core: remove np_linalg_matmul

.gitignore

@@ -1,3 +1,4 @@
 __pycache__
 /target
+/nac3standalone/demo/linalg/target
 nix/windows/msys2

Cargo.lock

@@ -73,15 +73,6 @@ dependencies = [
  "windows-sys",
 ]
 
-[[package]]
-name = "approx"
-version = "0.5.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "cab112f0a86d568ea0e627cc1d6be74a1e9cd55214684db5561995f6dad897c6"
-dependencies = [
- "num-traits",
-]
-
 [[package]]
 name = "ascii-canvas"
 version = "3.0.0"
@@ -256,12 +247,6 @@ version = "0.2.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7a81dae078cea95a014a339291cec439d2f232ebe854a9d672b796c6afafa9b7"
 
-[[package]]
-name = "cslice"
-version = "0.3.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0f8cb7306107e4b10e64994de6d3274bd08996a7c1322a27b86482392f96be0a"
-
 [[package]]
 name = "dirs-next"
 version = "2.0.0"
@@ -536,12 +521,6 @@ dependencies = [
  "windows-targets",
 ]
 
-[[package]]
-name = "libm"
-version = "0.2.8"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4ec2a862134d2a7d32d7983ddcdd1c4923530833c9f2ea1a44fc5fa473989058"
-
 [[package]]
 name = "libredox"
 version = "0.1.3"
@@ -552,14 +531,6 @@ dependencies = [
  "libc",
 ]
 
-[[package]]
-name = "linalg"
-version = "0.1.0"
-dependencies = [
- "cslice",
- "nalgebra",
-]
-
 [[package]]
 name = "linked-hash-map"
 version = "0.5.6"
@@ -688,70 +659,17 @@ version = "0.1.0"
 dependencies = [
  "clap",
  "inkwell",
- "linalg",
  "nac3core",
  "nac3parser",
  "parking_lot",
 ]
 
-[[package]]
-name = "nalgebra"
-version = "0.32.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7b5c17de023a86f59ed79891b2e5d5a94c705dbe904a5b5c9c952ea6221b03e4"
-dependencies = [
- "approx",
- "num-complex",
- "num-rational",
- "num-traits",
- "simba",
- "typenum",
-]
-
 [[package]]
 name = "new_debug_unreachable"
 version = "1.0.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "650eef8c711430f1a879fdd01d4745a7deea475becfb90269c06775983bbf086"
 
-[[package]]
-name = "num-complex"
-version = "0.4.6"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495"
-dependencies = [
- "num-traits",
-]
-
-[[package]]
-name = "num-integer"
-version = "0.1.46"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7969661fd2958a5cb096e56c8e1ad0444ac2bbcd0061bd28660485a44879858f"
-dependencies = [
- "num-traits",
-]
-
-[[package]]
-name = "num-rational"
-version = "0.4.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f83d14da390562dca69fc84082e73e548e1ad308d24accdedd2720017cb37824"
-dependencies = [
- "num-integer",
- "num-traits",
-]
-
-[[package]]
-name = "num-traits"
-version = "0.2.19"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "071dfc062690e90b734c0b2273ce72ad0ffa95f0c74596bc250dcfd960262841"
-dependencies = [
- "autocfg",
- "libm",
-]
-
 [[package]]
 name = "once_cell"
 version = "1.19.0"
@@ -781,12 +699,6 @@ dependencies = [
  "windows-targets",
 ]
 
-[[package]]
-name = "paste"
-version = "1.0.15"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a"
-
 [[package]]
 name = "petgraph"
 version = "0.6.5"
@@ -1158,18 +1070,6 @@ dependencies = [
  "yaml-rust",
 ]
 
-[[package]]
-name = "simba"
-version = "0.8.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "061507c94fc6ab4ba1c9a0305018408e312e17c041eb63bef8aa726fa33aceae"
-dependencies = [
- "approx",
- "num-complex",
- "num-traits",
- "paste",
-]
-
 [[package]]
 name = "similar"
 version = "2.6.0"
@@ -1330,12 +1230,6 @@ dependencies = [
  "crunchy",
 ]
 
-[[package]]
-name = "typenum"
-version = "1.17.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825"
-
 [[package]]
 name = "unic-char-property"
 version = "0.9.0"

flake.nix

@@ -161,9 +161,7 @@
           clippy
           pre-commit
           rustfmt
-          rust-analyzer
         ];
-        RUST_SRC_PATH = "${pkgs.rust.packages.stable.rustPlatform.rustLibSrc}";
       };
       devShells.x86_64-linux.msys2 = pkgs.mkShell {
         name = "nac3-dev-shell-msys2";

nac3core/src/codegen/builtin_fns.rs

@@ -3,7 +3,9 @@ use inkwell::values::{BasicValue, BasicValueEnum, PointerValue};
 use inkwell::{FloatPredicate, IntPredicate, OptimizationLevel};
 use itertools::Itertools;
 
-use crate::codegen::classes::{NDArrayValue, ProxyValue, UntypedArrayLikeAccessor};
+use crate::codegen::classes::{
+    NDArrayValue, ProxyValue, UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
+};
 use crate::codegen::numpy::ndarray_elementwise_unaryop_impl;
 use crate::codegen::stmt::gen_for_callback_incrementing;
 use crate::codegen::{extern_fns, irrt, llvm_intrinsics, numpy, CodeGenContext, CodeGenerator};
@@ -1865,83 +1867,6 @@ fn build_output_struct<'ctx>(
     out_ptr
 }
 
-/// Invokes the `np_dot` linalg function
-pub fn call_np_dot<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    x1: (Type, BasicValueEnum<'ctx>),
-    x2: (Type, BasicValueEnum<'ctx>),
-) -> Result<BasicValueEnum<'ctx>, String> {
-    const FN_NAME: &str = "np_dot";
-    let (x1_ty, x1) = x1;
-    let (x2_ty, x2) = x2;
-
-    if let (BasicValueEnum::PointerValue(_), BasicValueEnum::PointerValue(_)) = (x1, x2) {
-        let (n1_elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
-        let n1_elem_ty = ctx.get_llvm_type(generator, n1_elem_ty);
-        let (n2_elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x2_ty);
-        let n2_elem_ty = ctx.get_llvm_type(generator, n2_elem_ty);
-
-        let (BasicTypeEnum::FloatType(_), BasicTypeEnum::FloatType(_)) = (n1_elem_ty, n2_elem_ty)
-        else {
-            unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty]);
-        };
-
-        Ok(extern_fns::call_np_dot(ctx, x1, x2, None).into())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty])
-    }
-}
-
-/// Invokes the `np_linalg_matmul` linalg function
-pub fn call_np_linalg_matmul<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    x1: (Type, BasicValueEnum<'ctx>),
-    x2: (Type, BasicValueEnum<'ctx>),
-) -> Result<BasicValueEnum<'ctx>, String> {
-    const FN_NAME: &str = "np_linalg_matmul";
-    let (x1_ty, x1) = x1;
-    let (x2_ty, x2) = x2;
-
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    if let (BasicValueEnum::PointerValue(n1), BasicValueEnum::PointerValue(n2)) = (x1, x2) {
-        let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
-        let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
-        let (n2_elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x2_ty);
-        let n2_elem_ty = ctx.get_llvm_type(generator, n2_elem_ty);
-
-        let (BasicTypeEnum::FloatType(_), BasicTypeEnum::FloatType(_)) = (n1_elem_ty, n2_elem_ty)
-        else {
-            unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty]);
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-        let n2 = NDArrayValue::from_ptr_val(n2, llvm_usize, None);
-
-        let outdim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let outdim1 = unsafe {
-            n2.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
-                .into_int_value()
-        };
-
-        let out = numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[outdim0, outdim1])
-            .unwrap()
-            .as_base_value()
-            .as_basic_value_enum();
-
-        extern_fns::call_np_linalg_matmul(ctx, x1, x2, out, None);
-        Ok(out)
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty])
-    }
-}
-
 /// Invokes the `np_linalg_cholesky` linalg function
 pub fn call_np_linalg_cholesky<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
@@ -2224,6 +2149,104 @@ pub fn call_sp_linalg_lu<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
+/// Invokes the `np_linalg_matrix_power` linalg function
+pub fn call_np_linalg_matrix_power<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    x1: (Type, BasicValueEnum<'ctx>),
+    x2: (Type, BasicValueEnum<'ctx>),
+) -> Result<BasicValueEnum<'ctx>, String> {
+    const FN_NAME: &str = "np_linalg_matrix_power";
+    let (x1_ty, x1) = x1;
+    let (x2_ty, x2) = x2;
+    let x2 = call_float(generator, ctx, (x2_ty, x2)).unwrap();
+
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    if let (BasicValueEnum::PointerValue(n1), BasicValueEnum::FloatValue(n2)) = (x1, x2) {
+        let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
+        let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
+
+        let BasicTypeEnum::FloatType(_) = n1_elem_ty else {
+            unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty]);
+        };
+
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
+        // Changing second parameter to a `NDArray` for uniformity in function call
+        let n2_array = numpy::create_ndarray_const_shape(
+            generator,
+            ctx,
+            elem_ty,
+            &[llvm_usize.const_int(1, false)],
+        )
+        .unwrap();
+        unsafe {
+            n2_array.data().set_unchecked(
+                ctx,
+                generator,
+                &llvm_usize.const_zero(),
+                n2.as_basic_value_enum(),
+            );
+        };
+        let n2_array = n2_array.as_base_value().as_basic_value_enum();
+
+        let outdim0 = unsafe {
+            n1.dim_sizes()
+                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
+                .into_int_value()
+        };
+        let outdim1 = unsafe {
+            n1.dim_sizes()
+                .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
+                .into_int_value()
+        };
+
+        let out = numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[outdim0, outdim1])
+            .unwrap()
+            .as_base_value()
+            .as_basic_value_enum();
+
+        extern_fns::call_np_linalg_matrix_power(ctx, x1, n2_array, out, None);
+        Ok(out)
+    } else {
+        unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty])
+    }
+}
+
+/// Invokes the `np_linalg_det` linalg function
+pub fn call_np_linalg_det<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    x1: (Type, BasicValueEnum<'ctx>),
+) -> Result<BasicValueEnum<'ctx>, String> {
+    const FN_NAME: &str = "np_linalg_matrix_power";
+    let (x1_ty, x1) = x1;
+
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    if let BasicValueEnum::PointerValue(_) = x1 {
+        let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
+        let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
+
+        let BasicTypeEnum::FloatType(_) = n1_elem_ty else {
+            unsupported_type(ctx, FN_NAME, &[x1_ty]);
+        };
+
+        // Changing second parameter to a `NDArray` for uniformity in function call
+        let out = numpy::create_ndarray_const_shape(
+            generator,
+            ctx,
+            elem_ty,
+            &[llvm_usize.const_int(1, false)],
+        )
+        .unwrap();
+        extern_fns::call_np_linalg_det(ctx, x1, out.as_base_value().as_basic_value_enum(), None);
+        let res =
+            unsafe { out.data().get_unchecked(ctx, generator, &llvm_usize.const_zero(), None) };
+        Ok(res)
+    } else {
+        unsupported_type(ctx, FN_NAME, &[x1_ty])
+    }
+}
+
 /// Invokes the `sp_linalg_schur` linalg function
 pub fn call_sp_linalg_schur<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,

nac3core/src/codegen/extern_fns.rs

@@ -179,42 +179,13 @@ macro_rules! generate_linalg_extern_fn {
     };
 }
 
-generate_linalg_extern_fn!(call_np_linalg_matmul, "np_linalg_matmul", 3);
 generate_linalg_extern_fn!(call_np_linalg_cholesky, "np_linalg_cholesky", 2);
 generate_linalg_extern_fn!(call_np_linalg_qr, "np_linalg_qr", 3);
 generate_linalg_extern_fn!(call_np_linalg_svd, "np_linalg_svd", 4);
 generate_linalg_extern_fn!(call_np_linalg_inv, "np_linalg_inv", 2);
 generate_linalg_extern_fn!(call_np_linalg_pinv, "np_linalg_pinv", 2);
+generate_linalg_extern_fn!(call_np_linalg_matrix_power, "np_linalg_matrix_power", 3);
+generate_linalg_extern_fn!(call_np_linalg_det, "np_linalg_det", 2);
 generate_linalg_extern_fn!(call_sp_linalg_lu, "sp_linalg_lu", 3);
 generate_linalg_extern_fn!(call_sp_linalg_schur, "sp_linalg_schur", 3);
 generate_linalg_extern_fn!(call_sp_linalg_hessenberg, "sp_linalg_hessenberg", 3);
-
-/// Invokes the linalg `np_dot` function.
-pub fn call_np_dot<'ctx>(
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    mat1: BasicValueEnum<'ctx>,
-    mat2: BasicValueEnum<'ctx>,
-    name: Option<&str>,
-) -> FloatValue<'ctx> {
-    const FN_NAME: &str = "np_dot";
-
-    let extern_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
-        let fn_type =
-            ctx.ctx.f64_type().fn_type(&[mat1.get_type().into(), mat2.get_type().into()], false);
-        let func = ctx.module.add_function(FN_NAME, fn_type, None);
-        for attr in ["mustprogress", "nofree", "nounwind", "willreturn", "writeonly"] {
-            func.add_attribute(
-                AttributeLoc::Function,
-                ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
-            );
-        }
-        func
-    });
-
-    ctx.builder
-        .build_call(extern_fn, &[mat1.into(), mat2.into()], name.unwrap_or_default())
-        .map(CallSiteValue::try_as_basic_value)
-        .map(|v| v.map_left(BasicValueEnum::into_float_value))
-        .map(Either::unwrap_left)
-        .unwrap()
-}

nac3core/src/codegen/numpy.rs

@@ -26,12 +26,15 @@ use crate::{
         typedef::{FunSignature, Type, TypeEnum},
     },
 };
-use inkwell::types::{AnyTypeEnum, BasicTypeEnum, PointerType};
 use inkwell::{
     types::BasicType,
     values::{BasicValueEnum, IntValue, PointerValue},
     AddressSpace, IntPredicate, OptimizationLevel,
 };
+use inkwell::{
+    types::{AnyTypeEnum, BasicTypeEnum, PointerType},
+    values::BasicValue,
+};
 use nac3parser::ast::{Operator, StrRef};
 
 /// Creates an uninitialized `NDArray` instance.
@@ -2027,6 +2030,7 @@ pub fn gen_ndarray_fill<'ctx>(
     Ok(())
 }
 
+/// Generates LLVM IR for `ndarray.transpose`.
 pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -2041,6 +2045,7 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
         let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
         let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
 
+        // Dimensions are reversed in the transposed array
         let out = create_ndarray_dyn_shape(
             generator,
             ctx,
@@ -2066,30 +2071,16 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
             (n_sz, false),
             |generator, ctx, _, idx| {
                 let elem = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
-                // Calculate transposed idx
-                // 2, 3 => idx = row * num_col + col = 3 + 2 = 5
-                // 2, 3, 4 => idx = row * (num_col*num_z) + col * (num_z) + z => 12 + 8 + 3 | 18, [4, 2] 15, ,1,2
-                // num_z (col + num_col * row) + z
-                // 4D => 2, 3, 4, 5 = idx = num_w (num_z (col + num_col*row) + z) + w = 119
-                // [1, 1, 2]?
-                // 4 (1 + 3*1) + 2 = 6
-                // z = 2, col = 1, row = 0
-                // 0,1,
-                // 2, 3 => idx = row * num_col + col | 2, 3, 4 => idx = row * (num_col * num_z) + col * (num_z) + num_z
-                // ND => idx = 1 * (dim0 + dim1 + ... dimn) + dim[-1] * (dim0 + dim1 + ... + dimn-1) + ... + dim[1] * dim0
-                // 6 + 12 + 6 = 24 num_z * (row*num_col + col + 1) 4*6=24
-                // 2, 3, 4, 5 at idx 1 should go to
-                // 5, 4, 3, 2
 
-                // 18 => [2, 4] dim = 4
-                // 0 * 4 + 2 = 2
-                // 4 => [1, 1] dim = 3
-                // 2 * 3 + 1
                 let new_idx = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
                 let rem_idx = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
                 ctx.builder.build_store(new_idx, llvm_usize.const_zero()).unwrap();
                 ctx.builder.build_store(rem_idx, idx).unwrap();
 
+                // Incrementally calculate the new index in the transposed array
+                // For each index, we first decompose it into the n-dims and use those to reconstruct the new index
+                // The formula used for indexing is:
+                // idx = dim_n * ( ... (dim2 * (dim0 * dim1) + dim1) + dim2 ... ) + dim_n
                 gen_for_callback_incrementing(
                     generator,
                     ctx,
@@ -2145,6 +2136,15 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
+/// LLVM-typed implementation for generating the implementation for `ndarray.reshape`.
+///
+/// * `x1` - `NDArray` to reshape.
+/// * `shape` - The `shape` parameter used to construct the new `NDArray`.
+/// Just like numpy, the `shape` argument can be:
+///   1. A list of `int32`;   e.g., `np.reshape(arr, [600, -1, 3])`
+///   2. A tuple of `int32`;  e.g., `np.reshape(arr, (-1, 800, 3))`
+///   3. A scalar `int32`;     e.g., `np.reshape(arr, 3)`
+/// Note that unlike other generating functions, one of the dimesions in the shape can be negative
 pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -2162,44 +2162,19 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
         let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
         let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
 
-        // Check for -1 in the shapec
-        let ndim_ty = match shape {
-            BasicValueEnum::PointerValue(shape_list_ptr)
-                if ListValue::is_instance(shape_list_ptr, llvm_usize).is_ok() =>
-            {
-                let shape_list = ListValue::from_ptr_val(shape_list_ptr, llvm_usize, None);
-                shape_list
-                    .data()
-                    .get(ctx, generator, &llvm_usize.const_zero(), None)
-                    .into_int_value()
-                    .get_type()
-            }
-            BasicValueEnum::StructValue(shape_tuple) => ctx
-                .builder
-                .build_extract_value(shape_tuple, 0, "")
-                .unwrap()
-                .into_int_value()
-                .get_type(),
-            BasicValueEnum::IntValue(shape_int) => shape_int.get_type(),
-            _ => unreachable!(),
-        };
-
-        let n_sz = ctx
-            .builder
-            .build_cast(inkwell::values::InstructionOpcode::Trunc, n_sz, ndim_ty, "")
-            .unwrap()
-            .into_int_value();
-
-        let acc = generator.gen_var_alloc(ctx, ndim_ty.into(), None)?;
+        let acc = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
         let num_neg = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-        ctx.builder.build_store(acc, ndim_ty.const_int(1, false)).unwrap();
+        ctx.builder.build_store(acc, llvm_usize.const_int(1, false)).unwrap();
         ctx.builder.build_store(num_neg, llvm_usize.const_zero()).unwrap();
 
         let out = match shape {
             BasicValueEnum::PointerValue(shape_list_ptr)
                 if ListValue::is_instance(shape_list_ptr, llvm_usize).is_ok() =>
             {
+                // 1. A list of ints; e.g., `np.reshape(arr, [int64(600), int64(800, -1])`
+
                 let shape_list = ListValue::from_ptr_val(shape_list_ptr, llvm_usize, None);
+                // Check for -1 in dimensions
                 gen_for_callback_incrementing(
                     generator,
                     ctx,
@@ -2209,6 +2184,7 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                     |generator, ctx, _, idx| {
                         let ele =
                             shape_list.data().get(ctx, generator, &idx, None).into_int_value();
+                        let ele = ctx.builder.build_int_s_extend(ele, llvm_usize, "").unwrap();
 
                         gen_if_else_expr_callback(
                             generator,
@@ -2219,7 +2195,7 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                                     .build_int_compare(
                                         IntPredicate::SLT,
                                         ele,
-                                        ndim_ty.const_zero(),
+                                        llvm_usize.const_zero(),
                                         "",
                                     )
                                     .unwrap())
@@ -2253,6 +2229,7 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                 )?;
                 let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
                 let rem = ctx.builder.build_int_unsigned_div(n_sz, acc_val, "").unwrap();
+                // Generate the output shape by filling -1 with `rem`
                 create_ndarray_dyn_shape(
                     generator,
                     ctx,
@@ -2262,6 +2239,8 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                     |generator, ctx, shape_list, idx| {
                         let dim =
                             shape_list.data().get(ctx, generator, &idx, None).into_int_value();
+                        let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
+
                         Ok(gen_if_else_expr_callback(
                             generator,
                             ctx,
@@ -2271,7 +2250,7 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                                     .build_int_compare(
                                         IntPredicate::SLT,
                                         dim,
-                                        ndim_ty.const_zero(),
+                                        llvm_usize.const_zero(),
                                         "",
                                     )
                                     .unwrap())
@@ -2285,16 +2264,17 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                 )
             }
             BasicValueEnum::StructValue(shape_tuple) => {
-                let ndims = shape_tuple.get_type().count_fields();
-                let acc = ctx.builder.build_alloca(ndim_ty, "").unwrap();
-                ctx.builder.build_store(acc, ndim_ty.const_int(1, false)).unwrap();
+                // 2. A tuple of `int32`;  e.g., `np.reshape(arr, (-1, 800, 3))`
 
+                let ndims = shape_tuple.get_type().count_fields();
+                // Check for -1 in dims
                 for dim_i in 0..ndims {
                     let dim = ctx
                         .builder
                         .build_extract_value(shape_tuple, dim_i, "")
                         .unwrap()
                         .into_int_value();
+                    let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
 
                     gen_if_else_expr_callback(
                         generator,
@@ -2302,7 +2282,12 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                         |_, ctx| {
                             Ok(ctx
                                 .builder
-                                .build_int_compare(IntPredicate::SLT, dim, ndim_ty.const_zero(), "")
+                                .build_int_compare(
+                                    IntPredicate::SLT,
+                                    dim,
+                                    llvm_usize.const_zero(),
+                                    "",
+                                )
                                 .unwrap())
                         },
                         |_, ctx| -> Result<Option<IntValue>, String> {
@@ -2328,12 +2313,14 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                 let rem = ctx.builder.build_int_unsigned_div(n_sz, acc_val, "").unwrap();
                 let mut shape = Vec::with_capacity(ndims as usize);
 
+                // Reconstruct shape filling negatives with rem
                 for dim_i in 0..ndims {
                     let dim = ctx
                         .builder
                         .build_extract_value(shape_tuple, dim_i, "")
                         .unwrap()
                         .into_int_value();
+                    let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
 
                     let dim = gen_if_else_expr_callback(
                         generator,
@@ -2341,7 +2328,12 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                         |_, ctx| {
                             Ok(ctx
                                 .builder
-                                .build_int_compare(IntPredicate::SLT, dim, ndim_ty.const_zero(), "")
+                                .build_int_compare(
+                                    IntPredicate::SLT,
+                                    dim,
+                                    llvm_usize.const_zero(),
+                                    "",
+                                )
                                 .unwrap())
                         },
                         |_, _| Ok(Some(rem)),
@@ -2354,6 +2346,7 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                 create_ndarray_const_shape(generator, ctx, elem_ty, shape.as_slice())
             }
             BasicValueEnum::IntValue(shape_int) => {
+                // 3. A scalar `int32`;     e.g., `np.reshape(arr, 3)`
                 let shape_int = gen_if_else_expr_callback(
                     generator,
                     ctx,
@@ -2363,13 +2356,15 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
                             .build_int_compare(
                                 IntPredicate::SLT,
                                 shape_int,
-                                ndim_ty.const_zero(),
+                                llvm_usize.const_zero(),
                                 "",
                             )
                             .unwrap())
                     },
                     |_, _| Ok(Some(n_sz)),
-                    |_, _| Ok(Some(shape_int)),
+                    |_, ctx| {
+                        Ok(Some(ctx.builder.build_int_s_extend(shape_int, llvm_usize, "").unwrap()))
+                    },
                 )?
                 .unwrap()
                 .into_int_value();
@@ -2379,6 +2374,7 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
         }
         .unwrap();
 
+        // Only allow one dimension to be negative
         let num_negs = ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
         ctx.make_assert(
             generator,
@@ -2391,17 +2387,13 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
             ctx.current_loc,
         );
 
+        // The new shape must be compatible with the old shape
         let out_sz = call_ndarray_calc_size(generator, ctx, &out.dim_sizes(), (None, None));
-        let out_sz = ctx
-            .builder
-            .build_cast(inkwell::values::InstructionOpcode::Trunc, out_sz, ndim_ty, "")
-            .unwrap()
-            .into_int_value();
         ctx.make_assert(
             generator,
             ctx.builder.build_int_compare(IntPredicate::EQ, out_sz, n_sz, "").unwrap(),
             "0:ValueError",
-            "cannot reshape array of size {} into provided shape of size {}",
+            "cannot reshape array of size {0} into provided shape of size {1}",
             [Some(n_sz), Some(out_sz), None],
             ctx.current_loc,
         );
@@ -2428,3 +2420,102 @@ pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
         )
     }
 }
+
+/// Generates LLVM IR for `ndarray.dot`.
+/// Calculate inner product of two vectors or literals
+/// For matrix multiplication use `np_matmul`
+///
+/// The input `NDArray` are flattened and treated as 1D
+/// The operation is equivalent to `np.dot(arr1.ravel(), arr2.ravel())`
+pub fn ndarray_dot<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    x1: (Type, BasicValueEnum<'ctx>),
+    x2: (Type, BasicValueEnum<'ctx>),
+) -> Result<BasicValueEnum<'ctx>, String> {
+    const FN_NAME: &str = "ndarray_dot";
+    let (x1_ty, x1) = x1;
+    let (_, x2) = x2;
+
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    match (x1, x2) {
+        (BasicValueEnum::PointerValue(n1), BasicValueEnum::PointerValue(n2)) => {
+            let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
+            let n2 = NDArrayValue::from_ptr_val(n2, llvm_usize, None);
+
+            let n1_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
+            let n2_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
+
+            ctx.make_assert(
+                generator,
+                ctx.builder.build_int_compare(IntPredicate::EQ, n1_sz, n2_sz, "").unwrap(),
+                "0:ValueError",
+                "shapes ({0}), ({1}) not aligned",
+                [Some(n1_sz), Some(n2_sz), None],
+                ctx.current_loc,
+            );
+
+            let identity =
+                unsafe { n1.data().get_unchecked(ctx, generator, &llvm_usize.const_zero(), None) };
+            let acc = ctx.builder.build_alloca(identity.get_type(), "").unwrap();
+            ctx.builder.build_store(acc, identity.get_type().const_zero()).unwrap();
+
+            gen_for_callback_incrementing(
+                generator,
+                ctx,
+                None,
+                llvm_usize.const_zero(),
+                (n1_sz, false),
+                |generator, ctx, _, idx| {
+                    let elem1 = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
+                    let elem2 = unsafe { n2.data().get_unchecked(ctx, generator, &idx, None) };
+
+                    let product = match elem1 {
+                        BasicValueEnum::IntValue(e1) => ctx
+                            .builder
+                            .build_int_mul(e1, elem2.into_int_value(), "")
+                            .unwrap()
+                            .as_basic_value_enum(),
+                        BasicValueEnum::FloatValue(e1) => ctx
+                            .builder
+                            .build_float_mul(e1, elem2.into_float_value(), "")
+                            .unwrap()
+                            .as_basic_value_enum(),
+                        _ => unreachable!(),
+                    };
+                    let acc_val = ctx.builder.build_load(acc, "").unwrap();
+                    let acc_val = match acc_val {
+                        BasicValueEnum::IntValue(e1) => ctx
+                            .builder
+                            .build_int_add(e1, product.into_int_value(), "")
+                            .unwrap()
+                            .as_basic_value_enum(),
+                        BasicValueEnum::FloatValue(e1) => ctx
+                            .builder
+                            .build_float_add(e1, product.into_float_value(), "")
+                            .unwrap()
+                            .as_basic_value_enum(),
+                        _ => unreachable!(),
+                    };
+                    ctx.builder.build_store(acc, acc_val).unwrap();
+
+                    Ok(())
+                },
+                llvm_usize.const_int(1, false),
+            )?;
+            let acc_val = ctx.builder.build_load(acc, "").unwrap();
+            Ok(acc_val)
+        }
+        (BasicValueEnum::IntValue(e1), BasicValueEnum::IntValue(e2)) => {
+            Ok(ctx.builder.build_int_mul(e1, e2, "").unwrap().as_basic_value_enum())
+        }
+        (BasicValueEnum::FloatValue(e1), BasicValueEnum::FloatValue(e2)) => {
+            Ok(ctx.builder.build_float_mul(e1, e2, "").unwrap().as_basic_value_enum())
+        }
+        _ => unreachable!(
+            "{FN_NAME}() not supported for '{}'",
+            format!("'{}'", ctx.unifier.stringify(x1_ty))
+        ),
+    }
+}

nac3core/src/toplevel/builtins.rs

@@ -558,16 +558,17 @@ impl<'a> BuiltinBuilder<'a> {
             | PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
 
             PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
-                self.build_np_sp_ndarray_1ary_function(prim)
+                self.build_np_sp_ndarray_function(prim)
             }
 
             PrimDef::FunNpDot
-            | PrimDef::FunNpLinalgMatmul
             | PrimDef::FunNpLinalgCholesky
             | PrimDef::FunNpLinalgQr
             | PrimDef::FunNpLinalgSvd
             | PrimDef::FunNpLinalgInv
             | PrimDef::FunNpLinalgPinv
+            | PrimDef::FunNpLinalgMatrixPower
+            | PrimDef::FunNpLinalgDet
             | PrimDef::FunSpLinalgLu
             | PrimDef::FunSpLinalgSchur
             | PrimDef::FunSpLinalgHessenberg => self.build_linalg_methods(prim),
@@ -1889,62 +1890,53 @@ impl<'a> BuiltinBuilder<'a> {
         }
     }
 
-    /// Build 1-ary numpy/scipy functions that take in an ndarray and return a value of the same type as the input.
-    fn build_np_sp_ndarray_1ary_function(&mut self, prim: PrimDef) -> TopLevelDef {
+    /// 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]);
 
-        let elem_type = self.unifier.get_fresh_var(Some("R".into()), None);
-        let ndarray_type = make_ndarray_ty(self.unifier, self.primitives, Some(elem_type.ty), None);
-        let ndarray_ty =
-            self.unifier.get_fresh_var_with_range(&[ndarray_type], Some("T".into()), None);
-        let var_map = into_var_map([elem_type, ndarray_ty]);
-
         match prim {
-            PrimDef::FunNpTranspose => create_fn_by_codegen(
-                self.unifier,
-                &var_map,
-                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))?))
-                }),
-            ),
-
-            PrimDef::FunNpReshape => {
-                // Return type can have differend ndims
-                let ret_ty =
-                    self.unifier.get_fresh_var_with_range(&[ndarray_type], Some("U".into()), None);
-                let var_map = var_map
-                    .clone()
-                    .into_iter()
-                    .chain(once((ret_ty.id, ret_ty.ty)))
-                    .chain(once((
-                        self.ndarray_factory_fn_shape_arg_tvar.id,
-                        self.ndarray_factory_fn_shape_arg_tvar.ty,
-                    )))
-                    .collect::<IndexMap<_, _>>();
-
+            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,
-                    &var_map,
+                    &into_var_map([ndarray_ty]),
                     prim.name(),
-                    ret_ty.ty,
-                    &[(ndarray_ty.ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
+                    ndarray_ty.ty,
+                    &[(ndarray_ty.ty, "x")],
                     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))?))
+                        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!(),
         }
     }
@@ -1957,12 +1949,13 @@ impl<'a> BuiltinBuilder<'a> {
             prim,
             &[
                 PrimDef::FunNpDot,
-                PrimDef::FunNpLinalgMatmul,
                 PrimDef::FunNpLinalgCholesky,
                 PrimDef::FunNpLinalgQr,
                 PrimDef::FunNpLinalgSvd,
                 PrimDef::FunNpLinalgInv,
                 PrimDef::FunNpLinalgPinv,
+                PrimDef::FunNpLinalgMatrixPower,
+                PrimDef::FunNpLinalgDet,
                 PrimDef::FunSpLinalgLu,
                 PrimDef::FunSpLinalgSchur,
                 PrimDef::FunSpLinalgHessenberg,
@@ -1974,7 +1967,7 @@ impl<'a> BuiltinBuilder<'a> {
                 self.unifier,
                 &self.num_or_ndarray_var_map,
                 prim.name(),
-                self.primitives.float,
+                self.num_ty.ty,
                 &[(self.num_or_ndarray_ty.ty, "x1"), (self.num_or_ndarray_ty.ty, "x2")],
                 Box::new(move |ctx, _, fun, args, generator| {
                     let x1_ty = fun.0.args[0].ty;
@@ -1982,33 +1975,7 @@ impl<'a> BuiltinBuilder<'a> {
                     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(builtin_fns::call_np_dot(
-                        generator,
-                        ctx,
-                        (x1_ty, x1_val),
-                        (x2_ty, x2_val),
-                    )?))
-                }),
-            ),
-
-            PrimDef::FunNpLinalgMatmul => create_fn_by_codegen(
-                self.unifier,
-                &VarMap::new(),
-                prim.name(),
-                self.ndarray_float_2d,
-                &[(self.ndarray_float_2d, "x1"), (self.ndarray_float_2d, "x2")],
-                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(builtin_fns::call_np_linalg_matmul(
-                        generator,
-                        ctx,
-                        (x1_ty, x1_val),
-                        (x2_ty, x2_val),
-                    )?))
+                    Ok(Some(ndarray_dot(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
                 }),
             ),
 
@@ -2086,10 +2053,39 @@ impl<'a> BuiltinBuilder<'a> {
                     }),
                 )
             }
-            _ => {
-                println!("{:?}", prim.name());
-                unreachable!()
-            }
+            PrimDef::FunNpLinalgMatrixPower => create_fn_by_codegen(
+                self.unifier,
+                &VarMap::new(),
+                prim.name(),
+                self.ndarray_float_2d,
+                &[(self.ndarray_float_2d, "x1"), (self.primitives.int32, "power")],
+                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(builtin_fns::call_np_linalg_matrix_power(
+                        generator,
+                        ctx,
+                        (x1_ty, x1_val),
+                        (x2_ty, x2_val),
+                    )?))
+                }),
+            ),
+            PrimDef::FunNpLinalgDet => create_fn_by_codegen(
+                self.unifier,
+                &VarMap::new(),
+                prim.name(),
+                self.primitives.float,
+                &[(self.ndarray_float_2d, "x1")],
+                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)?;
+                    Ok(Some(builtin_fns::call_np_linalg_det(generator, ctx, (x1_ty, x1_val))?))
+                }),
+            ),
+            _ => unreachable!(),
         }
     }
 

nac3core/src/toplevel/helper.rs

@@ -104,12 +104,13 @@ pub enum PrimDef {
 
     // Linalg functions
     FunNpDot,
-    FunNpLinalgMatmul,
     FunNpLinalgCholesky,
     FunNpLinalgQr,
     FunNpLinalgSvd,
     FunNpLinalgInv,
     FunNpLinalgPinv,
+    FunNpLinalgMatrixPower,
+    FunNpLinalgDet,
     FunSpLinalgLu,
     FunSpLinalgSchur,
     FunSpLinalgHessenberg,
@@ -289,12 +290,13 @@ impl PrimDef {
 
             // Linalg functions
             PrimDef::FunNpDot => fun("np_dot", None),
-            PrimDef::FunNpLinalgMatmul => fun("np_linalg_matmul", None),
             PrimDef::FunNpLinalgCholesky => fun("np_linalg_cholesky", None),
             PrimDef::FunNpLinalgQr => fun("np_linalg_qr", None),
             PrimDef::FunNpLinalgSvd => fun("np_linalg_svd", None),
             PrimDef::FunNpLinalgInv => fun("np_linalg_inv", None),
             PrimDef::FunNpLinalgPinv => fun("np_linalg_pinv", None),
+            PrimDef::FunNpLinalgMatrixPower => fun("np_linalg_matrix_power", None),
+            PrimDef::FunNpLinalgDet => fun("np_linalg_det", None),
             PrimDef::FunSpLinalgLu => fun("sp_linalg_lu", None),
             PrimDef::FunSpLinalgSchur => fun("sp_linalg_schur", None),
             PrimDef::FunSpLinalgHessenberg => fun("sp_linalg_hessenberg", None),

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap

@@ -5,7 +5,7 @@ expression: res_vec
 [
     "Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
     "Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(245)]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(246)]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [\"aa\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap

@@ -7,7 +7,7 @@ expression: res_vec
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
-    "Class {\nname: \"B\",\nancestors: [\"B[typevar234]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar234\"]\n}\n",
+    "Class {\nname: \"B\",\nancestors: [\"B[typevar235]\", \"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: [\"typevar235\"]\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap

@@ -5,8 +5,8 @@ expression: res_vec
 [
     "Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
-    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(247)]\n}\n",
-    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(252)]\n}\n",
+    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(248)]\n}\n",
+    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(253)]\n}\n",
     "Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap

@@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
 expression: res_vec
 ---
 [
-    "Class {\nname: \"A\",\nancestors: [\"A[typevar233, typevar234]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar233\", \"typevar234\"]\n}\n",
+    "Class {\nname: \"A\",\nancestors: [\"A[typevar234, typevar235]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar234\", \"typevar235\"]\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap

@@ -6,12 +6,12 @@ expression: res_vec
     "Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(253)]\n}\n",
+    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(254)]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(261)]\n}\n",
+    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(262)]\n}\n",
 ]

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -1130,6 +1130,44 @@ impl<'a> Inferencer<'a> {
             }));
         }
 
+        if id == &"np_dot".into() {
+            let arg0 = self.fold_expr(args.remove(0))?;
+            let arg1 = self.fold_expr(args.remove(0))?;
+            let arg0_ty = arg0.custom.unwrap();
+
+            let ret = if arg0_ty.obj_id(self.unifier).is_some_and(|id| id == PrimDef::NDArray.id())
+            {
+                let (ndarray_dtype, _) = unpack_ndarray_var_tys(self.unifier, arg0_ty);
+
+                ndarray_dtype
+            } else {
+                arg0_ty
+            };
+
+            let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
+                args: vec![
+                    FuncArg { name: "x1".into(), ty: arg0.custom.unwrap(), default_value: None },
+                    FuncArg { name: "x2".into(), ty: arg1.custom.unwrap(), default_value: None },
+                ],
+                ret,
+                vars: VarMap::new(),
+            }));
+
+            return Ok(Some(Located {
+                location,
+                custom: Some(ret),
+                node: ExprKind::Call {
+                    func: Box::new(Located {
+                        custom: Some(custom),
+                        location: func.location,
+                        node: ExprKind::Name { id: *id, ctx: *ctx },
+                    }),
+                    args: vec![arg0, arg1],
+                    keywords: vec![],
+                },
+            }));
+        }
+
         if ["np_min", "np_max"].iter().any(|fun_id| id == &(*fun_id).into()) && args.len() == 1 {
             let arg0 = self.fold_expr(args.remove(0))?;
             let arg0_ty = arg0.custom.unwrap();
@@ -1389,7 +1427,45 @@ impl<'a> Inferencer<'a> {
                 },
             }));
         }
+        // 2-argument ndarray n-dimensional factory functions
+        if id == &"np_reshape".into() && args.len() == 2 {
+            let arg0 = self.fold_expr(args.remove(0))?;
 
+            let shape_expr = args.remove(0);
+            let (ndims, shape) =
+                self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?; // Special handling for `shape`
+
+            let ndims = self.unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None);
+            let (elem_ty, _) = unpack_ndarray_var_tys(self.unifier, arg0.custom.unwrap());
+            let ret = make_ndarray_ty(self.unifier, self.primitives, Some(elem_ty), Some(ndims));
+
+            let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
+                args: vec![
+                    FuncArg { name: "x1".into(), ty: arg0.custom.unwrap(), default_value: None },
+                    FuncArg {
+                        name: "shape".into(),
+                        ty: shape.custom.unwrap(),
+                        default_value: None,
+                    },
+                ],
+                ret,
+                vars: VarMap::new(),
+            }));
+
+            return Ok(Some(Located {
+                location,
+                custom: Some(ret),
+                node: ExprKind::Call {
+                    func: Box::new(Located {
+                        custom: Some(custom),
+                        location: func.location,
+                        node: ExprKind::Name { id: *id, ctx: *ctx },
+                    }),
+                    args: vec![arg0, shape],
+                    keywords: vec![],
+                },
+            }));
+        }
         // 2-argument ndarray n-dimensional creation functions
         if id == &"np_full".into() && args.len() == 2 {
             let ExprKind::List { elts, .. } = &args[0].node else {

nac3standalone/Cargo.toml

@@ -8,7 +8,6 @@ edition = "2021"
 parking_lot = "0.12"
 nac3parser = { path = "../nac3parser" }
 nac3core = { path = "../nac3core" }
-linalg = { path = "./demo/linalg" }
 
 [dependencies.clap]
 version = "4.5"

nac3standalone/demo/interpret_demo.py

@@ -5,8 +5,8 @@ import importlib.util
 import importlib.machinery
 import math
 import numpy as np
-import scipy as sp
 import numpy.typing as npt
+import scipy as sp
 import pathlib
 
 from numpy import int32, int64, uint32, uint64
@@ -231,12 +231,13 @@ def patch(module):
 
     # Linalg functions
     module.np_dot = np.dot
-    module.np_linalg_matmul = np.matmul
     module.np_linalg_cholesky = np.linalg.cholesky
     module.np_linalg_qr = np.linalg.qr
     module.np_linalg_svd = np.linalg.svd
     module.np_linalg_inv = np.linalg.inv
     module.np_linalg_pinv = np.linalg.pinv
+    module.np_linalg_matrix_power = np.linalg.matrix_power
+    module.np_linalg_det = np.linalg.det
     
     module.sp_linalg_lu = lambda x: sp.linalg.lu(x, True)
     module.sp_linalg_schur = sp.linalg.schur

nac3standalone/demo/linalg/Cargo.lock

@@ -0,0 +1,114 @@
+# This file is automatically @generated by Cargo.
+# It is not intended for manual editing.
+version = 3
+
+[[package]]
+name = "approx"
+version = "0.5.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cab112f0a86d568ea0e627cc1d6be74a1e9cd55214684db5561995f6dad897c6"
+dependencies = [
+ "num-traits",
+]
+
+[[package]]
+name = "autocfg"
+version = "1.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0c4b4d0bd25bd0b74681c0ad21497610ce1b7c91b1022cd21c80c6fbdd9476b0"
+
+[[package]]
+name = "cslice"
+version = "0.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0f8cb7306107e4b10e64994de6d3274bd08996a7c1322a27b86482392f96be0a"
+
+[[package]]
+name = "libm"
+version = "0.2.8"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4ec2a862134d2a7d32d7983ddcdd1c4923530833c9f2ea1a44fc5fa473989058"
+
+[[package]]
+name = "linalg"
+version = "0.1.0"
+dependencies = [
+ "cslice",
+ "nalgebra",
+]
+
+[[package]]
+name = "nalgebra"
+version = "0.32.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7b5c17de023a86f59ed79891b2e5d5a94c705dbe904a5b5c9c952ea6221b03e4"
+dependencies = [
+ "approx",
+ "num-complex",
+ "num-rational",
+ "num-traits",
+ "simba",
+ "typenum",
+]
+
+[[package]]
+name = "num-complex"
+version = "0.4.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495"
+dependencies = [
+ "num-traits",
+]
+
+[[package]]
+name = "num-integer"
+version = "0.1.46"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7969661fd2958a5cb096e56c8e1ad0444ac2bbcd0061bd28660485a44879858f"
+dependencies = [
+ "num-traits",
+]
+
+[[package]]
+name = "num-rational"
+version = "0.4.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f83d14da390562dca69fc84082e73e548e1ad308d24accdedd2720017cb37824"
+dependencies = [
+ "num-integer",
+ "num-traits",
+]
+
+[[package]]
+name = "num-traits"
+version = "0.2.19"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "071dfc062690e90b734c0b2273ce72ad0ffa95f0c74596bc250dcfd960262841"
+dependencies = [
+ "autocfg",
+ "libm",
+]
+
+[[package]]
+name = "paste"
+version = "1.0.15"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a"
+
+[[package]]
+name = "simba"
+version = "0.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "061507c94fc6ab4ba1c9a0305018408e312e17c041eb63bef8aa726fa33aceae"
+dependencies = [
+ "approx",
+ "num-complex",
+ "num-traits",
+ "paste",
+]
+
+[[package]]
+name = "typenum"
+version = "1.17.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "42ff0bf0c66b8238c6f3b578df37d0b7848e55df8577b3f74f92a69acceeb825"

nac3standalone/demo/linalg/Cargo.toml

@@ -9,3 +9,5 @@ crate-type = ["staticlib"]
 [dependencies]
 nalgebra = {version = "0.32.6", default-features = false, features = ["libm", "alloc"]}
 cslice = "0.3.0"
+
+[workspace]

nac3standalone/demo/linalg/build.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+# Uses rustup to compile the linalg library for i386 and x86_84 architecture
+
+nix-shell -p rustup --command "RUSTC_BOOTSTRAP=1 cargo build -Z unstable-options --target x86_64-unknown-linux-gnu --out-dir liblinalg/x86_64"
+nix-shell -p rustup --command "RUSTC_BOOTSTRAP=1 RUSTFLAGS=\"-C target-cpu=i386 -C target-feature=+sse2\" cargo build -Z unstable-options --target i686-unknown-linux-gnu --out-dir liblinalg/i386"

nac3standalone/demo/linalg/src/lib.rs

@@ -34,83 +34,6 @@ impl InputMatrix {
     }
 }
 
-/// # Safety
-///
-/// `mat1` and `mat2` should point to a valid 1DArray of `f64` floats in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn np_dot(mat1: *mut InputMatrix, mat2: *mut InputMatrix) -> f64 {
-    let mat1 = mat1.as_mut().unwrap();
-    let mat2 = mat2.as_mut().unwrap();
-
-    if !(mat1.ndims == 1 && mat2.ndims == 1) {
-        let err_msg = format!(
-            "expected 1D Vector Input, but received {}-D and {}-D input",
-            mat1.ndims, mat2.ndims
-        );
-        report_error("ValueError", "np_dot", file!(), line!(), column!(), &err_msg);
-    }
-
-    let dim1 = (*mat1).get_dims();
-    let dim2 = (*mat2).get_dims();
-
-    if dim1[0] != dim2[0] {
-        let err_msg = format!("shapes ({},) and ({},) not aligned", dim1[0], dim2[0]);
-        report_error("ValueError", "np_dot", file!(), line!(), column!(), &err_msg);
-    }
-    let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0]) };
-    let data_slice2 = unsafe { slice::from_raw_parts_mut(mat2.data, dim2[0]) };
-
-    let matrix1 = DMatrix::from_row_slice(dim1[0], 1, data_slice1);
-    let matrix2 = DMatrix::from_row_slice(dim2[0], 1, data_slice2);
-
-    matrix1.dot(&matrix2)
-}
-
-/// # Safety
-///
-/// `mat1` and `mat2` should point to a valid 2DArray of `f64` floats in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn np_linalg_matmul(
-    mat1: *mut InputMatrix,
-    mat2: *mut InputMatrix,
-    out: *mut InputMatrix,
-) {
-    let mat1 = mat1.as_mut().unwrap();
-    let mat2 = mat2.as_mut().unwrap();
-    let out = out.as_mut().unwrap();
-
-    if !(mat1.ndims == 2 && mat2.ndims == 2) {
-        let err_msg = format!(
-            "expected 2D Vector Input, but received {}-D and {}-D input",
-            mat1.ndims, mat2.ndims
-        );
-        report_error("ValueError", "np_matmul", file!(), line!(), column!(), &err_msg);
-    }
-
-    let dim1 = (*mat1).get_dims();
-    let dim2 = (*mat2).get_dims();
-
-    if dim1[1] != dim2[0] {
-        let err_msg = format!(
-            "shapes ({},{}) and ({},{}) not aligned: {} (dim 1) != {} (dim 0)",
-            dim1[0], dim1[1], dim2[0], dim2[1], dim1[1], dim2[0]
-        );
-        report_error("ValueError", "np_matmul", file!(), line!(), column!(), &err_msg);
-    }
-
-    let outdim = out.get_dims();
-    let out_slice = unsafe { slice::from_raw_parts_mut(out.data, outdim[0] * outdim[1]) };
-    let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
-    let data_slice2 = unsafe { slice::from_raw_parts_mut(mat2.data, dim2[0] * dim2[1]) };
-
-    let matrix1 = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
-    let matrix2 = DMatrix::from_row_slice(dim2[0], dim2[1], data_slice2);
-    let mut result = DMatrix::<f64>::zeros(outdim[0], outdim[1]);
-
-    matrix1.mul_to(&matrix2, &mut result);
-    out_slice.copy_from_slice(result.transpose().as_slice());
-}
-
 /// # Safety
 ///
 /// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
@@ -120,7 +43,7 @@ pub unsafe extern "C" fn np_linalg_cholesky(mat1: *mut InputMatrix, out: *mut In
     let out = out.as_mut().unwrap();
 
     if mat1.ndims != 2 {
-        let err_msg = format!("expected 2D Vector Input, but received {}-D input", mat1.ndims);
+        let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
         report_error("ValueError", "np_linalg_cholesky", file!(), line!(), column!(), &err_msg);
     }
 
@@ -168,7 +91,7 @@ pub unsafe extern "C" fn np_linalg_qr(
     let out_r = out_r.as_mut().unwrap();
 
     if mat1.ndims != 2 {
-        let err_msg = format!("expected 2D Vector Input, but received {}-D input", mat1.ndims);
+        let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
         report_error("ValueError", "np_linalg_cholesky", file!(), line!(), column!(), &err_msg);
     }
 
@@ -207,7 +130,7 @@ pub unsafe extern "C" fn np_linalg_svd(
     let outvh = outvh.as_mut().unwrap();
 
     if mat1.ndims != 2 {
-        let err_msg = format!("expected 2D Vector Input, but received {}-D input", mat1.ndims);
+        let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
         report_error("ValueError", "np_linalg_svd", file!(), line!(), column!(), &err_msg);
     }
 
@@ -238,7 +161,7 @@ pub unsafe extern "C" fn np_linalg_inv(mat1: *mut InputMatrix, out: *mut InputMa
     let out = out.as_mut().unwrap();
 
     if mat1.ndims != 2 {
-        let err_msg = format!("expected 2D Vector Input, but received {}-D input", mat1.ndims);
+        let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
         report_error("ValueError", "np_linalg_inv", file!(), line!(), column!(), &err_msg);
     }
     let dim1 = (*mat1).get_dims();
@@ -277,7 +200,7 @@ pub unsafe extern "C" fn np_linalg_pinv(mat1: *mut InputMatrix, out: *mut InputM
     let out = out.as_mut().unwrap();
 
     if mat1.ndims != 2 {
-        let err_msg = format!("expected 2D Vector Input, but received {}-D input", mat1.ndims);
+        let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
         report_error("ValueError", "np_linalg_pinv", file!(), line!(), column!(), &err_msg);
     }
     let dim1 = (*mat1).get_dims();
@@ -299,6 +222,76 @@ pub unsafe extern "C" fn np_linalg_pinv(mat1: *mut InputMatrix, out: *mut InputM
     }
 }
 
+/// # Safety
+///
+/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
+#[no_mangle]
+pub unsafe extern "C" fn np_linalg_matrix_power(
+    mat1: *mut InputMatrix,
+    mat2: *mut InputMatrix,
+    out: *mut InputMatrix,
+) {
+    let mat1 = mat1.as_mut().unwrap();
+    let mat2 = mat2.as_mut().unwrap();
+    let out = out.as_mut().unwrap();
+
+    if mat1.ndims != 2 {
+        let err_msg = format!("expected 2D Vector Input, but received {}D", mat1.ndims);
+        report_error("ValueError", "np_linalg_matrix_power", file!(), line!(), column!(), &err_msg);
+    }
+
+    let dim1 = (*mat1).get_dims();
+    let power = unsafe { slice::from_raw_parts_mut(mat2.data, 1) };
+    let power = power[0];
+    let outdim = out.get_dims();
+    let out_slice = unsafe { slice::from_raw_parts_mut(out.data, outdim[0] * outdim[1]) };
+    let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
+
+    let abs_pow = power.abs();
+    let matrix1 = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
+    let mut result = matrix1.pow(abs_pow as u32);
+
+    if power < 0.0 {
+        if !result.is_invertible() {
+            report_error(
+                "LinAlgError",
+                "np_linalg_inv",
+                file!(),
+                line!(),
+                column!(),
+                "no inverse for Singular Matrix",
+            );
+        }
+        result = result.try_inverse().unwrap();
+    }
+    out_slice.copy_from_slice(result.transpose().as_slice());
+}
+
+/// # Safety
+///
+/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
+#[no_mangle]
+pub unsafe extern "C" fn np_linalg_det(mat1: *mut InputMatrix, out: *mut InputMatrix) {
+    let mat1 = mat1.as_mut().unwrap();
+    let out = out.as_mut().unwrap();
+
+    if mat1.ndims != 2 {
+        let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
+        report_error("ValueError", "np_linalg_det", file!(), line!(), column!(), &err_msg);
+    }
+    let dim1 = (*mat1).get_dims();
+    let out_slice = unsafe { slice::from_raw_parts_mut(out.data, 1) };
+    let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
+
+    let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
+    if !matrix.is_square() {
+        let err_msg =
+            format!("last 2 dimensions of the array must be square: {0} != {1}", dim1[0], dim1[1]);
+        report_error("LinAlgError", "np_linalg_inv", file!(), line!(), column!(), &err_msg);
+    }
+    out_slice[0] = matrix.determinant();
+}
+
 /// # Safety
 ///
 /// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
@@ -313,7 +306,7 @@ pub unsafe extern "C" fn sp_linalg_lu(
     let out_u = out_u.as_mut().unwrap();
 
     if mat1.ndims != 2 {
-        let err_msg = format!("expected 2D Vector Input, but received {}-D input", mat1.ndims);
+        let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
         report_error("ValueError", "sp_linalg_lu", file!(), line!(), column!(), &err_msg);
     }
 
@@ -346,7 +339,7 @@ pub unsafe extern "C" fn sp_linalg_schur(
     let out_z = out_z.as_mut().unwrap();
 
     if mat1.ndims != 2 {
-        let err_msg = format!("expected 2D Vector Input, but received {}-D input", mat1.ndims);
+        let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
         report_error("ValueError", "sp_linalg_schur", file!(), line!(), column!(), &err_msg);
     }
 
@@ -386,7 +379,7 @@ pub unsafe extern "C" fn sp_linalg_hessenberg(
     let out_q = out_q.as_mut().unwrap();
 
     if mat1.ndims != 2 {
-        let err_msg = format!("expected 2D Vector Input, but received {}-D input", mat1.ndims);
+        let err_msg = format!("expected 2D Vector Input, but received {}D input", mat1.ndims);
         report_error("ValueError", "sp_linalg_hessenberg", file!(), line!(), column!(), &err_msg);
     }
 

nac3standalone/demo/run_demo.sh

@@ -42,14 +42,11 @@ done
 
 if [ -n "$debug" ] && [ -e ../../target/debug/nac3standalone ]; then
     nac3standalone=../../target/debug/nac3standalone
-    linalg=../../target/debug/deps/liblinalg-?*.a
 elif [ -e ../../target/release/nac3standalone ]; then
     nac3standalone=../../target/release/nac3standalone
-    linalg=../../target/release/deps/liblinalg-?*.a
 else
     # used by Nix builds
     nac3standalone=../../target/x86_64-unknown-linux-gnu/release/nac3standalone
-    linalg=../../target/x86_64-unknown-linux-gnu/release/deps/liblinalg-?*.a
 fi
 
 rm -f ./*.o ./*.bc demo
@@ -57,21 +54,16 @@ rm -f ./*.o ./*.bc demo
 if [ -z "$i386" ]; then
   $nac3standalone "${nac3args[@]}"
 
+  cd linalg && cargo build -q && cd ..
   clang -c -std=gnu11 -Wall -Wextra -O3 -o demo.o demo.c
-  clang -lm -Wl,--no-warn-search-mismatch -o demo module.o demo.o $linalg
+  clang -lm -Wl,--no-warn-search-mismatch -o demo module.o demo.o linalg/liblinalg/x86_64/liblinalg.a
 else
    # Enable SSE2 to avoid rounding errors with X87's 80-bit fp precision computations
 
    $nac3standalone --triple i386-pc-linux-gnu --target-features +sse2 "${nac3args[@]}"
    
-   # Compile linalg crate to provide functions compatible with i386 architecture
-   if [ ! -f ../../target/i686-unknown-linux-gnu/release/liblinalg.a ]; then
-     cd linalg && nix-shell -p rustup --command "RUSTFLAGS=\"-C target-cpu=i386 -C target-feature=+sse2\" cargo build -q --release --target=i686-unknown-linux-gnu" && cd ..
-   fi
-
-   linalg=../../target/i686-unknown-linux-gnu/release/liblinalg.a
    clang -m32 -c -std=gnu11 -Wall -Wextra -O3 -msse2 -o demo.o demo.c 
-   clang -m32 -lm -Wl,--no-warn-search-mismatch -o demo module.o demo.o $linalg
+   clang -m32 -lm -Wl,--no-warn-search-mismatch -o demo module.o demo.o linalg/liblinalg/i386/liblinalg.a
 fi
 
 if [ -z "$outfile" ]; then

nac3standalone/demo/src/ndarray.py

@@ -68,12 +68,6 @@ 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 r in range(len(n)):
-        for c in range(len(n[r])):
-            for z in range(len(n[r][c])):
-                output_float64(n[r][c][z])
-
 def consume_ndarray_1(n: ndarray[float, Literal[1]]):
     pass
 
@@ -1436,43 +1430,49 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
     output_ndarray_float_2(nextafter_x_ones)
 
 def test_ndarray_transpose():
-    # x: ndarray[float, 3] = np_array([[[1., 2.], [3., 4.], [5., 6.]]])
-    x: ndarray[float, 1] = np_array([1., 2., 3.])
+    x: ndarray[float, 2] = np_array([[1., 2., 3.], [4., 5., 6.]])
     y = np_transpose(x)
+    z = np_transpose(y)
 
-    output_ndarray_float_1(x)
-    output_ndarray_float_1(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: ndarray[float, 4] = np_reshape(w, (1, 2, 1, -1))
-    y: ndarray[float, 2] = np_reshape(x, [2, -1])
-    z: ndarray[float, 1] = np_reshape(w, 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():
-    x: ndarray[float, 1] = np_array([5.0, 1.0])
-    y: ndarray[float, 1] = np_array([5.0, 1.0])
-    z = np_dot(x, y)
+    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])
+    z1 = np_dot(x1, y1)
 
-    output_ndarray_float_1(x)
-    output_ndarray_float_1(y)
-    output_float64(z)
+    x2: ndarray[int32, 1] = np_array([5, 1, 4, 2])
+    y2: ndarray[int32, 1] = np_array([5, 1, 6, 6])
+    z2 = np_dot(x2, y2)
 
-def test_ndarray_linalg_matmul():
-    x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
-    y: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
-    z = np_linalg_matmul(x, y)
+    x3: ndarray[bool, 1] = np_array([True, True, True, True])
+    y3: ndarray[bool, 1] = np_array([True, True, True, True])
+    z3 = np_dot(x3, y3)
 
-    m = np_argmax(z)
+    z4 = np_dot(2, 3)
+    z5 = np_dot(2., 3.)
+    z6 = np_dot(True, False)
 
-    output_ndarray_float_2(x)
-    output_ndarray_float_2(y)
-    output_ndarray_float_2(z)
-    output_int64(m)
+    output_float64(z1)
+    output_int32(z2)
+    output_bool(z3)
+    output_int32(z4)
+    output_float64(z5)
+    output_bool(z6)
 
 def test_ndarray_cholesky():
     x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
@@ -1489,7 +1489,7 @@ def test_ndarray_qr():
 
     # QR Factorization is not unique and gives different results in numpy and nalgebra
     # Reverting the decomposition to compare the initial arrays
-    a = np_linalg_matmul(y, z)
+    a = y @ z
     output_ndarray_float_2(a)
 
 def test_ndarray_linalg_inv():
@@ -1506,6 +1506,20 @@ def test_ndarray_pinv():
     output_ndarray_float_2(x)
     output_ndarray_float_2(y)
 
+def test_ndarray_matrix_power():
+    x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
+    y = np_linalg_matrix_power(x, -9)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_det():
+    x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
+    y = np_linalg_det(x)
+
+    output_ndarray_float_2(x)
+    output_float64(y)
+
 def test_ndarray_schur():
     x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
     t, z = sp_linalg_schur(x)
@@ -1514,7 +1528,7 @@ def test_ndarray_schur():
 
     # Schur Factorization is not unique and gives different results in scipy and nalgebra
     # Reverting the decomposition to compare the initial arrays
-    a = np_linalg_matmul(np_linalg_matmul(z, t), np_linalg_inv(z))
+    a = (z @ t) @ np_linalg_inv(z)
     output_ndarray_float_2(a)
 
 def test_ndarray_hessenberg():
@@ -1525,7 +1539,7 @@ def test_ndarray_hessenberg():
 
     # Hessenberg Factorization is not unique and gives different results in scipy and nalgebra
     # Reverting the decomposition to compare the initial arrays
-    a = np_linalg_matmul(np_linalg_matmul(q, h), np_linalg_inv(q))
+    a = (q @ h) @ np_linalg_inv(q)
     output_ndarray_float_2(a)
 
 
@@ -1546,7 +1560,7 @@ def test_ndarray_svd():
 
     # SVD Factorization is not unique and gives different results in numpy and nalgebra
     # Reverting the decomposition to compare the initial arrays
-    a = np_linalg_matmul(x, z)
+    a = x @ z
     output_ndarray_float_2(a)
     output_ndarray_float_1(y)
 
@@ -1733,12 +1747,13 @@ def run() -> int32:
     test_ndarray_reshape()
     
     test_ndarray_dot()
-    test_ndarray_linalg_matmul()
     test_ndarray_cholesky()
     test_ndarray_qr()
     test_ndarray_svd()
     test_ndarray_linalg_inv()
     test_ndarray_pinv()
+    test_ndarray_matrix_power()
+    test_ndarray_det()
     test_ndarray_lu()
     test_ndarray_schur()
     test_ndarray_hessenberg()

pyo3_output/nac3artiq.so

@@ -1 +0,0 @@
-../target/debug/libnac3artiq.so