9 @@ checksum = "bbd2bcb4c963f2ddae06a2efc7e9f3591312473c50c6685e1f298068316e66fe"
 
 [[package]]
 name = "libc"
-version = "0.2.168"
+version = "0.2.169"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5aaeb2981e0606ca11d79718f8bb01164f1d6ed75080182d3abf017e6d244b6d"
+checksum = "b5aba8db14291edd000dfcc4d620c7ebfb122c613afb886ca8803fa4e128a20a"
 
 [[package]]
 name = "libloading"
@@ -678,7 +678,7 @@ dependencies = [
  "proc-macro-error",
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
  "trybuild",
 ]
 
@@ -799,7 +799,7 @@ dependencies = [
  "phf_shared 0.11.2",
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -927,7 +927,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-macros-backend",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -940,14 +940,14 @@ dependencies = [
  "proc-macro2",
  "pyo3-build-config",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
 name = "quote"
-version = "1.0.37"
+version = "1.0.38"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b5b9d34b8991d19d98081b46eacdd8eb58c6f2b201139f7c5f643cc155a633af"
+checksum = "0e4dccaaaf89514f546c693ddc140f729f958c247918a13380cccc6078391acc"
 dependencies = [
  "proc-macro2",
 ]
@@ -1062,9 +1062,9 @@ dependencies = [
 
 [[package]]
 name = "rustversion"
-version = "1.0.18"
+version = "1.0.19"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0e819f2bc632f285be6d7cd36e25940d45b2391dd6d9b939e79de557f7014248"
+checksum = "f7c45b9784283f1b2e7fb61b42047c2fd678ef0960d4f6f1eba131594cc369d4"
 
 [[package]]
 name = "ryu"
@@ -1095,29 +1095,29 @@ checksum = "3cb6eb87a131f756572d7fb904f6e7b68633f09cca868c5df1c4b8d1a694bbba"
 
 [[package]]
 name = "serde"
-version = "1.0.216"
+version = "1.0.217"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0b9781016e935a97e8beecf0c933758c97a5520d32930e460142b4cd80c6338e"
+checksum = "02fc4265df13d6fa1d00ecff087228cc0a2b5f3c0e87e258d8b94a156e984c70"
 dependencies = [
  "serde_derive",
 ]
 
 [[package]]
 name = "serde_derive"
-version = "1.0.216"
+version = "1.0.217"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "46f859dbbf73865c6627ed570e78961cd3ac92407a2d117204c49232485da55e"
+checksum = "5a9bf7cf98d04a2b28aead066b7496853d4779c9cc183c440dbac457641e19a0"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
 name = "serde_json"
-version = "1.0.133"
+version = "1.0.134"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c7fceb2473b9166b2294ef05efcb65a3db80803f0b03ef86a5fc88a2b85ee377"
+checksum = "d00f4175c42ee48b15416f6193a959ba3a0d67fc699a0db9ad12df9f83991c7d"
 dependencies = [
  "itoa",
  "memchr",
@@ -1226,7 +1226,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustversion",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -1242,9 +1242,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.90"
+version = "2.0.94"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "919d3b74a5dd0ccd15aeb8f93e7006bd9e14c295087c9896a110f490752bcf31"
+checksum = "987bc0be1cdea8b10216bd06e2ca407d40b9543468fafd3ddfb02f36e77f71f3"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -1265,12 +1265,13 @@ checksum = "42a4d50cdb458045afc8131fd91b64904da29548bcb63c7236e0844936c13078"
 
 [[package]]
 name = "tempfile"
-version = "3.14.0"
+version = "3.15.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "28cce251fcbc87fac86a866eeb0d6c2d536fc16d06f184bb61aeae11aa4cee0c"
+checksum = "9a8a559c81686f576e8cd0290cd2a24a2a9ad80c98b3478856500fcbd7acd704"
 dependencies = [
  "cfg-if",
  "fastrand",
+ "getrandom",
  "once_cell",
  "rustix",
  "windows-sys 0.59.0",
@@ -1278,9 +1279,9 @@ dependencies = [
 
 [[package]]
 name = "term"
-version = "1.0.0"
+version = "1.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4df4175de05129f31b80458c6df371a15e7fc3fd367272e6bf938e5c351c7ea0"
+checksum = "a3bb6001afcea98122260987f8b7b5da969ecad46dbf0b5453702f776b491a41"
 dependencies = [
  "home",
  "windows-sys 0.52.0",
@@ -1325,7 +1326,7 @@ checksum = "4fee6c4efc90059e10f81e6d42c60a18f76588c3d74cb83a0b242a2b6c7504c1"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]
 
 [[package]]
@@ -1603,9 +1604,9 @@ checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec"
 
 [[package]]
 name = "winnow"
-version = "0.6.20"
+version = "0.6.22"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "36c1fec1a2bb5866f07c25f68c26e565c4c200aebb96d7e55710c19d3e8ac49b"
+checksum = "39281189af81c07ec09db316b302a3e67bf9bd7cbf6c820b50e35fee9c2fa980"
 dependencies = [
  "memchr",
 ]
@@ -1637,5 +1638,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.90",
+ "syn 2.0.94",
 ]

flake.lock

@@ -2,11 +2,11 @@
   "nodes": {
     "nixpkgs": {
       "locked": {
-        "lastModified": 1733940404,
-        "narHash": "sha256-Pj39hSoUA86ZePPF/UXiYHHM7hMIkios8TYG29kQT4g=",
+        "lastModified": 1735834308,
+        "narHash": "sha256-dklw3AXr3OGO4/XT1Tu3Xz9n/we8GctZZ75ZWVqAVhk=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "5d67ea6b4b63378b9c13be21e2ec9d1afc921713",
+        "rev": "6df24922a1400241dae323af55f30e4318a6ca65",
         "type": "github"
       },
       "original": {

nac3artiq/demo/mangling_test.py

@@ -1,37 +0,0 @@
-from min_artiq import *
-from numpy import int32
-
-@nac3
-class NameManglingTest:
-    core: KernelInvariant[Core]
-    __var1: int32
-    __var2__: int32
-    __: int32
-
-    def __init__(self):
-        self.core = Core()
-        self.__var1 = 42
-        self.__var2__ = 10
-        self.__ = 99
-
-    @rpc
-    def get_var1(self) -> int32:
-        return self.__var1
-
-    @rpc
-    def get_var2(self) -> int32:
-        return self.__var2__ 
-
-    @rpc
-    def get_var3(self) -> int32:
-        return self.__ 
-
-    @kernel
-    def run(self):
-        assert self.get_var1() == 42 
-        assert self.get_var2() == 10 
-        assert self.get_var3() == 99  
-
-
-if __name__ == "__main__":
-    NameManglingTest().run()

nac3artiq/demo/numpy_primitives_decay.py

@@ -0,0 +1,29 @@
+from min_artiq import *
+import numpy
+from numpy import int32
+
+
+@nac3
+class NumpyBoolDecay:
+    core: KernelInvariant[Core]
+    np_true: KernelInvariant[bool]
+    np_false: KernelInvariant[bool]
+    np_int: KernelInvariant[int32]
+    np_float: KernelInvariant[float]
+    np_str: KernelInvariant[str]
+
+    def __init__(self):
+        self.core = Core()
+        self.np_true = numpy.True_
+        self.np_false = numpy.False_
+        self.np_int = numpy.int32(0)
+        self.np_float = numpy.float64(0.0)
+        self.np_str = numpy.str_("")
+
+    @kernel
+    def run(self):
+        pass
+
+
+if __name__ == "__main__":
+    NumpyBoolDecay().run()

nac3artiq/src/codegen.rs

@@ -162,7 +162,7 @@ impl<'a> ArtiqCodeGenerator<'a> {
     }
 }
 
-impl<'b> CodeGenerator for ArtiqCodeGenerator<'b> {
+impl CodeGenerator for ArtiqCodeGenerator<'_> {
     fn get_name(&self) -> &str {
         &self.name
     }
@@ -464,7 +464,7 @@ fn format_rpc_arg<'ctx>(
             let (elem_ty, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, arg_ty);
             let ndims = extract_ndims(&ctx.unifier, ndims);
             let dtype = ctx.get_llvm_type(generator, elem_ty);
-            let ndarray = NDArrayType::new(generator, ctx.ctx, dtype, Some(ndims))
+            let ndarray = NDArrayType::new(generator, ctx.ctx, dtype, ndims)
                 .map_value(arg.into_pointer_value(), None);
 
             let ndims = llvm_usize.const_int(ndims, false);
@@ -597,7 +597,7 @@ fn format_rpc_ret<'ctx>(
             let (dtype, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, ret_ty);
             let dtype_llvm = ctx.get_llvm_type(generator, dtype);
             let ndims = extract_ndims(&ctx.unifier, ndims);
-            let ndarray = NDArrayType::new(generator, ctx.ctx, dtype_llvm, Some(ndims))
+            let ndarray = NDArrayType::new(generator, ctx.ctx, dtype_llvm, ndims)
                 .construct_uninitialized(generator, ctx, None);
 
             // NOTE: Current content of `ndarray`:
@@ -1505,7 +1505,7 @@ pub fn call_rtio_log_impl<'ctx>(
 /// Generates a call to `core_log`.
 pub fn gen_core_log<'ctx>(
     ctx: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    obj: Option<&(Type, ValueEnum<'ctx>)>,
     fun: (&FunSignature, DefinitionId),
     args: &[(Option<StrRef>, ValueEnum<'ctx>)],
     generator: &mut dyn CodeGenerator,
@@ -1522,7 +1522,7 @@ pub fn gen_core_log<'ctx>(
 /// Generates a call to `rtio_log`.
 pub fn gen_rtio_log<'ctx>(
     ctx: &mut CodeGenContext<'ctx, '_>,
-    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    obj: Option<&(Type, ValueEnum<'ctx>)>,
     fun: (&FunSignature, DefinitionId),
     args: &[(Option<StrRef>, ValueEnum<'ctx>)],
     generator: &mut dyn CodeGenerator,

nac3artiq/src/lib.rs

@@ -330,7 +330,7 @@ impl Nac3 {
                         vars: into_var_map([arg_ty]),
                     },
                     Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
-                        gen_core_log(ctx, &obj, fun, &args, generator)?;
+                        gen_core_log(ctx, obj.as_ref(), fun, &args, generator)?;
 
                         Ok(None)
                     }))),
@@ -360,7 +360,7 @@ impl Nac3 {
                         vars: into_var_map([arg_ty]),
                     },
                     Arc::new(GenCall::new(Box::new(move |ctx, obj, fun, args, generator| {
-                        gen_rtio_log(ctx, &obj, fun, &args, generator)?;
+                        gen_rtio_log(ctx, obj.as_ref(), fun, &args, generator)?;
 
                         Ok(None)
                     }))),

nac3artiq/src/symbol_resolver.rs

@@ -931,10 +931,13 @@ impl InnerResolver {
                         |_| Ok(Ok(extracted_ty)),
                     )
                 } else if unifier.unioned(extracted_ty, primitives.bool) {
-                    obj.extract::<bool>().map_or_else(
-                        |_| Ok(Err(format!("{obj} is not in the range of bool"))),
-                        |_| Ok(Ok(extracted_ty)),
-                    )
+                    if obj.extract::<bool>().is_ok()
+                        || obj.call_method("__bool__", (), None)?.extract::<bool>().is_ok()
+                    {
+                        Ok(Ok(extracted_ty))
+                    } else {
+                        Ok(Err(format!("{obj} is not in the range of bool")))
+                    }
                 } else if unifier.unioned(extracted_ty, primitives.float) {
                     obj.extract::<f64>().map_or_else(
                         |_| Ok(Err(format!("{obj} is not in the range of float64"))),
@@ -947,28 +950,6 @@ impl InnerResolver {
         }
     }
 
-    pub fn get_class_name(&self, field_id: StrRef, defs: &[Arc<RwLock<TopLevelDef>>]) -> Option<String> {
-
-        let field_to_val = self.field_to_val.read();
-        let class_id = field_to_val.iter().find_map(|(&(id, ref name), _)| {
-            if *name == field_id {
-                Some(id)
-            } else {
-                None
-            }
-        })?;
-
-        let id_to_def = self.id_to_def.read();
-        let def_id = id_to_def.get(&StrRef::from(class_id.to_string()))?;
-
-        let definition = defs[def_id.0].read();
-        if let TopLevelDef::Class { name, .. } = &*definition {
-            Some(name.to_string()) // return class name
-        } else {
-            None // then not a class
-        }
-    }
-    
     pub fn get_obj_value<'ctx>(
         &self,
         py: Python,
@@ -996,10 +977,14 @@ impl InnerResolver {
             let val: u64 = obj.extract().unwrap();
             self.id_to_primitive.write().insert(id, PrimitiveValue::U64(val));
             Ok(Some(ctx.ctx.i64_type().const_int(val, false).into()))
-        } else if ty_id == self.primitive_ids.bool || ty_id == self.primitive_ids.np_bool_ {
+        } else if ty_id == self.primitive_ids.bool {
             let val: bool = obj.extract().unwrap();
             self.id_to_primitive.write().insert(id, PrimitiveValue::Bool(val));
             Ok(Some(ctx.ctx.i8_type().const_int(u64::from(val), false).into()))
+        } else if ty_id == self.primitive_ids.np_bool_ {
+            let val: bool = obj.call_method("__bool__", (), None)?.extract().unwrap();
+            self.id_to_primitive.write().insert(id, PrimitiveValue::Bool(val));
+            Ok(Some(ctx.ctx.i8_type().const_int(u64::from(val), false).into()))
         } else if ty_id == self.primitive_ids.string || ty_id == self.primitive_ids.np_str_ {
             let val: String = obj.extract().unwrap();
             self.id_to_primitive.write().insert(id, PrimitiveValue::Str(val.clone()));
@@ -1129,7 +1114,7 @@ impl InnerResolver {
                 self.global_value_ids.write().insert(id, obj.into());
             }
 
-            let ndims = llvm_ndarray.ndims().unwrap();
+            let ndims = llvm_ndarray.ndims();
 
             // Obtain the shape of the ndarray
             let shape_tuple: &PyTuple = obj.getattr("shape")?.downcast()?;
@@ -1146,7 +1131,10 @@ impl InnerResolver {
                             super::CompileError::new_err(format!("Error getting element {i}: {e}"))
                         })?
                         .unwrap();
-                    let value = value.into_int_value();
+                    let value = ctx
+                        .builder
+                        .build_int_z_extend(value.into_int_value(), llvm_usize, "")
+                        .unwrap();
                     Ok(value)
                 })
                 .collect::<Result<Vec<_>, PyErr>>()?;
@@ -1225,8 +1213,16 @@ impl InnerResolver {
             data_global.set_initializer(&data);
 
             // Get the constant itemsize.
-            let itemsize = dtype.size_of().unwrap();
-            let itemsize = itemsize.get_zero_extended_constant().unwrap();
+            //
+            // NOTE: dtype.size_of() may return a non-constant, where `TargetData::get_store_size`
+            // will always return a constant size.
+            let itemsize = ctx
+                .registry
+                .llvm_options
+                .create_target_machine()
+                .map(|tm| tm.get_target_data().get_store_size(&dtype))
+                .unwrap();
+            assert_ne!(itemsize, 0);
 
             // Create the strides needed for ndarray.strides
             let strides = make_contiguous_strides(itemsize, ndims, &shape_u64s);
@@ -1236,7 +1232,7 @@ impl InnerResolver {
 
             // create a global for ndarray.strides and initialize it
             let strides_global = ctx.module.add_global(
-                llvm_i8.array_type(ndims as u32),
+                llvm_usize.array_type(ndims as u32),
                 Some(AddressSpace::default()),
                 &format!("${id_str}.strides"),
             );
@@ -1252,9 +1248,30 @@ impl InnerResolver {
 
             let ndarray_ndims = llvm_usize.const_int(ndims, false);
 
+            // calling as_pointer_value on shape and strides returns [i64 x ndims]*
+            // convert into i64* to conform with expected layout of ndarray
+
             let ndarray_shape = shape_global.as_pointer_value();
+            let ndarray_shape = unsafe {
+                ctx.builder
+                    .build_in_bounds_gep(
+                        ndarray_shape,
+                        &[llvm_usize.const_zero(), llvm_usize.const_zero()],
+                        "",
+                    )
+                    .unwrap()
+            };
 
             let ndarray_strides = strides_global.as_pointer_value();
+            let ndarray_strides = unsafe {
+                ctx.builder
+                    .build_in_bounds_gep(
+                        ndarray_strides,
+                        &[llvm_usize.const_zero(), llvm_usize.const_zero()],
+                        "",
+                    )
+                    .unwrap()
+            };
 
             let ndarray = llvm_ndarray
                 .as_base_type()
@@ -1435,9 +1452,12 @@ impl InnerResolver {
         } else if ty_id == self.primitive_ids.uint64 {
             let val: u64 = obj.extract()?;
             Ok(SymbolValue::U64(val))
-        } else if ty_id == self.primitive_ids.bool || ty_id == self.primitive_ids.np_bool_ {
+        } else if ty_id == self.primitive_ids.bool {
             let val: bool = obj.extract()?;
             Ok(SymbolValue::Bool(val))
+        } else if ty_id == self.primitive_ids.np_bool_ {
+            let val: bool = obj.call_method("__bool__", (), None)?.extract()?;
+            Ok(SymbolValue::Bool(val))
         } else if ty_id == self.primitive_ids.string || ty_id == self.primitive_ids.np_str_ {
             let val: String = obj.extract()?;
             Ok(SymbolValue::Str(val))
@@ -1535,25 +1555,12 @@ impl SymbolResolver for Resolver {
     fn get_symbol_value<'ctx>(
         &self,
         id: StrRef,
-        ctx: &mut CodeGenContext<'ctx, '_>,
+        _: &mut CodeGenContext<'ctx, '_>,
         _: &mut dyn CodeGenerator,
     ) -> Option<ValueEnum<'ctx>> {
-        let (resolved_id, _is_dunder) = if id.to_string().starts_with("__") && !id.to_string().ends_with("__") && !id.to_string().contains('.') {
-            let inner_resolver = &self.0;
-
-            if let Some(class_name) = inner_resolver.get_class_name(id, &ctx.top_level.definitions.read()) {
-                let stripped_class_name = class_name.trim_start_matches('_');
-                let mangled_id: StrRef = format!("_{}{}", stripped_class_name, id).into();
-                (mangled_id, true)
-            } else {
-                (id, false)
-            }
-        } else {
-            (id, false)
-        };
         let sym_value = {
             let id_to_val = self.0.id_to_pyval.read();
-            id_to_val.get(&resolved_id).cloned()
+            id_to_val.get(&id).cloned()
         }
         .or_else(|| {
             Python::with_gil(|py| -> PyResult<Option<(u64, PyObject)>> {
@@ -1562,14 +1569,14 @@ impl SymbolResolver for Resolver {
                 let members: &PyDict = obj.getattr("__dict__").unwrap().downcast().unwrap();
                 for (key, val) in members {
                     let key: &str = key.extract()?;
-                    if key == resolved_id.to_string() {
-                        let pyid = self.0.helper.id_fn.call1(py, (val,))?.extract(py)?;
-                        sym_value = Some((pyid, val.extract()?));
+                    if key == id.to_string() {
+                        let id = self.0.helper.id_fn.call1(py, (val,))?.extract(py)?;
+                        sym_value = Some((id, val.extract()?));
                         break;
                     }
                 }
                 if let Some((pyid, val)) = &sym_value {
-                    self.0.id_to_pyval.write().insert(resolved_id, (*pyid, val.clone()));
+                    self.0.id_to_pyval.write().insert(id, (*pyid, val.clone()));
                 }
                 Ok(sym_value)
             })

nac3core/irrt/irrt.cpp

@@ -1,10 +1,15 @@
 #include "irrt/exception.hpp"
 #include "irrt/list.hpp"
 #include "irrt/math.hpp"
-#include "irrt/ndarray.hpp"
 #include "irrt/range.hpp"
 #include "irrt/slice.hpp"
+#include "irrt/string.hpp"
 #include "irrt/ndarray/basic.hpp"
 #include "irrt/ndarray/def.hpp"
 #include "irrt/ndarray/iter.hpp"
 #include "irrt/ndarray/indexing.hpp"
+#include "irrt/ndarray/array.hpp"
+#include "irrt/ndarray/reshape.hpp"
+#include "irrt/ndarray/broadcast.hpp"
+#include "irrt/ndarray/transpose.hpp"
+#include "irrt/ndarray/matmul.hpp"

nac3core/irrt/irrt/int_types.hpp

@@ -21,7 +21,5 @@ using uint64_t = unsigned _ExtInt(64);
 
 #endif
 
-// NDArray indices are always `uint32_t`.
-using NDIndexInt = uint32_t;
 // The type of an index or a value describing the length of a range/slice is always `int32_t`.
 using SliceIndex = int32_t;

nac3core/irrt/irrt/list.hpp

@@ -2,6 +2,21 @@
 
 #include "irrt/int_types.hpp"
 #include "irrt/math_util.hpp"
+#include "irrt/slice.hpp"
+
+namespace {
+/**
+ * @brief A list in NAC3.
+ *
+ * The `items` field is opaque. You must rely on external contexts to
+ * know how to interpret it.
+ */
+template<typename SizeT>
+struct List {
+    uint8_t* items;
+    SizeT len;
+};
+}  // namespace
 
 extern "C" {
 // Handle list assignment and dropping part of the list when

nac3core/irrt/irrt/math.hpp

@@ -1,5 +1,7 @@
 #pragma once
 
+#include "irrt/int_types.hpp"
+
 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

nac3core/irrt/irrt/ndarray.hpp

@@ -1,151 +0,0 @@
-#pragma once
-
-#include "irrt/int_types.hpp"
-
-// TODO: To be deleted since NDArray with strides is done.
-
-namespace {
-template<typename SizeT>
-SizeT __nac3_ndarray_calc_size_impl(const SizeT* list_data, SizeT list_len, SizeT begin_idx, SizeT end_idx) {
-    __builtin_assume(end_idx <= list_len);
-
-    SizeT num_elems = 1;
-    for (SizeT i = begin_idx; i < end_idx; ++i) {
-        SizeT val = list_data[i];
-        __builtin_assume(val > 0);
-        num_elems *= val;
-    }
-    return num_elems;
-}
-
-template<typename SizeT>
-void __nac3_ndarray_calc_nd_indices_impl(SizeT index, const SizeT* dims, SizeT num_dims, 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" {
-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);
-}
-}  // namespace

nac3core/irrt/irrt/ndarray/array.hpp

@@ -0,0 +1,132 @@
+#pragma once
+
+#include "irrt/debug.hpp"
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/list.hpp"
+#include "irrt/ndarray/basic.hpp"
+#include "irrt/ndarray/def.hpp"
+
+namespace {
+namespace ndarray::array {
+/**
+ * @brief In the context of `np.array(<list>)`, deduce the ndarray's shape produced by `<list>` and raise
+ * an exception if there is anything wrong with `<shape>` (e.g., inconsistent dimensions `np.array([[1.0, 2.0],
+ * [3.0]])`)
+ *
+ * If this function finds no issues with `<list>`, the deduced shape is written to `shape`. The caller has the
+ * responsibility to allocate `[SizeT; ndims]` for `shape`. The caller must also initialize `shape` with `-1`s because
+ * of implementation details.
+ */
+template<typename SizeT>
+void set_and_validate_list_shape_helper(SizeT axis, List<SizeT>* list, SizeT ndims, SizeT* shape) {
+    if (shape[axis] == -1) {
+        // Dimension is unspecified. Set it.
+        shape[axis] = list->len;
+    } else {
+        // Dimension is specified. Check.
+        if (shape[axis] != list->len) {
+            // Mismatch, throw an error.
+            // NOTE: NumPy's error message is more complex and needs more PARAMS to display.
+            raise_exception(SizeT, EXN_VALUE_ERROR,
+                            "The requested array has an inhomogenous shape "
+                            "after {0} dimension(s).",
+                            axis, shape[axis], list->len);
+        }
+    }
+
+    if (axis + 1 == ndims) {
+        // `list` has type `list[ItemType]`
+        // Do nothing
+    } else {
+        // `list` has type `list[list[...]]`
+        List<SizeT>** lists = (List<SizeT>**)(list->items);
+        for (SizeT i = 0; i < list->len; i++) {
+            set_and_validate_list_shape_helper<SizeT>(axis + 1, lists[i], ndims, shape);
+        }
+    }
+}
+
+/**
+ * @brief See `set_and_validate_list_shape_helper`.
+ */
+template<typename SizeT>
+void set_and_validate_list_shape(List<SizeT>* list, SizeT ndims, SizeT* shape) {
+    for (SizeT axis = 0; axis < ndims; axis++) {
+        shape[axis] = -1;  // Sentinel to say this dimension is unspecified.
+    }
+    set_and_validate_list_shape_helper<SizeT>(0, list, ndims, shape);
+}
+
+/**
+ * @brief In the context of `np.array(<list>)`, copied the contents stored in `list` to `ndarray`.
+ *
+ * `list` is assumed to be "legal". (i.e., no inconsistent dimensions)
+ *
+ * # Notes on `ndarray`
+ * The caller is responsible for allocating space for `ndarray`.
+ * Here is what this function expects from `ndarray` when called:
+ *   - `ndarray->data` has to be allocated, contiguous, and may contain uninitialized values.
+ *   - `ndarray->itemsize` has to be initialized.
+ *   - `ndarray->ndims` has to be initialized.
+ *   - `ndarray->shape` has to be initialized.
+ *   - `ndarray->strides` is ignored, but note that `ndarray->data` is contiguous.
+ * When this function call ends:
+ *   - `ndarray->data` is written with contents from `<list>`.
+ */
+template<typename SizeT>
+void write_list_to_array_helper(SizeT axis, SizeT* index, List<SizeT>* list, NDArray<SizeT>* ndarray) {
+    debug_assert_eq(SizeT, list->len, ndarray->shape[axis]);
+    if (IRRT_DEBUG_ASSERT_BOOL) {
+        if (!ndarray::basic::is_c_contiguous(ndarray)) {
+            raise_debug_assert(SizeT, "ndarray is not C-contiguous", ndarray->strides[0], ndarray->strides[1],
+                               NO_PARAM);
+        }
+    }
+
+    if (axis + 1 == ndarray->ndims) {
+        // `list` has type `list[scalar]`
+        // `ndarray` is contiguous, so we can do this, and this is fast.
+        uint8_t* dst = static_cast<uint8_t*>(ndarray->data) + (ndarray->itemsize * (*index));
+        __builtin_memcpy(dst, list->items, ndarray->itemsize * list->len);
+        *index += list->len;
+    } else {
+        // `list` has type `list[list[...]]`
+        List<SizeT>** lists = (List<SizeT>**)(list->items);
+
+        for (SizeT i = 0; i < list->len; i++) {
+            write_list_to_array_helper<SizeT>(axis + 1, index, lists[i], ndarray);
+        }
+    }
+}
+
+/**
+ * @brief See `write_list_to_array_helper`.
+ */
+template<typename SizeT>
+void write_list_to_array(List<SizeT>* list, NDArray<SizeT>* ndarray) {
+    SizeT index = 0;
+    write_list_to_array_helper<SizeT>((SizeT)0, &index, list, ndarray);
+}
+}  // namespace ndarray::array
+}  // namespace
+
+extern "C" {
+using namespace ndarray::array;
+
+void __nac3_ndarray_array_set_and_validate_list_shape(List<int32_t>* list, int32_t ndims, int32_t* shape) {
+    set_and_validate_list_shape(list, ndims, shape);
+}
+
+void __nac3_ndarray_array_set_and_validate_list_shape64(List<int64_t>* list, int64_t ndims, int64_t* shape) {
+    set_and_validate_list_shape(list, ndims, shape);
+}
+
+void __nac3_ndarray_array_write_list_to_array(List<int32_t>* list, NDArray<int32_t>* ndarray) {
+    write_list_to_array(list, ndarray);
+}
+
+void __nac3_ndarray_array_write_list_to_array64(List<int64_t>* list, NDArray<int64_t>* ndarray) {
+    write_list_to_array(list, ndarray);
+}
+}

nac3core/irrt/irrt/ndarray/basic.hpp

@@ -6,8 +6,7 @@
 #include "irrt/ndarray/def.hpp"
 
 namespace {
-namespace ndarray {
-namespace basic {
+namespace ndarray::basic {
 /**
  * @brief Assert that `shape` does not contain negative dimensions.
  *
@@ -247,8 +246,7 @@ void copy_data(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
         ndarray::basic::set_pelement_value(dst_ndarray, dst_element, src_element);
     }
 }
-}  // namespace basic
-}  // namespace ndarray
+}  // namespace ndarray::basic
 }  // namespace
 
 extern "C" {

nac3core/irrt/irrt/ndarray/broadcast.hpp

@@ -0,0 +1,165 @@
+#pragma once
+
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/def.hpp"
+#include "irrt/slice.hpp"
+
+namespace {
+template<typename SizeT>
+struct ShapeEntry {
+    SizeT ndims;
+    SizeT* shape;
+};
+}  // namespace
+
+namespace {
+namespace ndarray::broadcast {
+/**
+ * @brief Return true if `src_shape` can broadcast to `dst_shape`.
+ *
+ * See https://numpy.org/doc/stable/user/basics.broadcasting.html
+ */
+template<typename SizeT>
+bool can_broadcast_shape_to(SizeT target_ndims, const SizeT* target_shape, SizeT src_ndims, const SizeT* src_shape) {
+    if (src_ndims > target_ndims) {
+        return false;
+    }
+
+    for (SizeT i = 0; i < src_ndims; i++) {
+        SizeT target_dim = target_shape[target_ndims - i - 1];
+        SizeT src_dim = src_shape[src_ndims - i - 1];
+        if (!(src_dim == 1 || target_dim == src_dim)) {
+            return false;
+        }
+    }
+    return true;
+}
+
+/**
+ * @brief Performs `np.broadcast_shapes(<shapes>)`
+ *
+ * @param num_shapes Number of entries in `shapes`
+ * @param shapes The list of shape to do `np.broadcast_shapes` on.
+ * @param dst_ndims The length of `dst_shape`.
+ *   `dst_ndims` must be `max([shape.ndims for shape in shapes])`, but the caller has to calculate it/provide it.
+ *   for this function since they should already know in order to allocate `dst_shape` in the first place.
+ * @param dst_shape The resulting shape. Must be pre-allocated by the caller. This function calculate the result
+ *   of `np.broadcast_shapes` and write it here.
+ */
+template<typename SizeT>
+void broadcast_shapes(SizeT num_shapes, const ShapeEntry<SizeT>* shapes, SizeT dst_ndims, SizeT* dst_shape) {
+    for (SizeT dst_axis = 0; dst_axis < dst_ndims; dst_axis++) {
+        dst_shape[dst_axis] = 1;
+    }
+
+#ifdef IRRT_DEBUG_ASSERT
+    SizeT max_ndims_found = 0;
+#endif
+
+    for (SizeT i = 0; i < num_shapes; i++) {
+        ShapeEntry<SizeT> entry = shapes[i];
+
+        // Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
+        debug_assert(SizeT, entry.ndims <= dst_ndims);
+
+#ifdef IRRT_DEBUG_ASSERT
+        max_ndims_found = max(max_ndims_found, entry.ndims);
+#endif
+
+        for (SizeT j = 0; j < entry.ndims; j++) {
+            SizeT entry_axis = entry.ndims - j - 1;
+            SizeT dst_axis = dst_ndims - j - 1;
+
+            SizeT entry_dim = entry.shape[entry_axis];
+            SizeT dst_dim = dst_shape[dst_axis];
+
+            if (dst_dim == 1) {
+                dst_shape[dst_axis] = entry_dim;
+            } else if (entry_dim == 1 || entry_dim == dst_dim) {
+                // Do nothing
+            } else {
+                raise_exception(SizeT, EXN_VALUE_ERROR,
+                                "shape mismatch: objects cannot be broadcast "
+                                "to a single shape.",
+                                NO_PARAM, NO_PARAM, NO_PARAM);
+            }
+        }
+    }
+
+#ifdef IRRT_DEBUG_ASSERT
+    // Check pre-condition: `dst_ndims` must be `max([shape.ndims for shape in shapes])`
+    debug_assert_eq(SizeT, max_ndims_found, dst_ndims);
+#endif
+}
+
+/**
+ * @brief Perform `np.broadcast_to(<ndarray>, <target_shape>)` and appropriate assertions.
+ *
+ * This function attempts to broadcast `src_ndarray` to a new shape defined by `dst_ndarray.shape`,
+ * and return the result by modifying `dst_ndarray`.
+ *
+ * # Notes on `dst_ndarray`
+ * The caller is responsible for allocating space for the resulting ndarray.
+ * Here is what this function expects from `dst_ndarray` when called:
+ *   - `dst_ndarray->data` does not have to be initialized.
+ *   - `dst_ndarray->itemsize` does not have to be initialized.
+ *   - `dst_ndarray->ndims` must be initialized, determining the length of `dst_ndarray->shape`
+ *   - `dst_ndarray->shape` must be allocated, and must contain the desired target broadcast shape.
+ *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
+ * When this function call ends:
+ *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
+ *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
+ *   - `dst_ndarray->ndims` is unchanged.
+ *   - `dst_ndarray->shape` is unchanged.
+ *   - `dst_ndarray->strides` is updated accordingly by how ndarray broadcast_to works.
+ */
+template<typename SizeT>
+void broadcast_to(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray) {
+    if (!ndarray::broadcast::can_broadcast_shape_to(dst_ndarray->ndims, dst_ndarray->shape, src_ndarray->ndims,
+                                                    src_ndarray->shape)) {
+        raise_exception(SizeT, EXN_VALUE_ERROR, "operands could not be broadcast together", NO_PARAM, NO_PARAM,
+                        NO_PARAM);
+    }
+
+    dst_ndarray->data = src_ndarray->data;
+    dst_ndarray->itemsize = src_ndarray->itemsize;
+
+    for (SizeT i = 0; i < dst_ndarray->ndims; i++) {
+        SizeT src_axis = src_ndarray->ndims - i - 1;
+        SizeT dst_axis = dst_ndarray->ndims - i - 1;
+        if (src_axis < 0 || (src_ndarray->shape[src_axis] == 1 && dst_ndarray->shape[dst_axis] != 1)) {
+            // Freeze the steps in-place
+            dst_ndarray->strides[dst_axis] = 0;
+        } else {
+            dst_ndarray->strides[dst_axis] = src_ndarray->strides[src_axis];
+        }
+    }
+}
+}  // namespace ndarray::broadcast
+}  // namespace
+
+extern "C" {
+using namespace ndarray::broadcast;
+
+void __nac3_ndarray_broadcast_to(NDArray<int32_t>* src_ndarray, NDArray<int32_t>* dst_ndarray) {
+    broadcast_to(src_ndarray, dst_ndarray);
+}
+
+void __nac3_ndarray_broadcast_to64(NDArray<int64_t>* src_ndarray, NDArray<int64_t>* dst_ndarray) {
+    broadcast_to(src_ndarray, dst_ndarray);
+}
+
+void __nac3_ndarray_broadcast_shapes(int32_t num_shapes,
+                                     const ShapeEntry<int32_t>* shapes,
+                                     int32_t dst_ndims,
+                                     int32_t* dst_shape) {
+    broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
+}
+
+void __nac3_ndarray_broadcast_shapes64(int64_t num_shapes,
+                                       const ShapeEntry<int64_t>* shapes,
+                                       int64_t dst_ndims,
+                                       int64_t* dst_shape) {
+    broadcast_shapes(num_shapes, shapes, dst_ndims, dst_shape);
+}
+}

nac3core/irrt/irrt/ndarray/indexing.hpp

@@ -65,8 +65,7 @@ struct NDIndex {
 }  // namespace
 
 namespace {
-namespace ndarray {
-namespace indexing {
+namespace ndarray::indexing {
 /**
  * @brief Perform ndarray "basic indexing" (https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
  *
@@ -162,7 +161,8 @@ void index(SizeT num_indices, const NDIndex* indices, const NDArray<SizeT>* src_
 
             Range<int32_t> range = slice->indices_checked<SizeT>(src_ndarray->shape[src_axis]);
 
-            dst_ndarray->data = static_cast<uint8_t*>(dst_ndarray->data) + (SizeT)range.start * src_ndarray->strides[src_axis];
+            dst_ndarray->data =
+                static_cast<uint8_t*>(dst_ndarray->data) + (SizeT)range.start * src_ndarray->strides[src_axis];
             dst_ndarray->strides[dst_axis] = ((SizeT)range.step) * src_ndarray->strides[src_axis];
             dst_ndarray->shape[dst_axis] = (SizeT)range.len<SizeT>();
 
@@ -197,8 +197,7 @@ void index(SizeT num_indices, const NDIndex* indices, const NDArray<SizeT>* src_
     debug_assert_eq(SizeT, src_ndarray->ndims, src_axis);
     debug_assert_eq(SizeT, dst_ndarray->ndims, dst_axis);
 }
-}  // namespace indexing
-}  // namespace ndarray
+}  // namespace ndarray::indexing
 }  // namespace
 
 extern "C" {

nac3core/irrt/irrt/ndarray/matmul.hpp

@@ -0,0 +1,98 @@
+#pragma once
+
+#include "irrt/debug.hpp"
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/basic.hpp"
+#include "irrt/ndarray/broadcast.hpp"
+#include "irrt/ndarray/iter.hpp"
+
+// NOTE: Everything would be much easier and elegant if einsum is implemented.
+
+namespace {
+namespace ndarray::matmul {
+
+/**
+ * @brief Perform the broadcast in `np.einsum("...ij,...jk->...ik", a, b)`.
+ *
+ * Example:
+ *   Suppose      `a_shape ==      [1, 97, 4, 2]`
+ *       and      `b_shape == [99, 98,  1, 2, 5]`,
+ *
+ *   ...then  `new_a_shape == [99, 98, 97, 4, 2]`,
+ *            `new_b_shape == [99, 98, 97, 2, 5]`,
+ *      and     `dst_shape == [99, 98, 97, 4, 5]`.
+ *                             ^^^^^^^^^^  ^^^^
+ *                          (broadcasted)  (4x2 @ 2x5 => 4x5)
+ *
+ * @param a_ndims Length of `a_shape`.
+ * @param a_shape Shape of `a`.
+ * @param b_ndims Length of `b_shape`.
+ * @param b_shape Shape of `b`.
+ * @param final_ndims Should be equal to `max(a_ndims, b_ndims)`. This is the length of `new_a_shape`,
+ * `new_b_shape`, and `dst_shape` - the number of dimensions after broadcasting.
+ */
+template<typename SizeT>
+void calculate_shapes(SizeT a_ndims,
+                      SizeT* a_shape,
+                      SizeT b_ndims,
+                      SizeT* b_shape,
+                      SizeT final_ndims,
+                      SizeT* new_a_shape,
+                      SizeT* new_b_shape,
+                      SizeT* dst_shape) {
+    debug_assert(SizeT, a_ndims >= 2);
+    debug_assert(SizeT, b_ndims >= 2);
+    debug_assert_eq(SizeT, max(a_ndims, b_ndims), final_ndims);
+
+    // Check that a and b are compatible for matmul
+    if (a_shape[a_ndims - 1] != b_shape[b_ndims - 2]) {
+        // This is a custom error message. Different from NumPy.
+        raise_exception(SizeT, EXN_VALUE_ERROR, "Cannot multiply LHS (shape ?x{0}) with RHS (shape {1}x?})",
+                        a_shape[a_ndims - 1], b_shape[b_ndims - 2], NO_PARAM);
+    }
+
+    const SizeT num_entries = 2;
+    ShapeEntry<SizeT> entries[num_entries] = {{.ndims = a_ndims - 2, .shape = a_shape},
+                                              {.ndims = b_ndims - 2, .shape = b_shape}};
+
+    // TODO: Optimize this
+    ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_a_shape);
+    ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, new_b_shape);
+    ndarray::broadcast::broadcast_shapes<SizeT>(num_entries, entries, final_ndims - 2, dst_shape);
+
+    new_a_shape[final_ndims - 2] = a_shape[a_ndims - 2];
+    new_a_shape[final_ndims - 1] = a_shape[a_ndims - 1];
+    new_b_shape[final_ndims - 2] = b_shape[b_ndims - 2];
+    new_b_shape[final_ndims - 1] = b_shape[b_ndims - 1];
+    dst_shape[final_ndims - 2] = a_shape[a_ndims - 2];
+    dst_shape[final_ndims - 1] = b_shape[b_ndims - 1];
+}
+}  // namespace ndarray::matmul
+}  // namespace
+
+extern "C" {
+using namespace ndarray::matmul;
+
+void __nac3_ndarray_matmul_calculate_shapes(int32_t a_ndims,
+                                            int32_t* a_shape,
+                                            int32_t b_ndims,
+                                            int32_t* b_shape,
+                                            int32_t final_ndims,
+                                            int32_t* new_a_shape,
+                                            int32_t* new_b_shape,
+                                            int32_t* dst_shape) {
+    calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
+}
+
+void __nac3_ndarray_matmul_calculate_shapes64(int64_t a_ndims,
+                                              int64_t* a_shape,
+                                              int64_t b_ndims,
+                                              int64_t* b_shape,
+                                              int64_t final_ndims,
+                                              int64_t* new_a_shape,
+                                              int64_t* new_b_shape,
+                                              int64_t* dst_shape) {
+    calculate_shapes(a_ndims, a_shape, b_ndims, b_shape, final_ndims, new_a_shape, new_b_shape, dst_shape);
+}
+}

nac3core/irrt/irrt/ndarray/reshape.hpp

@@ -0,0 +1,97 @@
+#pragma once
+
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/def.hpp"
+
+namespace {
+namespace ndarray::reshape {
+/**
+ * @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
+ *
+ * If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
+ * modified to contain the resolved dimension.
+ *
+ * To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
+ * `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
+ *
+ * @param size The `.size` of `<ndarray>`
+ * @param new_ndims Number of elements in `new_shape`
+ * @param new_shape Target shape to reshape to
+ */
+template<typename SizeT>
+void resolve_and_check_new_shape(SizeT size, SizeT new_ndims, SizeT* new_shape) {
+    // Is there a -1 in `new_shape`?
+    bool neg1_exists = false;
+    // Location of -1, only initialized if `neg1_exists` is true
+    SizeT neg1_axis_i;
+    // The computed ndarray size of `new_shape`
+    SizeT new_size = 1;
+
+    for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
+        SizeT dim = new_shape[axis_i];
+        if (dim < 0) {
+            if (dim == -1) {
+                if (neg1_exists) {
+                    // Multiple `-1` found. Throw an error.
+                    raise_exception(SizeT, EXN_VALUE_ERROR, "can only specify one unknown dimension", NO_PARAM,
+                                    NO_PARAM, NO_PARAM);
+                } else {
+                    neg1_exists = true;
+                    neg1_axis_i = axis_i;
+                }
+            } else {
+                // TODO: What? In `np.reshape` any negative dimensions is
+                // treated like its `-1`.
+                //
+                // Try running `np.zeros((3, 4)).reshape((-999, 2))`
+                //
+                // It is not documented by numpy.
+                // Throw an error for now...
+
+                raise_exception(SizeT, EXN_VALUE_ERROR, "Found non -1 negative dimension {0} on axis {1}", dim, axis_i,
+                                NO_PARAM);
+            }
+        } else {
+            new_size *= dim;
+        }
+    }
+
+    bool can_reshape;
+    if (neg1_exists) {
+        // Let `x` be the unknown dimension
+        // Solve `x * <new_size> = <size>`
+        if (new_size == 0 && size == 0) {
+            // `x` has infinitely many solutions
+            can_reshape = false;
+        } else if (new_size == 0 && size != 0) {
+            // `x` has no solutions
+            can_reshape = false;
+        } else if (size % new_size != 0) {
+            // `x` has no integer solutions
+            can_reshape = false;
+        } else {
+            can_reshape = true;
+            new_shape[neg1_axis_i] = size / new_size;  // Resolve dimension
+        }
+    } else {
+        can_reshape = (new_size == size);
+    }
+
+    if (!can_reshape) {
+        raise_exception(SizeT, EXN_VALUE_ERROR, "cannot reshape array of size {0} into given shape", size, NO_PARAM,
+                        NO_PARAM);
+    }
+}
+}  // namespace ndarray::reshape
+}  // namespace
+
+extern "C" {
+void __nac3_ndarray_reshape_resolve_and_check_new_shape(int32_t size, int32_t new_ndims, int32_t* new_shape) {
+    ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
+}
+
+void __nac3_ndarray_reshape_resolve_and_check_new_shape64(int64_t size, int64_t new_ndims, int64_t* new_shape) {
+    ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
+}
+}

nac3core/irrt/irrt/ndarray/transpose.hpp

@@ -0,0 +1,143 @@
+#pragma once
+
+#include "irrt/debug.hpp"
+#include "irrt/exception.hpp"
+#include "irrt/int_types.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::transpose {
+/**
+ * @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
+ *
+ * Note that `np.transpose`'s `<axe>` argument is optional. If the argument
+ * is specified but the user, use this function to do assertions on it.
+ *
+ * @param ndims The number of dimensions of `<array>`
+ * @param num_axes Number of elements in `<axes>` as specified by the user.
+ *   This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
+ * @param axes The user specified `<axes>`.
+ */
+template<typename SizeT>
+void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
+    if (ndims != num_axes) {
+        raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array", NO_PARAM, NO_PARAM, NO_PARAM);
+    }
+
+    // TODO: Optimize this
+    bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
+    for (SizeT i = 0; i < ndims; i++)
+        axe_specified[i] = false;
+
+    for (SizeT i = 0; i < ndims; i++) {
+        SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
+        if (axis == -1) {
+            // TODO: numpy actually throws a `numpy.exceptions.AxisError`
+            raise_exception(SizeT, EXN_VALUE_ERROR, "axis {0} is out of bounds for array of dimension {1}", axis, ndims,
+                            NO_PARAM);
+        }
+
+        if (axe_specified[axis]) {
+            raise_exception(SizeT, EXN_VALUE_ERROR, "repeated axis in transpose", NO_PARAM, NO_PARAM, NO_PARAM);
+        }
+
+        axe_specified[axis] = true;
+    }
+}
+
+/**
+ * @brief Create a transpose view of `src_ndarray` and perform proper assertions.
+ *
+ * This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
+ * If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
+ *
+ * The transpose view created is returned by modifying `dst_ndarray`.
+ *
+ * The caller is responsible for setting up `dst_ndarray` before calling this function.
+ * Here is what this function expects from `dst_ndarray` when called:
+ *   - `dst_ndarray->data` does not have to be initialized.
+ *   - `dst_ndarray->itemsize` does not have to be initialized.
+ *   - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
+ *   - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
+ *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
+ * When this function call ends:
+ *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
+ *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
+ *   - `dst_ndarray->ndims` is unchanged
+ *   - `dst_ndarray->shape` is updated according to how `np.transpose` works
+ *   - `dst_ndarray->strides` is updated according to how `np.transpose` works
+ *
+ * @param src_ndarray The NDArray to build a transpose view on
+ * @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
+ * @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
+ * @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
+ */
+template<typename SizeT>
+void transpose(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray, SizeT num_axes, const SizeT* axes) {
+    debug_assert_eq(SizeT, src_ndarray->ndims, dst_ndarray->ndims);
+    const auto ndims = src_ndarray->ndims;
+
+    if (axes != nullptr)
+        assert_transpose_axes(ndims, num_axes, axes);
+
+    dst_ndarray->data = src_ndarray->data;
+    dst_ndarray->itemsize = src_ndarray->itemsize;
+
+    // Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
+    if (axes == nullptr) {
+        // `np.transpose(<array>, axes=None)`
+
+        /*
+         * Minor note: `np.transpose(<array>, axes=None)` is equivalent to
+         * `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
+         * is reversing the order of strides and shape.
+         *
+         * This is a fast implementation to handle this special (but very common) case.
+         */
+
+        for (SizeT axis = 0; axis < ndims; axis++) {
+            dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
+            dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
+        }
+    } else {
+        // `np.transpose(<array>, <axes>)`
+
+        // Permute strides and shape according to `axes`, while resolving negative indices in `axes`
+        for (SizeT axis = 0; axis < ndims; axis++) {
+            // `i` cannot be OUT_OF_BOUNDS because of assertions
+            SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
+
+            dst_ndarray->shape[axis] = src_ndarray->shape[i];
+            dst_ndarray->strides[axis] = src_ndarray->strides[i];
+        }
+    }
+}
+}  // namespace ndarray::transpose
+}  // namespace
+
+extern "C" {
+using namespace ndarray::transpose;
+void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
+                              NDArray<int32_t>* dst_ndarray,
+                              int32_t num_axes,
+                              const int32_t* axes) {
+    transpose(src_ndarray, dst_ndarray, num_axes, axes);
+}
+
+void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
+                                NDArray<int64_t>* dst_ndarray,
+                                int64_t num_axes,
+                                const int64_t* axes) {
+    transpose(src_ndarray, dst_ndarray, num_axes, axes);
+}
+}

nac3core/irrt/irrt/string.hpp

@@ -0,0 +1,23 @@
+#pragma once
+
+#include "irrt/int_types.hpp"
+
+namespace {
+template<typename SizeT>
+bool __nac3_str_eq_impl(const char* str1, SizeT len1, const char* str2, SizeT len2) {
+    if (len1 != len2) {
+        return 0;
+    }
+    return __builtin_memcmp(str1, str2, static_cast<SizeT>(len1)) == 0;
+}
+}  // namespace
+
+extern "C" {
+bool nac3_str_eq(const char* str1, uint32_t len1, const char* str2, uint32_t len2) {
+    return __nac3_str_eq_impl<uint32_t>(str1, len1, str2, len2);
+}
+
+bool nac3_str_eq64(const char* str1, uint64_t len1, const char* str2, uint64_t len2) {
+    return __nac3_str_eq_impl<uint64_t>(str1, len1, str2, len2);
+}
+}

nac3core/src/codegen/expr.rs

@@ -11,7 +11,7 @@ use inkwell::{
     values::{BasicValueEnum, CallSiteValue, FunctionValue, IntValue, PointerValue, StructValue},
     AddressSpace, IntPredicate, OptimizationLevel,
 };
-use itertools::{chain, izip, Either, Itertools};
+use itertools::{izip, Either, Itertools};
 
 use nac3parser::ast::{
     self, Boolop, Cmpop, Comprehension, Constant, Expr, ExprKind, Location, Operator, StrRef,
@@ -24,26 +24,26 @@ use super::{
     irrt::*,
     llvm_intrinsics::{
         call_expect, call_float_floor, call_float_pow, call_float_powi, call_int_smax,
-        call_int_umin, call_memcpy_generic,
+        call_memcpy_generic,
     },
     macros::codegen_unreachable,
-    need_sret, numpy,
+    need_sret,
     stmt::{
         gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
         gen_var,
     },
     types::{ndarray::NDArrayType, ListType},
     values::{
-        ndarray::{NDArrayValue, RustNDIndex},
+        ndarray::{NDArrayOut, RustNDIndex, ScalarOrNDArray},
         ArrayLikeIndexer, ArrayLikeValue, ListValue, ProxyValue, RangeValue,
-        TypedArrayLikeAccessor, UntypedArrayLikeAccessor,
+        UntypedArrayLikeAccessor,
     },
     CodeGenContext, CodeGenTask, CodeGenerator,
 };
 use crate::{
     symbol_resolver::{SymbolValue, ValueEnum},
     toplevel::{
-        helper::{extract_ndims, PrimDef},
+        helper::{arraylike_flatten_element_type, PrimDef},
         numpy::unpack_ndarray_var_tys,
         DefinitionId, TopLevelDef,
     },
@@ -79,7 +79,7 @@ pub fn get_subst_key(
         .join(", ")
 }
 
-impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
+impl<'ctx> CodeGenContext<'ctx, '_> {
     /// Builds a sequence of `getelementptr` and `load` instructions which stores the value of a
     /// struct field into an LLVM value.
     pub fn build_gep_and_load(
@@ -1095,33 +1095,6 @@ pub fn destructure_range<'ctx>(
     (start, end, step)
 }
 
-/// Allocates a List structure with the given [type][ty] and [length]. The name of the resulting
-/// LLVM value is `{name}.addr`, or `list.addr` if [name] is not specified.
-///
-/// Setting `ty` to [`None`] implies that the list is empty **and** does not have a known element
-/// type, and will therefore set the `list.data` type as `size_t*`. It is undefined behavior to
-/// generate a sized list with an unknown element type.
-pub fn allocate_list<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ty: Option<BasicTypeEnum<'ctx>>,
-    length: IntValue<'ctx>,
-    name: Option<&'ctx str>,
-) -> ListValue<'ctx> {
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    let llvm_elem_ty = ty.unwrap_or(llvm_usize.into());
-
-    // List structure; type { ty*, size_t }
-    let arr_ty = ListType::new(generator, ctx.ctx, llvm_elem_ty);
-    let list = arr_ty.alloca(generator, ctx, name);
-
-    let length = ctx.builder.build_int_z_extend(length, llvm_usize, "").unwrap();
-    list.store_size(ctx, generator, length);
-    list.create_data(ctx, llvm_elem_ty, None);
-
-    list
-}
-
 /// Generates LLVM IR for a [list comprehension expression][expr].
 pub fn gen_comprehension<'ctx, G: CodeGenerator>(
     generator: &mut G,
@@ -1194,12 +1167,11 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
                     "listcomp.alloc_size",
                 )
                 .unwrap();
-            list = allocate_list(
+            list = ListType::new(generator, ctx.ctx, elem_ty).construct(
                 generator,
                 ctx,
-                Some(elem_ty),
                 list_alloc_size.into_int_value(),
-                Some("listcomp.addr"),
+                Some("listcomp"),
             );
 
             let i = generator.gen_store_target(ctx, target, Some("i.addr"))?.unwrap();
@@ -1246,7 +1218,12 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
                     Some("length"),
                 )
                 .into_int_value();
-            list = allocate_list(generator, ctx, Some(elem_ty), length, Some("listcomp"));
+            list = ListType::new(generator, ctx.ctx, elem_ty).construct(
+                generator,
+                ctx,
+                length,
+                Some("listcomp"),
+            );
 
             let counter = generator.gen_var_alloc(ctx, size_t.into(), Some("counter.addr"))?;
             // counter = -1
@@ -1411,7 +1388,8 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
                     .build_int_add(lhs.load_size(ctx, None), rhs.load_size(ctx, None), "")
                     .unwrap();
 
-                let new_list = allocate_list(generator, ctx, Some(llvm_elem_ty), size, None);
+                let new_list = ListType::new(generator, ctx.ctx, llvm_elem_ty)
+                    .construct(generator, ctx, size, None);
 
                 let lhs_size = ctx
                     .builder
@@ -1498,10 +1476,9 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
                 let elem_llvm_ty = ctx.get_llvm_type(generator, elem_ty);
                 let sizeof_elem = elem_llvm_ty.size_of().unwrap();
 
-                let new_list = allocate_list(
+                let new_list = ListType::new(generator, ctx.ctx, elem_llvm_ty).construct(
                     generator,
                     ctx,
-                    Some(elem_llvm_ty),
                     ctx.builder.build_int_mul(list_val.load_size(ctx, None), int_val, "").unwrap(),
                     None,
                 );
@@ -1554,98 +1531,77 @@ pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
     } else if ty1.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
         || ty2.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
     {
-        let is_ndarray1 = ty1.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
-        let is_ndarray2 = ty2.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
+        let left = ScalarOrNDArray::from_value(generator, ctx, (ty1, left_val));
+        let right = ScalarOrNDArray::from_value(generator, ctx, (ty2, right_val));
 
-        if is_ndarray1 && is_ndarray2 {
-            let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
-            let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty2);
+        let ty1_dtype = arraylike_flatten_element_type(&mut ctx.unifier, ty1);
+        let ty2_dtype = arraylike_flatten_element_type(&mut ctx.unifier, ty2);
 
-            assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+        // Inhomogeneous binary operations are not supported.
+        assert!(ctx.unifier.unioned(ty1_dtype, ty2_dtype));
 
-            let left_val = NDArrayType::from_unifier_type(generator, ctx, ty1)
-                .map_value(left_val.into_pointer_value(), None);
-            let right_val = NDArrayType::from_unifier_type(generator, ctx, ty2)
-                .map_value(right_val.into_pointer_value(), None);
+        let common_dtype = ty1_dtype;
+        let llvm_common_dtype = left.get_dtype();
 
-            let res = if op.base == Operator::MatMult {
-                // MatMult is the only binop which is not an elementwise op
-                numpy::ndarray_matmul_2d(
-                    generator,
-                    ctx,
-                    ndarray_dtype1,
-                    match op.variant {
-                        BinopVariant::Normal => None,
-                        BinopVariant::AugAssign => Some(left_val),
-                    },
-                    left_val,
-                    right_val,
-                )?
-            } else {
-                numpy::ndarray_elementwise_binop_impl(
-                    generator,
-                    ctx,
-                    ndarray_dtype1,
-                    match op.variant {
-                        BinopVariant::Normal => None,
-                        BinopVariant::AugAssign => Some(left_val),
-                    },
-                    (ty1, left_val.as_base_value().into(), false),
-                    (ty2, right_val.as_base_value().into(), false),
-                    |generator, ctx, (lhs, rhs)| {
-                        gen_binop_expr_with_values(
-                            generator,
-                            ctx,
-                            (&Some(ndarray_dtype1), lhs),
-                            op,
-                            (&Some(ndarray_dtype2), rhs),
-                            ctx.current_loc,
-                        )?
-                        .unwrap()
-                        .to_basic_value_enum(
-                            ctx,
-                            generator,
-                            ndarray_dtype1,
-                        )
-                    },
-                )?
-            };
+        let out = match op.variant {
+            BinopVariant::Normal => NDArrayOut::NewNDArray { dtype: llvm_common_dtype },
+            BinopVariant::AugAssign => {
+                // Augmented assignment - `left` has to be an ndarray. If it were a scalar then NAC3
+                // simply doesn't support it.
+                if let ScalarOrNDArray::NDArray(out_ndarray) = left {
+                    NDArrayOut::WriteToNDArray { ndarray: out_ndarray }
+                } else {
+                    panic!("left must be an ndarray")
+                }
+            }
+        };
 
-            Ok(Some(res.as_base_value().into()))
+        if op.base == Operator::MatMult {
+            let left = left.to_ndarray(generator, ctx);
+            let right = right.to_ndarray(generator, ctx);
+            let result = left
+                .matmul(generator, ctx, ty1, (ty2, right), (common_dtype, out))
+                .split_unsized(generator, ctx);
+            Ok(Some(result.to_basic_value_enum().into()))
         } else {
-            let (ndarray_dtype, _) =
-                unpack_ndarray_var_tys(&mut ctx.unifier, if is_ndarray1 { ty1 } else { ty2 });
-            let ndarray_val =
-                NDArrayType::from_unifier_type(generator, ctx, if is_ndarray1 { ty1 } else { ty2 })
-                    .map_value(
-                        if is_ndarray1 { left_val } else { right_val }.into_pointer_value(),
-                        None,
-                    );
-            let res = numpy::ndarray_elementwise_binop_impl(
-                generator,
-                ctx,
-                ndarray_dtype,
-                match op.variant {
-                    BinopVariant::Normal => None,
-                    BinopVariant::AugAssign => Some(ndarray_val),
-                },
-                (ty1, left_val, !is_ndarray1),
-                (ty2, right_val, !is_ndarray2),
-                |generator, ctx, (lhs, rhs)| {
-                    gen_binop_expr_with_values(
-                        generator,
-                        ctx,
-                        (&Some(ndarray_dtype), lhs),
-                        op,
-                        (&Some(ndarray_dtype), rhs),
-                        ctx.current_loc,
-                    )?
-                    .unwrap()
-                    .to_basic_value_enum(ctx, generator, ndarray_dtype)
-                },
-            )?;
+            // For other operations, they are all elementwise operations.
 
-            Ok(Some(res.as_base_value().into()))
+            // There are only three cases:
+            // - LHS is a scalar, RHS is an ndarray.
+            // - LHS is an ndarray, RHS is a scalar.
+            // - LHS is an ndarray, RHS is an ndarray.
+            //
+            // For all cases, the scalar operand is promoted to an ndarray,
+            // the two are then broadcasted, and starmapped through.
+
+            let left = left.to_ndarray(generator, ctx);
+            let right = right.to_ndarray(generator, ctx);
+
+            let result = NDArrayType::new_broadcast(
+                generator,
+                ctx.ctx,
+                llvm_common_dtype,
+                &[left.get_type(), right.get_type()],
+            )
+            .broadcast_starmap(generator, ctx, &[left, right], out, |generator, ctx, scalars| {
+                let left_value = scalars[0];
+                let right_value = scalars[1];
+
+                let result = gen_binop_expr_with_values(
+                    generator,
+                    ctx,
+                    (&Some(ty1_dtype), left_value),
+                    op,
+                    (&Some(ty2_dtype), right_value),
+                    ctx.current_loc,
+                )?
+                .unwrap()
+                .to_basic_value_enum(ctx, generator, common_dtype)?;
+
+                Ok(result)
+            })
+            .unwrap();
+            Ok(Some(result.as_base_value().into()))
         }
     } else {
         let left_ty_enum = ctx.unifier.get_ty_immutable(left_ty.unwrap());
@@ -1808,10 +1764,10 @@ pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
             _ => val.into(),
         }
     } else if ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) {
-        let llvm_ndarray_ty = NDArrayType::from_unifier_type(generator, ctx, ty);
         let (ndarray_dtype, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty);
 
-        let val = llvm_ndarray_ty.map_value(val.into_pointer_value(), None);
+        let ndarray = NDArrayType::from_unifier_type(generator, ctx, ty)
+            .map_value(val.into_pointer_value(), None);
 
         // ndarray uses `~` rather than `not` to perform elementwise inversion, convert it before
         // passing it to the elementwise codegen function
@@ -1829,20 +1785,18 @@ pub fn gen_unaryop_expr_with_values<'ctx, G: CodeGenerator>(
             op
         };
 
-        let res = numpy::ndarray_elementwise_unaryop_impl(
+        let mapped_ndarray = ndarray.map(
             generator,
             ctx,
-            ndarray_dtype,
-            None,
-            val,
-            |generator, ctx, val| {
-                gen_unaryop_expr_with_values(generator, ctx, op, (&Some(ndarray_dtype), val))?
+            NDArrayOut::NewNDArray { dtype: ndarray.get_type().element_type() },
+            |generator, ctx, scalar| {
+                gen_unaryop_expr_with_values(generator, ctx, op, (&Some(ndarray_dtype), scalar))?
+                    .map(|val| val.to_basic_value_enum(ctx, generator, ndarray_dtype))
                     .unwrap()
-                    .to_basic_value_enum(ctx, generator, ndarray_dtype)
             },
         )?;
 
-        res.as_base_value().into()
+        mapped_ndarray.as_base_value().into()
     } else {
         unimplemented!()
     }))
@@ -1885,87 +1839,56 @@ pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
         if left_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
             || right_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
         {
-            let (Some(left_ty), lhs) = left else { codegen_unreachable!(ctx) };
-            let (Some(right_ty), rhs) = comparators[0] else { codegen_unreachable!(ctx) };
+            let (Some(left_ty), left) = left else { codegen_unreachable!(ctx) };
+            let (Some(right_ty), right) = comparators[0] else { codegen_unreachable!(ctx) };
             let op = ops[0];
 
-            let is_ndarray1 =
-                left_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
-            let is_ndarray2 =
-                right_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id());
+            let left_ty_dtype = arraylike_flatten_element_type(&mut ctx.unifier, left_ty);
+            let right_ty_dtype = arraylike_flatten_element_type(&mut ctx.unifier, right_ty);
 
-            return if is_ndarray1 && is_ndarray2 {
-                let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, left_ty);
-                let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, right_ty);
+            let left = ScalarOrNDArray::from_value(generator, ctx, (left_ty, left))
+                .to_ndarray(generator, ctx);
+            let right = ScalarOrNDArray::from_value(generator, ctx, (right_ty, right))
+                .to_ndarray(generator, ctx);
 
-                assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+            let result_ndarray = NDArrayType::new_broadcast(
+                generator,
+                ctx.ctx,
+                ctx.ctx.i8_type().into(),
+                &[left.get_type(), right.get_type()],
+            )
+            .broadcast_starmap(
+                generator,
+                ctx,
+                &[left, right],
+                NDArrayOut::NewNDArray { dtype: ctx.ctx.i8_type().into() },
+                |generator, ctx, scalars| {
+                    let left_scalar = scalars[0];
+                    let right_scalar = scalars[1];
 
-                let left_val = NDArrayType::from_unifier_type(generator, ctx, left_ty)
-                    .map_value(lhs.into_pointer_value(), None);
-                let res = numpy::ndarray_elementwise_binop_impl(
-                    generator,
-                    ctx,
-                    ctx.primitives.bool,
-                    None,
-                    (left_ty, left_val.as_base_value().into(), false),
-                    (right_ty, rhs, false),
-                    |generator, ctx, (lhs, rhs)| {
-                        let val = gen_cmpop_expr_with_values(
-                            generator,
-                            ctx,
-                            (Some(ndarray_dtype1), lhs),
-                            &[op],
-                            &[(Some(ndarray_dtype2), rhs)],
-                        )?
-                        .unwrap()
-                        .to_basic_value_enum(
-                            ctx,
-                            generator,
-                            ctx.primitives.bool,
-                        )?;
+                    let val = gen_cmpop_expr_with_values(
+                        generator,
+                        ctx,
+                        (Some(left_ty_dtype), left_scalar),
+                        &[op],
+                        &[(Some(right_ty_dtype), right_scalar)],
+                    )?
+                    .unwrap()
+                    .to_basic_value_enum(
+                        ctx,
+                        generator,
+                        ctx.primitives.bool,
+                    )?;
 
-                        Ok(generator.bool_to_i8(ctx, val.into_int_value()).into())
-                    },
-                )?;
+                    Ok(generator.bool_to_i8(ctx, val.into_int_value()).into())
+                },
+            )?;
 
-                Ok(Some(res.as_base_value().into()))
-            } else {
-                let (ndarray_dtype, _) = unpack_ndarray_var_tys(
-                    &mut ctx.unifier,
-                    if is_ndarray1 { left_ty } else { right_ty },
-                );
-                let res = numpy::ndarray_elementwise_binop_impl(
-                    generator,
-                    ctx,
-                    ctx.primitives.bool,
-                    None,
-                    (left_ty, lhs, !is_ndarray1),
-                    (right_ty, rhs, !is_ndarray2),
-                    |generator, ctx, (lhs, rhs)| {
-                        let val = gen_cmpop_expr_with_values(
-                            generator,
-                            ctx,
-                            (Some(ndarray_dtype), lhs),
-                            &[op],
-                            &[(Some(ndarray_dtype), rhs)],
-                        )?
-                        .unwrap()
-                        .to_basic_value_enum(
-                            ctx,
-                            generator,
-                            ctx.primitives.bool,
-                        )?;
-
-                        Ok(generator.bool_to_i8(ctx, val.into_int_value()).into())
-                    },
-                )?;
-
-                Ok(Some(res.as_base_value().into()))
-            };
+            return Ok(Some(result_ndarray.as_base_value().into()));
         }
     }
 
-    let cmp_val = izip!(chain(once(&left), comparators.iter()), comparators.iter(), ops.iter(),)
+    let cmp_val = izip!(once(&left).chain(comparators.iter()), comparators.iter(), ops.iter(),)
         .fold(Ok(None), |prev: Result<Option<_>, String>, (lhs, rhs, op)| {
             let (left_ty, lhs) = lhs;
             let (right_ty, rhs) = rhs;
@@ -2045,111 +1968,43 @@ pub fn gen_cmpop_expr_with_values<'ctx, G: CodeGenerator>(
             } else if left_ty == ctx.primitives.str {
                 assert!(ctx.unifier.unioned(left_ty, right_ty));
 
-                let llvm_i1 = ctx.ctx.bool_type();
-                let llvm_i32 = ctx.ctx.i32_type();
-                let llvm_usize = generator.get_size_type(ctx.ctx);
-
                 let lhs = lhs.into_struct_value();
                 let rhs = rhs.into_struct_value();
 
+                let llvm_i32 = ctx.ctx.i32_type();
+                let llvm_usize = generator.get_size_type(ctx.ctx);
+
                 let plhs = generator.gen_var_alloc(ctx, lhs.get_type().into(), None).unwrap();
                 ctx.builder.build_store(plhs, lhs).unwrap();
                 let prhs = generator.gen_var_alloc(ctx, lhs.get_type().into(), None).unwrap();
                 ctx.builder.build_store(prhs, rhs).unwrap();
 
+                let lhs_ptr = ctx.build_in_bounds_gep_and_load(
+                    plhs,
+                    &[llvm_usize.const_zero(), llvm_i32.const_zero()],
+                    None,
+                ).into_pointer_value();
                 let lhs_len = ctx.build_in_bounds_gep_and_load(
                     plhs,
-                    &[llvm_i32.const_zero(), llvm_i32.const_int(1, false)],
+                    &[llvm_usize.const_zero(), llvm_i32.const_int(1, false)],
                     None,
                 ).into_int_value();
+
+                let rhs_ptr = ctx.build_in_bounds_gep_and_load(
+                    prhs,
+                    &[llvm_usize.const_zero(), llvm_i32.const_zero()],
+                    None,
+                ).into_pointer_value();
                 let rhs_len = ctx.build_in_bounds_gep_and_load(
                     prhs,
-                    &[llvm_i32.const_zero(), llvm_i32.const_int(1, false)],
+                    &[llvm_usize.const_zero(), llvm_i32.const_int(1, false)],
                     None,
                 ).into_int_value();
-
-                let len = call_int_umin(ctx, lhs_len, rhs_len, None);
-
-                let current_bb = ctx.builder.get_insert_block().unwrap();
-                let post_foreach_cmp = ctx.ctx.insert_basic_block_after(current_bb, "foreach.cmp.end");
-
-                ctx.builder.position_at_end(post_foreach_cmp);
-                let cmp_phi = ctx.builder.build_phi(llvm_i1, "").unwrap();
-                ctx.builder.position_at_end(current_bb);
-
-                gen_for_callback_incrementing(
-                    generator,
-                    ctx,
-                    None,
-                    llvm_usize.const_zero(),
-                    (len, false),
-                    |generator, ctx, _, i| {
-                        let lhs_char = {
-                            let plhs_data = ctx.build_in_bounds_gep_and_load(
-                                plhs,
-                                &[llvm_i32.const_zero(), llvm_i32.const_zero()],
-                                None,
-                            ).into_pointer_value();
-
-                            ctx.build_in_bounds_gep_and_load(
-                                plhs_data,
-                                &[i],
-                                None
-                            ).into_int_value()
-                        };
-                        let rhs_char = {
-                            let prhs_data = ctx.build_in_bounds_gep_and_load(
-                                prhs,
-                                &[llvm_i32.const_zero(), llvm_i32.const_zero()],
-                                None,
-                            ).into_pointer_value();
-
-                            ctx.build_in_bounds_gep_and_load(
-                                prhs_data,
-                                &[i],
-                                None
-                            ).into_int_value()
-                        };
-
-                        gen_if_callback(
-                            generator,
-                            ctx,
-                            |_, ctx| {
-                                Ok(ctx.builder.build_int_compare(IntPredicate::NE, lhs_char, rhs_char, "").unwrap())
-                            },
-                            |_, ctx| {
-                                let bb = ctx.builder.get_insert_block().unwrap();
-                                cmp_phi.add_incoming(&[(&llvm_i1.const_zero(), bb)]);
-                                ctx.builder.build_unconditional_branch(post_foreach_cmp).unwrap();
-
-                                Ok(())
-                            },
-                            |_, _| Ok(()),
-                        )?;
-
-                        Ok(())
-                    },
-                    llvm_usize.const_int(1, false),
-                )?;
-
-                let bb = ctx.builder.get_insert_block().unwrap();
-                let is_len_eq = ctx.builder.build_int_compare(
-                    IntPredicate::EQ,
-                    lhs_len,
-                    rhs_len,
-                    "",
-                ).unwrap();
-                cmp_phi.add_incoming(&[(&is_len_eq, bb)]);
-                ctx.builder.build_unconditional_branch(post_foreach_cmp).unwrap();
-
-                ctx.builder.position_at_end(post_foreach_cmp);
-                let cmp_phi = cmp_phi.as_basic_value().into_int_value();
-
-                // Invert the final value if __ne__
+                let result = call_string_eq(generator, ctx, lhs_ptr, lhs_len, rhs_ptr, rhs_len);
                 if *op == Cmpop::NotEq {
-                    ctx.builder.build_not(cmp_phi, "").unwrap()
+                    ctx.builder.build_not(result, "").unwrap() 
                 } else {
-                    cmp_phi
+                    result
                 }
             } else if [left_ty, right_ty]
                 .iter()
@@ -2512,319 +2367,6 @@ pub fn gen_cmpop_expr<'ctx, G: CodeGenerator>(
     )
 }
 
-/// Generates code for a subscript expression on an `ndarray`.
-///
-/// * `ty` - The `Type` of the `NDArray` elements.
-/// * `ndims` - The `Type` of the `NDArray` number-of-dimensions `Literal`.
-/// * `v` - The `NDArray` value.
-/// * `slice` - The slice expression used to subscript into the `ndarray`.
-fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    ty: Type,
-    ndims_ty: Type,
-    v: NDArrayValue<'ctx>,
-    slice: &Expr<Option<Type>>,
-) -> Result<Option<ValueEnum<'ctx>>, String> {
-    let llvm_i1 = ctx.ctx.bool_type();
-    let llvm_i32 = ctx.ctx.i32_type();
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let TypeEnum::TLiteral { values, .. } = &*ctx.unifier.get_ty_immutable(ndims_ty) else {
-        codegen_unreachable!(ctx)
-    };
-
-    let ndims = values
-        .iter()
-        .map(|ndim| u64::try_from(ndim.clone()).map_err(|()| ndim.clone()))
-        .collect::<Result<Vec<_>, _>>()
-        .map_err(|val| {
-            format!(
-                "Expected non-negative literal for ndarray.ndims, got {}",
-                i128::try_from(val).unwrap()
-            )
-        })?;
-
-    assert!(!ndims.is_empty());
-
-    // The number of dimensions subscripted by the index expression.
-    // Slicing a ndarray will yield the same number of dimensions, whereas indexing into a
-    // dimension will remove a dimension.
-    let subscripted_dims = match &slice.node {
-        ExprKind::Tuple { elts, .. } => elts.iter().fold(0, |acc, value_subexpr| {
-            if let ExprKind::Slice { .. } = &value_subexpr.node {
-                acc
-            } else {
-                acc + 1
-            }
-        }),
-
-        ExprKind::Slice { .. } => 0,
-        _ => 1,
-    };
-
-    let llvm_ndarray_data_t = ctx.get_llvm_type(generator, ty).as_basic_type_enum();
-    let sizeof_elem = llvm_ndarray_data_t.size_of().unwrap();
-
-    // Check that len is non-zero
-    let len = v.load_ndims(ctx);
-    ctx.make_assert(
-        generator,
-        ctx.builder.build_int_compare(IntPredicate::SGT, len, llvm_usize.const_zero(), "").unwrap(),
-        "0:IndexError",
-        "too many indices for array: array is {0}-dimensional but 1 were indexed",
-        [Some(len), None, None],
-        slice.location,
-    );
-
-    // Normalizes a possibly-negative index to its corresponding positive index
-    let normalize_index = |generator: &mut G,
-                           ctx: &mut CodeGenContext<'ctx, '_>,
-                           index: IntValue<'ctx>,
-                           dim: u64| {
-        gen_if_else_expr_callback(
-            generator,
-            ctx,
-            |_, ctx| {
-                Ok(ctx
-                    .builder
-                    .build_int_compare(IntPredicate::SGE, index, index.get_type().const_zero(), "")
-                    .unwrap())
-            },
-            |_, _| Ok(Some(index)),
-            |generator, ctx| {
-                let llvm_i32 = ctx.ctx.i32_type();
-
-                let len = unsafe {
-                    v.shape().get_typed_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(dim, true),
-                        None,
-                    )
-                };
-
-                let index = ctx
-                    .builder
-                    .build_int_add(
-                        len,
-                        ctx.builder.build_int_s_extend(index, llvm_usize, "").unwrap(),
-                        "",
-                    )
-                    .unwrap();
-
-                Ok(Some(ctx.builder.build_int_truncate(index, llvm_i32, "").unwrap()))
-            },
-        )
-        .map(|v| v.map(BasicValueEnum::into_int_value))
-    };
-
-    // Converts a slice expression into a slice-range tuple
-    let expr_to_slice = |generator: &mut G,
-                         ctx: &mut CodeGenContext<'ctx, '_>,
-                         node: &ExprKind<Option<Type>>,
-                         dim: u64| {
-        match node {
-            ExprKind::Constant { value: Constant::Int(v), .. } => {
-                let Some(index) =
-                    normalize_index(generator, ctx, llvm_i32.const_int(*v as u64, true), dim)?
-                else {
-                    return Ok(None);
-                };
-
-                Ok(Some((index, index, llvm_i32.const_int(1, true))))
-            }
-
-            ExprKind::Slice { lower, upper, step } => {
-                let dim_sz = unsafe {
-                    v.shape().get_typed_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(dim, false),
-                        None,
-                    )
-                };
-
-                handle_slice_indices(lower, upper, step, ctx, generator, dim_sz)
-            }
-
-            _ => {
-                let Some(index) = generator.gen_expr(ctx, slice)? else { return Ok(None) };
-                let index = index
-                    .to_basic_value_enum(ctx, generator, slice.custom.unwrap())?
-                    .into_int_value();
-                let Some(index) = normalize_index(generator, ctx, index, dim)? else {
-                    return Ok(None);
-                };
-
-                Ok(Some((index, index, llvm_i32.const_int(1, true))))
-            }
-        }
-    };
-
-    let make_indices_arr = |generator: &mut G,
-                            ctx: &mut CodeGenContext<'ctx, '_>|
-     -> Result<_, String> {
-        Ok(if let ExprKind::Tuple { elts, .. } = &slice.node {
-            let llvm_int_ty = ctx.get_llvm_type(generator, elts[0].custom.unwrap());
-            let index_addr = generator.gen_array_var_alloc(
-                ctx,
-                llvm_int_ty,
-                llvm_usize.const_int(elts.len() as u64, false),
-                None,
-            )?;
-
-            for (i, elt) in elts.iter().enumerate() {
-                let Some(index) = generator.gen_expr(ctx, elt)? else {
-                    return Ok(None);
-                };
-
-                let index = index
-                    .to_basic_value_enum(ctx, generator, elt.custom.unwrap())?
-                    .into_int_value();
-                let Some(index) = normalize_index(generator, ctx, index, 0)? else {
-                    return Ok(None);
-                };
-
-                let store_ptr = unsafe {
-                    index_addr.ptr_offset_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(i as u64, false),
-                        None,
-                    )
-                };
-                ctx.builder.build_store(store_ptr, index).unwrap();
-            }
-
-            Some(index_addr)
-        } else if let Some(index) = generator.gen_expr(ctx, slice)? {
-            let llvm_int_ty = ctx.get_llvm_type(generator, slice.custom.unwrap());
-            let index_addr = generator.gen_array_var_alloc(
-                ctx,
-                llvm_int_ty,
-                llvm_usize.const_int(1u64, false),
-                None,
-            )?;
-
-            let index =
-                index.to_basic_value_enum(ctx, generator, slice.custom.unwrap())?.into_int_value();
-            let Some(index) = normalize_index(generator, ctx, index, 0)? else { return Ok(None) };
-
-            let store_ptr = unsafe {
-                index_addr.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-            };
-            ctx.builder.build_store(store_ptr, index).unwrap();
-
-            Some(index_addr)
-        } else {
-            None
-        })
-    };
-
-    Ok(Some(if ndims.len() == 1 && ndims[0] - subscripted_dims == 0 {
-        let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
-
-        v.data().get(ctx, generator, &index_addr, None).into()
-    } else {
-        match &slice.node {
-            ExprKind::Tuple { elts, .. } => {
-                let slices = elts
-                    .iter()
-                    .enumerate()
-                    .map(|(dim, elt)| expr_to_slice(generator, ctx, &elt.node, dim as u64))
-                    .take_while_inclusive(|slice| slice.as_ref().is_ok_and(Option::is_some))
-                    .collect::<Result<Vec<_>, _>>()?;
-                if slices.len() < elts.len() {
-                    return Ok(None);
-                }
-
-                let slices = slices.into_iter().map(Option::unwrap).collect_vec();
-
-                numpy::ndarray_sliced_copy(generator, ctx, ty, v, &slices)?.as_base_value().into()
-            }
-
-            ExprKind::Slice { .. } => {
-                let Some(slice) = expr_to_slice(generator, ctx, &slice.node, 0)? else {
-                    return Ok(None);
-                };
-
-                numpy::ndarray_sliced_copy(generator, ctx, ty, v, &[slice])?.as_base_value().into()
-            }
-
-            _ => {
-                // Accessing an element from a multi-dimensional `ndarray`
-                let Some(index_addr) = make_indices_arr(generator, ctx)? else { return Ok(None) };
-
-                let num_dims = extract_ndims(&ctx.unifier, ndims_ty) - 1;
-
-                // Create a new array, remove the top dimension from the dimension-size-list, and copy the
-                // elements over
-                let ndarray =
-                    NDArrayType::new(generator, ctx.ctx, llvm_ndarray_data_t, Some(num_dims))
-                        .construct_uninitialized(generator, ctx, None);
-
-                let ndarray_num_dims = ctx
-                    .builder
-                    .build_int_z_extend_or_bit_cast(
-                        ndarray.load_ndims(ctx),
-                        llvm_usize.size_of().get_type(),
-                        "",
-                    )
-                    .unwrap();
-                let v_dims_src_ptr = unsafe {
-                    v.shape().ptr_offset_unchecked(
-                        ctx,
-                        generator,
-                        &llvm_usize.const_int(1, false),
-                        None,
-                    )
-                };
-                call_memcpy_generic(
-                    ctx,
-                    ndarray.shape().base_ptr(ctx, generator),
-                    v_dims_src_ptr,
-                    ctx.builder
-                        .build_int_mul(ndarray_num_dims, llvm_usize.size_of(), "")
-                        .map(Into::into)
-                        .unwrap(),
-                    llvm_i1.const_zero(),
-                );
-
-                let ndarray_num_elems = ndarray::call_ndarray_calc_size(
-                    generator,
-                    ctx,
-                    &ndarray.shape().as_slice_value(ctx, generator),
-                    (None, None),
-                );
-                let ndarray_num_elems = ctx
-                    .builder
-                    .build_int_z_extend_or_bit_cast(ndarray_num_elems, sizeof_elem.get_type(), "")
-                    .unwrap();
-                unsafe { ndarray.create_data(generator, ctx) };
-
-                let v_data_src_ptr = v.data().ptr_offset(ctx, generator, &index_addr, None);
-                call_memcpy_generic(
-                    ctx,
-                    ndarray.data().base_ptr(ctx, generator),
-                    v_data_src_ptr,
-                    ctx.builder
-                        .build_int_mul(
-                            ndarray_num_elems,
-                            llvm_ndarray_data_t.size_of().unwrap(),
-                            "",
-                        )
-                        .map(Into::into)
-                        .unwrap(),
-                    llvm_i1.const_zero(),
-                );
-
-                ndarray.as_base_value().into()
-            }
-        }
-    }))
-}
-
 /// See [`CodeGenerator::gen_expr`].
 pub fn gen_expr<'ctx, G: CodeGenerator>(
     generator: &mut G,
@@ -2939,7 +2481,20 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
                 Some(elements[0].get_type())
             };
             let length = generator.get_size_type(ctx.ctx).const_int(elements.len() as u64, false);
-            let arr_str_ptr = allocate_list(generator, ctx, ty, length, Some("list"));
+            let arr_str_ptr = if let Some(ty) = ty {
+                ListType::new(generator, ctx.ctx, ty).construct(
+                    generator,
+                    ctx,
+                    length,
+                    Some("list"),
+                )
+            } else {
+                ListType::new_untyped(generator, ctx.ctx).construct_empty(
+                    generator,
+                    ctx,
+                    Some("list"),
+                )
+            };
             let arr_ptr = arr_str_ptr.data();
             for (i, v) in elements.iter().enumerate() {
                 let elem_ptr = arr_ptr.ptr_offset(
@@ -3417,8 +2972,12 @@ pub fn gen_expr<'ctx, G: CodeGenerator>(
                                 .unwrap(),
                             step,
                         );
-                        let res_array_ret =
-                            allocate_list(generator, ctx, Some(ty), length, Some("ret"));
+                        let res_array_ret = ListType::new(generator, ctx.ctx, ty).construct(
+                            generator,
+                            ctx,
+                            length,
+                            Some("ret"),
+                        );
                         let Some(res_ind) = handle_slice_indices(
                             &None,
                             &None,

nac3core/src/codegen/generator.rs

@@ -17,6 +17,7 @@ pub trait CodeGenerator {
     /// Return the module name for the code generator.
     fn get_name(&self) -> &str;
 
+    /// Return an instance of [`IntType`] corresponding to the type of `size_t` for this instance.
     fn get_size_type<'ctx>(&self, ctx: &'ctx Context) -> IntType<'ctx>;
 
     /// Generate function call and returns the function return value.

nac3core/src/codegen/irrt/list.rs

@@ -24,42 +24,52 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
     src_arr: ListValue<'ctx>,
     src_idx: (IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>),
 ) {
-    let size_ty = generator.get_size_type(ctx.ctx);
-    let int8_ptr = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
-    let int32 = ctx.ctx.i32_type();
-    let (fun_symbol, elem_ptr_type) = ("__nac3_list_slice_assign_var_size", int8_ptr);
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pi8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
+    let llvm_i32 = ctx.ctx.i32_type();
+
+    assert_eq!(dest_idx.0.get_type(), llvm_i32);
+    assert_eq!(dest_idx.1.get_type(), llvm_i32);
+    assert_eq!(dest_idx.2.get_type(), llvm_i32);
+    assert_eq!(src_idx.0.get_type(), llvm_i32);
+    assert_eq!(src_idx.1.get_type(), llvm_i32);
+    assert_eq!(src_idx.2.get_type(), llvm_i32);
+
+    let (fun_symbol, elem_ptr_type) = ("__nac3_list_slice_assign_var_size", llvm_pi8);
     let slice_assign_fun = {
         let ty_vec = vec![
-            int32.into(),         // dest start idx
-            int32.into(),         // dest end idx
-            int32.into(),         // dest step
+            llvm_i32.into(),      // dest start idx
+            llvm_i32.into(),      // dest end idx
+            llvm_i32.into(),      // dest step
             elem_ptr_type.into(), // dest arr ptr
-            int32.into(),         // dest arr len
-            int32.into(),         // src start idx
-            int32.into(),         // src end idx
-            int32.into(),         // src step
+            llvm_i32.into(),      // dest arr len
+            llvm_i32.into(),      // src start idx
+            llvm_i32.into(),      // src end idx
+            llvm_i32.into(),      // src step
             elem_ptr_type.into(), // src arr ptr
-            int32.into(),         // src arr len
-            int32.into(),         // size
+            llvm_i32.into(),      // src arr len
+            llvm_i32.into(),      // size
         ];
         ctx.module.get_function(fun_symbol).unwrap_or_else(|| {
-            let fn_t = int32.fn_type(ty_vec.as_slice(), false);
+            let fn_t = llvm_i32.fn_type(ty_vec.as_slice(), false);
             ctx.module.add_function(fun_symbol, fn_t, None)
         })
     };
 
-    let zero = int32.const_zero();
-    let one = int32.const_int(1, false);
+    let zero = llvm_i32.const_zero();
+    let one = llvm_i32.const_int(1, false);
     let dest_arr_ptr = dest_arr.data().base_ptr(ctx, generator);
     let dest_arr_ptr =
         ctx.builder.build_pointer_cast(dest_arr_ptr, elem_ptr_type, "dest_arr_ptr_cast").unwrap();
     let dest_len = dest_arr.load_size(ctx, Some("dest.len"));
-    let dest_len = ctx.builder.build_int_truncate_or_bit_cast(dest_len, int32, "srclen32").unwrap();
+    let dest_len =
+        ctx.builder.build_int_truncate_or_bit_cast(dest_len, llvm_i32, "srclen32").unwrap();
     let src_arr_ptr = src_arr.data().base_ptr(ctx, generator);
     let src_arr_ptr =
         ctx.builder.build_pointer_cast(src_arr_ptr, elem_ptr_type, "src_arr_ptr_cast").unwrap();
     let src_len = src_arr.load_size(ctx, Some("src.len"));
-    let src_len = ctx.builder.build_int_truncate_or_bit_cast(src_len, int32, "srclen32").unwrap();
+    let src_len =
+        ctx.builder.build_int_truncate_or_bit_cast(src_len, llvm_i32, "srclen32").unwrap();
 
     // index in bound and positive should be done
     // assert if dest.step == 1 then len(src) <= len(dest) else len(src) == len(dest), and
@@ -136,7 +146,7 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
                     BasicTypeEnum::StructType(t) => t.size_of().unwrap(),
                     _ => codegen_unreachable!(ctx),
                 };
-                ctx.builder.build_int_truncate_or_bit_cast(s, int32, "size").unwrap()
+                ctx.builder.build_int_truncate_or_bit_cast(s, llvm_i32, "size").unwrap()
             }
             .into(),
         ];
@@ -147,6 +157,7 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
             .map(Either::unwrap_left)
             .unwrap()
     };
+
     // update length
     let need_update =
         ctx.builder.build_int_compare(IntPredicate::NE, new_len, dest_len, "need_update").unwrap();
@@ -155,7 +166,8 @@ pub fn list_slice_assignment<'ctx, G: CodeGenerator + ?Sized>(
     let cont_bb = ctx.ctx.append_basic_block(current, "cont");
     ctx.builder.build_conditional_branch(need_update, update_bb, cont_bb).unwrap();
     ctx.builder.position_at_end(update_bb);
-    let new_len = ctx.builder.build_int_z_extend_or_bit_cast(new_len, size_ty, "new_len").unwrap();
+    let new_len =
+        ctx.builder.build_int_z_extend_or_bit_cast(new_len, llvm_usize, "new_len").unwrap();
     dest_arr.store_size(ctx, generator, new_len);
     ctx.builder.build_unconditional_branch(cont_bb).unwrap();
     ctx.builder.position_at_end(cont_bb);

nac3core/src/codegen/irrt/math.rs

@@ -62,8 +62,13 @@ pub fn call_isinf<'ctx, G: CodeGenerator + ?Sized>(
     ctx: &CodeGenContext<'ctx, '_>,
     v: FloatValue<'ctx>,
 ) -> IntValue<'ctx> {
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_f64 = ctx.ctx.f64_type();
+
+    assert_eq!(v.get_type(), llvm_f64);
+
     let intrinsic_fn = ctx.module.get_function("__nac3_isinf").unwrap_or_else(|| {
-        let fn_type = ctx.ctx.i32_type().fn_type(&[ctx.ctx.f64_type().into()], false);
+        let fn_type = llvm_i32.fn_type(&[llvm_f64.into()], false);
         ctx.module.add_function("__nac3_isinf", fn_type, None)
     });
 
@@ -84,8 +89,13 @@ pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
     ctx: &CodeGenContext<'ctx, '_>,
     v: FloatValue<'ctx>,
 ) -> IntValue<'ctx> {
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_f64 = ctx.ctx.f64_type();
+
+    assert_eq!(v.get_type(), llvm_f64);
+
     let intrinsic_fn = ctx.module.get_function("__nac3_isnan").unwrap_or_else(|| {
-        let fn_type = ctx.ctx.i32_type().fn_type(&[ctx.ctx.f64_type().into()], false);
+        let fn_type = llvm_i32.fn_type(&[llvm_f64.into()], false);
         ctx.module.add_function("__nac3_isnan", fn_type, None)
     });
 
@@ -104,6 +114,8 @@ pub fn call_isnan<'ctx, G: CodeGenerator + ?Sized>(
 pub fn call_gamma<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
     let llvm_f64 = ctx.ctx.f64_type();
 
+    assert_eq!(v.get_type(), llvm_f64);
+
     let intrinsic_fn = ctx.module.get_function("__nac3_gamma").unwrap_or_else(|| {
         let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
         ctx.module.add_function("__nac3_gamma", fn_type, None)
@@ -121,6 +133,8 @@ pub fn call_gamma<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) ->
 pub fn call_gammaln<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
     let llvm_f64 = ctx.ctx.f64_type();
 
+    assert_eq!(v.get_type(), llvm_f64);
+
     let intrinsic_fn = ctx.module.get_function("__nac3_gammaln").unwrap_or_else(|| {
         let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
         ctx.module.add_function("__nac3_gammaln", fn_type, None)
@@ -138,6 +152,8 @@ pub fn call_gammaln<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -
 pub fn call_j0<'ctx>(ctx: &CodeGenContext<'ctx, '_>, v: FloatValue<'ctx>) -> FloatValue<'ctx> {
     let llvm_f64 = ctx.ctx.f64_type();
 
+    assert_eq!(v.get_type(), llvm_f64);
+
     let intrinsic_fn = ctx.module.get_function("__nac3_j0").unwrap_or_else(|| {
         let fn_type = llvm_f64.fn_type(&[llvm_f64.into()], false);
         ctx.module.add_function("__nac3_j0", fn_type, None)

nac3core/src/codegen/irrt/mod.rs

@@ -15,12 +15,14 @@ pub use list::*;
 pub use math::*;
 pub use range::*;
 pub use slice::*;
+pub use string::*;
 
 mod list;
 mod math;
 pub mod ndarray;
 mod range;
 mod slice;
+mod string;
 
 #[must_use]
 pub fn load_irrt<'ctx>(ctx: &'ctx Context, symbol_resolver: &dyn SymbolResolver) -> Module<'ctx> {
@@ -130,10 +132,11 @@ pub fn handle_slice_indices<'ctx, G: CodeGenerator>(
     generator: &mut G,
     length: IntValue<'ctx>,
 ) -> Result<Option<(IntValue<'ctx>, IntValue<'ctx>, IntValue<'ctx>)>, String> {
-    let int32 = ctx.ctx.i32_type();
-    let zero = int32.const_zero();
-    let one = int32.const_int(1, false);
-    let length = ctx.builder.build_int_truncate_or_bit_cast(length, int32, "leni32").unwrap();
+    let llvm_i32 = ctx.ctx.i32_type();
+
+    let zero = llvm_i32.const_zero();
+    let one = llvm_i32.const_int(1, false);
+    let length = ctx.builder.build_int_truncate_or_bit_cast(length, llvm_i32, "leni32").unwrap();
     Ok(Some(match (start, end, step) {
         (s, e, None) => (
             if let Some(s) = s.as_ref() {
@@ -142,7 +145,7 @@ pub fn handle_slice_indices<'ctx, G: CodeGenerator>(
                     None => return Ok(None),
                 }
             } else {
-                int32.const_zero()
+                llvm_i32.const_zero()
             },
             {
                 let e = if let Some(s) = e.as_ref() {

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

@@ -0,0 +1,80 @@
+use inkwell::{types::BasicTypeEnum, values::IntValue};
+
+use crate::codegen::{
+    expr::infer_and_call_function,
+    irrt::get_usize_dependent_function_name,
+    values::{ndarray::NDArrayValue, ListValue, ProxyValue, TypedArrayLikeAccessor},
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_array_set_and_validate_list_shape`.
+///
+/// Deduces the target shape of the `ndarray` from the provided `list`, raising an exception if
+/// there is any issue with the resultant `shape`.
+///
+/// `shape` must be pre-allocated by the caller of this function to `[usize; ndims]`, and must be
+/// initialized to all `-1`s.
+pub fn call_nac3_ndarray_array_set_and_validate_list_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    list: ListValue<'ctx>,
+    ndims: IntValue<'ctx>,
+    shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    assert_eq!(list.get_type().element_type().unwrap(), ctx.ctx.i8_type().into());
+    assert_eq!(ndims.get_type(), llvm_usize);
+    assert_eq!(
+        BasicTypeEnum::try_from(shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
+    let name = get_usize_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_array_set_and_validate_list_shape",
+    );
+
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[list.as_base_value().into(), ndims.into(), shape.base_ptr(ctx, generator).into()],
+        None,
+        None,
+    );
+}
+
+/// Generates a call to `__nac3_ndarray_array_write_list_to_array`.
+///
+/// Copies the contents stored in `list` into `ndarray`.
+///
+/// The `ndarray` must fulfill the following preconditions:
+///
+/// - `ndarray.itemsize`: Must be initialized.
+/// - `ndarray.ndims`: Must be initialized.
+/// - `ndarray.shape`: Must be initialized.
+/// - `ndarray.data`: Must be allocated and contiguous.
+pub fn call_nac3_ndarray_array_write_list_to_array<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    list: ListValue<'ctx>,
+    ndarray: NDArrayValue<'ctx>,
+) {
+    assert_eq!(list.get_type().element_type().unwrap(), ctx.ctx.i8_type().into());
+
+    let name = get_usize_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_array_write_list_to_array",
+    );
+
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[list.as_base_value().into(), ndarray.as_base_value().into()],
+        None,
+        None,
+    );
+}

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

@@ -1,4 +1,5 @@
 use inkwell::{
+    types::BasicTypeEnum,
     values::{BasicValueEnum, IntValue, PointerValue},
     AddressSpace,
 };
@@ -7,19 +8,26 @@ use crate::codegen::{
     expr::{create_and_call_function, infer_and_call_function},
     irrt::get_usize_dependent_function_name,
     types::ProxyType,
-    values::{ndarray::NDArrayValue, ProxyValue},
+    values::{ndarray::NDArrayValue, ProxyValue, TypedArrayLikeAccessor},
     CodeGenContext, CodeGenerator,
 };
 
+/// Generates a call to `__nac3_ndarray_util_assert_shape_no_negative`.
+///
+/// Assets that `shape` does not contain negative dimensions.
 pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
-    ndims: IntValue<'ctx>,
-    shape: PointerValue<'ctx>,
+    shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
 ) {
     let llvm_usize = generator.get_size_type(ctx.ctx);
     let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
 
+    assert_eq!(
+        BasicTypeEnum::try_from(shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
     let name = get_usize_dependent_function_name(
         generator,
         ctx,
@@ -30,23 +38,37 @@ pub fn call_nac3_ndarray_util_assert_shape_no_negative<'ctx, G: CodeGenerator +
         ctx,
         &name,
         Some(llvm_usize.into()),
-        &[(llvm_usize.into(), ndims.into()), (llvm_pusize.into(), shape.into())],
+        &[
+            (llvm_usize.into(), shape.size(ctx, generator).into()),
+            (llvm_pusize.into(), shape.base_ptr(ctx, generator).into()),
+        ],
         None,
         None,
     );
 }
 
+/// Generates a call to `__nac3_ndarray_util_assert_shape_output_shape_same`.
+///
+/// Asserts that `ndarray_shape` and `output_shape` are the same in the context of writing output to
+/// an `ndarray`.
 pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
-    ndarray_ndims: IntValue<'ctx>,
-    ndarray_shape: PointerValue<'ctx>,
-    output_ndims: IntValue<'ctx>,
-    output_shape: IntValue<'ctx>,
+    ndarray_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    output_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
 ) {
     let llvm_usize = generator.get_size_type(ctx.ctx);
     let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
 
+    assert_eq!(
+        BasicTypeEnum::try_from(ndarray_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+    assert_eq!(
+        BasicTypeEnum::try_from(output_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
     let name = get_usize_dependent_function_name(
         generator,
         ctx,
@@ -58,16 +80,20 @@ pub fn call_nac3_ndarray_util_assert_output_shape_same<'ctx, G: CodeGenerator +
         &name,
         Some(llvm_usize.into()),
         &[
-            (llvm_usize.into(), ndarray_ndims.into()),
-            (llvm_pusize.into(), ndarray_shape.into()),
-            (llvm_usize.into(), output_ndims.into()),
-            (llvm_pusize.into(), output_shape.into()),
+            (llvm_usize.into(), ndarray_shape.size(ctx, generator).into()),
+            (llvm_pusize.into(), ndarray_shape.base_ptr(ctx, generator).into()),
+            (llvm_usize.into(), output_shape.size(ctx, generator).into()),
+            (llvm_pusize.into(), output_shape.base_ptr(ctx, generator).into()),
         ],
         None,
         None,
     );
 }
 
+/// Generates a call to `__nac3_ndarray_size`.
+///
+/// Returns a [`usize`][CodeGenerator::get_size_type] value of the number of elements of an
+/// `ndarray`, corresponding to the value of `ndarray.size`.
 pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
@@ -90,6 +116,10 @@ pub fn call_nac3_ndarray_size<'ctx, G: CodeGenerator + ?Sized>(
     .unwrap()
 }
 
+/// Generates a call to `__nac3_ndarray_nbytes`.
+///
+/// Returns a [`usize`][CodeGenerator::get_size_type] value of the number of bytes consumed by the
+/// data of the `ndarray`, corresponding to the value of `ndarray.nbytes`.
 pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
@@ -112,6 +142,10 @@ pub fn call_nac3_ndarray_nbytes<'ctx, G: CodeGenerator + ?Sized>(
     .unwrap()
 }
 
+/// Generates a call to `__nac3_ndarray_len`.
+///
+/// Returns a [`usize`][CodeGenerator::get_size_type] value of the size of the topmost dimension of
+/// the `ndarray`, corresponding to the value of `ndarray.__len__`.
 pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
@@ -134,6 +168,9 @@ pub fn call_nac3_ndarray_len<'ctx, G: CodeGenerator + ?Sized>(
     .unwrap()
 }
 
+/// Generates a call to `__nac3_ndarray_is_c_contiguous`.
+///
+/// Returns an `i1` value indicating whether the `ndarray` is C-contiguous.
 pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
@@ -156,6 +193,9 @@ pub fn call_nac3_ndarray_is_c_contiguous<'ctx, G: CodeGenerator + ?Sized>(
     .unwrap()
 }
 
+/// Generates a call to `__nac3_ndarray_get_nth_pelement`.
+///
+/// Returns a [`PointerValue`] to the `index`-th flattened element of the `ndarray`.
 pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
@@ -167,6 +207,8 @@ pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
     let llvm_usize = generator.get_size_type(ctx.ctx);
     let llvm_ndarray = ndarray.get_type().as_base_type();
 
+    assert_eq!(index.get_type(), llvm_usize);
+
     let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_get_nth_pelement");
 
     create_and_call_function(
@@ -181,11 +223,16 @@ pub fn call_nac3_ndarray_get_nth_pelement<'ctx, G: CodeGenerator + ?Sized>(
     .unwrap()
 }
 
+/// Generates a call to `__nac3_ndarray_get_pelement_by_indices`.
+///
+/// `indices` must have the same number of elements as the number of dimensions in `ndarray`.
+///
+/// Returns a [`PointerValue`] to the element indexed by `indices`.
 pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
     ndarray: NDArrayValue<'ctx>,
-    indices: PointerValue<'ctx>,
+    indices: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
 ) -> PointerValue<'ctx> {
     let llvm_i8 = ctx.ctx.i8_type();
     let llvm_pi8 = llvm_i8.ptr_type(AddressSpace::default());
@@ -193,6 +240,11 @@ pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized
     let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
     let llvm_ndarray = ndarray.get_type().as_base_type();
 
+    assert_eq!(
+        BasicTypeEnum::try_from(indices.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
     let name =
         get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_get_pelement_by_indices");
 
@@ -202,7 +254,7 @@ pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized
         Some(llvm_pi8.into()),
         &[
             (llvm_ndarray.into(), ndarray.as_base_value().into()),
-            (llvm_pusize.into(), indices.into()),
+            (llvm_pusize.into(), indices.base_ptr(ctx, generator).into()),
         ],
         Some("pelement"),
         None,
@@ -211,6 +263,9 @@ pub fn call_nac3_ndarray_get_pelement_by_indices<'ctx, G: CodeGenerator + ?Sized
     .unwrap()
 }
 
+/// Generates a call to `__nac3_ndarray_set_strides_by_shape`.
+///
+/// Sets `ndarray.strides` assuming that `ndarray.shape` is C-contiguous.
 pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
@@ -231,6 +286,11 @@ pub fn call_nac3_ndarray_set_strides_by_shape<'ctx, G: CodeGenerator + ?Sized>(
     );
 }
 
+/// Generates a call to `__nac3_ndarray_copy_data`.
+///
+/// Copies all elements from `src_ndarray` to `dst_ndarray` using their flattened views. The number
+/// of elements in `src_ndarray` must be greater than or equal to the number of elements in
+/// `dst_ndarray`.
 pub fn call_nac3_ndarray_copy_data<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,

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

@@ -0,0 +1,82 @@
+use inkwell::values::IntValue;
+
+use crate::codegen::{
+    expr::infer_and_call_function,
+    irrt::get_usize_dependent_function_name,
+    types::{ndarray::ShapeEntryType, ProxyType},
+    values::{
+        ndarray::NDArrayValue, ArrayLikeValue, ArraySliceValue, ProxyValue, TypedArrayLikeAccessor,
+        TypedArrayLikeMutator,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_broadcast_to`.
+///
+/// Attempts to broadcast `src_ndarray` to the new shape defined by `dst_ndarray`.
+///
+/// `dst_ndarray` must meet the following preconditions:
+///
+/// - `dst_ndarray.ndims` must be initialized and matching the length of `dst_ndarray.shape`.
+/// - `dst_ndarray.shape` must be initialized and contains the target broadcast shape.
+/// - `dst_ndarray.strides` must be allocated and may contain uninitialized values.
+pub fn call_nac3_ndarray_broadcast_to<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    src_ndarray: NDArrayValue<'ctx>,
+    dst_ndarray: NDArrayValue<'ctx>,
+) {
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_to");
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[src_ndarray.as_base_value().into(), dst_ndarray.as_base_value().into()],
+        None,
+        None,
+    );
+}
+
+/// Generates a call to `__nac3_ndarray_broadcast_shapes`.
+///
+/// Attempts to calculate the resultant shape from broadcasting all shapes in `shape_entries`,
+/// writing the result to `dst_shape`.
+pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G, Shape>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    num_shape_entries: IntValue<'ctx>,
+    shape_entries: ArraySliceValue<'ctx>,
+    dst_ndims: IntValue<'ctx>,
+    dst_shape: &Shape,
+) where
+    G: CodeGenerator + ?Sized,
+    Shape: TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
+        + TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    assert_eq!(num_shape_entries.get_type(), llvm_usize);
+    assert!(ShapeEntryType::is_type(
+        generator,
+        ctx.ctx,
+        shape_entries.base_ptr(ctx, generator).get_type()
+    )
+    .is_ok());
+    assert_eq!(dst_ndims.get_type(), llvm_usize);
+    assert_eq!(dst_shape.element_type(ctx, generator), llvm_usize.into());
+
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_broadcast_shapes");
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[
+            num_shape_entries.into(),
+            shape_entries.base_ptr(ctx, generator).into(),
+            dst_ndims.into(),
+            dst_shape.base_ptr(ctx, generator).into(),
+        ],
+        None,
+        None,
+    );
+}

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

@@ -5,6 +5,11 @@ use crate::codegen::{
     CodeGenContext, CodeGenerator,
 };
 
+/// Generates a call to `__nac3_ndarray_index`.
+///
+/// Performs [basic indexing](https://numpy.org/doc/stable/user/basics.indexing.html#basic-indexing)
+/// on `src_ndarray` using `indices`, writing the result to `dst_ndarray`, corresponding to the
+/// operation `dst_ndarray = src_ndarray[indices]`.
 pub fn call_nac3_ndarray_index<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,

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

@@ -1,4 +1,5 @@
 use inkwell::{
+    types::BasicTypeEnum,
     values::{BasicValueEnum, IntValue},
     AddressSpace,
 };
@@ -9,21 +10,29 @@ use crate::codegen::{
     types::ProxyType,
     values::{
         ndarray::{NDArrayValue, NDIterValue},
-        ArrayLikeValue, ArraySliceValue, ProxyValue,
+        ProxyValue, TypedArrayLikeAccessor,
     },
     CodeGenContext, CodeGenerator,
 };
 
+/// Generates a call to `__nac3_nditer_initialize`.
+///
+/// Initializes the `iter` object.
 pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
     iter: NDIterValue<'ctx>,
     ndarray: NDArrayValue<'ctx>,
-    indices: ArraySliceValue<'ctx>,
+    indices: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
 ) {
     let llvm_usize = generator.get_size_type(ctx.ctx);
     let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
 
+    assert_eq!(
+        BasicTypeEnum::try_from(indices.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
     let name = get_usize_dependent_function_name(generator, ctx, "__nac3_nditer_initialize");
 
     create_and_call_function(
@@ -40,6 +49,10 @@ pub fn call_nac3_nditer_initialize<'ctx, G: CodeGenerator + ?Sized>(
     );
 }
 
+/// Generates a call to `__nac3_nditer_initialize_has_element`.
+///
+/// Returns an `i1` value indicating whether there are elements left to traverse for the `iter`
+/// object.
 pub fn call_nac3_nditer_has_element<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,
@@ -59,6 +72,9 @@ pub fn call_nac3_nditer_has_element<'ctx, G: CodeGenerator + ?Sized>(
     .unwrap()
 }
 
+/// Generates a call to `__nac3_nditer_next`.
+///
+/// Moves `iter` to point to the next element.
 pub fn call_nac3_nditer_next<'ctx, G: CodeGenerator + ?Sized>(
     generator: &G,
     ctx: &CodeGenContext<'ctx, '_>,

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

@@ -0,0 +1,66 @@
+use inkwell::{types::BasicTypeEnum, values::IntValue};
+
+use crate::codegen::{
+    expr::infer_and_call_function, irrt::get_usize_dependent_function_name,
+    values::TypedArrayLikeAccessor, CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_matmul_calculate_shapes`.
+///
+/// Calculates the broadcasted shapes for `a`, `b`, and the `ndarray` holding the final values of
+/// `a @ b`.
+#[allow(clippy::too_many_arguments)]
+pub fn call_nac3_ndarray_matmul_calculate_shapes<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    a_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    b_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    final_ndims: IntValue<'ctx>,
+    new_a_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    new_b_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    dst_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    assert_eq!(
+        BasicTypeEnum::try_from(a_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+    assert_eq!(
+        BasicTypeEnum::try_from(b_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+    assert_eq!(
+        BasicTypeEnum::try_from(new_a_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+    assert_eq!(
+        BasicTypeEnum::try_from(new_b_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+    assert_eq!(
+        BasicTypeEnum::try_from(dst_shape.element_type(ctx, generator)).unwrap(),
+        llvm_usize.into()
+    );
+
+    let name =
+        get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_matmul_calculate_shapes");
+
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[
+            a_shape.size(ctx, generator).into(),
+            a_shape.base_ptr(ctx, generator).into(),
+            b_shape.size(ctx, generator).into(),
+            b_shape.base_ptr(ctx, generator).into(),
+            final_ndims.into(),
+            new_a_shape.base_ptr(ctx, generator).into(),
+            new_b_shape.base_ptr(ctx, generator).into(),
+            dst_shape.base_ptr(ctx, generator).into(),
+        ],
+        None,
+        None,
+    );
+}

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

@@ -1,391 +1,17 @@
-use inkwell::{
-    types::IntType,
-    values::{BasicValueEnum, CallSiteValue, IntValue},
-    AddressSpace, IntPredicate,
-};
-use itertools::Either;
-
-use crate::codegen::{
-    llvm_intrinsics,
-    macros::codegen_unreachable,
-    stmt::gen_for_callback_incrementing,
-    values::{
-        ndarray::NDArrayValue, ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue,
-        TypedArrayLikeAccessor, TypedArrayLikeAdapter, UntypedArrayLikeAccessor,
-    },
-    CodeGenContext, CodeGenerator,
-};
+pub use array::*;
 pub use basic::*;
+pub use broadcast::*;
 pub use indexing::*;
 pub use iter::*;
+pub use matmul::*;
+pub use reshape::*;
+pub use transpose::*;
 
+mod array;
 mod basic;
+mod broadcast;
 mod indexing;
 mod iter;
-
-/// Generates a call to `__nac3_ndarray_calc_size`. Returns an [`IntValue`] representing the
-/// calculated total size.
-///
-/// * `dims` - An [`ArrayLikeIndexer`] containing the size of each dimension.
-/// * `range` - The dimension index to begin and end (exclusively) calculating the dimensions for,
-///   or [`None`] if starting from the first dimension and ending at the last dimension
-///   respectively.
-pub fn call_ndarray_calc_size<'ctx, G, Dims>(
-    generator: &G,
-    ctx: &CodeGenContext<'ctx, '_>,
-    dims: &Dims,
-    (begin, end): (Option<IntValue<'ctx>>, Option<IntValue<'ctx>>),
-) -> IntValue<'ctx>
-where
-    G: CodeGenerator + ?Sized,
-    Dims: ArrayLikeIndexer<'ctx>,
-{
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
-
-    let ndarray_calc_size_fn_name = match llvm_usize.get_bit_width() {
-        32 => "__nac3_ndarray_calc_size",
-        64 => "__nac3_ndarray_calc_size64",
-        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-    };
-    let ndarray_calc_size_fn_t = llvm_usize.fn_type(
-        &[llvm_pusize.into(), llvm_usize.into(), llvm_usize.into(), llvm_usize.into()],
-        false,
-    );
-    let ndarray_calc_size_fn =
-        ctx.module.get_function(ndarray_calc_size_fn_name).unwrap_or_else(|| {
-            ctx.module.add_function(ndarray_calc_size_fn_name, ndarray_calc_size_fn_t, None)
-        });
-
-    let begin = begin.unwrap_or_else(|| llvm_usize.const_zero());
-    let end = end.unwrap_or_else(|| dims.size(ctx, generator));
-    ctx.builder
-        .build_call(
-            ndarray_calc_size_fn,
-            &[
-                dims.base_ptr(ctx, generator).into(),
-                dims.size(ctx, generator).into(),
-                begin.into(),
-                end.into(),
-            ],
-            "",
-        )
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_int_value))
-        .map(Either::unwrap_left)
-        .unwrap()
-}
-
-/// Generates a call to `__nac3_ndarray_calc_nd_indices`. Returns a [`TypeArrayLikeAdpater`]
-/// containing `i32` indices of the flattened index.
-///
-/// * `index` - The index to compute the multidimensional index for.
-/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
-///   `NDArray`.
-pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &G,
-    ctx: &CodeGenContext<'ctx, '_>,
-    index: IntValue<'ctx>,
-    ndarray: NDArrayValue<'ctx>,
-) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
-    let llvm_void = ctx.ctx.void_type();
-    let llvm_i32 = ctx.ctx.i32_type();
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
-    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
-
-    let ndarray_calc_nd_indices_fn_name = match llvm_usize.get_bit_width() {
-        32 => "__nac3_ndarray_calc_nd_indices",
-        64 => "__nac3_ndarray_calc_nd_indices64",
-        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-    };
-    let ndarray_calc_nd_indices_fn =
-        ctx.module.get_function(ndarray_calc_nd_indices_fn_name).unwrap_or_else(|| {
-            let fn_type = llvm_void.fn_type(
-                &[llvm_usize.into(), llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into()],
-                false,
-            );
-
-            ctx.module.add_function(ndarray_calc_nd_indices_fn_name, fn_type, None)
-        });
-
-    let ndarray_num_dims = ndarray.load_ndims(ctx);
-    let ndarray_dims = ndarray.shape();
-
-    let indices = ctx.builder.build_array_alloca(llvm_i32, ndarray_num_dims, "").unwrap();
-
-    ctx.builder
-        .build_call(
-            ndarray_calc_nd_indices_fn,
-            &[
-                index.into(),
-                ndarray_dims.base_ptr(ctx, generator).into(),
-                ndarray_num_dims.into(),
-                indices.into(),
-            ],
-            "",
-        )
-        .unwrap();
-
-    TypedArrayLikeAdapter::from(
-        ArraySliceValue::from_ptr_val(indices, ndarray_num_dims, None),
-        Box::new(|_, v| v.into_int_value()),
-        Box::new(|_, v| v.into()),
-    )
-}
-
-fn call_ndarray_flatten_index_impl<'ctx, G, Indices>(
-    generator: &G,
-    ctx: &CodeGenContext<'ctx, '_>,
-    ndarray: NDArrayValue<'ctx>,
-    indices: &Indices,
-) -> IntValue<'ctx>
-where
-    G: CodeGenerator + ?Sized,
-    Indices: ArrayLikeIndexer<'ctx>,
-{
-    let llvm_i32 = ctx.ctx.i32_type();
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
-    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
-
-    debug_assert_eq!(
-        IntType::try_from(indices.element_type(ctx, generator))
-            .map(IntType::get_bit_width)
-            .unwrap_or_default(),
-        llvm_i32.get_bit_width(),
-        "Expected i32 value for argument `indices` to `call_ndarray_flatten_index_impl`"
-    );
-    debug_assert_eq!(
-        indices.size(ctx, generator).get_type().get_bit_width(),
-        llvm_usize.get_bit_width(),
-        "Expected usize integer value for argument `indices_size` to `call_ndarray_flatten_index_impl`"
-    );
-
-    let ndarray_flatten_index_fn_name = match llvm_usize.get_bit_width() {
-        32 => "__nac3_ndarray_flatten_index",
-        64 => "__nac3_ndarray_flatten_index64",
-        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-    };
-    let ndarray_flatten_index_fn =
-        ctx.module.get_function(ndarray_flatten_index_fn_name).unwrap_or_else(|| {
-            let fn_type = llvm_usize.fn_type(
-                &[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_usize.into()],
-                false,
-            );
-
-            ctx.module.add_function(ndarray_flatten_index_fn_name, fn_type, None)
-        });
-
-    let ndarray_num_dims = ndarray.load_ndims(ctx);
-    let ndarray_dims = ndarray.shape();
-
-    let index = ctx
-        .builder
-        .build_call(
-            ndarray_flatten_index_fn,
-            &[
-                ndarray_dims.base_ptr(ctx, generator).into(),
-                ndarray_num_dims.into(),
-                indices.base_ptr(ctx, generator).into(),
-                indices.size(ctx, generator).into(),
-            ],
-            "",
-        )
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_int_value))
-        .map(Either::unwrap_left)
-        .unwrap();
-
-    index
-}
-
-/// Generates a call to `__nac3_ndarray_flatten_index`. Returns the flattened index for the
-/// multidimensional index.
-///
-/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
-///   `NDArray`.
-/// * `indices` - The multidimensional index to compute the flattened index for.
-pub fn call_ndarray_flatten_index<'ctx, G, Index>(
-    generator: &G,
-    ctx: &CodeGenContext<'ctx, '_>,
-    ndarray: NDArrayValue<'ctx>,
-    indices: &Index,
-) -> IntValue<'ctx>
-where
-    G: CodeGenerator + ?Sized,
-    Index: ArrayLikeIndexer<'ctx>,
-{
-    call_ndarray_flatten_index_impl(generator, ctx, ndarray, indices)
-}
-
-/// Generates a call to `__nac3_ndarray_calc_broadcast`. Returns a tuple containing the number of
-/// dimension and size of each dimension of the resultant `ndarray`.
-pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    lhs: NDArrayValue<'ctx>,
-    rhs: NDArrayValue<'ctx>,
-) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
-
-    let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
-        32 => "__nac3_ndarray_calc_broadcast",
-        64 => "__nac3_ndarray_calc_broadcast64",
-        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-    };
-    let ndarray_calc_broadcast_fn =
-        ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
-            let fn_type = llvm_usize.fn_type(
-                &[
-                    llvm_pusize.into(),
-                    llvm_usize.into(),
-                    llvm_pusize.into(),
-                    llvm_usize.into(),
-                    llvm_pusize.into(),
-                ],
-                false,
-            );
-
-            ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
-        });
-
-    let lhs_ndims = lhs.load_ndims(ctx);
-    let rhs_ndims = rhs.load_ndims(ctx);
-    let min_ndims = llvm_intrinsics::call_int_umin(ctx, lhs_ndims, rhs_ndims, None);
-
-    gen_for_callback_incrementing(
-        generator,
-        ctx,
-        None,
-        llvm_usize.const_zero(),
-        (min_ndims, false),
-        |generator, ctx, _, idx| {
-            let idx = ctx.builder.build_int_sub(min_ndims, idx, "").unwrap();
-            let (lhs_dim_sz, rhs_dim_sz) = unsafe {
-                (
-                    lhs.shape().get_typed_unchecked(ctx, generator, &idx, None),
-                    rhs.shape().get_typed_unchecked(ctx, generator, &idx, None),
-                )
-            };
-
-            let llvm_usize_const_one = llvm_usize.const_int(1, false);
-            let lhs_eqz = ctx
-                .builder
-                .build_int_compare(IntPredicate::EQ, lhs_dim_sz, llvm_usize_const_one, "")
-                .unwrap();
-            let rhs_eqz = ctx
-                .builder
-                .build_int_compare(IntPredicate::EQ, rhs_dim_sz, llvm_usize_const_one, "")
-                .unwrap();
-            let lhs_or_rhs_eqz = ctx.builder.build_or(lhs_eqz, rhs_eqz, "").unwrap();
-
-            let lhs_eq_rhs = ctx
-                .builder
-                .build_int_compare(IntPredicate::EQ, lhs_dim_sz, rhs_dim_sz, "")
-                .unwrap();
-
-            let is_compatible = ctx.builder.build_or(lhs_or_rhs_eqz, lhs_eq_rhs, "").unwrap();
-
-            ctx.make_assert(
-                generator,
-                is_compatible,
-                "0:ValueError",
-                "operands could not be broadcast together",
-                [None, None, None],
-                ctx.current_loc,
-            );
-
-            Ok(())
-        },
-        llvm_usize.const_int(1, false),
-    )
-    .unwrap();
-
-    let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
-    let lhs_dims = lhs.shape().base_ptr(ctx, generator);
-    let lhs_ndims = lhs.load_ndims(ctx);
-    let rhs_dims = rhs.shape().base_ptr(ctx, generator);
-    let rhs_ndims = rhs.load_ndims(ctx);
-    let out_dims = ctx.builder.build_array_alloca(llvm_usize, max_ndims, "").unwrap();
-    let out_dims = ArraySliceValue::from_ptr_val(out_dims, max_ndims, None);
-
-    ctx.builder
-        .build_call(
-            ndarray_calc_broadcast_fn,
-            &[
-                lhs_dims.into(),
-                lhs_ndims.into(),
-                rhs_dims.into(),
-                rhs_ndims.into(),
-                out_dims.base_ptr(ctx, generator).into(),
-            ],
-            "",
-        )
-        .unwrap();
-
-    TypedArrayLikeAdapter::from(
-        out_dims,
-        Box::new(|_, v| v.into_int_value()),
-        Box::new(|_, v| v.into()),
-    )
-}
-
-/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
-/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
-/// array `broadcast_idx`.
-pub fn call_ndarray_calc_broadcast_index<
-    'ctx,
-    G: CodeGenerator + ?Sized,
-    BroadcastIdx: UntypedArrayLikeAccessor<'ctx>,
->(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    array: NDArrayValue<'ctx>,
-    broadcast_idx: &BroadcastIdx,
-) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
-    let llvm_i32 = ctx.ctx.i32_type();
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
-    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
-
-    let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
-        32 => "__nac3_ndarray_calc_broadcast_idx",
-        64 => "__nac3_ndarray_calc_broadcast_idx64",
-        bw => codegen_unreachable!(ctx, "Unsupported size type bit width: {}", bw),
-    };
-    let ndarray_calc_broadcast_fn =
-        ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
-            let fn_type = llvm_usize.fn_type(
-                &[llvm_pusize.into(), llvm_usize.into(), llvm_pi32.into(), llvm_pi32.into()],
-                false,
-            );
-
-            ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
-        });
-
-    let broadcast_size = broadcast_idx.size(ctx, generator);
-    let out_idx = ctx.builder.build_array_alloca(llvm_i32, broadcast_size, "").unwrap();
-
-    let array_dims = array.shape().base_ptr(ctx, generator);
-    let array_ndims = array.load_ndims(ctx);
-    let broadcast_idx_ptr = unsafe {
-        broadcast_idx.ptr_offset_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-    };
-
-    ctx.builder
-        .build_call(
-            ndarray_calc_broadcast_fn,
-            &[array_dims.into(), array_ndims.into(), broadcast_idx_ptr.into(), out_idx.into()],
-            "",
-        )
-        .unwrap();
-
-    TypedArrayLikeAdapter::from(
-        ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None),
-        Box::new(|_, v| v.into_int_value()),
-        Box::new(|_, v| v.into()),
-    )
-}
+mod matmul;
+mod reshape;
+mod transpose;

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

@@ -0,0 +1,40 @@
+use inkwell::values::IntValue;
+
+use crate::codegen::{
+    expr::infer_and_call_function,
+    irrt::get_usize_dependent_function_name,
+    values::{ArrayLikeValue, ArraySliceValue},
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_reshape_resolve_and_check_new_shape`.
+///
+/// Resolves unknown dimensions in `new_shape` for `numpy.reshape(<ndarray>, new_shape)`, raising an
+/// assertion if multiple dimensions are unknown (`-1`).
+pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    size: IntValue<'ctx>,
+    new_ndims: IntValue<'ctx>,
+    new_shape: ArraySliceValue<'ctx>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    assert_eq!(size.get_type(), llvm_usize);
+    assert_eq!(new_ndims.get_type(), llvm_usize);
+    assert_eq!(new_shape.element_type(ctx, generator), llvm_usize.into());
+
+    let name = get_usize_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_reshape_resolve_and_check_new_shape",
+    );
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[size.into(), new_ndims.into(), new_shape.base_ptr(ctx, generator).into()],
+        None,
+        None,
+    );
+}

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

@@ -0,0 +1,48 @@
+use inkwell::{values::IntValue, AddressSpace};
+
+use crate::codegen::{
+    expr::infer_and_call_function,
+    irrt::get_usize_dependent_function_name,
+    values::{ndarray::NDArrayValue, ProxyValue, TypedArrayLikeAccessor},
+    CodeGenContext, CodeGenerator,
+};
+
+/// Generates a call to `__nac3_ndarray_transpose`.
+///
+/// Creates a transpose view of `src_ndarray` and writes the result to `dst_ndarray`.
+///
+/// `dst_ndarray` must fulfill the following preconditions:
+///
+/// - `dst_ndarray.ndims` must be initialized and must be equal to `src_ndarray.ndims`.
+/// - `dst_ndarray.shape` must be allocated and may contain uninitialized values.
+/// - `dst_ndarray.strides` must be allocated and may contain uninitialized values.
+pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    src_ndarray: NDArrayValue<'ctx>,
+    dst_ndarray: NDArrayValue<'ctx>,
+    axes: Option<&impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    assert!(axes.is_none_or(|axes| axes.size(ctx, generator).get_type() == llvm_usize));
+    assert!(axes.is_none_or(|axes| axes.element_type(ctx, generator) == llvm_usize.into()));
+
+    let name = get_usize_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[
+            src_ndarray.as_base_value().into(),
+            dst_ndarray.as_base_value().into(),
+            axes.map_or(llvm_usize.const_zero(), |axes| axes.size(ctx, generator)).into(),
+            axes.map_or(llvm_usize.ptr_type(AddressSpace::default()).const_null(), |axes| {
+                axes.base_ptr(ctx, generator)
+            })
+            .into(),
+        ],
+        None,
+        None,
+    );
+}

nac3core/src/codegen/irrt/range.rs

@@ -6,6 +6,13 @@ use itertools::Either;
 
 use crate::codegen::{CodeGenContext, CodeGenerator};
 
+/// Invokes the `__nac3_range_slice_len` in IRRT.
+///
+/// - `start`: The `i32` start value for the slice.
+/// - `end`: The `i32` end value for the slice.
+/// - `step`: The `i32` step value for the slice.
+///
+/// Returns an `i32` value of the length of the slice.
 pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -14,9 +21,15 @@ pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
     step: IntValue<'ctx>,
 ) -> IntValue<'ctx> {
     const SYMBOL: &str = "__nac3_range_slice_len";
+
+    let llvm_i32 = ctx.ctx.i32_type();
+
+    assert_eq!(start.get_type(), llvm_i32);
+    assert_eq!(end.get_type(), llvm_i32);
+    assert_eq!(step.get_type(), llvm_i32);
+
     let len_func = ctx.module.get_function(SYMBOL).unwrap_or_else(|| {
-        let i32_t = ctx.ctx.i32_type();
-        let fn_t = i32_t.fn_type(&[i32_t.into(), i32_t.into(), i32_t.into()], false);
+        let fn_t = llvm_i32.fn_type(&[llvm_i32.into(), llvm_i32.into(), llvm_i32.into()], false);
         ctx.module.add_function(SYMBOL, fn_t, None)
     });
 
@@ -33,6 +46,7 @@ pub fn calculate_len_for_slice_range<'ctx, G: CodeGenerator + ?Sized>(
         [None, None, None],
         ctx.current_loc,
     );
+
     ctx.builder
         .build_call(len_func, &[start.into(), end.into(), step.into()], "calc_len")
         .map(CallSiteValue::try_as_basic_value)

nac3core/src/codegen/irrt/string.rs

@@ -0,0 +1,46 @@
+use inkwell::values::{BasicValueEnum, CallSiteValue, IntValue, PointerValue};
+use itertools::Either;
+
+use super::get_usize_dependent_function_name;
+use crate::codegen::{CodeGenContext, CodeGenerator};
+
+/// Generates a call to string equality comparison. Returns an `i1` representing whether the strings are equal.
+pub fn call_string_eq<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    str1_ptr: PointerValue<'ctx>,
+    str1_len: IntValue<'ctx>,
+    str2_ptr: PointerValue<'ctx>,
+    str2_len: IntValue<'ctx>,
+) -> IntValue<'ctx> {
+    let llvm_i1 = ctx.ctx.bool_type();
+
+    let func_name = get_usize_dependent_function_name(generator, ctx, "nac3_str_eq");
+
+    let func = ctx.module.get_function(&func_name).unwrap_or_else(|| {
+        ctx.module.add_function(
+            &func_name,
+            llvm_i1.fn_type(
+                &[
+                    str1_ptr.get_type().into(),
+                    str1_len.get_type().into(),
+                    str2_ptr.get_type().into(),
+                    str2_len.get_type().into(),
+                ],
+                false,
+            ),
+            None,
+        )
+    });
+
+    ctx.builder
+        .build_call(
+            func,
+            &[str1_ptr.into(), str1_len.into(), str2_ptr.into(), str2_len.into()],
+            "str_eq_call",
+        )
+        .map(CallSiteValue::try_as_basic_value)
+        .map(|v| v.map_left(BasicValueEnum::into_int_value))
+        .map(Either::unwrap_left)
+        .unwrap()
+}

nac3core/src/codegen/llvm_intrinsics.rs

@@ -1,7 +1,6 @@
 use inkwell::{
-    context::Context,
     intrinsics::Intrinsic,
-    types::{AnyTypeEnum::IntType, FloatType},
+    types::AnyTypeEnum::IntType,
     values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
     AddressSpace,
 };
@@ -9,34 +8,6 @@ use itertools::Either;
 
 use super::CodeGenContext;
 
-/// Returns the string representation for the floating-point type `ft` when used in intrinsic
-/// functions.
-fn get_float_intrinsic_repr(ctx: &Context, ft: FloatType) -> &'static str {
-    // Standard LLVM floating-point types
-    if ft == ctx.f16_type() {
-        return "f16";
-    }
-    if ft == ctx.f32_type() {
-        return "f32";
-    }
-    if ft == ctx.f64_type() {
-        return "f64";
-    }
-    if ft == ctx.f128_type() {
-        return "f128";
-    }
-
-    // Non-standard floating-point types
-    if ft == ctx.x86_f80_type() {
-        return "f80";
-    }
-    if ft == ctx.ppc_f128_type() {
-        return "ppcf128";
-    }
-
-    unreachable!()
-}
-
 /// Invokes the [`llvm.va_start`](https://llvm.org/docs/LangRef.html#llvm-va-start-intrinsic)
 /// intrinsic.
 pub fn call_va_start<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue<'ctx>) {
@@ -54,7 +25,7 @@ pub fn call_va_start<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue
     ctx.builder.build_call(intrinsic_fn, &[arglist.into()], "").unwrap();
 }
 
-/// Invokes the [`llvm.va_start`](https://llvm.org/docs/LangRef.html#llvm-va-start-intrinsic)
+/// Invokes the [`llvm.va_end`](https://llvm.org/docs/LangRef.html#llvm-va-end-intrinsic)
 /// intrinsic.
 pub fn call_va_end<'ctx>(ctx: &CodeGenContext<'ctx, '_>, arglist: PointerValue<'ctx>) {
     const FN_NAME: &str = "llvm.va_end";

nac3core/src/codegen/mod.rs

@@ -42,7 +42,7 @@ use crate::{
 };
 use concrete_type::{ConcreteType, ConcreteTypeEnum, ConcreteTypeStore};
 pub use generator::{CodeGenerator, DefaultCodeGenerator};
-use types::{ndarray::NDArrayType, ListType, ProxyType, RangeType};
+use types::{ndarray::NDArrayType, ListType, ProxyType, RangeType, TupleType};
 
 pub mod builtin_fns;
 pub mod concrete_type;
@@ -228,7 +228,7 @@ pub struct CodeGenContext<'ctx, 'a> {
     pub current_loc: Location,
 }
 
-impl<'ctx, 'a> CodeGenContext<'ctx, 'a> {
+impl CodeGenContext<'_, '_> {
     /// Whether the [current basic block][Builder::get_insert_block] referenced by `builder`
     /// contains a [terminator statement][BasicBlock::get_terminator].
     pub fn is_terminated(&self) -> bool {
@@ -520,7 +520,7 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                                 ctx, module, generator, unifier, top_level, type_cache, dtype,
                             );
 
-                            NDArrayType::new(generator, ctx, element_type, Some(ndims)).as_base_type().into()
+                            NDArrayType::new(generator, ctx, element_type, ndims).as_base_type().into()
                         }
 
                         _ => unreachable!(
@@ -574,7 +574,7 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
                         get_llvm_type(ctx, module, generator, unifier, top_level, type_cache, *ty)
                     })
                     .collect_vec();
-                ctx.struct_type(&fields, false).into()
+                TupleType::new(generator, ctx, &fields).as_base_type().into()
             }
             TVirtual { .. } => unimplemented!(),
             _ => unreachable!("{}", ty_enum.get_type_name()),

nac3core/src/codegen/stmt.rs

@@ -16,7 +16,11 @@ use super::{
     gen_in_range_check,
     irrt::{handle_slice_indices, list_slice_assignment},
     macros::codegen_unreachable,
-    values::{ArrayLikeIndexer, ArraySliceValue, ListValue, RangeValue},
+    types::ndarray::NDArrayType,
+    values::{
+        ndarray::{RustNDIndex, ScalarOrNDArray},
+        ArrayLikeIndexer, ArraySliceValue, ListValue, ProxyValue, RangeValue,
+    },
     CodeGenContext, CodeGenerator,
 };
 use crate::{
@@ -411,7 +415,52 @@ pub fn gen_setitem<'ctx, G: CodeGenerator>(
             if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
         {
             // Handle NDArray item assignment
-            todo!("ndarray subscript assignment is not yet implemented");
+            // Process target
+            let target = generator
+                .gen_expr(ctx, target)?
+                .unwrap()
+                .to_basic_value_enum(ctx, generator, target_ty)?;
+
+            // Process key
+            let key = RustNDIndex::from_subscript_expr(generator, ctx, key)?;
+
+            // Process value
+            let value = value.to_basic_value_enum(ctx, generator, value_ty)?;
+
+            // Reference code:
+            // ```python
+            // target = target[key]
+            // value = np.asarray(value)
+            //
+            // shape = np.broadcast_shape((target, value))
+            //
+            // target = np.broadcast_to(target, shape)
+            // value = np.broadcast_to(value, shape)
+            //
+            // # ...and finally copy 1-1 from value to target.
+            // ```
+
+            let target = NDArrayType::from_unifier_type(generator, ctx, target_ty)
+                .map_value(target.into_pointer_value(), None);
+            let target = target.index(generator, ctx, &key);
+
+            let value = ScalarOrNDArray::from_value(generator, ctx, (value_ty, value))
+                .to_ndarray(generator, ctx);
+
+            let broadcast_ndims =
+                [target.get_type().ndims(), value.get_type().ndims()].into_iter().max().unwrap();
+            let broadcast_result = NDArrayType::new(
+                generator,
+                ctx.ctx,
+                value.get_type().element_type(),
+                broadcast_ndims,
+            )
+            .broadcast(generator, ctx, &[target, value]);
+
+            let target = broadcast_result.ndarrays[0];
+            let value = broadcast_result.ndarrays[1];
+
+            target.copy_data_from(generator, ctx, value);
         }
         _ => {
             panic!("encountered unknown target type: {}", ctx.unifier.stringify(target_ty));

nac3core/src/codegen/test.rs

@@ -36,7 +36,6 @@ use crate::{
 struct Resolver {
     id_to_type: HashMap<StrRef, Type>,
     id_to_def: RwLock<HashMap<StrRef, DefinitionId>>,
-    class_names: HashMap<StrRef, Type>,
 }
 
 impl Resolver {
@@ -104,11 +103,9 @@ fn test_primitives() {
     let top_level = Arc::new(composer.make_top_level_context());
     unifier.top_level = Some(top_level.clone());
 
-    let resolver = Arc::new(Resolver {
-        id_to_type: HashMap::new(),
-        id_to_def: RwLock::new(HashMap::new()),
-        class_names: HashMap::default(),
-    }) as Arc<dyn SymbolResolver + Send + Sync>;
+    let resolver =
+        Arc::new(Resolver { id_to_type: HashMap::new(), id_to_def: RwLock::new(HashMap::new()) })
+            as Arc<dyn SymbolResolver + Send + Sync>;
 
     let threads = vec![DefaultCodeGenerator::new("test".into(), 32).into()];
     let signature = FunSignature {
@@ -298,11 +295,7 @@ fn test_simple_call() {
         loc: None,
     })));
 
-    let resolver = Resolver {
-        id_to_type: HashMap::new(),
-        id_to_def: RwLock::new(HashMap::new()),
-        class_names: HashMap::default(),
-    };
+    let resolver = Resolver { id_to_type: HashMap::new(), id_to_def: RwLock::new(HashMap::new()) };
     resolver.add_id_def("foo".into(), DefinitionId(foo_id));
     let resolver = Arc::new(resolver) as Arc<dyn SymbolResolver + Send + Sync>;
 
@@ -471,6 +464,6 @@ fn test_classes_ndarray_type_new() {
     let llvm_i32 = ctx.i32_type();
     let llvm_usize = generator.get_size_type(&ctx);
 
-    let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into(), None);
+    let llvm_ndarray = NDArrayType::new(&generator, &ctx, llvm_i32.into(), 2);
     assert!(NDArrayType::is_representable(llvm_ndarray.as_base_type(), llvm_usize).is_ok());
 }

nac3core/src/codegen/types/list.rs

@@ -1,69 +1,113 @@
 use inkwell::{
-    context::Context,
+    context::{AsContextRef, Context},
     types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
-    values::IntValue,
-    AddressSpace,
+    values::{IntValue, PointerValue},
+    AddressSpace, IntPredicate, OptimizationLevel,
 };
+use itertools::Itertools;
+
+use nac3core_derive::StructFields;
 
 use super::ProxyType;
-use crate::codegen::{
-    values::{ArraySliceValue, ListValue, ProxyValue},
-    CodeGenContext, CodeGenerator,
+use crate::{
+    codegen::{
+        types::structure::{
+            check_struct_type_matches_fields, FieldIndexCounter, StructField, StructFields,
+        },
+        values::{ListValue, ProxyValue},
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::{iter_type_vars, Type, TypeEnum},
 };
 
 /// Proxy type for a `list` type in LLVM.
 #[derive(Debug, PartialEq, Eq, Clone, Copy)]
 pub struct ListType<'ctx> {
     ty: PointerType<'ctx>,
+    item: Option<BasicTypeEnum<'ctx>>,
     llvm_usize: IntType<'ctx>,
 }
 
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct ListStructFields<'ctx> {
+    /// Array pointer to content.
+    #[value_type(i8_type().ptr_type(AddressSpace::default()))]
+    pub items: StructField<'ctx, PointerValue<'ctx>>,
+
+    /// Number of items in the array.
+    #[value_type(usize)]
+    pub len: StructField<'ctx, IntValue<'ctx>>,
+}
+
+impl<'ctx> ListStructFields<'ctx> {
+    #[must_use]
+    pub fn new_typed(item: BasicTypeEnum<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        let mut counter = FieldIndexCounter::default();
+
+        ListStructFields {
+            items: StructField::create(
+                &mut counter,
+                "items",
+                item.ptr_type(AddressSpace::default()),
+            ),
+            len: StructField::create(&mut counter, "len", llvm_usize),
+        }
+    }
+}
+
 impl<'ctx> ListType<'ctx> {
     /// Checks whether `llvm_ty` represents a `list` type, returning [Err] if it does not.
     pub fn is_representable(
         llvm_ty: PointerType<'ctx>,
         llvm_usize: IntType<'ctx>,
     ) -> Result<(), String> {
-        let llvm_list_ty = llvm_ty.get_element_type();
-        let AnyTypeEnum::StructType(llvm_list_ty) = llvm_list_ty else {
-            return Err(format!("Expected struct type for `list` type, got {llvm_list_ty}"));
-        };
-        if llvm_list_ty.count_fields() != 2 {
-            return Err(format!(
-                "Expected 2 fields in `list`, got {}",
-                llvm_list_ty.count_fields()
-            ));
-        }
+        let ctx = llvm_ty.get_context();
 
-        let list_size_ty = llvm_list_ty.get_field_type_at_index(0).unwrap();
-        let Ok(_) = PointerType::try_from(list_size_ty) else {
-            return Err(format!("Expected pointer type for `list.0`, got {list_size_ty}"));
+        let llvm_ty = llvm_ty.get_element_type();
+        let AnyTypeEnum::StructType(llvm_ty) = llvm_ty else {
+            return Err(format!("Expected struct type for `list` type, got {llvm_ty}"));
         };
 
-        let list_data_ty = llvm_list_ty.get_field_type_at_index(1).unwrap();
-        let Ok(list_data_ty) = IntType::try_from(list_data_ty) else {
-            return Err(format!("Expected int type for `list.1`, got {list_data_ty}"));
-        };
-        if list_data_ty.get_bit_width() != llvm_usize.get_bit_width() {
-            return Err(format!(
-                "Expected {}-bit int type for `list.1`, got {}-bit int",
-                llvm_usize.get_bit_width(),
-                list_data_ty.get_bit_width()
-            ));
-        }
+        let fields = ListStructFields::new(ctx, llvm_usize);
 
-        Ok(())
+        check_struct_type_matches_fields(
+            fields,
+            llvm_ty,
+            "list",
+            &[(fields.items.name(), &|ty| {
+                if ty.is_pointer_type() {
+                    Ok(())
+                } else {
+                    Err(format!("Expected T* for `list.items`, got {ty}"))
+                }
+            })],
+        )
+    }
+
+    /// Returns an instance of [`StructFields`] containing all field accessors for this type.
+    #[must_use]
+    fn fields(item: BasicTypeEnum<'ctx>, llvm_usize: IntType<'ctx>) -> ListStructFields<'ctx> {
+        ListStructFields::new_typed(item, llvm_usize)
+    }
+
+    /// See [`ListType::fields`].
+    // TODO: Move this into e.g. StructProxyType
+    #[must_use]
+    pub fn get_fields(&self, _ctx: &impl AsContextRef<'ctx>) -> ListStructFields<'ctx> {
+        Self::fields(self.item.unwrap_or(self.llvm_usize.into()), self.llvm_usize)
     }
 
     /// Creates an LLVM type corresponding to the expected structure of a `List`.
     #[must_use]
     fn llvm_type(
         ctx: &'ctx Context,
-        element_type: BasicTypeEnum<'ctx>,
+        element_type: Option<BasicTypeEnum<'ctx>>,
         llvm_usize: IntType<'ctx>,
     ) -> PointerType<'ctx> {
-        // struct List { data: T*, size: size_t }
-        let field_tys = [element_type.ptr_type(AddressSpace::default()).into(), llvm_usize.into()];
+        let element_type = element_type.unwrap_or(llvm_usize.into());
+
+        let field_tys =
+            Self::fields(element_type, llvm_usize).into_iter().map(|field| field.1).collect_vec();
 
         ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
     }
@@ -76,9 +120,50 @@ impl<'ctx> ListType<'ctx> {
         element_type: BasicTypeEnum<'ctx>,
     ) -> Self {
         let llvm_usize = generator.get_size_type(ctx);
-        let llvm_list = Self::llvm_type(ctx, element_type, llvm_usize);
+        let llvm_list = Self::llvm_type(ctx, Some(element_type), llvm_usize);
 
-        ListType::from_type(llvm_list, llvm_usize)
+        Self { ty: llvm_list, item: Some(element_type), llvm_usize }
+    }
+
+    /// Creates an instance of [`ListType`] with an unknown element type.
+    #[must_use]
+    pub fn new_untyped<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        let llvm_list = Self::llvm_type(ctx, None, llvm_usize);
+
+        Self { ty: llvm_list, item: None, llvm_usize }
+    }
+
+    /// Creates an [`ListType`] from a [unifier type][Type].
+    #[must_use]
+    pub fn from_unifier_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ty: Type,
+    ) -> Self {
+        // Check unifier type and extract `item_type`
+        let elem_type = match &*ctx.unifier.get_ty_immutable(ty) {
+            TypeEnum::TObj { obj_id, params, .. }
+                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+            {
+                iter_type_vars(params).next().unwrap().ty
+            }
+
+            _ => panic!("Expected `list` type, but got {}", ctx.unifier.stringify(ty)),
+        };
+
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+        let llvm_elem_type = if let TypeEnum::TVar { .. } = &*ctx.unifier.get_ty_immutable(ty) {
+            None
+        } else {
+            Some(ctx.get_llvm_type(generator, elem_type))
+        };
+
+        Self {
+            ty: Self::llvm_type(ctx.ctx, llvm_elem_type, llvm_usize),
+            item: llvm_elem_type,
+            llvm_usize,
+        }
     }
 
     /// Creates an [`ListType`] from a [`PointerType`].
@@ -86,47 +171,141 @@ impl<'ctx> ListType<'ctx> {
     pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
         debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
 
-        ListType { ty: ptr_ty, llvm_usize }
+        let ctx = ptr_ty.get_context();
+
+        // We are just searching for the index off a field - Slot an arbitrary element type in.
+        let item_field_idx =
+            Self::fields(ctx.i8_type().into(), llvm_usize).index_of_field(|f| f.items);
+        let item = unsafe {
+            ptr_ty
+                .get_element_type()
+                .into_struct_type()
+                .get_field_type_at_index_unchecked(item_field_idx)
+                .into_pointer_type()
+                .get_element_type()
+        };
+        let item = BasicTypeEnum::try_from(item).unwrap_or_else(|()| {
+            panic!(
+                "Expected BasicTypeEnum for list element type, got {}",
+                ptr_ty.get_element_type().print_to_string()
+            )
+        });
+
+        ListType { ty: ptr_ty, item: Some(item), llvm_usize }
     }
 
     /// Returns the type of the `size` field of this `list` type.
     #[must_use]
     pub fn size_type(&self) -> IntType<'ctx> {
-        self.as_base_type()
-            .get_element_type()
-            .into_struct_type()
-            .get_field_type_at_index(1)
-            .map(BasicTypeEnum::into_int_type)
-            .unwrap()
+        self.llvm_usize
     }
 
     /// Returns the element type of this `list` type.
     #[must_use]
-    pub fn element_type(&self) -> AnyTypeEnum<'ctx> {
-        self.as_base_type()
-            .get_element_type()
-            .into_struct_type()
-            .get_field_type_at_index(0)
-            .map(BasicTypeEnum::into_pointer_type)
-            .map(PointerType::get_element_type)
-            .unwrap()
+    pub fn element_type(&self) -> Option<BasicTypeEnum<'ctx>> {
+        self.item
     }
 
     /// Allocates an instance of [`ListValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
     #[must_use]
-    pub fn alloca<G: CodeGenerator + ?Sized>(
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`ListValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            self.raw_alloca(generator, ctx, name),
+            self.raw_alloca_var(generator, ctx, name),
             self.llvm_usize,
             name,
         )
     }
 
+    /// Allocates a [`ListValue`] on the stack using `item` of this [`ListType`] instance.
+    ///
+    /// The returned list will contain:
+    ///
+    /// - `data`: Allocated with `len` number of elements.
+    /// - `len`: Initialized to the value of `len` passed to this function.
+    #[must_use]
+    pub fn construct<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        len: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let len = ctx.builder.build_int_z_extend(len, self.llvm_usize, "").unwrap();
+
+        // Generate a runtime assertion if allocating a non-empty list with unknown element type
+        if ctx.registry.llvm_options.opt_level == OptimizationLevel::None && self.item.is_none() {
+            let len_eqz = ctx
+                .builder
+                .build_int_compare(IntPredicate::EQ, len, self.llvm_usize.const_zero(), "")
+                .unwrap();
+
+            ctx.make_assert(
+                generator,
+                len_eqz,
+                "0:AssertionError",
+                "Cannot allocate a non-empty list with unknown element type",
+                [None, None, None],
+                ctx.current_loc,
+            );
+        }
+
+        let plist = self.alloca_var(generator, ctx, name);
+        plist.store_size(ctx, generator, len);
+
+        let item = self.item.unwrap_or(self.llvm_usize.into());
+        plist.create_data(ctx, item, None);
+
+        plist
+    }
+
+    /// Convenience function for creating a list with zero elements.
+    ///
+    /// This function is preferred over [`ListType::construct`] if the length is known to always be
+    /// 0, as this function avoids injecting an IR assertion for checking if a non-empty untyped
+    /// list is being allocated.
+    ///
+    /// The returned list will contain:
+    ///
+    /// - `data`: Initialized to `(T*) 0`.
+    /// - `len`: Initialized to `0`.
+    #[must_use]
+    pub fn construct_empty<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let plist = self.alloca_var(generator, ctx, name);
+
+        plist.store_size(ctx, generator, self.llvm_usize.const_zero());
+        plist.create_data(ctx, self.item.unwrap_or(self.llvm_usize.into()), None);
+
+        plist
+    }
+
     /// Converts an existing value into a [`ListValue`].
     #[must_use]
     pub fn map_value(
@@ -162,36 +341,8 @@ impl<'ctx> ProxyType<'ctx> for ListType<'ctx> {
         Self::is_representable(llvm_ty, generator.get_size_type(ctx))
     }
 
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base {
-        generator
-            .gen_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                name,
-            )
-            .unwrap()
-    }
-
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx> {
-        generator
-            .gen_array_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                size,
-                name,
-            )
-            .unwrap()
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
     }
 
     fn as_base_type(&self) -> Self::Base {

nac3core/src/codegen/types/mod.rs

@@ -16,7 +16,11 @@
 //!   the returned object. This is similar to a `new` expression in C++ but the object is allocated
 //!   on the stack.
 
-use inkwell::{context::Context, types::BasicType, values::IntValue};
+use inkwell::{
+    context::Context,
+    types::BasicType,
+    values::{IntValue, PointerValue},
+};
 
 use super::{
     values::{ArraySliceValue, ProxyValue},
@@ -24,11 +28,13 @@ use super::{
 };
 pub use list::*;
 pub use range::*;
+pub use tuple::*;
 
 mod list;
 pub mod ndarray;
 mod range;
 pub mod structure;
+mod tuple;
 pub mod utils;
 
 /// A LLVM type that is used to represent a corresponding type in NAC3.
@@ -53,23 +59,66 @@ pub trait ProxyType<'ctx>: Into<Self::Base> {
         llvm_ty: Self::Base,
     ) -> Result<(), String>;
 
-    /// Creates a new value of this type, returning the LLVM instance of this value.
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
+    /// Returns the type that should be used in `alloca` IR statements.
+    fn alloca_type(&self) -> impl BasicType<'ctx>;
+
+    /// Creates a new value of this type by invoking `alloca` at the current builder location,
+    /// returning a [`PointerValue`] instance representing the allocated value.
+    fn raw_alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> PointerValue<'ctx> {
+        ctx.builder
+            .build_alloca(self.alloca_type().as_basic_type_enum(), name.unwrap_or_default())
+            .unwrap()
+    }
+
+    /// Creates a new value of this type by invoking `alloca` at the beginning of the function,
+    /// returning a [`PointerValue`] instance representing the allocated value.
+    fn raw_alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base;
+    ) -> PointerValue<'ctx> {
+        generator.gen_var_alloc(ctx, self.alloca_type().as_basic_type_enum(), name).unwrap()
+    }
 
-    /// Creates a new array value of this type, returning an [`ArraySliceValue`] encapsulating the
-    /// resulting array.
-    fn array_alloca<G: CodeGenerator + ?Sized>(
+    /// Creates a new array value of this type by invoking `alloca` at the current builder location,
+    /// returning an [`ArraySliceValue`] encapsulating the resulting array.
+    fn array_alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        size: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> ArraySliceValue<'ctx> {
+        ArraySliceValue::from_ptr_val(
+            ctx.builder
+                .build_array_alloca(
+                    self.alloca_type().as_basic_type_enum(),
+                    size,
+                    name.unwrap_or_default(),
+                )
+                .unwrap(),
+            size,
+            name,
+        )
+    }
+
+    /// Creates a new array value of this type by invoking `alloca` at the beginning of the
+    /// function, returning an [`ArraySliceValue`] encapsulating the resulting array.
+    fn array_alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         size: IntValue<'ctx>,
         name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx>;
+    ) -> ArraySliceValue<'ctx> {
+        generator
+            .gen_array_var_alloc(ctx, self.alloca_type().as_basic_type_enum(), size, name)
+            .unwrap()
+    }
 
     /// Returns the [base type][Self::Base] of this proxy.
     fn as_base_type(&self) -> Self::Base;

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

@@ -0,0 +1,243 @@
+use inkwell::{
+    types::BasicTypeEnum,
+    values::{BasicValueEnum, IntValue},
+    AddressSpace,
+};
+
+use crate::{
+    codegen::{
+        irrt,
+        stmt::gen_if_else_expr_callback,
+        types::{ndarray::NDArrayType, ListType, ProxyType},
+        values::{
+            ndarray::NDArrayValue, ArrayLikeValue, ArraySliceValue, ListValue, ProxyValue,
+            TypedArrayLikeAdapter, TypedArrayLikeMutator,
+        },
+        CodeGenContext, CodeGenerator,
+    },
+    toplevel::helper::{arraylike_flatten_element_type, arraylike_get_ndims},
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+/// Get the expected `dtype` and `ndims` of the ndarray returned by `np_array(<list>)`.
+fn get_list_object_dtype_and_ndims<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    list_ty: Type,
+) -> (BasicTypeEnum<'ctx>, u64) {
+    let dtype = arraylike_flatten_element_type(&mut ctx.unifier, list_ty);
+    let ndims = arraylike_get_ndims(&mut ctx.unifier, list_ty);
+
+    (ctx.get_llvm_type(generator, dtype), ndims)
+}
+
+impl<'ctx> NDArrayType<'ctx> {
+    /// Implementation of `np_array(<list>, copy=True)`
+    fn construct_numpy_array_from_list_copy_true_impl<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        (list_ty, list): (Type, ListValue<'ctx>),
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let (dtype, ndims_int) = get_list_object_dtype_and_ndims(generator, ctx, list_ty);
+        assert!(self.ndims >= ndims_int);
+        assert_eq!(dtype, self.dtype);
+
+        let list_value = list.as_i8_list(generator, ctx);
+
+        // Validate `list` has a consistent shape.
+        // Raise an exception if `list` is something abnormal like `[[1, 2], [3]]`.
+        // If `list` has a consistent shape, deduce the shape and write it to `shape`.
+        let ndims = self.llvm_usize.const_int(ndims_int, false);
+        let shape = ctx.builder.build_array_alloca(self.llvm_usize, ndims, "").unwrap();
+        let shape = ArraySliceValue::from_ptr_val(shape, ndims, None);
+        let shape = TypedArrayLikeAdapter::from(
+            shape,
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+        irrt::ndarray::call_nac3_ndarray_array_set_and_validate_list_shape(
+            generator, ctx, list_value, ndims, &shape,
+        );
+
+        let ndarray = Self::new(generator, ctx.ctx, dtype, ndims_int)
+            .construct_uninitialized(generator, ctx, name);
+        ndarray.copy_shape_from_array(generator, ctx, shape.base_ptr(ctx, generator));
+        unsafe { ndarray.create_data(generator, ctx) };
+
+        // Copy all contents from the list.
+        irrt::ndarray::call_nac3_ndarray_array_write_list_to_array(
+            generator, ctx, list_value, ndarray,
+        );
+
+        ndarray
+    }
+
+    /// Implementation of `np_array(<list>, copy=None)`
+    fn construct_numpy_array_from_list_copy_none_impl<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        (list_ty, list): (Type, ListValue<'ctx>),
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        // np_array without copying is only possible `list` is not nested.
+        //
+        // If `list` is `list[T]`, we can create an ndarray with `data` set
+        // to the array pointer of `list`.
+        //
+        // If `list` is `list[list[T]]` or worse, copy.
+
+        let (dtype, ndims) = get_list_object_dtype_and_ndims(generator, ctx, list_ty);
+        if ndims == 1 {
+            // `list` is not nested
+            assert_eq!(ndims, 1);
+            assert!(self.ndims >= ndims);
+            assert_eq!(dtype, self.dtype);
+
+            let llvm_pi8 = ctx.ctx.i8_type().ptr_type(AddressSpace::default());
+
+            let ndarray = Self::new(generator, ctx.ctx, dtype, 1)
+                .construct_uninitialized(generator, ctx, name);
+
+            // Set data
+            let data = ctx
+                .builder
+                .build_pointer_cast(list.data().base_ptr(ctx, generator), llvm_pi8, "")
+                .unwrap();
+            ndarray.store_data(ctx, data);
+
+            // ndarray->shape[0] = list->len;
+            let shape = ndarray.shape();
+            let list_len = list.load_size(ctx, None);
+            unsafe {
+                shape.set_typed_unchecked(ctx, generator, &self.llvm_usize.const_zero(), list_len);
+            }
+
+            // Set strides, the `data` is contiguous
+            ndarray.set_strides_contiguous(generator, ctx);
+
+            ndarray
+        } else {
+            // `list` is nested, copy
+            self.construct_numpy_array_from_list_copy_true_impl(
+                generator,
+                ctx,
+                (list_ty, list),
+                name,
+            )
+        }
+    }
+
+    /// Implementation of `np_array(<list>, copy=copy)`
+    fn construct_numpy_array_list_impl<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        (list_ty, list): (Type, ListValue<'ctx>),
+        copy: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(copy.get_type(), ctx.ctx.bool_type());
+
+        let (dtype, ndims) = get_list_object_dtype_and_ndims(generator, ctx, list_ty);
+
+        let ndarray = gen_if_else_expr_callback(
+            generator,
+            ctx,
+            |_generator, _ctx| Ok(copy),
+            |generator, ctx| {
+                let ndarray = self.construct_numpy_array_from_list_copy_true_impl(
+                    generator,
+                    ctx,
+                    (list_ty, list),
+                    name,
+                );
+                Ok(Some(ndarray.as_base_value()))
+            },
+            |generator, ctx| {
+                let ndarray = self.construct_numpy_array_from_list_copy_none_impl(
+                    generator,
+                    ctx,
+                    (list_ty, list),
+                    name,
+                );
+                Ok(Some(ndarray.as_base_value()))
+            },
+        )
+        .unwrap()
+        .map(BasicValueEnum::into_pointer_value)
+        .unwrap();
+
+        NDArrayType::new(generator, ctx.ctx, dtype, ndims).map_value(ndarray, None)
+    }
+
+    /// Implementation of `np_array(<ndarray>, copy=copy)`.
+    pub fn construct_numpy_array_ndarray_impl<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarray: NDArrayValue<'ctx>,
+        copy: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(ndarray.get_type().dtype, self.dtype);
+        assert!(self.ndims >= ndarray.get_type().ndims);
+        assert_eq!(copy.get_type(), ctx.ctx.bool_type());
+
+        let ndarray_val = gen_if_else_expr_callback(
+            generator,
+            ctx,
+            |_generator, _ctx| Ok(copy),
+            |generator, ctx| {
+                let ndarray = ndarray.make_copy(generator, ctx); // Force copy
+                Ok(Some(ndarray.as_base_value()))
+            },
+            |_generator, _ctx| {
+                // No need to copy. Return `ndarray` itself.
+                Ok(Some(ndarray.as_base_value()))
+            },
+        )
+        .unwrap()
+        .map(BasicValueEnum::into_pointer_value)
+        .unwrap();
+
+        ndarray.get_type().map_value(ndarray_val, name)
+    }
+
+    /// Create a new ndarray like
+    /// [`np.array()`](https://numpy.org/doc/stable/reference/generated/numpy.array.html).
+    ///
+    /// Note that the returned [`NDArrayValue`] may have fewer dimensions than is specified by this
+    /// instance. Use [`NDArrayValue::atleast_nd`] on the returned value if an `ndarray` instance
+    /// with the exact number of dimensions is needed.
+    pub fn construct_numpy_array<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        (object_ty, object): (Type, BasicValueEnum<'ctx>),
+        copy: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        match &*ctx.unifier.get_ty_immutable(object_ty) {
+            TypeEnum::TObj { obj_id, .. }
+                if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+            {
+                let list = ListType::from_unifier_type(generator, ctx, object_ty)
+                    .map_value(object.into_pointer_value(), None);
+                self.construct_numpy_array_list_impl(generator, ctx, (object_ty, list), copy, name)
+            }
+
+            TypeEnum::TObj { obj_id, .. }
+                if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
+            {
+                let ndarray = NDArrayType::from_unifier_type(generator, ctx, object_ty)
+                    .map_value(object.into_pointer_value(), None);
+                self.construct_numpy_array_ndarray_impl(generator, ctx, ndarray, copy, name)
+            }
+
+            _ => panic!("Unrecognized object type: {}", ctx.unifier.stringify(object_ty)), // Typechecker ensures this
+        }
+    }
+}

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

@@ -0,0 +1,176 @@
+use inkwell::{
+    context::{AsContextRef, Context},
+    types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
+    values::{IntValue, PointerValue},
+    AddressSpace,
+};
+use itertools::Itertools;
+
+use nac3core_derive::StructFields;
+
+use crate::codegen::{
+    types::{
+        structure::{check_struct_type_matches_fields, StructField, StructFields},
+        ProxyType,
+    },
+    values::{ndarray::ShapeEntryValue, ProxyValue},
+    CodeGenContext, CodeGenerator,
+};
+
+#[derive(Debug, PartialEq, Eq, Clone, Copy)]
+pub struct ShapeEntryType<'ctx> {
+    ty: PointerType<'ctx>,
+    llvm_usize: IntType<'ctx>,
+}
+
+#[derive(PartialEq, Eq, Clone, Copy, StructFields)]
+pub struct ShapeEntryStructFields<'ctx> {
+    #[value_type(usize)]
+    pub ndims: StructField<'ctx, IntValue<'ctx>>,
+    #[value_type(usize.ptr_type(AddressSpace::default()))]
+    pub shape: StructField<'ctx, PointerValue<'ctx>>,
+}
+
+impl<'ctx> ShapeEntryType<'ctx> {
+    /// Checks whether `llvm_ty` represents a [`ShapeEntryType`], returning [Err] if it does not.
+    pub fn is_representable(
+        llvm_ty: PointerType<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        let ctx = llvm_ty.get_context();
+
+        let llvm_ndarray_ty = llvm_ty.get_element_type();
+        let AnyTypeEnum::StructType(llvm_ndarray_ty) = llvm_ndarray_ty else {
+            return Err(format!(
+                "Expected struct type for `ShapeEntry` type, got {llvm_ndarray_ty}"
+            ));
+        };
+
+        check_struct_type_matches_fields(
+            Self::fields(ctx, llvm_usize),
+            llvm_ndarray_ty,
+            "NDArray",
+            &[],
+        )
+    }
+
+    /// Returns an instance of [`StructFields`] containing all field accessors for this type.
+    #[must_use]
+    fn fields(
+        ctx: impl AsContextRef<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> ShapeEntryStructFields<'ctx> {
+        ShapeEntryStructFields::new(ctx, llvm_usize)
+    }
+
+    /// See [`ShapeEntryStructFields::fields`].
+    // TODO: Move this into e.g. StructProxyType
+    #[must_use]
+    pub fn get_fields(&self, ctx: impl AsContextRef<'ctx>) -> ShapeEntryStructFields<'ctx> {
+        Self::fields(ctx, self.llvm_usize)
+    }
+
+    /// Creates an LLVM type corresponding to the expected structure of a `ShapeEntry`.
+    #[must_use]
+    fn llvm_type(ctx: &'ctx Context, llvm_usize: IntType<'ctx>) -> PointerType<'ctx> {
+        let field_tys =
+            Self::fields(ctx, llvm_usize).into_iter().map(|field| field.1).collect_vec();
+
+        ctx.struct_type(&field_tys, false).ptr_type(AddressSpace::default())
+    }
+
+    /// Creates an instance of [`ShapeEntryType`].
+    #[must_use]
+    pub fn new<G: CodeGenerator + ?Sized>(generator: &G, ctx: &'ctx Context) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        let llvm_ty = Self::llvm_type(ctx, llvm_usize);
+
+        Self { ty: llvm_ty, llvm_usize }
+    }
+
+    /// Creates a [`ShapeEntryType`] from a [`PointerType`] representing an `ShapeEntry`.
+    #[must_use]
+    pub fn from_type(ptr_ty: PointerType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
+
+        Self { ty: ptr_ty, llvm_usize }
+    }
+
+    /// Allocates an instance of [`ShapeEntryValue`] as if by calling `alloca` on the base type.
+    #[must_use]
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`ShapeEntryValue`] as if by calling `alloca` on the base type.
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca_var(generator, ctx, name),
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Converts an existing value into a [`ShapeEntryValue`].
+    #[must_use]
+    pub fn map_value(
+        &self,
+        value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(value, self.llvm_usize, name)
+    }
+}
+
+impl<'ctx> ProxyType<'ctx> for ShapeEntryType<'ctx> {
+    type Base = PointerType<'ctx>;
+    type Value = ShapeEntryValue<'ctx>;
+
+    fn is_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: impl BasicType<'ctx>,
+    ) -> Result<(), String> {
+        if let BasicTypeEnum::PointerType(ty) = llvm_ty.as_basic_type_enum() {
+            <Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
+        } else {
+            Err(format!("Expected pointer type, got {llvm_ty:?}"))
+        }
+    }
+
+    fn is_representable<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: Self::Base,
+    ) -> Result<(), String> {
+        Self::is_representable(llvm_ty, generator.get_size_type(ctx))
+    }
+
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
+    }
+
+    fn as_base_type(&self) -> Self::Base {
+        self.ty
+    }
+}
+
+impl<'ctx> From<ShapeEntryType<'ctx>> for PointerType<'ctx> {
+    fn from(value: ShapeEntryType<'ctx>) -> Self {
+        value.as_base_type()
+    }
+}

nac3core/src/codegen/types/ndarray/contiguous.rs

@@ -16,7 +16,7 @@ use crate::{
             },
             ProxyType,
         },
-        values::{ndarray::ContiguousNDArrayValue, ArraySliceValue, ProxyValue},
+        values::{ndarray::ContiguousNDArrayValue, ProxyValue},
         CodeGenContext, CodeGenerator,
     },
     toplevel::numpy::unpack_ndarray_var_tys,
@@ -31,7 +31,7 @@ pub struct ContiguousNDArrayType<'ctx> {
 }
 
 #[derive(PartialEq, Eq, Clone, Copy, StructFields)]
-pub struct ContiguousNDArrayFields<'ctx> {
+pub struct ContiguousNDArrayStructFields<'ctx> {
     #[value_type(usize)]
     pub ndims: StructField<'ctx, IntValue<'ctx>>,
     #[value_type(usize.ptr_type(AddressSpace::default()))]
@@ -40,12 +40,12 @@ pub struct ContiguousNDArrayFields<'ctx> {
     pub data: StructField<'ctx, PointerValue<'ctx>>,
 }
 
-impl<'ctx> ContiguousNDArrayFields<'ctx> {
+impl<'ctx> ContiguousNDArrayStructFields<'ctx> {
     #[must_use]
     pub fn new_typed(item: BasicTypeEnum<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
         let mut counter = FieldIndexCounter::default();
 
-        ContiguousNDArrayFields {
+        ContiguousNDArrayStructFields {
             ndims: StructField::create(&mut counter, "ndims", llvm_usize),
             shape: StructField::create(
                 &mut counter,
@@ -72,7 +72,7 @@ impl<'ctx> ContiguousNDArrayType<'ctx> {
             ));
         };
 
-        let fields = ContiguousNDArrayFields::new(ctx, llvm_usize);
+        let fields = ContiguousNDArrayStructFields::new(ctx, llvm_usize);
 
         check_struct_type_matches_fields(
             fields,
@@ -93,14 +93,14 @@ impl<'ctx> ContiguousNDArrayType<'ctx> {
     fn fields(
         item: BasicTypeEnum<'ctx>,
         llvm_usize: IntType<'ctx>,
-    ) -> ContiguousNDArrayFields<'ctx> {
-        ContiguousNDArrayFields::new_typed(item, llvm_usize)
+    ) -> ContiguousNDArrayStructFields<'ctx> {
+        ContiguousNDArrayStructFields::new_typed(item, llvm_usize)
     }
 
     /// See [`NDArrayType::fields`].
     // TODO: Move this into e.g. StructProxyType
     #[must_use]
-    pub fn get_fields(&self) -> ContiguousNDArrayFields<'ctx> {
+    pub fn get_fields(&self) -> ContiguousNDArrayStructFields<'ctx> {
         Self::fields(self.item, self.llvm_usize)
     }
 
@@ -157,16 +157,37 @@ impl<'ctx> ContiguousNDArrayType<'ctx> {
         Self { ty: ptr_ty, item, llvm_usize }
     }
 
-    /// Allocates an instance of [`ContiguousNDArrayValue`] as if by calling `alloca` on the base type.
+    /// Allocates an instance of [`ContiguousNDArrayValue`] as if by calling `alloca` on the base
+    /// type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
     #[must_use]
-    pub fn alloca<G: CodeGenerator + ?Sized>(
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.item,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`ContiguousNDArrayValue`] as if by calling `alloca` on the base
+    /// type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            self.raw_alloca(generator, ctx, name),
+            self.raw_alloca_var(generator, ctx, name),
             self.item,
             self.llvm_usize,
             name,
@@ -213,36 +234,8 @@ impl<'ctx> ProxyType<'ctx> for ContiguousNDArrayType<'ctx> {
         Self::is_representable(llvm_ty, generator.get_size_type(ctx))
     }
 
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base {
-        generator
-            .gen_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                name,
-            )
-            .unwrap()
-    }
-
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx> {
-        generator
-            .gen_array_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                size,
-                name,
-            )
-            .unwrap()
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
     }
 
     fn as_base_type(&self) -> Self::Base {

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

@@ -0,0 +1,236 @@
+use inkwell::{
+    values::{BasicValueEnum, IntValue},
+    IntPredicate,
+};
+
+use super::NDArrayType;
+use crate::{
+    codegen::{
+        irrt, types::ProxyType, values::TypedArrayLikeAccessor, CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::Type,
+};
+
+/// Get the zero value in `np.zeros()` of a `dtype`.
+fn ndarray_zero_value<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    dtype: Type,
+) -> BasicValueEnum<'ctx> {
+    if [ctx.primitives.int32, ctx.primitives.uint32]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(dtype, *ty))
+    {
+        ctx.ctx.i32_type().const_zero().into()
+    } else if [ctx.primitives.int64, ctx.primitives.uint64]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(dtype, *ty))
+    {
+        ctx.ctx.i64_type().const_zero().into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
+        ctx.ctx.f64_type().const_zero().into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
+        ctx.ctx.bool_type().const_zero().into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
+        ctx.gen_string(generator, "").into()
+    } else {
+        panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
+    }
+}
+
+/// Get the one value in `np.ones()` of a `dtype`.
+fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    dtype: Type,
+) -> BasicValueEnum<'ctx> {
+    if [ctx.primitives.int32, ctx.primitives.uint32]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(dtype, *ty))
+    {
+        let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int32);
+        ctx.ctx.i32_type().const_int(1, is_signed).into()
+    } else if [ctx.primitives.int64, ctx.primitives.uint64]
+        .iter()
+        .any(|ty| ctx.unifier.unioned(dtype, *ty))
+    {
+        let is_signed = ctx.unifier.unioned(dtype, ctx.primitives.int64);
+        ctx.ctx.i64_type().const_int(1, is_signed).into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.float) {
+        ctx.ctx.f64_type().const_float(1.0).into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.bool) {
+        ctx.ctx.bool_type().const_int(1, false).into()
+    } else if ctx.unifier.unioned(dtype, ctx.primitives.str) {
+        ctx.gen_string(generator, "1").into()
+    } else {
+        panic!("unrecognized dtype: {}", ctx.unifier.stringify(dtype));
+    }
+}
+
+impl<'ctx> NDArrayType<'ctx> {
+    /// Create an ndarray like
+    /// [`np.empty`](https://numpy.org/doc/stable/reference/generated/numpy.empty.html).
+    pub fn construct_numpy_empty<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let ndarray = self.construct_uninitialized(generator, ctx, name);
+
+        // Validate `shape`
+        irrt::ndarray::call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, shape);
+
+        ndarray.copy_shape_from_array(generator, ctx, shape.base_ptr(ctx, generator));
+        unsafe { ndarray.create_data(generator, ctx) };
+
+        ndarray
+    }
+
+    /// Create an ndarray like
+    /// [`np.full`](https://numpy.org/doc/stable/reference/generated/numpy.full.html).
+    pub fn construct_numpy_full<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+        fill_value: BasicValueEnum<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let ndarray = self.construct_numpy_empty(generator, ctx, shape, name);
+        ndarray.fill(generator, ctx, fill_value);
+        ndarray
+    }
+
+    /// Create an ndarray like
+    /// [`np.zero`](https://numpy.org/doc/stable/reference/generated/numpy.zeros.html).
+    pub fn construct_numpy_zeros<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(
+            ctx.get_llvm_type(generator, dtype),
+            self.dtype,
+            "Expected LLVM dtype={} but got {}",
+            self.dtype.print_to_string(),
+            ctx.get_llvm_type(generator, dtype).print_to_string(),
+        );
+
+        let fill_value = ndarray_zero_value(generator, ctx, dtype);
+        self.construct_numpy_full(generator, ctx, shape, fill_value, name)
+    }
+
+    /// Create an ndarray like
+    /// [`np.ones`](https://numpy.org/doc/stable/reference/generated/numpy.ones.html).
+    pub fn construct_numpy_ones<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(
+            ctx.get_llvm_type(generator, dtype),
+            self.dtype,
+            "Expected LLVM dtype={} but got {}",
+            self.dtype.print_to_string(),
+            ctx.get_llvm_type(generator, dtype).print_to_string(),
+        );
+
+        let fill_value = ndarray_one_value(generator, ctx, dtype);
+        self.construct_numpy_full(generator, ctx, shape, fill_value, name)
+    }
+
+    /// Create an ndarray like
+    /// [`np.eye`](https://numpy.org/doc/stable/reference/generated/numpy.eye.html).
+    #[allow(clippy::too_many_arguments)]
+    pub fn construct_numpy_eye<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        nrows: IntValue<'ctx>,
+        ncols: IntValue<'ctx>,
+        offset: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        assert_eq!(
+            ctx.get_llvm_type(generator, dtype),
+            self.dtype,
+            "Expected LLVM dtype={} but got {}",
+            self.dtype.print_to_string(),
+            ctx.get_llvm_type(generator, dtype).print_to_string(),
+        );
+        assert_eq!(nrows.get_type(), self.llvm_usize);
+        assert_eq!(ncols.get_type(), self.llvm_usize);
+        assert_eq!(offset.get_type(), self.llvm_usize);
+
+        let ndzero = ndarray_zero_value(generator, ctx, dtype);
+        let ndone = ndarray_one_value(generator, ctx, dtype);
+
+        let ndarray = self.construct_dyn_shape(generator, ctx, &[nrows, ncols], name);
+
+        // Create data and make the matrix like look np.eye()
+        unsafe {
+            ndarray.create_data(generator, ctx);
+        }
+        ndarray
+            .foreach(generator, ctx, |generator, ctx, _, nditer| {
+                // NOTE: rows and cols can never be zero here, since this ndarray's `np.size` would be zero
+                // and this loop would not execute.
+
+                let indices = nditer.get_indices();
+
+                let row_i = unsafe {
+                    indices.get_typed_unchecked(ctx, generator, &self.llvm_usize.const_zero(), None)
+                };
+                let col_i = unsafe {
+                    indices.get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &self.llvm_usize.const_int(1, false),
+                        None,
+                    )
+                };
+
+                let be_one = ctx
+                    .builder
+                    .build_int_compare(
+                        IntPredicate::EQ,
+                        ctx.builder.build_int_add(row_i, offset, "").unwrap(),
+                        col_i,
+                        "",
+                    )
+                    .unwrap();
+                let value = ctx.builder.build_select(be_one, ndone, ndzero, "value").unwrap();
+
+                let p = nditer.get_pointer(ctx);
+                ctx.builder.build_store(p, value).unwrap();
+
+                Ok(())
+            })
+            .unwrap();
+
+        ndarray
+    }
+
+    /// Create an ndarray like
+    /// [`np.identity`](https://numpy.org/doc/stable/reference/generated/numpy.identity.html).
+    pub fn construct_numpy_identity<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        dtype: Type,
+        size: IntValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let offset = self.llvm_usize.const_zero();
+        self.construct_numpy_eye(generator, ctx, dtype, size, size, offset, name)
+    }
+}

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

@@ -90,15 +90,33 @@ impl<'ctx> NDIndexType<'ctx> {
         Self { ty: ptr_ty, llvm_usize }
     }
 
+    /// Allocates an instance of [`NDIndexValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
     #[must_use]
-    pub fn alloca<G: CodeGenerator + ?Sized>(
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.llvm_usize,
+            name,
+        )
+    }
+    /// Allocates an instance of [`NDIndexValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            self.raw_alloca(generator, ctx, name),
+            self.raw_alloca_var(generator, ctx, name),
             self.llvm_usize,
             name,
         )
@@ -114,7 +132,7 @@ impl<'ctx> NDIndexType<'ctx> {
     ) -> ArraySliceValue<'ctx> {
         // Allocate the LLVM ndindices.
         let num_ndindices = self.llvm_usize.const_int(in_ndindices.len() as u64, false);
-        let ndindices = self.array_alloca(generator, ctx, num_ndindices, None);
+        let ndindices = self.array_alloca_var(generator, ctx, num_ndindices, None);
 
         // Initialize all of them.
         for (i, in_ndindex) in in_ndindices.iter().enumerate() {
@@ -171,36 +189,8 @@ impl<'ctx> ProxyType<'ctx> for NDIndexType<'ctx> {
         Self::is_representable(llvm_ty, generator.get_size_type(ctx))
     }
 
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base {
-        generator
-            .gen_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                name,
-            )
-            .unwrap()
-    }
-
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx> {
-        generator
-            .gen_array_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                size,
-                name,
-            )
-            .unwrap()
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
     }
 
     fn as_base_type(&self) -> Self::Base {

nac3core/src/codegen/types/ndarray/map.rs

@@ -0,0 +1,187 @@
+use inkwell::{types::BasicTypeEnum, values::BasicValueEnum};
+use itertools::Itertools;
+
+use crate::codegen::{
+    stmt::gen_for_callback,
+    types::{
+        ndarray::{NDArrayType, NDIterType},
+        ProxyType,
+    },
+    values::{
+        ndarray::{NDArrayOut, NDArrayValue, ScalarOrNDArray},
+        ArrayLikeValue, ProxyValue,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+impl<'ctx> NDArrayType<'ctx> {
+    /// Generate LLVM IR to broadcast `ndarray`s together, and starmap through them with `mapping`
+    /// elementwise.
+    ///
+    /// `mapping` is an LLVM IR generator. The input of `mapping` is the list of elements when
+    /// iterating through the input `ndarrays` after broadcasting. The output of `mapping` is the
+    /// result of the elementwise operation.
+    ///
+    /// `out` specifies whether the result should be a new ndarray or to be written an existing
+    /// ndarray.
+    pub fn broadcast_starmap<'a, G, MappingFn>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        ndarrays: &[NDArrayValue<'ctx>],
+        out: NDArrayOut<'ctx>,
+        mapping: MappingFn,
+    ) -> Result<<Self as ProxyType<'ctx>>::Value, String>
+    where
+        G: CodeGenerator + ?Sized,
+        MappingFn: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            &[BasicValueEnum<'ctx>],
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        // Broadcast inputs
+        let broadcast_result = self.broadcast(generator, ctx, ndarrays);
+
+        let out_ndarray = match out {
+            NDArrayOut::NewNDArray { dtype } => {
+                // Create a new ndarray based on the broadcast shape.
+                let result_ndarray =
+                    NDArrayType::new(generator, ctx.ctx, dtype, broadcast_result.ndims)
+                        .construct_uninitialized(generator, ctx, None);
+                result_ndarray.copy_shape_from_array(
+                    generator,
+                    ctx,
+                    broadcast_result.shape.base_ptr(ctx, generator),
+                );
+                unsafe {
+                    result_ndarray.create_data(generator, ctx);
+                }
+                result_ndarray
+            }
+
+            NDArrayOut::WriteToNDArray { ndarray: result_ndarray } => {
+                // Use an existing ndarray.
+
+                // Check that its shape is compatible with the broadcast shape.
+                result_ndarray.assert_can_be_written_by_out(generator, ctx, broadcast_result.shape);
+                result_ndarray
+            }
+        };
+
+        // Map element-wise and store results into `mapped_ndarray`.
+        let nditer = NDIterType::new(generator, ctx.ctx).construct(generator, ctx, out_ndarray);
+        gen_for_callback(
+            generator,
+            ctx,
+            Some("broadcast_starmap"),
+            |generator, ctx| {
+                // Create NDIters for all broadcasted input ndarrays.
+                let other_nditers = broadcast_result
+                    .ndarrays
+                    .iter()
+                    .map(|ndarray| {
+                        NDIterType::new(generator, ctx.ctx).construct(generator, ctx, *ndarray)
+                    })
+                    .collect_vec();
+                Ok((nditer, other_nditers))
+            },
+            |generator, ctx, (out_nditer, _in_nditers)| {
+                // We can simply use `out_nditer`'s `has_element()`.
+                // `in_nditers`' `has_element()`s should return the same value.
+                Ok(out_nditer.has_element(generator, ctx))
+            },
+            |generator, ctx, _hooks, (out_nditer, in_nditers)| {
+                // Get all the scalars from the broadcasted input ndarrays, pass them to `mapping`,
+                // and write to `out_ndarray`.
+                let in_scalars =
+                    in_nditers.iter().map(|nditer| nditer.get_scalar(ctx)).collect_vec();
+
+                let result = mapping(generator, ctx, &in_scalars)?;
+
+                let p = out_nditer.get_pointer(ctx);
+                ctx.builder.build_store(p, result).unwrap();
+
+                Ok(())
+            },
+            |generator, ctx, (out_nditer, in_nditers)| {
+                // Advance all iterators
+                out_nditer.next(generator, ctx);
+                in_nditers.iter().for_each(|nditer| nditer.next(generator, ctx));
+                Ok(())
+            },
+        )?;
+
+        Ok(out_ndarray)
+    }
+}
+
+impl<'ctx> ScalarOrNDArray<'ctx> {
+    /// Starmap through a list of inputs using `mapping`, where an input could be an ndarray, a
+    /// scalar.
+    ///
+    /// This function is very helpful when implementing NumPy functions that takes on either scalars
+    /// or ndarrays or a mix of them as their inputs and produces either an ndarray with broadcast,
+    /// or a scalar if all its inputs are all scalars.
+    ///
+    /// For example ,this function can be used to implement `np.add`, which has the following
+    /// behaviors:
+    ///
+    /// - `np.add(3, 4) = 7` # (scalar, scalar) -> scalar
+    /// - `np.add(3, np.array([4, 5, 6]))` # (scalar, ndarray) -> ndarray; the first `scalar` is
+    ///   converted into an ndarray and broadcasted.
+    /// - `np.add(np.array([[1], [2], [3]]), np.array([[4, 5, 6]]))` # (ndarray, ndarray) ->
+    ///   ndarray; there is broadcasting.
+    ///
+    /// ## Details:
+    ///
+    /// If `inputs` are all [`ScalarOrNDArray::Scalar`], the output will be a
+    /// [`ScalarOrNDArray::Scalar`] with type `ret_dtype`.
+    ///
+    /// Otherwise (if there are any [`ScalarOrNDArray::NDArray`] in `inputs`), all inputs will be
+    /// 'as-ndarray'-ed into ndarrays, then all inputs (now all ndarrays) will be passed to
+    /// [`NDArrayValue::broadcasting_starmap`] and **create** a new ndarray with dtype `ret_dtype`.
+    pub fn broadcasting_starmap<'a, G, MappingFn>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        inputs: &[ScalarOrNDArray<'ctx>],
+        ret_dtype: BasicTypeEnum<'ctx>,
+        mapping: MappingFn,
+    ) -> Result<ScalarOrNDArray<'ctx>, String>
+    where
+        G: CodeGenerator + ?Sized,
+        MappingFn: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            &[BasicValueEnum<'ctx>],
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        // Check if all inputs are Scalars
+        let all_scalars: Option<Vec<_>> =
+            inputs.iter().map(BasicValueEnum::<'ctx>::try_from).try_collect().ok();
+
+        if let Some(scalars) = all_scalars {
+            let scalars = scalars.iter().copied().collect_vec();
+            let value = mapping(generator, ctx, &scalars)?;
+
+            Ok(ScalarOrNDArray::Scalar(value))
+        } else {
+            // Promote all input to ndarrays and map through them.
+            let inputs = inputs.iter().map(|input| input.to_ndarray(generator, ctx)).collect_vec();
+            let ndarray = NDArrayType::new_broadcast(
+                generator,
+                ctx.ctx,
+                ret_dtype,
+                &inputs.iter().map(NDArrayValue::get_type).collect_vec(),
+            )
+            .broadcast_starmap(
+                generator,
+                ctx,
+                &inputs,
+                NDArrayOut::NewNDArray { dtype: ret_dtype },
+                mapping,
+            )?;
+            Ok(ScalarOrNDArray::NDArray(ndarray))
+        }
+    }
+}

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

@@ -14,18 +14,23 @@ use super::{
 };
 use crate::{
     codegen::{
-        values::{ndarray::NDArrayValue, ArraySliceValue, ProxyValue, TypedArrayLikeMutator},
+        values::{ndarray::NDArrayValue, ProxyValue, TypedArrayLikeMutator},
         {CodeGenContext, CodeGenerator},
     },
     toplevel::{helper::extract_ndims, numpy::unpack_ndarray_var_tys},
     typecheck::typedef::Type,
 };
+pub use broadcast::*;
 pub use contiguous::*;
 pub use indexing::*;
 pub use nditer::*;
 
+mod array;
+mod broadcast;
 mod contiguous;
+pub mod factory;
 mod indexing;
+mod map;
 mod nditer;
 
 /// Proxy type for a `ndarray` type in LLVM.
@@ -33,7 +38,7 @@ mod nditer;
 pub struct NDArrayType<'ctx> {
     ty: PointerType<'ctx>,
     dtype: BasicTypeEnum<'ctx>,
-    ndims: Option<u64>,
+    ndims: u64,
     llvm_usize: IntType<'ctx>,
 }
 
@@ -108,7 +113,7 @@ impl<'ctx> NDArrayType<'ctx> {
         generator: &G,
         ctx: &'ctx Context,
         dtype: BasicTypeEnum<'ctx>,
-        ndims: Option<u64>,
+        ndims: u64,
     ) -> Self {
         let llvm_usize = generator.get_size_type(ctx);
         let llvm_ndarray = Self::llvm_type(ctx, llvm_usize);
@@ -116,6 +121,20 @@ impl<'ctx> NDArrayType<'ctx> {
         NDArrayType { ty: llvm_ndarray, dtype, ndims, llvm_usize }
     }
 
+    /// Creates an instance of [`NDArrayType`] as a result of a broadcast operation over one or more
+    /// `ndarray` operands.
+    #[must_use]
+    pub fn new_broadcast<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        dtype: BasicTypeEnum<'ctx>,
+        inputs: &[NDArrayType<'ctx>],
+    ) -> Self {
+        assert!(!inputs.is_empty());
+
+        Self::new(generator, ctx, dtype, inputs.iter().map(NDArrayType::ndims).max().unwrap())
+    }
+
     /// Creates an instance of [`NDArrayType`] with `ndims` of 0.
     #[must_use]
     pub fn new_unsized<G: CodeGenerator + ?Sized>(
@@ -126,7 +145,7 @@ impl<'ctx> NDArrayType<'ctx> {
         let llvm_usize = generator.get_size_type(ctx);
         let llvm_ndarray = Self::llvm_type(ctx, llvm_usize);
 
-        NDArrayType { ty: llvm_ndarray, dtype, ndims: Some(0), llvm_usize }
+        NDArrayType { ty: llvm_ndarray, dtype, ndims: 0, llvm_usize }
     }
 
     /// Creates an [`NDArrayType`] from a [unifier type][Type].
@@ -145,7 +164,7 @@ impl<'ctx> NDArrayType<'ctx> {
         NDArrayType {
             ty: Self::llvm_type(ctx.ctx, llvm_usize),
             dtype: llvm_dtype,
-            ndims: Some(ndims),
+            ndims,
             llvm_usize,
         }
     }
@@ -155,7 +174,7 @@ impl<'ctx> NDArrayType<'ctx> {
     pub fn from_type(
         ptr_ty: PointerType<'ctx>,
         dtype: BasicTypeEnum<'ctx>,
-        ndims: Option<u64>,
+        ndims: u64,
         llvm_usize: IntType<'ctx>,
     ) -> Self {
         debug_assert!(Self::is_representable(ptr_ty, llvm_usize).is_ok());
@@ -177,20 +196,40 @@ impl<'ctx> NDArrayType<'ctx> {
 
     /// Returns the number of dimensions of this `ndarray` type.
     #[must_use]
-    pub fn ndims(&self) -> Option<u64> {
+    pub fn ndims(&self) -> u64 {
         self.ndims
     }
 
     /// Allocates an instance of [`NDArrayValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
     #[must_use]
-    pub fn alloca<G: CodeGenerator + ?Sized>(
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.dtype,
+            self.ndims,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`NDArrayValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            self.raw_alloca(generator, ctx, name),
+            self.raw_alloca_var(generator, ctx, name),
             self.dtype,
             self.ndims,
             self.llvm_usize,
@@ -214,7 +253,7 @@ impl<'ctx> NDArrayType<'ctx> {
         ndims: IntValue<'ctx>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
-        let ndarray = self.alloca(generator, ctx, name);
+        let ndarray = self.alloca_var(generator, ctx, name);
 
         let itemsize = ctx
             .builder
@@ -247,35 +286,7 @@ impl<'ctx> NDArrayType<'ctx> {
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
-        assert!(self.ndims.is_some(), "NDArrayType::construct can only be called on an instance with compile-time known ndims (self.ndims = Some(ndims))");
-
-        let Some(ndims) = self.ndims.map(|ndims| self.llvm_usize.const_int(ndims, false)) else {
-            unreachable!()
-        };
-
-        self.construct_impl(generator, ctx, ndims, name)
-    }
-
-    /// Allocate an [`NDArrayValue`] on the stack given its `ndims` and `dtype`.
-    ///
-    /// `shape` and `strides` will be automatically allocated onto the stack.
-    ///
-    /// The returned ndarray's content will be:
-    /// - `data`: uninitialized.
-    /// - `itemsize`: set to the size of `dtype`.
-    /// - `ndims`: set to the value of `ndims`.
-    /// - `shape`: allocated with an array of length `ndims` with uninitialized values.
-    /// - `strides`: allocated with an array of length `ndims` with uninitialized values.
-    #[deprecated = "Prefer construct_uninitialized or construct_*_shape."]
-    #[must_use]
-    pub fn construct_dyn_ndims<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        ndims: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> <Self as ProxyType<'ctx>>::Value {
-        assert!(self.ndims.is_none(), "NDArrayType::construct_dyn_ndims can only be called on an instance with compile-time unknown ndims (self.ndims = None)");
+        let ndims = self.llvm_usize.const_int(self.ndims, false);
 
         self.construct_impl(generator, ctx, ndims, name)
     }
@@ -291,9 +302,9 @@ impl<'ctx> NDArrayType<'ctx> {
         shape: &[u64],
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
-        assert!(self.ndims.is_none_or(|ndims| shape.len() as u64 == ndims));
+        assert_eq!(shape.len() as u64, self.ndims);
 
-        let ndarray = Self::new(generator, ctx.ctx, self.dtype, Some(shape.len() as u64))
+        let ndarray = Self::new(generator, ctx.ctx, self.dtype, shape.len() as u64)
             .construct_uninitialized(generator, ctx, name);
 
         let llvm_usize = generator.get_size_type(ctx.ctx);
@@ -326,9 +337,9 @@ impl<'ctx> NDArrayType<'ctx> {
         shape: &[IntValue<'ctx>],
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
-        assert!(self.ndims.is_none_or(|ndims| shape.len() as u64 == ndims));
+        assert_eq!(shape.len() as u64, self.ndims);
 
-        let ndarray = Self::new(generator, ctx.ctx, self.dtype, Some(shape.len() as u64))
+        let ndarray = Self::new(generator, ctx.ctx, self.dtype, shape.len() as u64)
             .construct_uninitialized(generator, ctx, name);
 
         let llvm_usize = generator.get_size_type(ctx.ctx);
@@ -368,7 +379,7 @@ impl<'ctx> NDArrayType<'ctx> {
         let value = value.as_basic_value_enum();
 
         assert_eq!(value.get_type(), self.dtype);
-        assert!(self.ndims.is_none_or(|ndims| ndims == 0));
+        assert_eq!(self.ndims, 0);
 
         // We have to put the value on the stack to get a data pointer.
         let data = ctx.builder.build_alloca(value.get_type(), "construct_unsized").unwrap();
@@ -425,36 +436,8 @@ impl<'ctx> ProxyType<'ctx> for NDArrayType<'ctx> {
         Self::is_representable(llvm_ty, generator.get_size_type(ctx))
     }
 
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base {
-        generator
-            .gen_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                name,
-            )
-            .unwrap()
-    }
-
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx> {
-        generator
-            .gen_array_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                size,
-                name,
-            )
-            .unwrap()
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
     }
 
     fn as_base_type(&self) -> Self::Base {

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

@@ -14,7 +14,7 @@ use crate::codegen::{
     types::structure::{check_struct_type_matches_fields, StructField, StructFields},
     values::{
         ndarray::{NDArrayValue, NDIterValue},
-        ArraySliceValue, ProxyValue,
+        ArrayLikeValue, ArraySliceValue, ProxyValue, TypedArrayLikeAdapter,
     },
     CodeGenContext, CodeGenerator,
 };
@@ -109,8 +109,31 @@ impl<'ctx> NDIterType<'ctx> {
         self.llvm_usize
     }
 
+    /// Allocates an instance of [`NDIterValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
     #[must_use]
-    pub fn alloca<G: CodeGenerator + ?Sized>(
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        parent: NDArrayValue<'ctx>,
+        indices: ArraySliceValue<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            parent,
+            indices,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`NDIterValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
@@ -119,7 +142,7 @@ impl<'ctx> NDIterType<'ctx> {
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            self.raw_alloca(generator, ctx, name),
+            self.raw_alloca_var(generator, ctx, name),
             parent,
             indices,
             self.llvm_usize,
@@ -128,6 +151,11 @@ impl<'ctx> NDIterType<'ctx> {
     }
 
     /// Allocate an [`NDIter`] that iterates through the given `ndarray`.
+    ///
+    /// Note: This function allocates an array on the stack at the current builder location, which
+    /// may lead to stack explosion if called in a hot loop. Therefore, callers are recommended to
+    /// call `llvm.stacksave` before calling this function and call `llvm.stackrestore` after the
+    /// [`NDIter`] is no longer needed.
     #[must_use]
     pub fn construct<G: CodeGenerator + ?Sized>(
         &self,
@@ -135,22 +163,18 @@ impl<'ctx> NDIterType<'ctx> {
         ctx: &mut CodeGenContext<'ctx, '_>,
         ndarray: NDArrayValue<'ctx>,
     ) -> <Self as ProxyType<'ctx>>::Value {
-        let nditer = self.raw_alloca(generator, ctx, None);
-        let ndims = ndarray.load_ndims(ctx);
+        let nditer = self.raw_alloca_var(generator, ctx, None);
+        let ndims = self.llvm_usize.const_int(ndarray.get_type().ndims(), false);
 
         // The caller has the responsibility to allocate 'indices' for `NDIter`.
         let indices =
             generator.gen_array_var_alloc(ctx, self.llvm_usize.into(), ndims, None).unwrap();
+        let indices =
+            TypedArrayLikeAdapter::from(indices, |_, _, v| v.into_int_value(), |_, _, v| v.into());
 
-        let nditer = <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            nditer,
-            ndarray,
-            indices,
-            self.llvm_usize,
-            None,
-        );
+        let nditer = self.map_value(nditer, ndarray, indices.as_slice_value(ctx, generator), None);
 
-        irrt::ndarray::call_nac3_nditer_initialize(generator, ctx, nditer, ndarray, indices);
+        irrt::ndarray::call_nac3_nditer_initialize(generator, ctx, nditer, ndarray, &indices);
 
         nditer
     }
@@ -197,36 +221,8 @@ impl<'ctx> ProxyType<'ctx> for NDIterType<'ctx> {
         Self::is_representable(llvm_ty, generator.get_size_type(ctx))
     }
 
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base {
-        generator
-            .gen_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                name,
-            )
-            .unwrap()
-    }
-
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx> {
-        generator
-            .gen_array_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                size,
-                name,
-            )
-            .unwrap()
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
     }
 
     fn as_base_type(&self) -> Self::Base {

nac3core/src/codegen/types/range.rs

@@ -1,13 +1,12 @@
 use inkwell::{
     context::Context,
     types::{AnyTypeEnum, BasicType, BasicTypeEnum, IntType, PointerType},
-    values::IntValue,
     AddressSpace,
 };
 
 use super::ProxyType;
 use crate::codegen::{
-    values::{ArraySliceValue, ProxyValue, RangeValue},
+    values::{ProxyValue, RangeValue},
     {CodeGenContext, CodeGenerator},
 };
 
@@ -78,15 +77,29 @@ impl<'ctx> RangeType<'ctx> {
     }
 
     /// Allocates an instance of [`RangeValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
     #[must_use]
     pub fn alloca<G: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(self.raw_alloca(ctx, name), name)
+    }
+
+    /// Allocates an instance of [`RangeValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            self.raw_alloca(generator, ctx, name),
+            self.raw_alloca_var(generator, ctx, name),
             name,
         )
     }
@@ -126,36 +139,8 @@ impl<'ctx> ProxyType<'ctx> for RangeType<'ctx> {
         Self::is_representable(llvm_ty)
     }
 
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base {
-        generator
-            .gen_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                name,
-            )
-            .unwrap()
-    }
-
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx> {
-        generator
-            .gen_array_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                size,
-                name,
-            )
-            .unwrap()
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
     }
 
     fn as_base_type(&self) -> Self::Base {

nac3core/src/codegen/types/structure.rs

@@ -5,6 +5,7 @@ use inkwell::{
     types::{BasicTypeEnum, IntType, StructType},
     values::{BasicValue, BasicValueEnum, IntValue, PointerValue, StructValue},
 };
+use itertools::Itertools;
 
 use crate::codegen::CodeGenContext;
 
@@ -55,6 +56,20 @@ pub trait StructFields<'ctx>: Eq + Copy {
     {
         self.into_vec().into_iter()
     }
+
+    /// Returns the field index of a field in this structure.
+    fn index_of_field<V>(&self, name: impl FnOnce(&Self) -> StructField<'ctx, V>) -> u32
+    where
+        V: BasicValue<'ctx> + TryFrom<BasicValueEnum<'ctx>, Error = ()>,
+    {
+        let field_name = name(self).name;
+        self.index_of_field_name(field_name).unwrap()
+    }
+
+    /// Returns the field index of a field with the given name in this structure.
+    fn index_of_field_name(&self, field_name: &str) -> Option<u32> {
+        self.iter().find_position(|(name, _)| *name == field_name).map(|(idx, _)| idx as u32)
+    }
 }
 
 /// A single field of an LLVM structure.

nac3core/src/codegen/types/tuple.rs

@@ -0,0 +1,184 @@
+use inkwell::{
+    context::Context,
+    types::{BasicType, BasicTypeEnum, IntType, StructType},
+    values::BasicValueEnum,
+};
+use itertools::Itertools;
+
+use super::ProxyType;
+use crate::{
+    codegen::{
+        values::{ProxyValue, TupleValue},
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+#[derive(Debug, PartialEq, Eq, Clone)]
+pub struct TupleType<'ctx> {
+    ty: StructType<'ctx>,
+    llvm_usize: IntType<'ctx>,
+}
+
+impl<'ctx> TupleType<'ctx> {
+    /// Checks whether `llvm_ty` represents any tuple type, returning [Err] if it does not.
+    pub fn is_representable(_value: StructType<'ctx>) -> Result<(), String> {
+        Ok(())
+    }
+
+    /// Creates an LLVM type corresponding to the expected structure of a tuple.
+    #[must_use]
+    fn llvm_type(ctx: &'ctx Context, tys: &[BasicTypeEnum<'ctx>]) -> StructType<'ctx> {
+        ctx.struct_type(tys, false)
+    }
+
+    /// Creates an instance of [`TupleType`].
+    #[must_use]
+    pub fn new<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        tys: &[BasicTypeEnum<'ctx>],
+    ) -> Self {
+        let llvm_usize = generator.get_size_type(ctx);
+        let llvm_tuple = Self::llvm_type(ctx, tys);
+
+        Self { ty: llvm_tuple, llvm_usize }
+    }
+
+    /// Creates an [`TupleType`] from a [unifier type][Type].
+    #[must_use]
+    pub fn from_unifier_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ty: Type,
+    ) -> Self {
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+
+        // Sanity check on object type.
+        let TypeEnum::TTuple { ty: tys, .. } = &*ctx.unifier.get_ty_immutable(ty) else {
+            panic!("Expected type to be a TypeEnum::TTuple, got {}", ctx.unifier.stringify(ty));
+        };
+
+        let llvm_tys = tys.iter().map(|ty| ctx.get_llvm_type(generator, *ty)).collect_vec();
+        Self { ty: Self::llvm_type(ctx.ctx, &llvm_tys), llvm_usize }
+    }
+
+    /// Creates an [`TupleType`] from a [`StructType`].
+    #[must_use]
+    pub fn from_type(struct_ty: StructType<'ctx>, llvm_usize: IntType<'ctx>) -> Self {
+        debug_assert!(Self::is_representable(struct_ty).is_ok());
+
+        TupleType { ty: struct_ty, llvm_usize }
+    }
+
+    /// Returns the number of elements present in this [`TupleType`].
+    #[must_use]
+    pub fn num_elements(&self) -> u32 {
+        self.ty.count_fields()
+    }
+
+    /// Returns the type of the tuple element at the given `index`, or [`None`] if `index` is out of
+    /// range.
+    #[must_use]
+    pub fn type_at_index(&self, index: u32) -> Option<BasicTypeEnum<'ctx>> {
+        if index < self.num_elements() {
+            Some(unsafe { self.type_at_index_unchecked(index) })
+        } else {
+            None
+        }
+    }
+
+    /// Returns the type of the tuple element at the given `index`.
+    ///
+    /// # Safety
+    ///
+    /// The caller must ensure that the index is valid.
+    #[must_use]
+    pub unsafe fn type_at_index_unchecked(&self, index: u32) -> BasicTypeEnum<'ctx> {
+        self.ty.get_field_type_at_index_unchecked(index)
+    }
+
+    /// Constructs a [`TupleValue`] from this type by zero-initializing the tuple value.
+    #[must_use]
+    pub fn construct(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        self.map_value(Self::llvm_type(ctx.ctx, &self.ty.get_field_types()).const_zero(), name)
+    }
+
+    /// Constructs a [`TupleValue`] from `objects`. The resulting tuple preserves the order of
+    /// objects.
+    #[must_use]
+    pub fn construct_from_objects<I: IntoIterator<Item = BasicValueEnum<'ctx>>>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        objects: I,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        let values = objects.into_iter().collect_vec();
+
+        assert_eq!(values.len(), self.num_elements() as usize);
+        assert!(values
+            .iter()
+            .enumerate()
+            .all(|(i, v)| { v.get_type() == unsafe { self.type_at_index_unchecked(i as u32) } }));
+
+        let mut value = self.construct(ctx, name);
+        for (i, val) in values.into_iter().enumerate() {
+            value.store_element(ctx, i as u32, val);
+        }
+
+        value
+    }
+
+    /// Converts an existing value into a [`ListValue`].
+    #[must_use]
+    pub fn map_value(
+        &self,
+        value: <<Self as ProxyType<'ctx>>::Value as ProxyValue<'ctx>>::Base,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_struct_value(value, self.llvm_usize, name)
+    }
+}
+
+impl<'ctx> ProxyType<'ctx> for TupleType<'ctx> {
+    type Base = StructType<'ctx>;
+    type Value = TupleValue<'ctx>;
+
+    fn is_type<G: CodeGenerator + ?Sized>(
+        generator: &G,
+        ctx: &'ctx Context,
+        llvm_ty: impl BasicType<'ctx>,
+    ) -> Result<(), String> {
+        if let BasicTypeEnum::StructType(ty) = llvm_ty.as_basic_type_enum() {
+            <Self as ProxyType<'ctx>>::is_representable(generator, ctx, ty)
+        } else {
+            Err(format!("Expected struct type, got {llvm_ty:?}"))
+        }
+    }
+
+    fn is_representable<G: CodeGenerator + ?Sized>(
+        _generator: &G,
+        _ctx: &'ctx Context,
+        llvm_ty: Self::Base,
+    ) -> Result<(), String> {
+        Self::is_representable(llvm_ty)
+    }
+
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type()
+    }
+
+    fn as_base_type(&self) -> Self::Base {
+        self.ty
+    }
+}
+
+impl<'ctx> From<TupleType<'ctx>> for StructType<'ctx> {
+    fn from(value: TupleType<'ctx>) -> Self {
+        value.as_base_type()
+    }
+}

nac3core/src/codegen/types/utils/slice.rs

@@ -15,7 +15,7 @@ use crate::codegen::{
         },
         ProxyType,
     },
-    values::{utils::SliceValue, ArraySliceValue, ProxyValue},
+    values::{utils::SliceValue, ProxyValue},
     CodeGenContext, CodeGenerator,
 };
 
@@ -154,16 +154,35 @@ impl<'ctx> SliceType<'ctx> {
         self.int_ty
     }
 
-    /// Allocates an instance of [`ContiguousNDArrayValue`] as if by calling `alloca` on the base type.
+    /// Allocates an instance of [`SliceValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca`].
     #[must_use]
-    pub fn alloca<G: CodeGenerator + ?Sized>(
+    pub fn alloca(
+        &self,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> <Self as ProxyType<'ctx>>::Value {
+        <Self as ProxyType<'ctx>>::Value::from_pointer_value(
+            self.raw_alloca(ctx, name),
+            self.int_ty,
+            self.llvm_usize,
+            name,
+        )
+    }
+
+    /// Allocates an instance of [`SliceValue`] as if by calling `alloca` on the base type.
+    ///
+    /// See [`ProxyType::raw_alloca_var`].
+    #[must_use]
+    pub fn alloca_var<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
         name: Option<&'ctx str>,
     ) -> <Self as ProxyType<'ctx>>::Value {
         <Self as ProxyType<'ctx>>::Value::from_pointer_value(
-            self.raw_alloca(generator, ctx, name),
+            self.raw_alloca_var(generator, ctx, name),
             self.int_ty,
             self.llvm_usize,
             name,
@@ -210,36 +229,8 @@ impl<'ctx> ProxyType<'ctx> for SliceType<'ctx> {
         Self::is_representable(llvm_ty, generator.get_size_type(ctx))
     }
 
-    fn raw_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        name: Option<&'ctx str>,
-    ) -> <Self::Value as ProxyValue<'ctx>>::Base {
-        generator
-            .gen_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                name,
-            )
-            .unwrap()
-    }
-
-    fn array_alloca<G: CodeGenerator + ?Sized>(
-        &self,
-        generator: &mut G,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        size: IntValue<'ctx>,
-        name: Option<&'ctx str>,
-    ) -> ArraySliceValue<'ctx> {
-        generator
-            .gen_array_var_alloc(
-                ctx,
-                self.as_base_type().get_element_type().into_struct_type().into(),
-                size,
-                name,
-            )
-            .unwrap()
+    fn alloca_type(&self) -> impl BasicType<'ctx> {
+        self.as_base_type().get_element_type().into_struct_type()
     }
 
     fn as_base_type(&self) -> Self::Base {

nac3core/src/codegen/values/array.rs

@@ -51,8 +51,8 @@ pub trait ArrayLikeIndexer<'ctx, Index = IntValue<'ctx>>: ArrayLikeValue<'ctx> {
     /// This function should be called with a valid index.
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &Index,
         name: Option<&str>,
     ) -> PointerValue<'ctx>;
@@ -76,8 +76,8 @@ pub trait UntypedArrayLikeAccessor<'ctx, Index = IntValue<'ctx>>:
     /// This function should be called with a valid index.
     unsafe fn get_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &Index,
         name: Option<&str>,
     ) -> BasicValueEnum<'ctx> {
@@ -107,8 +107,8 @@ pub trait UntypedArrayLikeMutator<'ctx, Index = IntValue<'ctx>>:
     /// This function should be called with a valid index.
     unsafe fn set_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &Index,
         value: BasicValueEnum<'ctx>,
     ) {
@@ -130,32 +130,33 @@ pub trait UntypedArrayLikeMutator<'ctx, Index = IntValue<'ctx>>:
 }
 
 /// An array-like value that can have its array elements accessed as an arbitrary type `T`.
-pub trait TypedArrayLikeAccessor<'ctx, T, Index = IntValue<'ctx>>:
+pub trait TypedArrayLikeAccessor<'ctx, G: CodeGenerator + ?Sized, T, Index = IntValue<'ctx>>:
     UntypedArrayLikeAccessor<'ctx, Index>
 {
     /// Casts an element from [`BasicValueEnum`] into `T`.
     fn downcast_to_type(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         value: BasicValueEnum<'ctx>,
     ) -> T;
 
     /// # Safety
     ///
     /// This function should be called with a valid index.
-    unsafe fn get_typed_unchecked<G: CodeGenerator + ?Sized>(
+    unsafe fn get_typed_unchecked(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &Index,
         name: Option<&str>,
     ) -> T {
         let value = unsafe { self.get_unchecked(ctx, generator, idx, name) };
-        self.downcast_to_type(ctx, value)
+        self.downcast_to_type(ctx, generator, value)
     }
 
     /// Returns the data at the `idx`-th index.
-    fn get_typed<G: CodeGenerator + ?Sized>(
+    fn get_typed(
         &self,
         ctx: &mut CodeGenContext<'ctx, '_>,
         generator: &mut G,
@@ -163,62 +164,63 @@ pub trait TypedArrayLikeAccessor<'ctx, T, Index = IntValue<'ctx>>:
         name: Option<&str>,
     ) -> T {
         let value = self.get(ctx, generator, idx, name);
-        self.downcast_to_type(ctx, value)
+        self.downcast_to_type(ctx, generator, value)
     }
 }
 
 /// An array-like value that can have its array elements mutated as an arbitrary type `T`.
-pub trait TypedArrayLikeMutator<'ctx, T, Index = IntValue<'ctx>>:
+pub trait TypedArrayLikeMutator<'ctx, G: CodeGenerator + ?Sized, T, Index = IntValue<'ctx>>:
     UntypedArrayLikeMutator<'ctx, Index>
 {
     /// Casts an element from T into [`BasicValueEnum`].
     fn upcast_from_type(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         value: T,
     ) -> BasicValueEnum<'ctx>;
 
     /// # Safety
     ///
     /// This function should be called with a valid index.
-    unsafe fn set_typed_unchecked<G: CodeGenerator + ?Sized>(
+    unsafe fn set_typed_unchecked(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &Index,
         value: T,
     ) {
-        let value = self.upcast_from_type(ctx, value);
+        let value = self.upcast_from_type(ctx, generator, value);
         unsafe { self.set_unchecked(ctx, generator, idx, value) }
     }
 
     /// Sets the data at the `idx`-th index.
-    fn set_typed<G: CodeGenerator + ?Sized>(
+    fn set_typed(
         &self,
         ctx: &mut CodeGenContext<'ctx, '_>,
         generator: &mut G,
         idx: &Index,
         value: T,
     ) {
-        let value = self.upcast_from_type(ctx, value);
+        let value = self.upcast_from_type(ctx, generator, value);
         self.set(ctx, generator, idx, value);
     }
 }
 
-/// Type alias for a function that casts a [`BasicValueEnum`] into a `T`.
-type ValueDowncastFn<'ctx, T> =
-    Box<dyn Fn(&mut CodeGenContext<'ctx, '_>, BasicValueEnum<'ctx>) -> T + 'ctx>;
-/// Type alias for a function that casts a `T` into a [`BasicValueEnum`].
-type ValueUpcastFn<'ctx, T> = Box<dyn Fn(&mut CodeGenContext<'ctx, '_>, T) -> BasicValueEnum<'ctx>>;
-
 /// An adapter for constraining untyped array values as typed values.
-pub struct TypedArrayLikeAdapter<'ctx, T, Adapted: ArrayLikeValue<'ctx> = ArraySliceValue<'ctx>> {
+#[derive(Copy, Clone)]
+pub struct TypedArrayLikeAdapter<
+    'ctx,
+    G: CodeGenerator + ?Sized,
+    T,
+    Adapted: ArrayLikeValue<'ctx> = ArraySliceValue<'ctx>,
+> {
     adapted: Adapted,
-    downcast_fn: ValueDowncastFn<'ctx, T>,
-    upcast_fn: ValueUpcastFn<'ctx, T>,
+    downcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, BasicValueEnum<'ctx>) -> T,
+    upcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, T) -> BasicValueEnum<'ctx>,
 }
 
-impl<'ctx, T, Adapted> TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Adapted> TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: ArrayLikeValue<'ctx>,
 {
@@ -229,61 +231,70 @@ where
     /// * `upcast_fn` - The function converting a T into a [`BasicValueEnum`].
     pub fn from(
         adapted: Adapted,
-        downcast_fn: ValueDowncastFn<'ctx, T>,
-        upcast_fn: ValueUpcastFn<'ctx, T>,
+        downcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, BasicValueEnum<'ctx>) -> T,
+        upcast_fn: fn(&CodeGenContext<'ctx, '_>, &G, T) -> BasicValueEnum<'ctx>,
     ) -> Self {
         TypedArrayLikeAdapter { adapted, downcast_fn, upcast_fn }
     }
 }
 
-impl<'ctx, T, Adapted> ArrayLikeValue<'ctx> for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Adapted> ArrayLikeValue<'ctx>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: ArrayLikeValue<'ctx>,
 {
-    fn element_type<G: CodeGenerator + ?Sized>(
+    fn element_type<CG: CodeGenerator + ?Sized>(
         &self,
         ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
+        generator: &CG,
     ) -> AnyTypeEnum<'ctx> {
         self.adapted.element_type(ctx, generator)
     }
 
-    fn base_ptr<G: CodeGenerator + ?Sized>(
+    fn base_ptr<CG: CodeGenerator + ?Sized>(
         &self,
         ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
+        generator: &CG,
     ) -> PointerValue<'ctx> {
         self.adapted.base_ptr(ctx, generator)
     }
 
-    fn size<G: CodeGenerator + ?Sized>(
+    fn size<CG: CodeGenerator + ?Sized>(
         &self,
         ctx: &CodeGenContext<'ctx, '_>,
-        generator: &G,
+        generator: &CG,
     ) -> IntValue<'ctx> {
         self.adapted.size(ctx, generator)
     }
+
+    fn as_slice_value<CG: CodeGenerator + ?Sized>(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &CG,
+    ) -> ArraySliceValue<'ctx> {
+        self.adapted.as_slice_value(ctx, generator)
+    }
 }
 
-impl<'ctx, T, Index, Adapted> ArrayLikeIndexer<'ctx, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> ArrayLikeIndexer<'ctx, Index>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: ArrayLikeIndexer<'ctx, Index>,
 {
-    unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
+    unsafe fn ptr_offset_unchecked<CG: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &CG,
         idx: &Index,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
         unsafe { self.adapted.ptr_offset_unchecked(ctx, generator, idx, name) }
     }
 
-    fn ptr_offset<G: CodeGenerator + ?Sized>(
+    fn ptr_offset<CG: CodeGenerator + ?Sized>(
         &self,
         ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        generator: &mut CG,
         idx: &Index,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
@@ -291,44 +302,46 @@ where
     }
 }
 
-impl<'ctx, T, Index, Adapted> UntypedArrayLikeAccessor<'ctx, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> UntypedArrayLikeAccessor<'ctx, Index>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: UntypedArrayLikeAccessor<'ctx, Index>,
 {
 }
-impl<'ctx, T, Index, Adapted> UntypedArrayLikeMutator<'ctx, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> UntypedArrayLikeMutator<'ctx, Index>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: UntypedArrayLikeMutator<'ctx, Index>,
 {
 }
 
-impl<'ctx, T, Index, Adapted> TypedArrayLikeAccessor<'ctx, T, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> TypedArrayLikeAccessor<'ctx, G, T, Index>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: UntypedArrayLikeAccessor<'ctx, Index>,
 {
     fn downcast_to_type(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         value: BasicValueEnum<'ctx>,
     ) -> T {
-        (self.downcast_fn)(ctx, value)
+        (self.downcast_fn)(ctx, generator, value)
     }
 }
 
-impl<'ctx, T, Index, Adapted> TypedArrayLikeMutator<'ctx, T, Index>
-    for TypedArrayLikeAdapter<'ctx, T, Adapted>
+impl<'ctx, G: CodeGenerator + ?Sized, T, Index, Adapted> TypedArrayLikeMutator<'ctx, G, T, Index>
+    for TypedArrayLikeAdapter<'ctx, G, T, Adapted>
 where
     Adapted: UntypedArrayLikeMutator<'ctx, Index>,
 {
     fn upcast_from_type(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         value: T,
     ) -> BasicValueEnum<'ctx> {
-        (self.upcast_fn)(ctx, value)
+        (self.upcast_fn)(ctx, generator, value)
     }
 }
 
@@ -384,12 +397,12 @@ impl<'ctx> ArrayLikeValue<'ctx> for ArraySliceValue<'ctx> {
 impl<'ctx> ArrayLikeIndexer<'ctx> for ArraySliceValue<'ctx> {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &IntValue<'ctx>,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
-        let var_name = name.map(|v| format!("{v}.addr")).unwrap_or_default();
+        let var_name = name.or(self.2).map(|v| format!("{v}.addr")).unwrap_or_default();
 
         unsafe {
             ctx.builder

nac3core/src/codegen/values/list.rs

@@ -8,7 +8,7 @@ use super::{
     ArrayLikeIndexer, ArrayLikeValue, ProxyValue, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
 };
 use crate::codegen::{
-    types::ListType,
+    types::{structure::StructField, ListType, ProxyType},
     {CodeGenContext, CodeGenerator},
 };
 
@@ -42,48 +42,26 @@ impl<'ctx> ListValue<'ctx> {
         ListValue { value: ptr, llvm_usize, name }
     }
 
+    fn items_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, PointerValue<'ctx>> {
+        self.get_type().get_fields(&ctx.ctx).items
+    }
+
     /// Returns the double-indirection pointer to the `data` array, as if by calling `getelementptr`
     /// on the field.
     fn pptr_to_data(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        let llvm_i32 = ctx.ctx.i32_type();
-        let var_name = self.name.map(|v| format!("{v}.data.addr")).unwrap_or_default();
-
-        unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.as_base_value(),
-                    &[llvm_i32.const_zero(), llvm_i32.const_zero()],
-                    var_name.as_str(),
-                )
-                .unwrap()
-        }
-    }
-
-    /// Returns the pointer to the field storing the size of this `list`.
-    fn ptr_to_size(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        let llvm_i32 = ctx.ctx.i32_type();
-        let var_name = self.name.map(|v| format!("{v}.size.addr")).unwrap_or_default();
-
-        unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.as_base_value(),
-                    &[llvm_i32.const_zero(), llvm_i32.const_int(1, true)],
-                    var_name.as_str(),
-                )
-                .unwrap()
-        }
+        self.items_field(ctx).ptr_by_gep(ctx, self.value, self.name)
     }
 
     /// Stores the array of data elements `data` into this instance.
     fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
-        ctx.builder.build_store(self.pptr_to_data(ctx), data).unwrap();
+        self.items_field(ctx).set(ctx, self.value, data, self.name);
     }
 
     /// Convenience method for creating a new array storing data elements with the given element
     /// type `elem_ty` and `size`.
     ///
-    /// If `size` is [None], the size stored in the field of this instance is used instead.
+    /// If `size` is [None], the size stored in the field of this instance is used instead. If
+    /// `size` is resolved to `0` at runtime, `(T*) 0` will be assigned to `data`.
     pub fn create_data(
         &self,
         ctx: &mut CodeGenContext<'ctx, '_>,
@@ -114,6 +92,10 @@ impl<'ctx> ListValue<'ctx> {
         ListDataProxy(self)
     }
 
+    fn len_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields(&ctx.ctx).len
+    }
+
     /// Stores the `size` of this `list` into this instance.
     pub fn store_size<G: CodeGenerator + ?Sized>(
         &self,
@@ -123,22 +105,33 @@ impl<'ctx> ListValue<'ctx> {
     ) {
         debug_assert_eq!(size.get_type(), generator.get_size_type(ctx.ctx));
 
-        let psize = self.ptr_to_size(ctx);
-        ctx.builder.build_store(psize, size).unwrap();
+        self.len_field(ctx).set(ctx, self.value, size, self.name);
     }
 
     /// Returns the size of this `list` as a value.
-    pub fn load_size(&self, ctx: &CodeGenContext<'ctx, '_>, name: Option<&str>) -> IntValue<'ctx> {
-        let psize = self.ptr_to_size(ctx);
-        let var_name = name
-            .map(ToString::to_string)
-            .or_else(|| self.name.map(|v| format!("{v}.size")))
-            .unwrap_or_default();
+    pub fn load_size(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        name: Option<&'ctx str>,
+    ) -> IntValue<'ctx> {
+        self.len_field(ctx).get(ctx, self.value, name)
+    }
 
-        ctx.builder
-            .build_load(psize, var_name.as_str())
-            .map(BasicValueEnum::into_int_value)
-            .unwrap()
+    /// Returns an instance of [`ListValue`] with the `items` pointer cast to `i8*`.
+    #[must_use]
+    pub fn as_i8_list<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &G,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> ListValue<'ctx> {
+        let llvm_i8 = ctx.ctx.i8_type();
+        let llvm_list_i8 = <Self as ProxyValue>::Type::new(generator, ctx.ctx, llvm_i8.into());
+
+        Self::from_pointer_value(
+            ctx.builder.build_pointer_cast(self.value, llvm_list_i8.as_base_type(), "").unwrap(),
+            self.llvm_usize,
+            self.name,
+        )
     }
 }
 
@@ -199,8 +192,8 @@ impl<'ctx> ArrayLikeValue<'ctx> for ListDataProxy<'ctx, '_> {
 impl<'ctx> ArrayLikeIndexer<'ctx> for ListDataProxy<'ctx, '_> {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &IntValue<'ctx>,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {

nac3core/src/codegen/values/mod.rs

@@ -5,11 +5,13 @@ use crate::codegen::CodeGenerator;
 pub use array::*;
 pub use list::*;
 pub use range::*;
+pub use tuple::*;
 
 mod array;
 mod list;
 pub mod ndarray;
 mod range;
+mod tuple;
 pub mod utils;
 
 /// A LLVM type that is used to represent a non-primitive value in NAC3.

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

@@ -0,0 +1,243 @@
+use inkwell::{
+    types::IntType,
+    values::{IntValue, PointerValue},
+};
+use itertools::Itertools;
+
+use crate::codegen::{
+    irrt,
+    types::{
+        ndarray::{NDArrayType, ShapeEntryType},
+        structure::StructField,
+        ProxyType,
+    },
+    values::{
+        ndarray::NDArrayValue, ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ProxyValue,
+        TypedArrayLikeAccessor, TypedArrayLikeAdapter, TypedArrayLikeMutator,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+#[derive(Copy, Clone)]
+pub struct ShapeEntryValue<'ctx> {
+    value: PointerValue<'ctx>,
+    llvm_usize: IntType<'ctx>,
+    name: Option<&'ctx str>,
+}
+
+impl<'ctx> ShapeEntryValue<'ctx> {
+    /// Checks whether `value` is an instance of `ShapeEntry`, returning [Err] if `value` is
+    /// not an instance.
+    pub fn is_representable(
+        value: PointerValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        <Self as ProxyValue<'ctx>>::Type::is_representable(value.get_type(), llvm_usize)
+    }
+
+    /// Creates an [`ShapeEntryValue`] from a [`PointerValue`].
+    #[must_use]
+    pub fn from_pointer_value(
+        ptr: PointerValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> Self {
+        debug_assert!(Self::is_representable(ptr, llvm_usize).is_ok());
+
+        Self { value: ptr, llvm_usize, name }
+    }
+
+    fn ndims_field(&self) -> StructField<'ctx, IntValue<'ctx>> {
+        self.get_type().get_fields(self.value.get_type().get_context()).ndims
+    }
+
+    /// Stores the number of dimensions into this value.
+    pub fn store_ndims(&self, ctx: &CodeGenContext<'ctx, '_>, value: IntValue<'ctx>) {
+        self.ndims_field().set(ctx, self.value, value, self.name);
+    }
+
+    fn shape_field(&self) -> StructField<'ctx, PointerValue<'ctx>> {
+        self.get_type().get_fields(self.value.get_type().get_context()).shape
+    }
+
+    /// Stores the shape into this value.
+    pub fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, value: PointerValue<'ctx>) {
+        self.shape_field().set(ctx, self.value, value, self.name);
+    }
+}
+
+impl<'ctx> ProxyValue<'ctx> for ShapeEntryValue<'ctx> {
+    type Base = PointerValue<'ctx>;
+    type Type = ShapeEntryType<'ctx>;
+
+    fn get_type(&self) -> Self::Type {
+        Self::Type::from_type(self.value.get_type(), self.llvm_usize)
+    }
+
+    fn as_base_value(&self) -> Self::Base {
+        self.value
+    }
+}
+
+impl<'ctx> From<ShapeEntryValue<'ctx>> for PointerValue<'ctx> {
+    fn from(value: ShapeEntryValue<'ctx>) -> Self {
+        value.as_base_value()
+    }
+}
+
+impl<'ctx> NDArrayValue<'ctx> {
+    /// Create a broadcast view on this ndarray with a target shape.
+    ///
+    /// The input shape will be checked to make sure that it contains no negative values.
+    ///
+    /// * `target_ndims` - The ndims type after broadcasting to the given shape.
+    ///   The caller has to figure this out for this function.
+    /// * `target_shape` - An array pointer pointing to the target shape.
+    #[must_use]
+    pub fn broadcast_to<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        target_ndims: u64,
+        target_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    ) -> Self {
+        assert!(self.ndims <= target_ndims);
+        assert_eq!(target_shape.element_type(ctx, generator), self.llvm_usize.into());
+
+        let broadcast_ndarray = NDArrayType::new(generator, ctx.ctx, self.dtype, target_ndims)
+            .construct_uninitialized(generator, ctx, None);
+        broadcast_ndarray.copy_shape_from_array(
+            generator,
+            ctx,
+            target_shape.base_ptr(ctx, generator),
+        );
+
+        irrt::ndarray::call_nac3_ndarray_broadcast_to(generator, ctx, *self, broadcast_ndarray);
+        broadcast_ndarray
+    }
+}
+
+/// A result produced by [`broadcast_all_ndarrays`]
+#[derive(Clone)]
+pub struct BroadcastAllResult<'ctx, G: CodeGenerator + ?Sized> {
+    /// The statically known `ndims` of the broadcast result.
+    pub ndims: u64,
+
+    /// The broadcasting shape.
+    pub shape: TypedArrayLikeAdapter<'ctx, G, IntValue<'ctx>>,
+
+    /// Broadcasted views on the inputs.
+    ///
+    /// All of them will have `shape` [`BroadcastAllResult::shape`] and
+    /// `ndims` [`BroadcastAllResult::ndims`]. The length of the vector
+    /// is the same as the input.
+    pub ndarrays: Vec<NDArrayValue<'ctx>>,
+}
+
+/// Helper function to call [`irrt::ndarray::call_nac3_ndarray_broadcast_shapes`].
+fn broadcast_shapes<'ctx, G, Shape>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    in_shape_entries: &[(ArraySliceValue<'ctx>, u64)], // (shape, shape's length/ndims)
+    broadcast_ndims: u64,
+    broadcast_shape: &Shape,
+) where
+    G: CodeGenerator + ?Sized,
+    Shape: TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
+        + TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_shape_ty = ShapeEntryType::new(generator, ctx.ctx);
+
+    assert!(in_shape_entries
+        .iter()
+        .all(|entry| entry.0.element_type(ctx, generator) == llvm_usize.into()));
+    assert_eq!(broadcast_shape.element_type(ctx, generator), llvm_usize.into());
+
+    // Prepare input shape entries to be passed to `call_nac3_ndarray_broadcast_shapes`.
+    let num_shape_entries =
+        llvm_usize.const_int(u64::try_from(in_shape_entries.len()).unwrap(), false);
+    let shape_entries = llvm_shape_ty.array_alloca(ctx, num_shape_entries, None);
+    for (i, (in_shape, in_ndims)) in in_shape_entries.iter().enumerate() {
+        let pshape_entry = unsafe {
+            shape_entries.ptr_offset_unchecked(
+                ctx,
+                generator,
+                &llvm_usize.const_int(i as u64, false),
+                None,
+            )
+        };
+        let shape_entry = llvm_shape_ty.map_value(pshape_entry, None);
+
+        let in_ndims = llvm_usize.const_int(*in_ndims, false);
+        shape_entry.store_ndims(ctx, in_ndims);
+
+        shape_entry.store_shape(ctx, in_shape.base_ptr(ctx, generator));
+    }
+
+    let broadcast_ndims = llvm_usize.const_int(broadcast_ndims, false);
+    irrt::ndarray::call_nac3_ndarray_broadcast_shapes(
+        generator,
+        ctx,
+        num_shape_entries,
+        shape_entries,
+        broadcast_ndims,
+        broadcast_shape,
+    );
+}
+
+impl<'ctx> NDArrayType<'ctx> {
+    /// Broadcast all ndarrays according to
+    /// [`np.broadcast()`](https://numpy.org/doc/stable/reference/generated/numpy.broadcast.html)
+    /// and return a [`BroadcastAllResult`] containing all the information of the result of the
+    /// broadcast operation.
+    pub fn broadcast<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        ndarrays: &[NDArrayValue<'ctx>],
+    ) -> BroadcastAllResult<'ctx, G> {
+        assert!(!ndarrays.is_empty());
+
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+
+        // Infer the broadcast output ndims.
+        let broadcast_ndims_int =
+            ndarrays.iter().map(|ndarray| ndarray.get_type().ndims()).max().unwrap();
+        assert!(self.ndims() >= broadcast_ndims_int);
+
+        let broadcast_ndims = llvm_usize.const_int(broadcast_ndims_int, false);
+        let broadcast_shape = ArraySliceValue::from_ptr_val(
+            ctx.builder.build_array_alloca(llvm_usize, broadcast_ndims, "").unwrap(),
+            broadcast_ndims,
+            None,
+        );
+        let broadcast_shape = TypedArrayLikeAdapter::from(
+            broadcast_shape,
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+
+        let shape_entries = ndarrays
+            .iter()
+            .map(|ndarray| {
+                (ndarray.shape().as_slice_value(ctx, generator), ndarray.get_type().ndims())
+            })
+            .collect_vec();
+        broadcast_shapes(generator, ctx, &shape_entries, broadcast_ndims_int, &broadcast_shape);
+
+        // Broadcast all the inputs to shape `dst_shape`.
+        let broadcast_ndarrays = ndarrays
+            .iter()
+            .map(|ndarray| {
+                ndarray.broadcast_to(generator, ctx, broadcast_ndims_int, &broadcast_shape)
+            })
+            .collect_vec();
+
+        BroadcastAllResult {
+            ndims: broadcast_ndims_int,
+            shape: broadcast_shape,
+            ndarrays: broadcast_ndarrays,
+        }
+    }
+}

nac3core/src/codegen/values/ndarray/contiguous.rs

@@ -118,12 +118,10 @@ impl<'ctx> NDArrayValue<'ctx> {
         ctx: &mut CodeGenContext<'ctx, '_>,
     ) -> ContiguousNDArrayValue<'ctx> {
         let result = ContiguousNDArrayType::new(generator, ctx.ctx, self.dtype)
-            .alloca(generator, ctx, self.name);
+            .alloca_var(generator, ctx, self.name);
 
         // Set ndims and shape.
-        let ndims = self
-            .ndims
-            .map_or_else(|| self.load_ndims(ctx), |ndims| self.llvm_usize.const_int(ndims, false));
+        let ndims = self.llvm_usize.const_int(self.ndims, false);
         result.store_ndims(ctx, ndims);
 
         let shape = self.shape();
@@ -180,7 +178,7 @@ impl<'ctx> NDArrayValue<'ctx> {
         // TODO: Debug assert `ndims == carray.ndims` to catch bugs.
 
         // Allocate the resulting ndarray.
-        let ndarray = NDArrayType::new(generator, ctx.ctx, carray.item, Some(ndims))
+        let ndarray = NDArrayType::new(generator, ctx.ctx, carray.item, ndims)
             .construct_uninitialized(generator, ctx, carray.name);
 
         // Copy shape and update strides

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

@@ -98,8 +98,8 @@ impl<'ctx> From<NDIndexValue<'ctx>> for PointerValue<'ctx> {
 impl<'ctx> NDArrayValue<'ctx> {
     /// Get the expected `ndims` after indexing with `indices`.
     #[must_use]
-    fn deduce_ndims_after_indexing_with(&self, indices: &[RustNDIndex<'ctx>]) -> Option<u64> {
-        let mut ndims = self.ndims?;
+    fn deduce_ndims_after_indexing_with(&self, indices: &[RustNDIndex<'ctx>]) -> u64 {
+        let mut ndims = self.ndims;
 
         for index in indices {
             match index {
@@ -113,7 +113,7 @@ impl<'ctx> NDArrayValue<'ctx> {
             }
         }
 
-        Some(ndims)
+        ndims
     }
 
     /// Index into the ndarray, and return a newly-allocated view on this ndarray.
@@ -127,8 +127,6 @@ impl<'ctx> NDArrayValue<'ctx> {
         ctx: &mut CodeGenContext<'ctx, '_>,
         indices: &[RustNDIndex<'ctx>],
     ) -> Self {
-        assert!(self.ndims.is_some(), "NDArrayValue::index is only supported for instances with compile-time known ndims (self.ndims = Some(...))");
-
         let dst_ndims = self.deduce_ndims_after_indexing_with(indices);
         let dst_ndarray = NDArrayType::new(generator, ctx.ctx, self.dtype, dst_ndims)
             .construct_uninitialized(generator, ctx, None);
@@ -248,7 +246,7 @@ impl<'ctx> RustNDIndex<'ctx> {
             RustNDIndex::Slice(in_rust_slice) => {
                 let user_slice_ptr =
                     SliceType::new(ctx.ctx, ctx.ctx.i32_type(), generator.get_size_type(ctx.ctx))
-                        .alloca(generator, ctx, None);
+                        .alloca_var(generator, ctx, None);
                 in_rust_slice.write_to_slice(ctx, user_slice_ptr);
 
                 dst_ndindex.store_data(

nac3core/src/codegen/values/ndarray/map.rs

@@ -0,0 +1,69 @@
+use inkwell::{types::BasicTypeEnum, values::BasicValueEnum};
+
+use crate::codegen::{
+    values::{
+        ndarray::{NDArrayOut, NDArrayValue, ScalarOrNDArray},
+        ProxyValue,
+    },
+    CodeGenContext, CodeGenerator,
+};
+
+impl<'ctx> NDArrayValue<'ctx> {
+    /// Map through this ndarray with an elementwise function.
+    pub fn map<'a, G, Mapping>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        out: NDArrayOut<'ctx>,
+        mapping: Mapping,
+    ) -> Result<Self, String>
+    where
+        G: CodeGenerator + ?Sized,
+        Mapping: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            BasicValueEnum<'ctx>,
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        self.get_type().broadcast_starmap(
+            generator,
+            ctx,
+            &[*self],
+            out,
+            |generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
+        )
+    }
+}
+
+impl<'ctx> ScalarOrNDArray<'ctx> {
+    /// Map through this [`ScalarOrNDArray`] with an elementwise function.
+    ///
+    /// If this is a scalar, `mapping` will directly act on the scalar. This function will return a
+    /// [`ScalarOrNDArray::Scalar`] of that result.
+    ///
+    /// If this is an ndarray, `mapping` will be applied to the elements of the ndarray. A new
+    /// ndarray of the results will be created and returned as a [`ScalarOrNDArray::NDArray`].
+    pub fn map<'a, G, Mapping>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, 'a>,
+        ret_dtype: BasicTypeEnum<'ctx>,
+        mapping: Mapping,
+    ) -> Result<ScalarOrNDArray<'ctx>, String>
+    where
+        G: CodeGenerator + ?Sized,
+        Mapping: FnOnce(
+            &mut G,
+            &mut CodeGenContext<'ctx, 'a>,
+            BasicValueEnum<'ctx>,
+        ) -> Result<BasicValueEnum<'ctx>, String>,
+    {
+        ScalarOrNDArray::broadcasting_starmap(
+            generator,
+            ctx,
+            &[*self],
+            ret_dtype,
+            |generator, ctx, scalars| mapping(generator, ctx, scalars[0]),
+        )
+    }
+}

nac3core/src/codegen/values/ndarray/matmul.rs

@@ -0,0 +1,323 @@
+use std::cmp::max;
+
+use nac3parser::ast::Operator;
+
+use super::{NDArrayOut, NDArrayValue, RustNDIndex};
+use crate::{
+    codegen::{
+        expr::gen_binop_expr_with_values,
+        irrt,
+        stmt::gen_for_callback_incrementing,
+        types::ndarray::NDArrayType,
+        values::{
+            ArrayLikeValue, ArraySliceValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
+            UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
+        },
+        CodeGenContext, CodeGenerator,
+    },
+    toplevel::helper::arraylike_flatten_element_type,
+    typecheck::{magic_methods::Binop, typedef::Type},
+};
+
+/// Perform `np.einsum("...ij,...jk->...ik", in_a, in_b)`.
+///
+/// `dst_dtype` defines the dtype of the returned ndarray.
+fn matmul_at_least_2d<'ctx, G: CodeGenerator>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    dst_dtype: Type,
+    (in_a_ty, in_a): (Type, NDArrayValue<'ctx>),
+    (in_b_ty, in_b): (Type, NDArrayValue<'ctx>),
+) -> NDArrayValue<'ctx> {
+    assert!(in_a.ndims >= 2, "in_a (which is {}) must be >= 2", in_a.ndims);
+    assert!(in_b.ndims >= 2, "in_b (which is {}) must be >= 2", in_b.ndims);
+
+    let lhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, in_a_ty);
+    let rhs_dtype = arraylike_flatten_element_type(&mut ctx.unifier, in_b_ty);
+
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_dst_dtype = ctx.get_llvm_type(generator, dst_dtype);
+
+    // Deduce ndims of the result of matmul.
+    let ndims_int = max(in_a.ndims, in_b.ndims);
+    let ndims = llvm_usize.const_int(ndims_int, false);
+
+    // Broadcasts `in_a.shape[:-2]` and `in_b.shape[:-2]` together and allocate the
+    // destination ndarray to store the result of matmul.
+    let (lhs, rhs, dst) = {
+        let in_lhs_ndims = llvm_usize.const_int(in_a.ndims, false);
+        let in_lhs_shape = TypedArrayLikeAdapter::from(
+            ArraySliceValue::from_ptr_val(
+                in_a.shape().base_ptr(ctx, generator),
+                in_lhs_ndims,
+                None,
+            ),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+        let in_rhs_ndims = llvm_usize.const_int(in_b.ndims, false);
+        let in_rhs_shape = TypedArrayLikeAdapter::from(
+            ArraySliceValue::from_ptr_val(
+                in_b.shape().base_ptr(ctx, generator),
+                in_rhs_ndims,
+                None,
+            ),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+        let lhs_shape = TypedArrayLikeAdapter::from(
+            ArraySliceValue::from_ptr_val(
+                ctx.builder.build_array_alloca(llvm_usize, ndims, "").unwrap(),
+                ndims,
+                None,
+            ),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+        let rhs_shape = TypedArrayLikeAdapter::from(
+            ArraySliceValue::from_ptr_val(
+                ctx.builder.build_array_alloca(llvm_usize, ndims, "").unwrap(),
+                ndims,
+                None,
+            ),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+        let dst_shape = TypedArrayLikeAdapter::from(
+            ArraySliceValue::from_ptr_val(
+                ctx.builder.build_array_alloca(llvm_usize, ndims, "").unwrap(),
+                ndims,
+                None,
+            ),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
+        );
+
+        // Matmul dimension compatibility is checked here.
+        irrt::ndarray::call_nac3_ndarray_matmul_calculate_shapes(
+            generator,
+            ctx,
+            &in_lhs_shape,
+            &in_rhs_shape,
+            ndims,
+            &lhs_shape,
+            &rhs_shape,
+            &dst_shape,
+        );
+
+        let lhs = in_a.broadcast_to(generator, ctx, ndims_int, &lhs_shape);
+        let rhs = in_b.broadcast_to(generator, ctx, ndims_int, &rhs_shape);
+
+        let dst = NDArrayType::new(generator, ctx.ctx, llvm_dst_dtype, ndims_int)
+            .construct_uninitialized(generator, ctx, None);
+        dst.copy_shape_from_array(generator, ctx, dst_shape.base_ptr(ctx, generator));
+        unsafe {
+            dst.create_data(generator, ctx);
+        }
+
+        (lhs, rhs, dst)
+    };
+
+    let len = unsafe {
+        lhs.shape().get_typed_unchecked(
+            ctx,
+            generator,
+            &llvm_usize.const_int(ndims_int - 1, false),
+            None,
+        )
+    };
+
+    let at_row = i64::try_from(ndims_int - 2).unwrap();
+    let at_col = i64::try_from(ndims_int - 1).unwrap();
+
+    let dst_dtype_llvm = ctx.get_llvm_type(generator, dst_dtype);
+    let dst_zero = dst_dtype_llvm.const_zero();
+
+    dst.foreach(generator, ctx, |generator, ctx, _, hdl| {
+        let pdst_ij = hdl.get_pointer(ctx);
+
+        ctx.builder.build_store(pdst_ij, dst_zero).unwrap();
+
+        let indices = hdl.get_indices::<G>();
+        let i = unsafe {
+            indices.get_unchecked(ctx, generator, &llvm_usize.const_int(at_row as u64, true), None)
+        };
+        let j = unsafe {
+            indices.get_unchecked(ctx, generator, &llvm_usize.const_int(at_col as u64, true), None)
+        };
+
+        let num_0 = llvm_usize.const_int(0, false);
+        let num_1 = llvm_usize.const_int(1, false);
+
+        gen_for_callback_incrementing(
+            generator,
+            ctx,
+            None,
+            num_0,
+            (len, false),
+            |generator, ctx, _, k| {
+                // `indices` is modified to index into `a` and `b`, and restored.
+                unsafe {
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_row as u64, true),
+                        i,
+                    );
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_col as u64, true),
+                        k.into(),
+                    );
+                }
+                let a_ik = unsafe { lhs.data().get_unchecked(ctx, generator, &indices, None) };
+
+                unsafe {
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_row as u64, true),
+                        k.into(),
+                    );
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_col as u64, true),
+                        j,
+                    );
+                }
+                let b_kj = unsafe { rhs.data().get_unchecked(ctx, generator, &indices, None) };
+
+                // Restore `indices`.
+                unsafe {
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_row as u64, true),
+                        i,
+                    );
+                    indices.set_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(at_col as u64, true),
+                        j,
+                    );
+                }
+
+                // x = a_[...]ik * b_[...]kj
+                let x = gen_binop_expr_with_values(
+                    generator,
+                    ctx,
+                    (&Some(lhs_dtype), a_ik),
+                    Binop::normal(Operator::Mult),
+                    (&Some(rhs_dtype), b_kj),
+                    ctx.current_loc,
+                )?
+                .unwrap()
+                .to_basic_value_enum(ctx, generator, dst_dtype)?;
+
+                // dst_[...]ij += x
+                let dst_ij = ctx.builder.build_load(pdst_ij, "").unwrap();
+                let dst_ij = gen_binop_expr_with_values(
+                    generator,
+                    ctx,
+                    (&Some(dst_dtype), dst_ij),
+                    Binop::normal(Operator::Add),
+                    (&Some(dst_dtype), x),
+                    ctx.current_loc,
+                )?
+                .unwrap()
+                .to_basic_value_enum(ctx, generator, dst_dtype)?;
+                ctx.builder.build_store(pdst_ij, dst_ij).unwrap();
+
+                Ok(())
+            },
+            num_1,
+        )
+    })
+    .unwrap();
+
+    dst
+}
+
+impl<'ctx> NDArrayValue<'ctx> {
+    /// Perform [`np.matmul`](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).
+    ///
+    /// This function always return an [`NDArrayValue`]. You may want to use
+    /// [`NDArrayValue::split_unsized`] to handle when the output could be a scalar.
+    ///
+    /// `dst_dtype` defines the dtype of the returned ndarray.
+    #[must_use]
+    pub fn matmul<G: CodeGenerator>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        self_ty: Type,
+        (other_ty, other): (Type, Self),
+        (out_dtype, out): (Type, NDArrayOut<'ctx>),
+    ) -> Self {
+        // Sanity check, but type inference should prevent this.
+        assert!(self.ndims > 0 && other.ndims > 0, "np.matmul disallows scalar input");
+
+        // If both arguments are 2-D they are multiplied like conventional matrices.
+        //
+        // If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the
+        // last two indices and broadcast accordingly.
+        //
+        // If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its
+        // dimensions. After matrix multiplication the prepended 1 is removed.
+        //
+        // If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its
+        // dimensions. After matrix multiplication the appended 1 is removed.
+
+        let new_a = if self.ndims == 1 {
+            // Prepend 1 to its dimensions
+            self.index(generator, ctx, &[RustNDIndex::NewAxis, RustNDIndex::Ellipsis])
+        } else {
+            *self
+        };
+
+        let new_b = if other.ndims == 1 {
+            // Append 1 to its dimensions
+            other.index(generator, ctx, &[RustNDIndex::Ellipsis, RustNDIndex::NewAxis])
+        } else {
+            other
+        };
+
+        // NOTE: `result` will always be a newly allocated ndarray.
+        // Current implementation cannot do in-place matrix muliplication.
+        let mut result =
+            matmul_at_least_2d(generator, ctx, out_dtype, (self_ty, new_a), (other_ty, new_b));
+
+        // Postprocessing on the result to remove prepended/appended axes.
+        let mut postindices = vec![];
+        let zero = ctx.ctx.i32_type().const_zero();
+
+        if self.ndims == 1 {
+            // Remove the prepended 1
+            postindices.push(RustNDIndex::SingleElement(zero));
+        }
+
+        if other.ndims == 1 {
+            // Remove the appended 1
+            postindices.push(RustNDIndex::Ellipsis);
+            postindices.push(RustNDIndex::SingleElement(zero));
+        }
+
+        if !postindices.is_empty() {
+            result = result.index(generator, ctx, &postindices);
+        }
+
+        match out {
+            NDArrayOut::NewNDArray { .. } => result,
+            NDArrayOut::WriteToNDArray { ndarray: out_ndarray } => {
+                let result_shape = result.shape();
+                out_ndarray.assert_can_be_written_by_out(generator, ctx, result_shape);
+
+                out_ndarray.copy_data_from(generator, ctx, result);
+                out_ndarray
+            }
+        }
+    }
+}

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

@@ -1,29 +1,40 @@
+use std::iter::repeat_n;
+
 use inkwell::{
     types::{AnyType, AnyTypeEnum, BasicType, BasicTypeEnum, IntType},
-    values::{BasicValueEnum, IntValue, PointerValue},
+    values::{BasicValue, BasicValueEnum, IntValue, PointerValue},
     AddressSpace, IntPredicate,
 };
+use itertools::Itertools;
 
 use super::{
-    ArrayLikeIndexer, ArrayLikeValue, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeMutator,
-    UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
+    ArrayLikeIndexer, ArrayLikeValue, ProxyValue, TupleValue, TypedArrayLikeAccessor,
+    TypedArrayLikeAdapter, TypedArrayLikeMutator, UntypedArrayLikeAccessor,
+    UntypedArrayLikeMutator,
 };
-use crate::codegen::{
-    irrt,
-    llvm_intrinsics::{call_int_umin, call_memcpy_generic_array},
-    stmt::gen_for_callback_incrementing,
-    type_aligned_alloca,
-    types::{ndarray::NDArrayType, structure::StructField},
-    CodeGenContext, CodeGenerator,
+use crate::{
+    codegen::{
+        irrt,
+        llvm_intrinsics::{call_int_umin, call_memcpy_generic_array},
+        stmt::gen_for_callback_incrementing,
+        type_aligned_alloca,
+        types::{ndarray::NDArrayType, structure::StructField, TupleType},
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::{Type, TypeEnum},
 };
+pub use broadcast::*;
 pub use contiguous::*;
 pub use indexing::*;
 pub use nditer::*;
-pub use view::*;
 
+mod broadcast;
 mod contiguous;
 mod indexing;
+mod map;
+mod matmul;
 mod nditer;
+pub mod shape;
 mod view;
 
 /// Proxy type for accessing an `NDArray` value in LLVM.
@@ -31,7 +42,7 @@ mod view;
 pub struct NDArrayValue<'ctx> {
     value: PointerValue<'ctx>,
     dtype: BasicTypeEnum<'ctx>,
-    ndims: Option<u64>,
+    ndims: u64,
     llvm_usize: IntType<'ctx>,
     name: Option<&'ctx str>,
 }
@@ -51,7 +62,7 @@ impl<'ctx> NDArrayValue<'ctx> {
     pub fn from_pointer_value(
         ptr: PointerValue<'ctx>,
         dtype: BasicTypeEnum<'ctx>,
-        ndims: Option<u64>,
+        ndims: u64,
         llvm_usize: IntType<'ctx>,
         name: Option<&'ctx str>,
     ) -> Self {
@@ -113,12 +124,6 @@ impl<'ctx> NDArrayValue<'ctx> {
         self.get_type().get_fields(ctx.ctx).shape
     }
 
-    /// Returns the double-indirection pointer to the `shape` array, as if by calling
-    /// `getelementptr` on the field.
-    fn ptr_to_shape(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        self.shape_field(ctx).ptr_by_gep(ctx, self.value, self.name)
-    }
-
     /// Stores the array of dimension sizes `dims` into this instance.
     fn store_shape(&self, ctx: &CodeGenContext<'ctx, '_>, dims: PointerValue<'ctx>) {
         self.shape_field(ctx).set(ctx, self.as_base_value(), dims, self.name);
@@ -147,12 +152,6 @@ impl<'ctx> NDArrayValue<'ctx> {
         self.get_type().get_fields(ctx.ctx).strides
     }
 
-    /// Returns the double-indirection pointer to the `strides` array, as if by calling
-    /// `getelementptr` on the field.
-    fn ptr_to_strides(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
-        self.strides_field(ctx).ptr_by_gep(ctx, self.value, self.name)
-    }
-
     /// Stores the array of stride sizes `strides` into this instance.
     fn store_strides(&self, ctx: &CodeGenContext<'ctx, '_>, strides: PointerValue<'ctx>) {
         self.strides_field(ctx).set(ctx, self.as_base_value(), strides, self.name);
@@ -185,7 +184,7 @@ impl<'ctx> NDArrayValue<'ctx> {
     }
 
     /// Stores the array of data elements `data` into this instance.
-    fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
+    pub fn store_data(&self, ctx: &CodeGenContext<'ctx, '_>, data: PointerValue<'ctx>) {
         let data = ctx
             .builder
             .build_bit_cast(data, ctx.ctx.i8_type().ptr_type(AddressSpace::default()), "")
@@ -246,26 +245,7 @@ impl<'ctx> NDArrayValue<'ctx> {
         ctx: &mut CodeGenContext<'ctx, '_>,
         src_ndarray: NDArrayValue<'ctx>,
     ) {
-        if self.ndims.is_some() && src_ndarray.ndims.is_some() {
-            assert_eq!(self.ndims, src_ndarray.ndims);
-        } else {
-            let self_ndims = self.load_ndims(ctx);
-            let src_ndims = src_ndarray.load_ndims(ctx);
-
-            ctx.make_assert(
-                generator,
-                ctx.builder.build_int_compare(
-                    IntPredicate::EQ,
-                    self_ndims,
-                    src_ndims,
-                    ""
-                ).unwrap(),
-                "0:AssertionError",
-                "NDArrayValue::copy_shape_from_ndarray: Expected self.ndims ({0}) == src_ndarray.ndims ({1})",
-                [Some(self_ndims), Some(src_ndims), None],
-                ctx.current_loc
-            );
-        }
+        assert_eq!(self.ndims, src_ndarray.ndims);
 
         let src_shape = src_ndarray.shape().base_ptr(ctx, generator);
         self.copy_shape_from_array(generator, ctx, src_shape);
@@ -297,26 +277,7 @@ impl<'ctx> NDArrayValue<'ctx> {
         ctx: &mut CodeGenContext<'ctx, '_>,
         src_ndarray: NDArrayValue<'ctx>,
     ) {
-        if self.ndims.is_some() && src_ndarray.ndims.is_some() {
-            assert_eq!(self.ndims, src_ndarray.ndims);
-        } else {
-            let self_ndims = self.load_ndims(ctx);
-            let src_ndims = src_ndarray.load_ndims(ctx);
-
-            ctx.make_assert(
-                generator,
-                ctx.builder.build_int_compare(
-                    IntPredicate::EQ,
-                    self_ndims,
-                    src_ndims,
-                    ""
-                ).unwrap(),
-                "0:AssertionError",
-                "NDArrayValue::copy_shape_from_ndarray: Expected self.ndims ({0}) == src_ndarray.ndims ({1})",
-                [Some(self_ndims), Some(src_ndims), None],
-                ctx.current_loc
-            );
-        }
+        assert_eq!(self.ndims, src_ndarray.ndims);
 
         let src_strides = src_ndarray.strides().base_ptr(ctx, generator);
         self.copy_strides_from_array(generator, ctx, src_strides);
@@ -371,17 +332,17 @@ impl<'ctx> NDArrayValue<'ctx> {
         irrt::ndarray::call_nac3_ndarray_set_strides_by_shape(generator, ctx, *self);
     }
 
+    /// Clone/Copy this ndarray - Allocate a new ndarray with the same shape as this ndarray and
+    /// copy the contents over.
+    ///
+    /// The new ndarray will own its data and will be C-contiguous.
     #[must_use]
     pub fn make_copy<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
     ) -> Self {
-        let clone = if self.ndims.is_some() {
-            self.get_type().construct_uninitialized(generator, ctx, None)
-        } else {
-            self.get_type().construct_dyn_ndims(generator, ctx, self.load_ndims(ctx), None)
-        };
+        let clone = self.get_type().construct_uninitialized(generator, ctx, None);
 
         let shape = self.shape();
         clone.copy_shape_from_array(generator, ctx, shape.base_ptr(ctx, generator));
@@ -406,32 +367,149 @@ impl<'ctx> NDArrayValue<'ctx> {
         irrt::ndarray::call_nac3_ndarray_copy_data(generator, ctx, src, *self);
     }
 
-    /// Returns true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
-    #[must_use]
-    pub fn is_unsized(&self) -> Option<bool> {
-        self.ndims.map(|ndims| ndims == 0)
+    /// Fill the ndarray with a scalar.
+    ///
+    /// `fill_value` must have the same LLVM type as the `dtype` of this ndarray.
+    pub fn fill<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        value: BasicValueEnum<'ctx>,
+    ) {
+        // TODO: It is possible to optimize this by exploiting contiguous strides with memset.
+        //       Probably best to implement in IRRT.
+        self.foreach(generator, ctx, |_, ctx, _, nditer| {
+            let p = nditer.get_pointer(ctx);
+            ctx.builder.build_store(p, value).unwrap();
+            Ok(())
+        })
+        .unwrap();
     }
 
-    /// If this ndarray is unsized, return its sole value as an [`AnyObject`].
+    /// Create the shape tuple of this ndarray like
+    /// [`np.shape(<ndarray>)`](https://numpy.org/doc/stable/reference/generated/numpy.shape.html).
+    ///
+    /// All elements in the tuple are `i32`.
+    pub fn make_shape_tuple<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> TupleValue<'ctx> {
+        let llvm_i32 = ctx.ctx.i32_type();
+
+        let objects = (0..self.ndims)
+            .map(|i| {
+                let dim = unsafe {
+                    self.shape().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &self.llvm_usize.const_int(i, false),
+                        None,
+                    )
+                };
+                ctx.builder.build_int_truncate_or_bit_cast(dim, llvm_i32, "").unwrap()
+            })
+            .map(|obj| obj.as_basic_value_enum())
+            .collect_vec();
+
+        TupleType::new(
+            generator,
+            ctx.ctx,
+            &repeat_n(llvm_i32.into(), self.ndims as usize).collect_vec(),
+        )
+        .construct_from_objects(ctx, objects, None)
+    }
+
+    /// Create the strides tuple of this ndarray like
+    /// [`<ndarray>.strides`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.strides.html).
+    ///
+    /// All elements in the tuple are `i32`.
+    pub fn make_strides_tuple<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> TupleValue<'ctx> {
+        let llvm_i32 = ctx.ctx.i32_type();
+
+        let objects = (0..self.ndims)
+            .map(|i| {
+                let dim = unsafe {
+                    self.strides().get_typed_unchecked(
+                        ctx,
+                        generator,
+                        &self.llvm_usize.const_int(i, false),
+                        None,
+                    )
+                };
+                ctx.builder.build_int_truncate_or_bit_cast(dim, llvm_i32, "").unwrap()
+            })
+            .map(|obj| obj.as_basic_value_enum())
+            .collect_vec();
+
+        TupleType::new(
+            generator,
+            ctx.ctx,
+            &repeat_n(llvm_i32.into(), self.ndims as usize).collect_vec(),
+        )
+        .construct_from_objects(ctx, objects, None)
+    }
+
+    /// Returns true if this ndarray is unsized - `ndims == 0` and only contains a scalar.
+    #[must_use]
+    pub fn is_unsized(&self) -> bool {
+        self.ndims == 0
+    }
+
+    /// Returns the element present in this `ndarray` if this is unsized.
+    pub fn get_unsized_element<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> Option<BasicValueEnum<'ctx>> {
+        if self.is_unsized() {
+            // NOTE: `np.size(self) == 0` here is never possible.
+            let zero = generator.get_size_type(ctx.ctx).const_zero();
+            let value = unsafe { self.data().get_unchecked(ctx, generator, &zero, None) };
+
+            Some(value)
+        } else {
+            None
+        }
+    }
+
+    /// If this ndarray is unsized, return its sole value as an [`BasicValueEnum`].
     /// Otherwise, do nothing and return the ndarray itself.
-    // TODO: Rename to get_unsized_element
     pub fn split_unsized<G: CodeGenerator + ?Sized>(
         &self,
         generator: &mut G,
         ctx: &mut CodeGenContext<'ctx, '_>,
     ) -> ScalarOrNDArray<'ctx> {
-        let Some(is_unsized) = self.is_unsized() else { todo!() };
-
-        if is_unsized {
-            // NOTE: `np.size(self) == 0` here is never possible.
-            let zero = generator.get_size_type(ctx.ctx).const_zero();
-            let value = unsafe { self.data().get_unchecked(ctx, generator, &zero, None) };
-
-            ScalarOrNDArray::Scalar(value)
+        if let Some(unsized_elem) = self.get_unsized_element(generator, ctx) {
+            ScalarOrNDArray::Scalar(unsized_elem)
         } else {
             ScalarOrNDArray::NDArray(*self)
         }
     }
+
+    /// Check if this `NDArray` can be used as an `out` ndarray for an operation.
+    ///
+    /// Raise an exception if the shapes do not match.
+    pub fn assert_can_be_written_by_out<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        out_shape: impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    ) {
+        let ndarray_shape = self.shape();
+        let output_shape = out_shape;
+
+        irrt::ndarray::call_nac3_ndarray_util_assert_output_shape_same(
+            generator,
+            ctx,
+            &ndarray_shape,
+            &output_shape,
+        );
+    }
 }
 
 impl<'ctx> ProxyValue<'ctx> for NDArrayValue<'ctx> {
@@ -491,8 +569,8 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayShapeProxy<'ctx, '_> {
 impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &IntValue<'ctx>,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
@@ -530,20 +608,26 @@ impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_
 impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {}
 impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {}
 
-impl<'ctx> TypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
+impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
+    for NDArrayShapeProxy<'ctx, '_>
+{
     fn downcast_to_type(
         &self,
-        _: &mut CodeGenContext<'ctx, '_>,
+        _: &CodeGenContext<'ctx, '_>,
+        _: &G,
         value: BasicValueEnum<'ctx>,
     ) -> IntValue<'ctx> {
         value.into_int_value()
     }
 }
 
-impl<'ctx> TypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayShapeProxy<'ctx, '_> {
+impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>
+    for NDArrayShapeProxy<'ctx, '_>
+{
     fn upcast_from_type(
         &self,
-        _: &mut CodeGenContext<'ctx, '_>,
+        _: &CodeGenContext<'ctx, '_>,
+        _: &G,
         value: IntValue<'ctx>,
     ) -> BasicValueEnum<'ctx> {
         value.into()
@@ -583,8 +667,8 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayStridesProxy<'ctx, '_> {
 impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &IntValue<'ctx>,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
@@ -622,20 +706,26 @@ impl<'ctx> ArrayLikeIndexer<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx,
 impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {}
 impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {}
 
-impl<'ctx> TypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {
+impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>
+    for NDArrayStridesProxy<'ctx, '_>
+{
     fn downcast_to_type(
         &self,
-        _: &mut CodeGenContext<'ctx, '_>,
+        _: &CodeGenContext<'ctx, '_>,
+        _: &G,
         value: BasicValueEnum<'ctx>,
     ) -> IntValue<'ctx> {
         value.into_int_value()
     }
 }
 
-impl<'ctx> TypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayStridesProxy<'ctx, '_> {
+impl<'ctx, G: CodeGenerator + ?Sized> TypedArrayLikeMutator<'ctx, G, IntValue<'ctx>>
+    for NDArrayStridesProxy<'ctx, '_>
+{
     fn upcast_from_type(
         &self,
-        _: &mut CodeGenContext<'ctx, '_>,
+        _: &CodeGenContext<'ctx, '_>,
+        _: &G,
         value: IntValue<'ctx>,
     ) -> BasicValueEnum<'ctx> {
         value.into()
@@ -668,41 +758,19 @@ impl<'ctx> ArrayLikeValue<'ctx> for NDArrayDataProxy<'ctx, '_> {
         ctx: &CodeGenContext<'ctx, '_>,
         generator: &G,
     ) -> IntValue<'ctx> {
-        irrt::ndarray::call_ndarray_calc_size(
-            generator,
-            ctx,
-            &self.as_slice_value(ctx, generator),
-            (None, None),
-        )
+        irrt::ndarray::call_nac3_ndarray_len(generator, ctx, *self.0)
     }
 }
 
 impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         idx: &IntValue<'ctx>,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
-        let sizeof_elem = ctx
-            .builder
-            .build_int_truncate_or_bit_cast(
-                self.element_type(ctx, generator).size_of().unwrap(),
-                idx.get_type(),
-                "",
-            )
-            .unwrap();
-        let idx = ctx.builder.build_int_mul(*idx, sizeof_elem, "").unwrap();
-        let ptr = unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.base_ptr(ctx, generator),
-                    &[idx],
-                    name.unwrap_or_default(),
-                )
-                .unwrap()
-        };
+        let ptr = irrt::ndarray::call_nac3_ndarray_get_nth_pelement(generator, ctx, *self.0, *idx);
 
         // Current implementation is transparent - The returned pointer type is
         // already cast into the expected type, allowing for immediately
@@ -713,7 +781,7 @@ impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
                 BasicTypeEnum::try_from(self.element_type(ctx, generator))
                     .unwrap()
                     .ptr_type(AddressSpace::default()),
-                "",
+                name.unwrap_or_default(),
             )
             .unwrap()
     }
@@ -761,55 +829,33 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
 {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
+        ctx: &CodeGenContext<'ctx, '_>,
+        generator: &G,
         indices: &Index,
         name: Option<&str>,
     ) -> PointerValue<'ctx> {
-        let llvm_usize = generator.get_size_type(ctx.ctx);
+        assert_eq!(indices.element_type(ctx, generator), generator.get_size_type(ctx.ctx).into());
 
-        let indices_elem_ty = indices
-            .ptr_offset(ctx, generator, &llvm_usize.const_zero(), None)
-            .get_type()
-            .get_element_type();
-        let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
-            panic!("Expected list[int32] but got {indices_elem_ty}")
-        };
-        assert_eq!(
-            indices_elem_ty.get_bit_width(),
-            32,
-            "Expected list[int32] but got list[int{}]",
-            indices_elem_ty.get_bit_width()
+        let indices = TypedArrayLikeAdapter::from(
+            indices.as_slice_value(ctx, generator),
+            |_, _, v| v.into_int_value(),
+            |_, _, v| v.into(),
         );
 
-        let index = irrt::ndarray::call_ndarray_flatten_index(generator, ctx, *self.0, indices);
-        let sizeof_elem = ctx
-            .builder
-            .build_int_truncate_or_bit_cast(
-                self.element_type(ctx, generator).size_of().unwrap(),
-                index.get_type(),
-                "",
-            )
-            .unwrap();
-        let index = ctx.builder.build_int_mul(index, sizeof_elem, "").unwrap();
+        let ptr = irrt::ndarray::call_nac3_ndarray_get_pelement_by_indices(
+            generator, ctx, *self.0, &indices,
+        );
 
-        let ptr = unsafe {
-            ctx.builder
-                .build_in_bounds_gep(
-                    self.base_ptr(ctx, generator),
-                    &[index],
-                    name.unwrap_or_default(),
-                )
-                .unwrap()
-        };
-        // TODO: Current implementation is transparent
+        // Current implementation is transparent - The returned pointer type is
+        // already cast into the expected type, allowing for immediately
+        // load/store.
         ctx.builder
             .build_pointer_cast(
                 ptr,
                 BasicTypeEnum::try_from(self.element_type(ctx, generator))
                     .unwrap()
                     .ptr_type(AddressSpace::default()),
-                "",
+                name.unwrap_or_default(),
             )
             .unwrap()
     }
@@ -904,10 +950,9 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> UntypedArrayLikeMutator<'ctx,
 /// This function is used generating strides for globally defined contiguous ndarrays.
 #[must_use]
 pub fn make_contiguous_strides(itemsize: u64, ndims: u64, shape: &[u64]) -> Vec<u64> {
-    let mut strides = Vec::with_capacity(ndims as usize);
+    let mut strides = vec![0u64; ndims as usize];
     let mut stride_product = 1u64;
-    for i in 0..ndims {
-        let axis = ndims - i - 1;
+    for axis in (0..ndims).rev() {
         strides[axis as usize] = stride_product * itemsize;
         stride_product *= shape[axis as usize];
     }
@@ -921,7 +966,52 @@ pub enum ScalarOrNDArray<'ctx> {
     NDArray(NDArrayValue<'ctx>),
 }
 
+impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for BasicValueEnum<'ctx> {
+    type Error = ();
+
+    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
+        match value {
+            ScalarOrNDArray::Scalar(scalar) => Ok(*scalar),
+            ScalarOrNDArray::NDArray(_) => Err(()),
+        }
+    }
+}
+
+impl<'ctx> TryFrom<&ScalarOrNDArray<'ctx>> for NDArrayValue<'ctx> {
+    type Error = ();
+
+    fn try_from(value: &ScalarOrNDArray<'ctx>) -> Result<Self, Self::Error> {
+        match value {
+            ScalarOrNDArray::Scalar(_) => Err(()),
+            ScalarOrNDArray::NDArray(ndarray) => Ok(*ndarray),
+        }
+    }
+}
+
 impl<'ctx> ScalarOrNDArray<'ctx> {
+    /// Split on `object` either into a scalar or an ndarray.
+    ///
+    /// If `object` is an ndarray, [`ScalarOrNDArray::NDArray`].
+    ///
+    /// For everything else, it is wrapped with [`ScalarOrNDArray::Scalar`].
+    pub fn from_value<G: CodeGenerator + ?Sized>(
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        (object_ty, object): (Type, BasicValueEnum<'ctx>),
+    ) -> ScalarOrNDArray<'ctx> {
+        match &*ctx.unifier.get_ty(object_ty) {
+            TypeEnum::TObj { obj_id, .. }
+                if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
+            {
+                let ndarray = NDArrayType::from_unifier_type(generator, ctx, object_ty)
+                    .map_value(object.into_pointer_value(), None);
+                ScalarOrNDArray::NDArray(ndarray)
+            }
+
+            _ => ScalarOrNDArray::Scalar(object),
+        }
+    }
+
     /// Get the underlying [`BasicValueEnum<'ctx>`] of this [`ScalarOrNDArray`].
     #[must_use]
     pub fn to_basic_value_enum(self) -> BasicValueEnum<'ctx> {
@@ -930,4 +1020,57 @@ impl<'ctx> ScalarOrNDArray<'ctx> {
             ScalarOrNDArray::NDArray(ndarray) => ndarray.as_base_value().into(),
         }
     }
+
+    /// Convert this [`ScalarOrNDArray`] to an ndarray - behaves like `np.asarray`.
+    ///
+    /// - If this is an ndarray, the ndarray is returned.
+    /// - If this is a scalar, this function returns new ndarray created with
+    ///   [`NDArrayType::construct_unsized`].
+    pub fn to_ndarray<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+    ) -> NDArrayValue<'ctx> {
+        match self {
+            ScalarOrNDArray::NDArray(ndarray) => *ndarray,
+            ScalarOrNDArray::Scalar(scalar) => {
+                NDArrayType::new_unsized(generator, ctx.ctx, scalar.get_type())
+                    .construct_unsized(generator, ctx, scalar, None)
+            }
+        }
+    }
+
+    /// Get the dtype of the ndarray created if this were called with
+    /// [`ScalarOrNDArray::to_ndarray`].
+    #[must_use]
+    pub fn get_dtype(&self) -> BasicTypeEnum<'ctx> {
+        match self {
+            ScalarOrNDArray::NDArray(ndarray) => ndarray.dtype,
+            ScalarOrNDArray::Scalar(scalar) => scalar.get_type(),
+        }
+    }
+}
+
+/// An helper enum specifying how a function should produce its output.
+///
+/// Many functions in NumPy has an optional `out` parameter (e.g., `matmul`). If `out` is specified
+/// with an ndarray, the result of a function will be written to `out`. If `out` is not specified, a
+/// function will create a new ndarray and store the result in it.
+#[derive(Clone, Copy)]
+pub enum NDArrayOut<'ctx> {
+    /// Tell a function should create a new ndarray with the expected element type `dtype`.
+    NewNDArray { dtype: BasicTypeEnum<'ctx> },
+    /// Tell a function to write the result to `ndarray`.
+    WriteToNDArray { ndarray: NDArrayValue<'ctx> },
+}
+
+impl<'ctx> NDArrayOut<'ctx> {
+    /// Get the dtype of this output.
+    #[must_use]
+    pub fn get_dtype(&self) -> BasicTypeEnum<'ctx> {
+        match self {
+            NDArrayOut::NewNDArray { dtype } => *dtype,
+            NDArrayOut::WriteToNDArray { ndarray } => ndarray.dtype,
+        }
+    }
 }

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

@@ -4,7 +4,7 @@ use inkwell::{
     AddressSpace,
 };
 
-use super::{NDArrayValue, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeMutator};
+use super::{NDArrayValue, ProxyValue};
 use crate::codegen::{
     irrt,
     stmt::{gen_for_callback, BreakContinueHooks},
@@ -69,7 +69,10 @@ impl<'ctx> NDIterValue<'ctx> {
         irrt::ndarray::call_nac3_nditer_next(generator, ctx, *self);
     }
 
-    fn element(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, PointerValue<'ctx>> {
+    fn element_field(
+        &self,
+        ctx: &CodeGenContext<'ctx, '_>,
+    ) -> StructField<'ctx, PointerValue<'ctx>> {
         self.get_type().get_fields(ctx.ctx).element
     }
 
@@ -78,7 +81,7 @@ impl<'ctx> NDIterValue<'ctx> {
     pub fn get_pointer(&self, ctx: &CodeGenContext<'ctx, '_>) -> PointerValue<'ctx> {
         let elem_ty = self.parent.dtype;
 
-        let p = self.element(ctx).get(ctx, self.as_base_value(), None);
+        let p = self.element_field(ctx).get(ctx, self.as_base_value(), self.name);
         ctx.builder
             .build_pointer_cast(p, elem_ty.ptr_type(AddressSpace::default()), "element")
             .unwrap()
@@ -91,30 +94,25 @@ impl<'ctx> NDIterValue<'ctx> {
         ctx.builder.build_load(p, "value").unwrap()
     }
 
-    fn nth(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
+    fn nth_field(&self, ctx: &CodeGenContext<'ctx, '_>) -> StructField<'ctx, IntValue<'ctx>> {
         self.get_type().get_fields(ctx.ctx).nth
     }
 
     /// Get the index of the current element if this ndarray were a flat ndarray.
     #[must_use]
     pub fn get_index(&self, ctx: &CodeGenContext<'ctx, '_>) -> IntValue<'ctx> {
-        self.nth(ctx).get(ctx, self.as_base_value(), None)
+        self.nth_field(ctx).get(ctx, self.as_base_value(), self.name)
     }
 
     /// Get the indices of the current element.
     #[must_use]
-    pub fn get_indices(
-        &'ctx self,
-    ) -> impl TypedArrayLikeAccessor<'ctx, IntValue<'ctx>> + TypedArrayLikeMutator<'ctx, IntValue<'ctx>>
-    {
+    pub fn get_indices<G: CodeGenerator + ?Sized>(
+        &self,
+    ) -> TypedArrayLikeAdapter<'ctx, G, IntValue<'ctx>> {
         TypedArrayLikeAdapter::from(
             self.indices,
-            Box::new(|ctx, val| {
-                ctx.builder
-                    .build_int_z_extend_or_bit_cast(val.into_int_value(), self.llvm_usize, "")
-                    .unwrap()
-            }),
-            Box::new(|_, val| val.into()),
+            |_, _, val| val.into_int_value(),
+            |_, _, val| val.into(),
         )
     }
 }
@@ -143,6 +141,10 @@ impl<'ctx> NDArrayValue<'ctx> {
     ///
     /// `body` has access to [`BreakContinueHooks`] to short-circuit and [`NDIterValue`] to
     /// get properties of the current iteration (e.g., the current element, indices, etc.)
+    ///
+    /// Note: The caller is recommended to call `llvm.stacksave` and `llvm.stackrestore` before and
+    /// after invoking this function respectively. See [`NDIterType::construct`] for an explanation
+    /// on why this is suggested.
     pub fn foreach<'a, G, F>(
         &self,
         generator: &mut G,

nac3core/src/codegen/values/ndarray/shape.rs

@@ -0,0 +1,152 @@
+use inkwell::values::{BasicValueEnum, IntValue};
+
+use crate::{
+    codegen::{
+        stmt::gen_for_callback_incrementing,
+        types::{ListType, TupleType},
+        values::{
+            ArraySliceValue, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
+            TypedArrayLikeMutator, UntypedArrayLikeAccessor,
+        },
+        CodeGenContext, CodeGenerator,
+    },
+    typecheck::typedef::{Type, TypeEnum},
+};
+
+/// Parse a NumPy-like "int sequence" input and return the int sequence as an array and its length.
+///
+/// * `sequence` - The `sequence` parameter.
+/// * `sequence_ty` - The typechecker type of `sequence`
+///
+/// The `sequence` argument type may only be one of the following:
+///   1. A list of `int32`;   e.g., `np.empty([600, 800, 3])`
+///   2. A tuple of `int32`;  e.g., `np.empty((600, 800, 3))`
+///   3. A scalar `int32`;    e.g., `np.empty(3)`, this is functionally equivalent to
+///      `np.empty([3])`
+///
+/// All `int32` values will be sign-extended to `SizeT`.
+pub fn parse_numpy_int_sequence<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    (input_seq_ty, input_seq): (Type, BasicValueEnum<'ctx>),
+) -> impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>> {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let zero = llvm_usize.const_zero();
+    let one = llvm_usize.const_int(1, false);
+
+    // The result `list` to return.
+    match &*ctx.unifier.get_ty_immutable(input_seq_ty) {
+        TypeEnum::TObj { obj_id, .. }
+            if *obj_id == ctx.primitives.list.obj_id(&ctx.unifier).unwrap() =>
+        {
+            // 1. A list of `int32`; e.g., `np.empty([600, 800, 3])`
+
+            let input_seq = ListType::from_unifier_type(generator, ctx, input_seq_ty)
+                .map_value(input_seq.into_pointer_value(), None);
+
+            let len = input_seq.load_size(ctx, None);
+            // TODO: Find a way to remove this mid-BB allocation
+            let result = ctx.builder.build_array_alloca(llvm_usize, len, "").unwrap();
+            let result = TypedArrayLikeAdapter::from(
+                ArraySliceValue::from_ptr_val(result, len, None),
+                |_, _, val| val.into_int_value(),
+                |_, _, val| val.into(),
+            );
+
+            // Load all the `int32`s from the input_sequence, cast them to `SizeT`, and store them into `result`
+            gen_for_callback_incrementing(
+                generator,
+                ctx,
+                None,
+                zero,
+                (len, false),
+                |generator, ctx, _, i| {
+                    // Load the i-th int32 in the input sequence
+                    let int = unsafe {
+                        input_seq.data().get_unchecked(ctx, generator, &i, None).into_int_value()
+                    };
+
+                    // Cast to SizeT
+                    let int =
+                        ctx.builder.build_int_s_extend_or_bit_cast(int, llvm_usize, "").unwrap();
+
+                    // Store
+                    unsafe { result.set_typed_unchecked(ctx, generator, &i, int) };
+
+                    Ok(())
+                },
+                one,
+            )
+            .unwrap();
+
+            result
+        }
+
+        TypeEnum::TTuple { .. } => {
+            // 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
+
+            let input_seq = TupleType::from_unifier_type(generator, ctx, input_seq_ty)
+                .map_value(input_seq.into_struct_value(), None);
+
+            let len = input_seq.get_type().num_elements();
+
+            let result = generator
+                .gen_array_var_alloc(
+                    ctx,
+                    llvm_usize.into(),
+                    llvm_usize.const_int(u64::from(len), false),
+                    None,
+                )
+                .unwrap();
+            let result = TypedArrayLikeAdapter::from(
+                result,
+                |_, _, val| val.into_int_value(),
+                |_, _, val| val.into(),
+            );
+
+            for i in 0..input_seq.get_type().num_elements() {
+                // Get the i-th element off of the tuple and load it into `result`.
+                let int = input_seq.load_element(ctx, i).into_int_value();
+                let int = ctx.builder.build_int_s_extend_or_bit_cast(int, llvm_usize, "").unwrap();
+
+                unsafe {
+                    result.set_typed_unchecked(
+                        ctx,
+                        generator,
+                        &llvm_usize.const_int(u64::from(i), false),
+                        int,
+                    );
+                }
+            }
+
+            result
+        }
+
+        TypeEnum::TObj { obj_id, .. }
+            if *obj_id == ctx.primitives.int32.obj_id(&ctx.unifier).unwrap() =>
+        {
+            // 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
+
+            let input_int = input_seq.into_int_value();
+
+            let len = one;
+            let result = generator.gen_array_var_alloc(ctx, llvm_usize.into(), len, None).unwrap();
+            let result = TypedArrayLikeAdapter::from(
+                result,
+                |_, _, val| val.into_int_value(),
+                |_, _, val| val.into(),
+            );
+            let int =
+                ctx.builder.build_int_s_extend_or_bit_cast(input_int, llvm_usize, "").unwrap();
+
+            // Storing into result[0]
+            unsafe {
+                result.set_typed_unchecked(ctx, generator, &zero, int);
+            }
+
+            result
+        }
+
+        _ => panic!("encountered unknown sequence type: {}", ctx.unifier.stringify(input_seq_ty)),
+    }
+}

nac3core/src/codegen/values/ndarray/view.rs

@@ -1,9 +1,16 @@
 use std::iter::{once, repeat_n};
 
+use inkwell::values::{IntValue, PointerValue};
 use itertools::Itertools;
 
 use crate::codegen::{
-    values::ndarray::{NDArrayValue, RustNDIndex},
+    irrt,
+    stmt::gen_if_callback,
+    types::ndarray::NDArrayType,
+    values::{
+        ndarray::{NDArrayValue, RustNDIndex},
+        ArrayLikeValue, ArraySliceValue, ProxyValue, TypedArrayLikeAccessor, TypedArrayLikeAdapter,
+    },
     CodeGenContext, CodeGenerator,
 };
 
@@ -19,9 +26,7 @@ impl<'ctx> NDArrayValue<'ctx> {
         ctx: &mut CodeGenContext<'ctx, '_>,
         ndmin: u64,
     ) -> Self {
-        assert!(self.ndims.is_some(), "NDArrayValue::atleast_nd is only supported for instances with compile-time known ndims (self.ndims = Some(...))");
-
-        let ndims = self.ndims.unwrap();
+        let ndims = self.ndims;
 
         if ndims < ndmin {
             // Extend the dimensions with np.newaxis.
@@ -33,4 +38,117 @@ impl<'ctx> NDArrayValue<'ctx> {
             *self
         }
     }
+
+    /// Create a reshaped view on this ndarray like
+    /// [`np.reshape()`](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html).
+    ///
+    /// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be
+    /// modified as a result.
+    ///
+    /// If reshape without copying is impossible, this function will allocate a new ndarray and copy
+    /// contents.
+    ///
+    /// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
+    /// * `new_shape` - The target shape to do `np.reshape()`.
+    #[must_use]
+    pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        new_ndims: u64,
+        new_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    ) -> Self {
+        assert_eq!(new_shape.element_type(ctx, generator), self.llvm_usize.into());
+
+        // TODO: The current criterion for whether to do a full copy or not is by checking
+        //       `is_c_contiguous`, but this is not optimal - there are cases when the ndarray is
+        //       not contiguous but could be reshaped without copying data. Look into how numpy does
+        //       it.
+
+        let dst_ndarray = NDArrayType::new(generator, ctx.ctx, self.dtype, new_ndims)
+            .construct_uninitialized(generator, ctx, None);
+        dst_ndarray.copy_shape_from_array(generator, ctx, new_shape.base_ptr(ctx, generator));
+
+        // Resolve negative indices
+        let size = self.size(generator, ctx);
+        let dst_ndims = self.llvm_usize.const_int(dst_ndarray.get_type().ndims(), false);
+        let dst_shape = dst_ndarray.shape();
+        irrt::ndarray::call_nac3_ndarray_reshape_resolve_and_check_new_shape(
+            generator,
+            ctx,
+            size,
+            dst_ndims,
+            dst_shape.as_slice_value(ctx, generator),
+        );
+
+        gen_if_callback(
+            generator,
+            ctx,
+            |generator, ctx| Ok(self.is_c_contiguous(generator, ctx)),
+            |generator, ctx| {
+                // Reshape is possible without copying
+                dst_ndarray.set_strides_contiguous(generator, ctx);
+                dst_ndarray.store_data(ctx, self.data().base_ptr(ctx, generator));
+
+                Ok(())
+            },
+            |generator, ctx| {
+                // Reshape is impossible without copying
+                unsafe {
+                    dst_ndarray.create_data(generator, ctx);
+                }
+                dst_ndarray.copy_data_from(generator, ctx, *self);
+
+                Ok(())
+            },
+        )
+        .unwrap();
+
+        dst_ndarray
+    }
+
+    /// Create a transposed view on this ndarray like
+    /// [`np.transpose(<ndarray>, <axes> = None)`](https://numpy.org/doc/stable/reference/generated/numpy.transpose.html).
+    ///
+    /// * `axes` - If specified, should be an array of the permutation (negative indices are
+    ///   **allowed**).
+    #[must_use]
+    pub fn transpose<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        axes: Option<PointerValue<'ctx>>,
+    ) -> Self {
+        assert!(
+            axes.is_none_or(|axes| axes.get_type().get_element_type() == self.llvm_usize.into())
+        );
+
+        // Define models
+        let transposed_ndarray = self.get_type().construct_uninitialized(generator, ctx, None);
+
+        let axes = if let Some(axes) = axes {
+            let num_axes = self.llvm_usize.const_int(self.ndims, false);
+
+            // `axes = nullptr` if `axes` is unspecified.
+            let axes = ArraySliceValue::from_ptr_val(axes, num_axes, None);
+
+            Some(TypedArrayLikeAdapter::from(
+                axes,
+                |_, _, val| val.into_int_value(),
+                |_, _, val| val.into(),
+            ))
+        } else {
+            None
+        };
+
+        irrt::ndarray::call_nac3_ndarray_transpose(
+            generator,
+            ctx,
+            *self,
+            transposed_ndarray,
+            axes.as_ref(),
+        );
+
+        transposed_ndarray
+    }
 }

nac3core/src/codegen/values/tuple.rs

@@ -0,0 +1,85 @@
+use inkwell::{
+    types::IntType,
+    values::{BasicValue, BasicValueEnum, StructValue},
+};
+
+use super::ProxyValue;
+use crate::codegen::{types::TupleType, CodeGenContext};
+
+#[derive(Copy, Clone)]
+pub struct TupleValue<'ctx> {
+    value: StructValue<'ctx>,
+    llvm_usize: IntType<'ctx>,
+    name: Option<&'ctx str>,
+}
+
+impl<'ctx> TupleValue<'ctx> {
+    /// Checks whether `value` is an instance of `tuple`, returning [Err] if `value` is not an
+    /// instance.
+    pub fn is_representable(
+        value: StructValue<'ctx>,
+        _llvm_usize: IntType<'ctx>,
+    ) -> Result<(), String> {
+        TupleType::is_representable(value.get_type())
+    }
+
+    /// Creates an [`TupleValue`] from a [`StructValue`].
+    #[must_use]
+    pub fn from_struct_value(
+        value: StructValue<'ctx>,
+        llvm_usize: IntType<'ctx>,
+        name: Option<&'ctx str>,
+    ) -> Self {
+        debug_assert!(Self::is_representable(value, llvm_usize).is_ok());
+
+        Self { value, llvm_usize, name }
+    }
+
+    /// Stores a value into the tuple element at the given `index`.
+    pub fn store_element(
+        &mut self,
+        ctx: &CodeGenContext<'ctx, '_>,
+        index: u32,
+        element: impl BasicValue<'ctx>,
+    ) {
+        assert_eq!(element.as_basic_value_enum().get_type(), unsafe {
+            self.get_type().type_at_index_unchecked(index)
+        });
+
+        let new_value = ctx
+            .builder
+            .build_insert_value(self.value, element, index, self.name.unwrap_or_default())
+            .unwrap();
+        self.value = new_value.into_struct_value();
+    }
+
+    /// Loads a value from the tuple element at the given `index`.
+    pub fn load_element(&self, ctx: &CodeGenContext<'ctx, '_>, index: u32) -> BasicValueEnum<'ctx> {
+        ctx.builder
+            .build_extract_value(
+                self.value,
+                index,
+                &format!("{}[{{i}}]", self.name.unwrap_or("tuple")),
+            )
+            .unwrap()
+    }
+}
+
+impl<'ctx> ProxyValue<'ctx> for TupleValue<'ctx> {
+    type Base = StructValue<'ctx>;
+    type Type = TupleType<'ctx>;
+
+    fn get_type(&self) -> Self::Type {
+        TupleType::from_type(self.as_base_value().get_type(), self.llvm_usize)
+    }
+
+    fn as_base_value(&self) -> Self::Base {
+        self.value
+    }
+}
+
+impl<'ctx> From<TupleValue<'ctx>> for StructValue<'ctx> {
+    fn from(value: TupleValue<'ctx>) -> Self {
+        value.as_base_value()
+    }
+}

nac3core/src/symbol_resolver.rs

@@ -6,7 +6,7 @@ use std::{
 };
 
 use inkwell::values::{BasicValueEnum, FloatValue, IntValue, PointerValue, StructValue};
-use itertools::{chain, izip, Itertools};
+use itertools::{izip, Itertools};
 use parking_lot::RwLock;
 
 use nac3parser::ast::{Constant, Expr, Location, StrRef};
@@ -452,11 +452,11 @@ pub fn parse_type_annotation<T>(
                             type_vars.len()
                         )]));
                     }
-                    let fields = chain(
-                        fields.iter().map(|(k, v, m)| (*k, (*v, *m))),
-                        methods.iter().map(|(k, v, _)| (*k, (*v, false))),
-                    )
-                    .collect();
+                    let fields = fields
+                        .iter()
+                        .map(|(k, v, m)| (*k, (*v, *m)))
+                        .chain(methods.iter().map(|(k, v, _)| (*k, (*v, false))))
+                        .collect();
                     Ok(unifier.add_ty(TypeEnum::TObj { obj_id, fields, params: VarMap::default() }))
                 } else {
                     Err(HashSet::from([format!("Cannot use function name as type at {loc}")]))

nac3core/src/toplevel/builtins.rs

@@ -1,18 +1,14 @@
 use std::iter::once;
 
 use indexmap::IndexMap;
-use inkwell::{
-    attributes::{Attribute, AttributeLoc},
-    types::{BasicMetadataTypeEnum, BasicType},
-    values::{BasicMetadataValueEnum, BasicValue, CallSiteValue},
-    IntPredicate,
-};
-use itertools::Either;
+use inkwell::{values::BasicValue, IntPredicate};
 use strum::IntoEnumIterator;
 
 use super::{
-    helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDef, PrimDefDetails},
-    numpy::make_ndarray_ty,
+    helper::{
+        debug_assert_prim_is_allowed, extract_ndims, make_exception_fields, PrimDef, PrimDefDetails,
+    },
+    numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
     *,
 };
 use crate::{
@@ -20,7 +16,8 @@ use crate::{
         builtin_fns,
         numpy::*,
         stmt::exn_constructor,
-        values::{ProxyValue, RangeValue},
+        types::ndarray::NDArrayType,
+        values::{ndarray::shape::parse_numpy_int_sequence, ProxyValue, RangeValue},
     },
     symbol_resolver::SymbolValue,
     typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
@@ -148,144 +145,6 @@ fn create_fn_by_codegen(
     }
 }
 
-/// Creates a NumPy [`TopLevelDef`] function using an LLVM intrinsic.
-///
-/// * `name`: The name of the implemented NumPy function.
-/// * `ret_ty`: The return type of this function.
-/// * `param_ty`: The parameters accepted by this function, represented by a tuple of the
-///   [parameter type][Type] and the parameter symbol name.
-/// * `intrinsic_fn`: The fully-qualified name of the LLVM intrinsic function.
-fn create_fn_by_intrinsic(
-    unifier: &mut Unifier,
-    var_map: &VarMap,
-    name: &'static str,
-    ret_ty: Type,
-    params: &[(Type, &'static str)],
-    intrinsic_fn: &'static str,
-) -> TopLevelDef {
-    let param_tys = params.iter().map(|p| p.0).collect_vec();
-
-    create_fn_by_codegen(
-        unifier,
-        var_map,
-        name,
-        ret_ty,
-        params,
-        Box::new(move |ctx, _, fun, args, generator| {
-            let args_ty = fun.0.args.iter().map(|a| a.ty).collect_vec();
-
-            assert!(param_tys
-                .iter()
-                .zip(&args_ty)
-                .all(|(expected, actual)| ctx.unifier.unioned(*expected, *actual)));
-
-            let args_val = args_ty
-                .iter()
-                .zip_eq(args.iter())
-                .map(|(ty, arg)| arg.1.clone().to_basic_value_enum(ctx, generator, *ty).unwrap())
-                .map_into::<BasicMetadataValueEnum>()
-                .collect_vec();
-
-            let intrinsic_fn = ctx.module.get_function(intrinsic_fn).unwrap_or_else(|| {
-                let ret_llvm_ty = ctx.get_llvm_abi_type(generator, ret_ty);
-                let param_llvm_ty = param_tys
-                    .iter()
-                    .map(|p| ctx.get_llvm_abi_type(generator, *p))
-                    .map_into::<BasicMetadataTypeEnum>()
-                    .collect_vec();
-                let fn_type = ret_llvm_ty.fn_type(param_llvm_ty.as_slice(), false);
-
-                ctx.module.add_function(intrinsic_fn, fn_type, None)
-            });
-
-            let val = ctx
-                .builder
-                .build_call(intrinsic_fn, args_val.as_slice(), name)
-                .map(CallSiteValue::try_as_basic_value)
-                .map(Either::unwrap_left)
-                .unwrap();
-            Ok(val.into())
-        }),
-    )
-}
-
-/// Creates a unary NumPy [`TopLevelDef`] function using an extern function (e.g. from `libc` or
-/// `libm`).
-///
-/// * `name`: The name of the implemented NumPy function.
-/// * `ret_ty`: The return type of this function.
-/// * `param_ty`: The parameters accepted by this function, represented by a tuple of the
-///   [parameter type][Type] and the parameter symbol name.
-/// * `extern_fn`: The fully-qualified name of the extern function used as the implementation.
-/// * `attrs`: The list of attributes to apply to this function declaration. Note that `nounwind` is
-///   already implied by the C ABI.
-fn create_fn_by_extern(
-    unifier: &mut Unifier,
-    var_map: &VarMap,
-    name: &'static str,
-    ret_ty: Type,
-    params: &[(Type, &'static str)],
-    extern_fn: &'static str,
-    attrs: &'static [&str],
-) -> TopLevelDef {
-    let param_tys = params.iter().map(|p| p.0).collect_vec();
-
-    create_fn_by_codegen(
-        unifier,
-        var_map,
-        name,
-        ret_ty,
-        params,
-        Box::new(move |ctx, _, fun, args, generator| {
-            let args_ty = fun.0.args.iter().map(|a| a.ty).collect_vec();
-
-            assert!(param_tys
-                .iter()
-                .zip(&args_ty)
-                .all(|(expected, actual)| ctx.unifier.unioned(*expected, *actual)));
-
-            let args_val = args_ty
-                .iter()
-                .zip_eq(args.iter())
-                .map(|(ty, arg)| arg.1.clone().to_basic_value_enum(ctx, generator, *ty).unwrap())
-                .map_into::<BasicMetadataValueEnum>()
-                .collect_vec();
-
-            let intrinsic_fn = ctx.module.get_function(extern_fn).unwrap_or_else(|| {
-                let ret_llvm_ty = ctx.get_llvm_abi_type(generator, ret_ty);
-                let param_llvm_ty = param_tys
-                    .iter()
-                    .map(|p| ctx.get_llvm_abi_type(generator, *p))
-                    .map_into::<BasicMetadataTypeEnum>()
-                    .collect_vec();
-                let fn_type = ret_llvm_ty.fn_type(param_llvm_ty.as_slice(), false);
-                let func = ctx.module.add_function(extern_fn, fn_type, None);
-                func.add_attribute(
-                    AttributeLoc::Function,
-                    ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id("nounwind"), 0),
-                );
-
-                for attr in attrs {
-                    func.add_attribute(
-                        AttributeLoc::Function,
-                        ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
-                    );
-                }
-
-                func
-            });
-
-            let val = ctx
-                .builder
-                .build_call(intrinsic_fn, &args_val, name)
-                .map(CallSiteValue::try_as_basic_value)
-                .map(Either::unwrap_left)
-                .unwrap();
-            Ok(val.into())
-        }),
-    )
-}
-
 pub fn get_builtins(unifier: &mut Unifier, primitives: &PrimitiveStore) -> BuiltinInfo {
     BuiltinBuilder::new(unifier, primitives)
         .build_all_builtins()
@@ -336,7 +195,6 @@ struct BuiltinBuilder<'a> {
 
     ndarray_float: Type,
     ndarray_float_2d: Type,
-    ndarray_num_ty: Type,
 
     float_or_ndarray_ty: TypeVar,
     float_or_ndarray_var_map: VarMap,
@@ -450,7 +308,6 @@ impl<'a> BuiltinBuilder<'a> {
 
             ndarray_float,
             ndarray_float_2d,
-            ndarray_num_ty,
 
             float_or_ndarray_ty,
             float_or_ndarray_var_map,
@@ -512,6 +369,14 @@ impl<'a> BuiltinBuilder<'a> {
             | PrimDef::FunNpEye
             | PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
 
+            PrimDef::FunNpSize | PrimDef::FunNpShape | PrimDef::FunNpStrides => {
+                self.build_ndarray_property_getter_function(prim)
+            }
+
+            PrimDef::FunNpBroadcastTo | PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
+                self.build_ndarray_view_function(prim)
+            }
+
             PrimDef::FunStr => self.build_str_function(),
 
             PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
@@ -577,10 +442,6 @@ impl<'a> BuiltinBuilder<'a> {
             | PrimDef::FunNpHypot
             | PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
 
-            PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
-                self.build_np_sp_ndarray_function(prim)
-            }
-
             PrimDef::FunNpDot
             | PrimDef::FunNpLinalgCholesky
             | PrimDef::FunNpLinalgQr
@@ -1386,6 +1247,176 @@ impl<'a> BuiltinBuilder<'a> {
         }
     }
 
+    fn build_ndarray_property_getter_function(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(
+            prim,
+            &[PrimDef::FunNpSize, PrimDef::FunNpShape, PrimDef::FunNpStrides],
+        );
+
+        let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
+            &[self.primitives.ndarray],
+            Some("T".into()),
+            None,
+        );
+
+        match prim {
+            PrimDef::FunNpSize => create_fn_by_codegen(
+                self.unifier,
+                &into_var_map([in_ndarray_ty]),
+                prim.name(),
+                self.primitives.int32,
+                &[(in_ndarray_ty.ty, "a")],
+                Box::new(|ctx, obj, fun, args, generator| {
+                    assert!(obj.is_none());
+                    assert_eq!(args.len(), 1);
+
+                    let ndarray_ty = fun.0.args[0].ty;
+                    let ndarray =
+                        args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+                    let ndarray = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty)
+                        .map_value(ndarray.into_pointer_value(), None);
+
+                    let size = ctx
+                        .builder
+                        .build_int_truncate_or_bit_cast(
+                            ndarray.size(generator, ctx),
+                            ctx.ctx.i32_type(),
+                            "",
+                        )
+                        .unwrap();
+                    Ok(Some(size.into()))
+                }),
+            ),
+
+            PrimDef::FunNpShape | PrimDef::FunNpStrides => {
+                // The function signatures of `np_shape` an `np_size` are the same.
+                // Mixed together for convenience.
+
+                // The return type is a tuple of variable length depending on the ndims of the input ndarray.
+                let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special folding
+
+                create_fn_by_codegen(
+                    self.unifier,
+                    &into_var_map([in_ndarray_ty]),
+                    prim.name(),
+                    ret_ty,
+                    &[(in_ndarray_ty.ty, "a")],
+                    Box::new(move |ctx, obj, fun, args, generator| {
+                        assert!(obj.is_none());
+                        assert_eq!(args.len(), 1);
+
+                        let ndarray_ty = fun.0.args[0].ty;
+                        let ndarray =
+                            args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+
+                        let ndarray = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty)
+                            .map_value(ndarray.into_pointer_value(), None);
+
+                        let result_tuple = match prim {
+                            PrimDef::FunNpShape => ndarray.make_shape_tuple(generator, ctx),
+                            PrimDef::FunNpStrides => ndarray.make_strides_tuple(generator, ctx),
+                            _ => unreachable!(),
+                        };
+
+                        Ok(Some(result_tuple.as_base_value().into()))
+                    }),
+                )
+            }
+            _ => unreachable!(),
+        }
+    }
+
+    /// Build np/sp functions that take as input `NDArray` only
+    fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(
+            prim,
+            &[PrimDef::FunNpBroadcastTo, PrimDef::FunNpTranspose, PrimDef::FunNpReshape],
+        );
+
+        let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
+            &[self.primitives.ndarray],
+            Some("T".into()),
+            None,
+        );
+
+        match prim {
+            PrimDef::FunNpTranspose => create_fn_by_codegen(
+                self.unifier,
+                &into_var_map([in_ndarray_ty]),
+                prim.name(),
+                in_ndarray_ty.ty,
+                &[(in_ndarray_ty.ty, "x")],
+                Box::new(move |ctx, _, fun, args, generator| {
+                    let arg_ty = fun.0.args[0].ty;
+                    let arg_val = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
+
+                    let ndarray = NDArrayType::from_unifier_type(generator, ctx, arg_ty)
+                        .map_value(arg_val.into_pointer_value(), None);
+
+                    let ndarray = ndarray.transpose(generator, ctx, None); // TODO: Add axes argument
+                    Ok(Some(ndarray.as_base_value().into()))
+                }),
+            ),
+
+            // NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
+            // the `param_ty` for `create_fn_by_codegen`.
+            //
+            // Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
+            // to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
+            // and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
+            PrimDef::FunNpBroadcastTo | PrimDef::FunNpReshape => {
+                // These two functions have the same function signature.
+                // Mixed together for convenience.
+
+                let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special holding
+
+                create_fn_by_codegen(
+                    self.unifier,
+                    &VarMap::new(),
+                    prim.name(),
+                    ret_ty,
+                    &[
+                        (in_ndarray_ty.ty, "x"),
+                        (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"), // Handled by special folding
+                    ],
+                    Box::new(move |ctx, _, fun, args, generator| {
+                        let ndarray_ty = fun.0.args[0].ty;
+                        let ndarray_val =
+                            args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+
+                        let shape_ty = fun.0.args[1].ty;
+                        let shape_val =
+                            args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
+
+                        let ndarray = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty)
+                            .map_value(ndarray_val.into_pointer_value(), None);
+
+                        let shape = parse_numpy_int_sequence(generator, ctx, (shape_ty, shape_val));
+
+                        // The ndims after reshaping is gotten from the return type of the call.
+                        let (_, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
+                        let ndims = extract_ndims(&ctx.unifier, ndims);
+
+                        let new_ndarray = match prim {
+                            PrimDef::FunNpBroadcastTo => {
+                                ndarray.broadcast_to(generator, ctx, ndims, &shape)
+                            }
+
+                            PrimDef::FunNpReshape => {
+                                ndarray.reshape_or_copy(generator, ctx, ndims, &shape)
+                            }
+
+                            _ => unreachable!(),
+                        };
+                        Ok(Some(new_ndarray.as_base_value().as_basic_value_enum()))
+                    }),
+                )
+            }
+
+            _ => unreachable!(),
+        }
+    }
+
     /// Build the `str()` function.
     fn build_str_function(&mut self) -> TopLevelDef {
         let prim = PrimDef::FunStr;
@@ -1873,57 +1904,6 @@ impl<'a> BuiltinBuilder<'a> {
         }
     }
 
-    /// Build np/sp functions that take as input `NDArray` only
-    fn build_np_sp_ndarray_function(&mut self, prim: PrimDef) -> TopLevelDef {
-        debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
-
-        match prim {
-            PrimDef::FunNpTranspose => {
-                let ndarray_ty = self.unifier.get_fresh_var_with_range(
-                    &[self.ndarray_num_ty],
-                    Some("T".into()),
-                    None,
-                );
-                create_fn_by_codegen(
-                    self.unifier,
-                    &into_var_map([ndarray_ty]),
-                    prim.name(),
-                    ndarray_ty.ty,
-                    &[(ndarray_ty.ty, "x")],
-                    Box::new(move |ctx, _, fun, args, generator| {
-                        let arg_ty = fun.0.args[0].ty;
-                        let arg_val =
-                            args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
-                        Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
-                    }),
-                )
-            }
-
-            // NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
-            // the `param_ty` for `create_fn_by_codegen`.
-            //
-            // Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
-            // to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
-            // and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
-            PrimDef::FunNpReshape => create_fn_by_codegen(
-                self.unifier,
-                &VarMap::new(),
-                prim.name(),
-                self.ndarray_num_ty,
-                &[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
-                Box::new(move |ctx, _, fun, args, generator| {
-                    let x1_ty = fun.0.args[0].ty;
-                    let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
-                    let x2_ty = fun.0.args[1].ty;
-                    let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
-                    Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
-                }),
-            ),
-
-            _ => unreachable!(),
-        }
-    }
-
     /// Build `np_linalg` and `sp_linalg` functions
     ///
     /// The input to these functions must be floating point `NDArray`
@@ -1955,10 +1935,12 @@ impl<'a> BuiltinBuilder<'a> {
                 Box::new(move |ctx, _, fun, args, generator| {
                     let x1_ty = fun.0.args[0].ty;
                     let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
+
                     let x2_ty = fun.0.args[1].ty;
                     let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
 
-                    Ok(Some(ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
+                    let result = ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?;
+                    Ok(Some(result))
                 }),
             ),
 

nac3core/src/toplevel/composer.rs

@@ -1052,7 +1052,7 @@ impl TopLevelComposer {
                         }
                         let mut result = Vec::new();
                         let no_defaults = args.args.len() - args.defaults.len() - 1;
-                        for (idx, x) in itertools::enumerate(args.args.iter().skip(1)) {
+                        for (idx, x) in args.args.iter().skip(1).enumerate() {
                             let type_ann = {
                                 let Some(annotation_expr) = x.node.annotation.as_ref() else {return Err(HashSet::from([format!("type annotation needed for `{}` (at {})", x.node.arg, x.location)]));};
                                 parse_ast_to_type_annotation_kinds(

nac3core/src/toplevel/helper.rs

@@ -54,6 +54,16 @@ pub enum PrimDef {
     FunNpEye,
     FunNpIdentity,
 
+    // NumPy ndarray property getters
+    FunNpSize,
+    FunNpShape,
+    FunNpStrides,
+
+    // NumPy ndarray view functions
+    FunNpBroadcastTo,
+    FunNpTranspose,
+    FunNpReshape,
+
     // Miscellaneous NumPy & SciPy functions
     FunNpRound,
     FunNpFloor,
@@ -101,8 +111,6 @@ pub enum PrimDef {
     FunNpLdExp,
     FunNpHypot,
     FunNpNextAfter,
-    FunNpTranspose,
-    FunNpReshape,
 
     // Linalg functions
     FunNpDot,
@@ -240,6 +248,16 @@ impl PrimDef {
             PrimDef::FunNpEye => fun("np_eye", None),
             PrimDef::FunNpIdentity => fun("np_identity", None),
 
+            // NumPy NDArray property getters,
+            PrimDef::FunNpSize => fun("np_size", None),
+            PrimDef::FunNpShape => fun("np_shape", None),
+            PrimDef::FunNpStrides => fun("np_strides", None),
+
+            // NumPy NDArray view functions
+            PrimDef::FunNpBroadcastTo => fun("np_broadcast_to", None),
+            PrimDef::FunNpTranspose => fun("np_transpose", None),
+            PrimDef::FunNpReshape => fun("np_reshape", None),
+
             // Miscellaneous NumPy & SciPy functions
             PrimDef::FunNpRound => fun("np_round", None),
             PrimDef::FunNpFloor => fun("np_floor", None),
@@ -287,8 +305,6 @@ impl PrimDef {
             PrimDef::FunNpLdExp => fun("np_ldexp", None),
             PrimDef::FunNpHypot => fun("np_hypot", None),
             PrimDef::FunNpNextAfter => fun("np_nextafter", None),
-            PrimDef::FunNpTranspose => fun("np_transpose", None),
-            PrimDef::FunNpReshape => fun("np_reshape", None),
 
             // Linalg functions
             PrimDef::FunNpDot => fun("np_dot", None),

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap

@@ -8,5 +8,5 @@ expression: res_vec
     "Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",
     "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(246)]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(261)]\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[typevar230]\", \"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: [\"typevar230\"]\n}\n",
+    "Class {\nname: \"B\",\nancestors: [\"B[typevar245]\", \"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: [\"typevar245\"]\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(243)]\n}\n",
-    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
+    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(258)]\n}\n",
+    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(263)]\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[typevar229, typevar230]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar229\", \"typevar230\"]\n}\n",
+    "Class {\nname: \"A\",\nancestors: [\"A[typevar244, typevar245]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar244\", \"typevar245\"]\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(249)]\n}\n",
+    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(264)]\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",
     "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",
     "Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(257)]\n}\n",
+    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(272)]\n}\n",
 ]

nac3core/src/toplevel/test.rs

@@ -15,14 +15,13 @@ use crate::{
     symbol_resolver::{SymbolResolver, ValueEnum},
     typecheck::{
         type_inferencer::PrimitiveStore,
-        typedef::{into_var_map, Type, Unifier},
+        typedef::{Type, Unifier},
     },
 };
 
 struct ResolverInternal {
     id_to_type: Mutex<HashMap<StrRef, Type>>,
     id_to_def: Mutex<HashMap<StrRef, DefinitionId>>,
-    class_names: Mutex<HashMap<StrRef, Type>>,
 }
 
 impl ResolverInternal {
@@ -179,11 +178,8 @@ fn test_simple_function_analyze(source: &[&str], tys: &[&str], names: &[&str]) {
     let mut composer =
         TopLevelComposer::new(Vec::new(), Vec::new(), ComposerConfig::default(), 64).0;
 
-    let internal_resolver = Arc::new(ResolverInternal {
-        id_to_def: Mutex::default(),
-        id_to_type: Mutex::default(),
-        class_names: Mutex::default(),
-    });
+    let internal_resolver =
+        Arc::new(ResolverInternal { id_to_def: Mutex::default(), id_to_type: Mutex::default() });
     let resolver =
         Arc::new(Resolver(internal_resolver.clone())) as Arc<dyn SymbolResolver + Send + Sync>;
 
@@ -784,13 +780,6 @@ fn make_internal_resolver_with_tvar(
     unifier: &mut Unifier,
     print: bool,
 ) -> Arc<ResolverInternal> {
-    let list_elem_tvar = unifier.get_fresh_var(Some("list_elem".into()), None);
-    let list = unifier.add_ty(TypeEnum::TObj {
-        obj_id: PrimDef::List.id(),
-        fields: HashMap::new(),
-        params: into_var_map([list_elem_tvar]),
-    });
-
     let res: Arc<ResolverInternal> = ResolverInternal {
         id_to_def: Mutex::new(HashMap::from([("list".into(), PrimDef::List.id())])),
         id_to_type: tvars
@@ -806,7 +795,6 @@ fn make_internal_resolver_with_tvar(
             })
             .collect::<HashMap<_, _>>()
             .into(),
-        class_names: Mutex::new(HashMap::from([("list".into(), list)])),
     }
     .into();
     if print {
@@ -819,7 +807,7 @@ struct TypeToStringFolder<'a> {
     unifier: &'a mut Unifier,
 }
 
-impl<'a> Fold<Option<Type>> for TypeToStringFolder<'a> {
+impl Fold<Option<Type>> for TypeToStringFolder<'_> {
     type TargetU = String;
     type Error = String;
     fn map_user(&mut self, user: Option<Type>) -> Result<Self::TargetU, Self::Error> {

nac3core/src/typecheck/function_check.rs

@@ -15,7 +15,7 @@ use super::{
 };
 use crate::toplevel::helper::PrimDef;
 
-impl<'a> Inferencer<'a> {
+impl Inferencer<'_> {
     fn should_have_value(&mut self, expr: &Expr<Option<Type>>) -> Result<(), HashSet<String>> {
         if matches!(expr.custom, Some(ty) if self.unifier.unioned(ty, self.primitives.none)) {
             Err(HashSet::from([format!("Error at {}: cannot have value none", expr.location)]))
@@ -94,7 +94,7 @@ impl<'a> Inferencer<'a> {
         // there are some cases where the custom field is None
         if let Some(ty) = &expr.custom {
             if !matches!(&expr.node, ExprKind::Constant { value: Constant::Ellipsis, .. })
-                && !ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::List.id())
+                && ty.obj_id(self.unifier).is_none_or(|id| id != PrimDef::List.id())
                 && !self.unifier.is_concrete(*ty, &self.function_data.bound_variables)
             {
                 return Err(HashSet::from([format!(

nac3core/src/typecheck/magic_methods.rs

@@ -7,12 +7,12 @@ use nac3parser::ast::{Cmpop, Operator, StrRef, Unaryop};
 
 use super::{
     type_inferencer::*,
-    typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
+    typedef::{into_var_map, FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
 };
 use crate::{
     symbol_resolver::SymbolValue,
     toplevel::{
-        helper::PrimDef,
+        helper::{extract_ndims, PrimDef},
         numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
     },
 };
@@ -175,19 +175,8 @@ pub fn impl_binop(
     ops: &[Operator],
 ) {
     with_fields(unifier, ty, |unifier, fields| {
-        let (other_ty, other_var_id) = if other_ty.len() == 1 {
-            (other_ty[0], None)
-        } else {
-            let tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
-            (tvar.ty, Some(tvar.id))
-        };
-
-        let function_vars = if let Some(var_id) = other_var_id {
-            vec![(var_id, other_ty)].into_iter().collect::<VarMap>()
-        } else {
-            VarMap::new()
-        };
-
+        let other_tvar = unifier.get_fresh_var_with_range(other_ty, Some("N".into()), None);
+        let function_vars = into_var_map([other_tvar]);
         let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).ty);
 
         for (base_op, variant) in iproduct!(ops, [BinopVariant::Normal, BinopVariant::AugAssign]) {
@@ -198,7 +187,7 @@ pub fn impl_binop(
                         ret: ret_ty,
                         vars: function_vars.clone(),
                         args: vec![FuncArg {
-                            ty: other_ty,
+                            ty: other_tvar.ty,
                             default_value: None,
                             name: "other".into(),
                             is_vararg: false,
@@ -541,36 +530,43 @@ pub fn typeof_binop(
                 }
             }
 
-            let (_, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
-            let lhs_ndims = match &*unifier.get_ty_immutable(lhs_ndims) {
-                TypeEnum::TLiteral { values, .. } => {
-                    assert_eq!(values.len(), 1);
-                    u64::try_from(values[0].clone()).unwrap()
+            let (lhs_dtype, lhs_ndims) = unpack_ndarray_var_tys(unifier, lhs);
+            let lhs_ndims = extract_ndims(unifier, lhs_ndims);
+
+            let (rhs_dtype, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
+            let rhs_ndims = extract_ndims(unifier, rhs_ndims);
+
+            if !(unifier.unioned(lhs_dtype, primitives.float)
+                && unifier.unioned(rhs_dtype, primitives.float))
+            {
+                return Err(format!(
+                    "ndarray.__matmul__ only supports float64 operations, but LHS has type {} and RHS has type {}",
+                    unifier.stringify(lhs),
+                    unifier.stringify(rhs)
+                ));
+            }
+
+            // Deduce the ndims of the resulting ndarray.
+            // If this is 0 (an unsized ndarray), matmul returns a scalar just like NumPy.
+            let result_ndims = match (lhs_ndims, rhs_ndims) {
+                (0, _) | (_, 0) => {
+                    return Err(
+                        "ndarray.__matmul__ does not allow unsized ndarray input".to_string()
+                    )
                 }
-                _ => unreachable!(),
-            };
-            let (_, rhs_ndims) = unpack_ndarray_var_tys(unifier, rhs);
-            let rhs_ndims = match &*unifier.get_ty_immutable(rhs_ndims) {
-                TypeEnum::TLiteral { values, .. } => {
-                    assert_eq!(values.len(), 1);
-                    u64::try_from(values[0].clone()).unwrap()
-                }
-                _ => unreachable!(),
+                (1, 1) => 0,
+                (1, _) => rhs_ndims - 1,
+                (_, 1) => lhs_ndims - 1,
+                (m, n) => max(m, n),
             };
 
-            match (lhs_ndims, rhs_ndims) {
-                (2, 2) => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
-                (lhs, rhs) if lhs == 0 || rhs == 0 => {
-                    return Err(format!(
-                    "Input operand {} does not have enough dimensions (has {lhs}, requires {rhs})",
-                    u8::from(rhs == 0)
-                ))
-                }
-                (lhs, rhs) => {
-                    return Err(format!(
-                        "ndarray.__matmul__ on {lhs}D and {rhs}D operands not supported"
-                    ))
-                }
+            if result_ndims == 0 {
+                // If the result is unsized, NumPy returns a scalar.
+                primitives.float
+            } else {
+                let result_ndims_ty =
+                    unifier.get_fresh_literal(vec![SymbolValue::U64(result_ndims)], None);
+                make_ndarray_ty(unifier, primitives, Some(primitives.float), Some(result_ndims_ty))
             }
         }
 
@@ -773,7 +769,7 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
     impl_div(unifier, store, ndarray_t, &[ndarray_t, ndarray_dtype_t], None);
     impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
     impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
-    impl_matmul(unifier, store, ndarray_t, &[ndarray_t], Some(ndarray_t));
+    impl_matmul(unifier, store, ndarray_t, &[ndarray_unsized_t], None);
     impl_sign(unifier, store, ndarray_t, Some(ndarray_t));
     impl_invert(unifier, store, ndarray_t, Some(ndarray_t));
     impl_eq(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);

nac3core/src/typecheck/type_error.rs

@@ -94,7 +94,7 @@ fn loc_to_str(loc: Option<Location>) -> String {
     }
 }
 
-impl<'a> Display for DisplayTypeError<'a> {
+impl Display for DisplayTypeError<'_> {
     fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
         use TypeErrorKind::*;
         let mut notes = Some(HashMap::new());

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -3,7 +3,7 @@ use std::{
     cmp::max,
     collections::{HashMap, HashSet},
     convert::{From, TryInto},
-    iter::once,
+    iter::{once, repeat_n},
     sync::Arc,
 };
 
@@ -187,7 +187,7 @@ fn fix_assignment_target_context(node: &mut ast::Located<ExprKind>) {
     }
 }
 
-impl<'a> Fold<()> for Inferencer<'a> {
+impl Fold<()> for Inferencer<'_> {
     type TargetU = Option<Type>;
     type Error = InferenceError;
 
@@ -657,7 +657,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
 
 type InferenceResult = Result<Type, InferenceError>;
 
-impl<'a> Inferencer<'a> {
+impl Inferencer<'_> {
     /// Constrain a <: b
     /// Currently implemented as unification
     fn constrain(&mut self, a: Type, b: Type, location: &Location) -> Result<(), InferenceError> {
@@ -1234,6 +1234,45 @@ impl<'a> Inferencer<'a> {
             }));
         }
 
+        if ["np_shape".into(), "np_strides".into()].contains(id) && args.len() == 1 {
+            let ndarray = self.fold_expr(args.remove(0))?;
+
+            let ndims = arraylike_get_ndims(self.unifier, ndarray.custom.unwrap());
+
+            // Make a tuple of size `ndims` full of int32 (TODO: Make it usize)
+            let ret_ty = TypeEnum::TTuple {
+                ty: repeat_n(self.primitives.int32, ndims as usize).collect_vec(),
+                is_vararg_ctx: false,
+            };
+            let ret_ty = self.unifier.add_ty(ret_ty);
+
+            let func_ty = TypeEnum::TFunc(FunSignature {
+                args: vec![FuncArg {
+                    name: "a".into(),
+                    default_value: None,
+                    ty: ndarray.custom.unwrap(),
+                    is_vararg: false,
+                }],
+                ret: ret_ty,
+                vars: VarMap::new(),
+            });
+            let func_ty = self.unifier.add_ty(func_ty);
+
+            return Ok(Some(Located {
+                location,
+                custom: Some(ret_ty),
+                node: ExprKind::Call {
+                    func: Box::new(Located {
+                        custom: Some(func_ty),
+                        location: func.location,
+                        node: ExprKind::Name { id: *id, ctx: *ctx },
+                    }),
+                    args: vec![ndarray],
+                    keywords: vec![],
+                },
+            }));
+        }
+
         if id == &"np_dot".into() {
             let arg0 = self.fold_expr(args.remove(0))?;
             let arg1 = self.fold_expr(args.remove(0))?;
@@ -1555,7 +1594,7 @@ impl<'a> Inferencer<'a> {
             }));
         }
         // 2-argument ndarray n-dimensional factory functions
-        if id == &"np_reshape".into() && args.len() == 2 {
+        if ["np_reshape".into(), "np_broadcast_to".into()].contains(id) && args.len() == 2 {
             let arg0 = self.fold_expr(args.remove(0))?;
 
             let shape_expr = args.remove(0);

nac3core/src/typecheck/type_inferencer/test.rs

@@ -18,7 +18,6 @@ use crate::{
 struct Resolver {
     id_to_type: HashMap<StrRef, Type>,
     id_to_def: HashMap<StrRef, DefinitionId>,
-    class_names: HashMap<StrRef, Type>,
 }
 
 impl SymbolResolver for Resolver {
@@ -198,7 +197,6 @@ impl TestEnvironment {
         let resolver = Arc::new(Resolver {
             id_to_type: identifier_mapping.clone(),
             id_to_def: HashMap::default(),
-            class_names: HashMap::default(),
         }) as Arc<dyn SymbolResolver + Send + Sync>;
 
         TestEnvironment {
@@ -454,7 +452,6 @@ impl TestEnvironment {
                 vars: IndexMap::default(),
             })),
         );
-        let class_names: HashMap<_, _> = [("Bar".into(), bar), ("Bar2".into(), bar2)].into();
 
         let id_to_name = [
             "int32".into(),
@@ -492,7 +489,6 @@ impl TestEnvironment {
                 ("Bar2".into(), DefinitionId(defs + 3)),
             ]
             .into(),
-            class_names,
         }) as Arc<dyn SymbolResolver + Send + Sync>;
 
         TestEnvironment {

nac3core/src/typecheck/typedef/mod.rs

@@ -3,13 +3,13 @@ use std::{
     cell::RefCell,
     collections::{HashMap, HashSet},
     fmt::{self, Display},
-    iter::{repeat, zip},
+    iter::{repeat, repeat_n, zip},
     rc::Rc,
     sync::{Arc, Mutex},
 };
 
 use indexmap::IndexMap;
-use itertools::{repeat_n, Itertools};
+use itertools::Itertools;
 
 use nac3parser::ast::{Cmpop, Location, StrRef, Unaryop};
 

nac3ld/src/dwarf.rs

@@ -30,7 +30,7 @@ pub struct DwarfReader<'a> {
     pub virt_addr: u32,
 }
 
-impl<'a> DwarfReader<'a> {
+impl DwarfReader<'_> {
     pub fn new(slice: &[u8], virt_addr: u32) -> DwarfReader {
         DwarfReader { slice, virt_addr }
     }
@@ -113,7 +113,7 @@ pub struct DwarfWriter<'a> {
     pub offset: usize,
 }
 
-impl<'a> DwarfWriter<'a> {
+impl DwarfWriter<'_> {
     pub fn new(slice: &mut [u8]) -> DwarfWriter {
         DwarfWriter { slice, offset: 0 }
     }
@@ -375,7 +375,7 @@ pub struct FDE_Records<'a> {
     available: usize,
 }
 
-impl<'a> Iterator for FDE_Records<'a> {
+impl Iterator for FDE_Records<'_> {
     type Item = (u32, u32);
 
     fn next(&mut self) -> Option<Self::Item> {
@@ -423,7 +423,7 @@ pub struct EH_Frame_Hdr<'a> {
     fdes: Vec<(u32, u32)>,
 }
 
-impl<'a> EH_Frame_Hdr<'a> {
+impl EH_Frame_Hdr<'_> {
     /// Create a [EH_Frame_Hdr] object, and write out the fixed fields of `.eh_frame_hdr` to memory.
     ///
     /// Load address is not known at this point.

nac3ld/src/lib.rs

@@ -159,7 +159,7 @@ struct SymbolTableReader<'a> {
     strtab: &'a [u8],
 }
 
-impl<'a> SymbolTableReader<'a> {
+impl SymbolTableReader<'_> {
     pub fn find_index_by_name(&self, sym_name: &[u8]) -> Option<usize> {
         self.symtab.iter().position(|sym| {
             if let Ok(dynsym_name) = name_starting_at_slice(self.strtab, sym.st_name as usize) {

nac3standalone/demo/interpret_demo.py

@@ -67,7 +67,7 @@ def _bool(x):
 
 def _float(x):
     if isinstance(x, np.ndarray):
-        return np.float_(x)
+        return np.float64(x)
     else:
         return float(x)
 
@@ -111,6 +111,9 @@ def patch(module):
     def output_strln(x):
         print(x, end='')
 
+    def output_int32_list(x):
+        print([int(e) for e in x])
+
     def dbg_stack_address(_):
         return 0
 
@@ -126,11 +129,12 @@ def patch(module):
             return output_float
         elif name == "output_str":
             return output_strln
+        elif name == "output_int32_list":
+            return output_int32_list
         elif name in {
             "output_bool",
             "output_int32",
             "output_int64",
-            "output_int32_list",
             "output_uint32",
             "output_uint64",
             "output_strln",
@@ -179,6 +183,16 @@ def patch(module):
     module.np_identity = np.identity
     module.np_array = np.array
 
+    # NumPy NDArray view functions
+    module.np_broadcast_to = np.broadcast_to
+    module.np_transpose = np.transpose
+    module.np_reshape = np.reshape
+
+    # NumPy NDArray property getters
+    module.np_size = np.size
+    module.np_shape = np.shape
+    module.np_strides = lambda ndarray: ndarray.strides
+
     # NumPy Math functions
     module.np_isnan = np.isnan
     module.np_isinf = np.isinf
@@ -218,8 +232,6 @@ def patch(module):
     module.np_ldexp = np.ldexp
     module.np_hypot = np.hypot
     module.np_nextafter = np.nextafter
-    module.np_transpose = np.transpose
-    module.np_reshape = np.reshape
 
     # SciPy Math functions
     module.sp_spec_erf = special.erf

nac3standalone/demo/src/ndarray.py

@@ -68,6 +68,19 @@ def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
         for c in range(len(n[r])):
             output_float64(n[r][c])
 
+def output_ndarray_float_3(n: ndarray[float, Literal[3]]):
+    for d in range(len(n)):
+        for r in range(len(n[d])):
+            for c in range(len(n[d][r])):
+                output_float64(n[d][r][c])
+
+def output_ndarray_float_4(n: ndarray[float, Literal[4]]):
+    for x in range(len(n)):
+        for y in range(len(n[x])):
+            for z in range(len(n[x][y])):
+                for w in range(len(n[x][y][z])):
+                    output_float64(n[x][y][z][w])
+
 def consume_ndarray_1(n: ndarray[float, Literal[1]]):
     pass
 
@@ -197,6 +210,104 @@ def test_ndarray_nd_idx():
     output_float64(x[1, 0])
     output_float64(x[1, 1])
 
+def test_ndarray_transpose():
+    x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
+    y = np_transpose(x)
+    z = np_transpose(y)
+
+    output_int32(np_shape(x)[0])
+    output_int32(np_shape(x)[1])
+    output_ndarray_float_2(x)
+
+    output_int32(np_shape(y)[0])
+    output_int32(np_shape(y)[1])
+    output_ndarray_float_2(y)
+
+    output_int32(np_shape(z)[0])
+    output_int32(np_shape(z)[1])
+    output_ndarray_float_2(z)
+
+def test_ndarray_reshape():
+    w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
+    x = np_reshape(w, (1, 2, 1, -1))
+    y = np_reshape(x, [2, -1])
+    z = np_reshape(y, 10)
+
+    output_int32(np_shape(w)[0])
+    output_ndarray_float_1(w)
+
+    output_int32(np_shape(x)[0])
+    output_int32(np_shape(x)[1])
+    output_int32(np_shape(x)[2])
+    output_int32(np_shape(x)[3])
+    output_ndarray_float_4(x)
+
+    output_int32(np_shape(y)[0])
+    output_int32(np_shape(y)[1])
+    output_ndarray_float_2(y)
+
+    output_int32(np_shape(z)[0])
+    output_ndarray_float_1(z)
+
+    x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
+    x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
+
+    output_int32(np_shape(x1)[0])
+    output_ndarray_int32_1(x1)
+
+    output_int32(np_shape(x2)[0])
+    output_int32(np_shape(x2)[1])
+    output_ndarray_int32_2(x2)
+
+def test_ndarray_broadcast_to():
+    xs = np_array([1.0, 2.0, 3.0])
+    ys = np_broadcast_to(xs, (1, 3))
+    zs = np_broadcast_to(ys, (2, 4, 3))
+
+    output_int32(np_shape(xs)[0])
+    output_ndarray_float_1(xs)
+
+    output_int32(np_shape(ys)[0])
+    output_int32(np_shape(ys)[1])
+    output_ndarray_float_2(ys)
+
+    output_int32(np_shape(zs)[0])
+    output_int32(np_shape(zs)[1])
+    output_int32(np_shape(zs)[2])
+    output_ndarray_float_3(zs)
+
+def test_ndarray_subscript_assignment():
+    xs = np_array([[11.0, 22.0, 33.0, 44.0], [55.0, 66.0, 77.0, 88.0]])
+
+    xs[0, 0] = 99.0
+    output_ndarray_float_2(xs)
+
+    xs[0] = 100.0
+    output_ndarray_float_2(xs)
+
+    xs[:, ::2] = 101.0
+    output_ndarray_float_2(xs)
+
+    xs[1:, 0] = 102.0
+    output_ndarray_float_2(xs)
+
+    xs[0] = np_array([-1.0, -2.0, -3.0, -4.0])
+    output_ndarray_float_2(xs)
+
+    xs[:] = np_array([-5.0, -6.0, -7.0, -8.0])
+    output_ndarray_float_2(xs)
+
+    # Test assignment with memory sharing
+    ys1 = np_reshape(xs, (2, 4))
+    ys2 = np_transpose(ys1)
+    ys3 = ys2[::-1, 0]
+    ys3[0] = -999.0
+
+    output_ndarray_float_2(xs)
+    output_ndarray_float_2(ys1)
+    output_ndarray_float_2(ys2)
+    output_ndarray_float_1(ys3)
+
 def test_ndarray_add():
     x = np_identity(2)
     y = x + np_ones([2, 2])
@@ -1440,27 +1551,6 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
     output_ndarray_float_2(nextafter_x_zeros)
     output_ndarray_float_2(nextafter_x_ones)
 
-def test_ndarray_transpose():
-    x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
-    y = np_transpose(x)
-    z = np_transpose(y)
-
-    output_ndarray_float_2(x)
-    output_ndarray_float_2(y)
-
-def test_ndarray_reshape():
-    w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
-    x = np_reshape(w, (1, 2, 1, -1))
-    y = np_reshape(x, [2, -1])
-    z = np_reshape(y, 10)
-
-    x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
-    x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
-
-    output_ndarray_float_1(w)
-    output_ndarray_float_2(y)
-    output_ndarray_float_1(z)
-
 def test_ndarray_dot():
     x1: ndarray[float, 1] = np_array([5.0, 1.0, 4.0, 2.0])
     y1: ndarray[float, 1] = np_array([5.0, 1.0, 6.0, 6.0])
@@ -1592,6 +1682,11 @@ def run() -> int32:
     test_ndarray_slices()
     test_ndarray_nd_idx()
 
+    test_ndarray_transpose()
+    test_ndarray_reshape()
+    test_ndarray_broadcast_to()
+    test_ndarray_subscript_assignment()
+
     test_ndarray_add()
     test_ndarray_add_broadcast()
     test_ndarray_add_broadcast_lhs_scalar()
@@ -1755,8 +1850,6 @@ def run() -> int32:
     test_ndarray_nextafter_broadcast()
     test_ndarray_nextafter_broadcast_lhs_scalar()
     test_ndarray_nextafter_broadcast_rhs_scalar()
-    test_ndarray_transpose()
-    test_ndarray_reshape()
 
     test_ndarray_dot()
     test_ndarray_cholesky()

nac3standalone/demo/src/numeric_primitives.py

@@ -29,10 +29,10 @@ def u32_max() -> uint32:
     return ~uint32(0)
 
 def i32_min() -> int32:
-    return int32(1 << 31)
+    return int32(-(1 << 31))
 
 def i32_max() -> int32:
-    return int32(~(1 << 31))
+    return int32((1 << 31)-1)
 
 def u64_min() -> uint64:
     return uint64(0)
@@ -63,8 +63,9 @@ def test_conv_from_i32():
         i32_max()
     ]:
         output_int64(int64(x))
-        output_uint32(uint32(x))
-        output_uint64(uint64(x))
+        if x >= 0:
+            output_uint32(uint32(x))
+            output_uint64(uint64(x))
         output_float64(float(x))
 
 def test_conv_from_u32():
@@ -108,7 +109,6 @@ def test_conv_from_u64():
 
 def test_f64toi32():
     for x in [
-        float(i32_min()) - 1.0,
         float(i32_min()),
         float(i32_min()) + 1.0,
         -1.5,
@@ -117,7 +117,6 @@ def test_f64toi32():
         1.5,
         float(i32_max()) - 1.0,
         float(i32_max()),
-        float(i32_max()) + 1.0
     ]:
         output_int32(int32(x))
 
@@ -138,24 +137,17 @@ def test_f64toi64():
 
 def test_f64tou32():
     for x in [
-        -1.5,
-        float(u32_min()) - 1.0,
-        -0.5,
         float(u32_min()),
         0.5,
         float(u32_min()) + 1.0,
         1.5,
         float(u32_max()) - 1.0,
         float(u32_max()),
-        float(u32_max()) + 1.0
     ]:
         output_uint32(uint32(x))
 
 def test_f64tou64():
     for x in [
-        -1.5,
-        float(u64_min()) - 1.0,
-        -0.5,
         float(u64_min()),
         0.5,
         float(u64_min()) + 1.0,
@@ -181,4 +173,4 @@ def run() -> int32:
     test_f64tou32()
     test_f64tou64()
 
-    return 0
+    return 0