core: remove np_linalg_matmul

.gitignore

@@ -1,3 +1,4 @@
 __pycache__
 /target
+/nac3standalone/demo/linalg/target
 nix/windows/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};
@@ -1865,84 +1867,7 @@ fn build_output_struct<'ctx>(
     out_ptr
 }
 
-/// Invokes the `np_dot` using `nalgebra` crate
-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 {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
-        };
-
-        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` using `nalgebra` crate
-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 {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
-        };
-
-        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` using `nalgebra` crate
+/// Invokes the `np_linalg_cholesky` linalg function
 pub fn call_np_linalg_cholesky<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -1957,7 +1882,7 @@ pub fn call_np_linalg_cholesky<'ctx, G: CodeGenerator + ?Sized>(
         let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
         let BasicTypeEnum::FloatType(_) = n1_elem_ty else {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
+            unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
         let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
@@ -1984,7 +1909,7 @@ pub fn call_np_linalg_cholesky<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
-/// Invokes the `np_linalg_qr` using `nalgebra` crate
+/// Invokes the `np_linalg_qr` linalg function
 pub fn call_np_linalg_qr<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -2034,7 +1959,7 @@ pub fn call_np_linalg_qr<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
-/// Invokes the `np_linalg_svd` using `nalgebra` crate
+/// Invokes the `np_linalg_svd` linalg function
 pub fn call_np_linalg_svd<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -2049,7 +1974,7 @@ pub fn call_np_linalg_svd<'ctx, G: CodeGenerator + ?Sized>(
         let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
         let BasicTypeEnum::FloatType(_) = n1_elem_ty else {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
+            unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
         let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
@@ -2089,7 +2014,7 @@ pub fn call_np_linalg_svd<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
-/// Invokes the `np_linalg_inv` using `nalgebra` crate
+/// Invokes the `np_linalg_inv` linalg function
 pub fn call_np_linalg_inv<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -2104,7 +2029,7 @@ pub fn call_np_linalg_inv<'ctx, G: CodeGenerator + ?Sized>(
         let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
         let BasicTypeEnum::FloatType(_) = n1_elem_ty else {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
+            unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
         let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
@@ -2131,7 +2056,7 @@ pub fn call_np_linalg_inv<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
-/// Invokes the `np_linalg_pinv` using `nalgebra` crate
+/// Invokes the `np_linalg_pinv` linalg function
 pub fn call_np_linalg_pinv<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -2146,7 +2071,7 @@ pub fn call_np_linalg_pinv<'ctx, G: CodeGenerator + ?Sized>(
         let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
         let BasicTypeEnum::FloatType(_) = n1_elem_ty else {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
+            unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
         let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
@@ -2174,7 +2099,7 @@ pub fn call_np_linalg_pinv<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
-/// Invokes the `sp_linalg_lu` using `nalgebra` crate
+/// Invokes the `sp_linalg_lu` linalg function
 pub fn call_sp_linalg_lu<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -2189,7 +2114,7 @@ pub fn call_sp_linalg_lu<'ctx, G: CodeGenerator + ?Sized>(
         let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
         let BasicTypeEnum::FloatType(_) = n1_elem_ty else {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
+            unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
         let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
@@ -2224,7 +2149,105 @@ pub fn call_sp_linalg_lu<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
-/// Invokes the `sp_linalg_schur` using `nalgebra` crate
+/// 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,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -2239,7 +2262,7 @@ pub fn call_sp_linalg_schur<'ctx, G: CodeGenerator + ?Sized>(
         let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
         let BasicTypeEnum::FloatType(_) = n1_elem_ty else {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
+            unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
         let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
@@ -2267,7 +2290,7 @@ pub fn call_sp_linalg_schur<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
-/// Invokes the `sp_linalg_hessenberg` using `nalgebra` crate
+/// Invokes the `sp_linalg_hessenberg` linalg function
 pub fn call_sp_linalg_hessenberg<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
@@ -2282,7 +2305,7 @@ pub fn call_sp_linalg_hessenberg<'ctx, G: CodeGenerator + ?Sized>(
         let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
 
         let BasicTypeEnum::FloatType(_) = n1_elem_ty else {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
+            unsupported_type(ctx, FN_NAME, &[x1_ty]);
         };
 
         let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);

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.
@@ -2026,3 +2029,493 @@ 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, '_>,
+    x1: (Type, BasicValueEnum<'ctx>),
+) -> Result<BasicValueEnum<'ctx>, String> {
+    const FN_NAME: &str = "ndarray_transpose";
+    let (x1_ty, x1) = x1;
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    if let BasicValueEnum::PointerValue(n1) = x1 {
+        let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
+        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,
+            elem_ty,
+            &n1,
+            |_, ctx, n| Ok(n.load_ndims(ctx)),
+            |generator, ctx, n, idx| {
+                let new_idx = ctx.builder.build_int_sub(n.load_ndims(ctx), idx, "").unwrap();
+                let new_idx = ctx
+                    .builder
+                    .build_int_sub(new_idx, new_idx.get_type().const_int(1, false), "")
+                    .unwrap();
+                unsafe { Ok(n.dim_sizes().get_typed_unchecked(ctx, generator, &new_idx, None)) }
+            },
+        )
+        .unwrap();
+
+        gen_for_callback_incrementing(
+            generator,
+            ctx,
+            None,
+            llvm_usize.const_zero(),
+            (n_sz, false),
+            |generator, ctx, _, idx| {
+                let elem = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
+
+                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,
+                    None,
+                    llvm_usize.const_zero(),
+                    (n1.load_ndims(ctx), false),
+                    |generator, ctx, _, ndim| {
+                        let ndim_rev =
+                            ctx.builder.build_int_sub(n1.load_ndims(ctx), ndim, "").unwrap();
+                        let ndim_rev = ctx
+                            .builder
+                            .build_int_sub(ndim_rev, llvm_usize.const_int(1, false), "")
+                            .unwrap();
+                        let dim = unsafe {
+                            n1.dim_sizes().get_typed_unchecked(ctx, generator, &ndim_rev, None)
+                        };
+
+                        let rem_idx_val =
+                            ctx.builder.build_load(rem_idx, "").unwrap().into_int_value();
+                        let new_idx_val =
+                            ctx.builder.build_load(new_idx, "").unwrap().into_int_value();
+
+                        let add_component =
+                            ctx.builder.build_int_unsigned_rem(rem_idx_val, dim, "").unwrap();
+                        let rem_idx_val =
+                            ctx.builder.build_int_unsigned_div(rem_idx_val, dim, "").unwrap();
+
+                        let new_idx_val = ctx.builder.build_int_mul(new_idx_val, dim, "").unwrap();
+                        let new_idx_val =
+                            ctx.builder.build_int_add(new_idx_val, add_component, "").unwrap();
+
+                        ctx.builder.build_store(rem_idx, rem_idx_val).unwrap();
+                        ctx.builder.build_store(new_idx, new_idx_val).unwrap();
+
+                        Ok(())
+                    },
+                    llvm_usize.const_int(1, false),
+                )?;
+
+                let new_idx_val = ctx.builder.build_load(new_idx, "").unwrap().into_int_value();
+                unsafe { out.data().set_unchecked(ctx, generator, &new_idx_val, elem) };
+                Ok(())
+            },
+            llvm_usize.const_int(1, false),
+        )?;
+
+        Ok(out.as_base_value().into())
+    } else {
+        unreachable!(
+            "{FN_NAME}() not supported for '{}'",
+            format!("'{}'", ctx.unifier.stringify(x1_ty))
+        )
+    }
+}
+
+/// 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, '_>,
+    x1: (Type, BasicValueEnum<'ctx>),
+    shape: (Type, BasicValueEnum<'ctx>),
+) -> Result<BasicValueEnum<'ctx>, String> {
+    const FN_NAME: &str = "ndarray_reshape";
+    let (x1_ty, x1) = x1;
+    let (_, shape) = shape;
+
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    if let BasicValueEnum::PointerValue(n1) = x1 {
+        let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
+        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
+        let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, 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, 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,
+                    None,
+                    llvm_usize.const_zero(),
+                    (shape_list.load_size(ctx, None), false),
+                    |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,
+                            ctx,
+                            |_, ctx| {
+                                Ok(ctx
+                                    .builder
+                                    .build_int_compare(
+                                        IntPredicate::SLT,
+                                        ele,
+                                        llvm_usize.const_zero(),
+                                        "",
+                                    )
+                                    .unwrap())
+                            },
+                            |_, ctx| -> Result<Option<IntValue>, String> {
+                                let num_neg_value =
+                                    ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
+                                let num_neg_value = ctx
+                                    .builder
+                                    .build_int_add(
+                                        num_neg_value,
+                                        llvm_usize.const_int(1, false),
+                                        "",
+                                    )
+                                    .unwrap();
+                                ctx.builder.build_store(num_neg, num_neg_value).unwrap();
+                                Ok(None)
+                            },
+                            |_, ctx| {
+                                let acc_value =
+                                    ctx.builder.build_load(acc, "").unwrap().into_int_value();
+                                let acc_value =
+                                    ctx.builder.build_int_mul(acc_value, ele, "").unwrap();
+                                ctx.builder.build_store(acc, acc_value).unwrap();
+                                Ok(None)
+                            },
+                        )?;
+                        Ok(())
+                    },
+                    llvm_usize.const_int(1, false),
+                )?;
+                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,
+                    elem_ty,
+                    &shape_list,
+                    |_, ctx, _| Ok(shape_list.load_size(ctx, None)),
+                    |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,
+                            |_, ctx| {
+                                Ok(ctx
+                                    .builder
+                                    .build_int_compare(
+                                        IntPredicate::SLT,
+                                        dim,
+                                        llvm_usize.const_zero(),
+                                        "",
+                                    )
+                                    .unwrap())
+                            },
+                            |_, _| Ok(Some(rem)),
+                            |_, _| Ok(Some(dim)),
+                        )?
+                        .unwrap()
+                        .into_int_value())
+                    },
+                )
+            }
+            BasicValueEnum::StructValue(shape_tuple) => {
+                // 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,
+                        ctx,
+                        |_, ctx| {
+                            Ok(ctx
+                                .builder
+                                .build_int_compare(
+                                    IntPredicate::SLT,
+                                    dim,
+                                    llvm_usize.const_zero(),
+                                    "",
+                                )
+                                .unwrap())
+                        },
+                        |_, ctx| -> Result<Option<IntValue>, String> {
+                            let num_negs =
+                                ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
+                            let num_negs = ctx
+                                .builder
+                                .build_int_add(num_negs, llvm_usize.const_int(1, false), "")
+                                .unwrap();
+                            ctx.builder.build_store(num_neg, num_negs).unwrap();
+                            Ok(None)
+                        },
+                        |_, ctx| {
+                            let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
+                            let acc_val = ctx.builder.build_int_mul(acc_val, dim, "").unwrap();
+                            ctx.builder.build_store(acc, acc_val).unwrap();
+                            Ok(None)
+                        },
+                    )?;
+                }
+
+                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();
+                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,
+                        ctx,
+                        |_, ctx| {
+                            Ok(ctx
+                                .builder
+                                .build_int_compare(
+                                    IntPredicate::SLT,
+                                    dim,
+                                    llvm_usize.const_zero(),
+                                    "",
+                                )
+                                .unwrap())
+                        },
+                        |_, _| Ok(Some(rem)),
+                        |_, _| Ok(Some(dim)),
+                    )?
+                    .unwrap()
+                    .into_int_value();
+                    shape.push(dim);
+                }
+                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,
+                    |_, ctx| {
+                        Ok(ctx
+                            .builder
+                            .build_int_compare(
+                                IntPredicate::SLT,
+                                shape_int,
+                                llvm_usize.const_zero(),
+                                "",
+                            )
+                            .unwrap())
+                    },
+                    |_, _| Ok(Some(n_sz)),
+                    |_, ctx| {
+                        Ok(Some(ctx.builder.build_int_s_extend(shape_int, llvm_usize, "").unwrap()))
+                    },
+                )?
+                .unwrap()
+                .into_int_value();
+                create_ndarray_const_shape(generator, ctx, elem_ty, &[shape_int])
+            }
+            _ => unreachable!(),
+        }
+        .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,
+            ctx.builder
+                .build_int_compare(IntPredicate::ULT, num_negs, llvm_usize.const_int(2, false), "")
+                .unwrap(),
+            "0:ValueError",
+            "can only specify one unknown dimension",
+            [None, None, None],
+            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));
+        ctx.make_assert(
+            generator,
+            ctx.builder.build_int_compare(IntPredicate::EQ, out_sz, n_sz, "").unwrap(),
+            "0:ValueError",
+            "cannot reshape array of size {0} into provided shape of size {1}",
+            [Some(n_sz), Some(out_sz), None],
+            ctx.current_loc,
+        );
+
+        gen_for_callback_incrementing(
+            generator,
+            ctx,
+            None,
+            llvm_usize.const_zero(),
+            (n_sz, false),
+            |generator, ctx, _, idx| {
+                let elem = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
+                unsafe { out.data().set_unchecked(ctx, generator, &idx, elem) };
+                Ok(())
+            },
+            llvm_usize.const_int(1, false),
+        )?;
+
+        Ok(out.as_base_value().into())
+    } else {
+        unreachable!(
+            "{FN_NAME}() not supported for '{}'",
+            format!("'{}'", ctx.unifier.stringify(x1_ty))
+        )
+    }
+}
+
+/// 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

@@ -557,13 +557,18 @@ 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::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),
@@ -1885,6 +1890,57 @@ 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`
@@ -1893,12 +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,
@@ -1910,7 +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;
@@ -1918,33 +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))?))
                 }),
             ),
 
@@ -2022,10 +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

@@ -99,14 +99,18 @@ pub enum PrimDef {
     FunNpLdExp,
     FunNpHypot,
     FunNpNextAfter,
+    FunNpTranspose,
+    FunNpReshape,
 
+    // Linalg functions
     FunNpDot,
-    FunNpLinalgMatmul,
     FunNpLinalgCholesky,
     FunNpLinalgQr,
     FunNpLinalgSvd,
     FunNpLinalgInv,
     FunNpLinalgPinv,
+    FunNpLinalgMatrixPower,
+    FunNpLinalgDet,
     FunSpLinalgLu,
     FunSpLinalgSchur,
     FunSpLinalgHessenberg,
@@ -281,13 +285,18 @@ 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),
-            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/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
@@ -218,6 +218,8 @@ 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
@@ -229,12 +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

@@ -0,0 +1,13 @@
+[package]
+name = "linalg"
+version = "0.1.0"
+edition = "2021"
+
+[lib]
+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

@@ -0,0 +1,406 @@
+// Uses `nalgebra` crate to invoke `np_linalg` and `sp_linalg` functions
+// When converting between `nalgebra::Matrix` and `NDArray` following considerations are necessary
+//
+// * Both `nalgebra::Matrix` and `NDArray` require their content to be stored in row-major order
+// * `NDArray` data pointer can be directly read and converted to `nalgebra::Matrix` (row and column number must be known)
+// * `nalgebra::Matrix::as_slice` returns the content of matrix in column-major order and initial data needs to be transposed before storing it in `NDArray` data pointer
+
+use core::slice;
+use nalgebra::DMatrix;
+
+fn report_error(
+    error_name: &str,
+    fn_name: &str,
+    file_name: &str,
+    line_num: u32,
+    col_num: u32,
+    err_msg: &str,
+) -> ! {
+    panic!(
+        "Exception {} from {} in {}:{}:{}, message: {}",
+        error_name, fn_name, file_name, line_num, col_num, err_msg
+    );
+}
+
+pub struct InputMatrix {
+    pub ndims: usize,
+    pub dims: *const usize,
+    pub data: *mut f64,
+}
+impl InputMatrix {
+    fn get_dims(&mut self) -> Vec<usize> {
+        let dims = unsafe { slice::from_raw_parts(self.dims, self.ndims) };
+        dims.to_vec()
+    }
+}
+
+/// # Safety
+///
+/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
+#[no_mangle]
+pub unsafe extern "C" fn np_linalg_cholesky(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_cholesky", file!(), line!(), column!(), &err_msg);
+    }
+
+    let dim1 = (*mat1).get_dims();
+    if dim1[0] != dim1[1] {
+        let err_msg =
+            format!("last 2 dimensions of the array must be square: {0} != {1}", dim1[0], dim1[1]);
+        report_error("LinAlgError", "np_linalg_cholesky", 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 matrix1 = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
+    let result = matrix1.cholesky();
+    match result {
+        Some(res) => {
+            out_slice.copy_from_slice(res.unpack().transpose().as_slice());
+        }
+        None => {
+            report_error(
+                "LinAlgError",
+                "np_linalg_cholesky",
+                file!(),
+                line!(),
+                column!(),
+                "Matrix is not positive definite",
+            );
+        }
+    };
+}
+
+/// # Safety
+///
+/// `mat1` should point to a valid 2DArray of `f64` floats in row-major order
+#[no_mangle]
+pub unsafe extern "C" fn np_linalg_qr(
+    mat1: *mut InputMatrix,
+    out_q: *mut InputMatrix,
+    out_r: *mut InputMatrix,
+) {
+    let mat1 = mat1.as_mut().unwrap();
+    let out_q = out_q.as_mut().unwrap();
+    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);
+        report_error("ValueError", "np_linalg_cholesky", file!(), line!(), column!(), &err_msg);
+    }
+
+    let dim1 = (*mat1).get_dims();
+    let outq_dim = (*out_q).get_dims();
+    let outr_dim = (*out_r).get_dims();
+
+    let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
+    let out_q_slice = unsafe { slice::from_raw_parts_mut(out_q.data, outq_dim[0] * outq_dim[1]) };
+    let out_r_slice = unsafe { slice::from_raw_parts_mut(out_r.data, outr_dim[0] * outr_dim[1]) };
+
+    // Refer to https://github.com/dimforge/nalgebra/issues/735
+    let matrix1 = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
+
+    let res = matrix1.qr();
+    let (q, r) = res.unpack();
+
+    // Uses different algo need to match numpy
+    out_q_slice.copy_from_slice(q.transpose().as_slice());
+    out_r_slice.copy_from_slice(r.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_svd(
+    mat1: *mut InputMatrix,
+    outu: *mut InputMatrix,
+    outs: *mut InputMatrix,
+    outvh: *mut InputMatrix,
+) {
+    let mat1 = mat1.as_mut().unwrap();
+    let outu = outu.as_mut().unwrap();
+    let outs = outs.as_mut().unwrap();
+    let outvh = outvh.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_svd", file!(), line!(), column!(), &err_msg);
+    }
+
+    let dim1 = (*mat1).get_dims();
+    let outu_dim = (*outu).get_dims();
+    let outs_dim = (*outs).get_dims();
+    let outvh_dim = (*outvh).get_dims();
+
+    let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
+    let out_u_slice = unsafe { slice::from_raw_parts_mut(outu.data, outu_dim[0] * outu_dim[1]) };
+    let out_s_slice = unsafe { slice::from_raw_parts_mut(outs.data, outs_dim[0]) };
+    let out_vh_slice =
+        unsafe { slice::from_raw_parts_mut(outvh.data, outvh_dim[0] * outvh_dim[1]) };
+
+    let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
+    let result = matrix.svd(true, true);
+    out_u_slice.copy_from_slice(result.u.unwrap().transpose().as_slice());
+    out_s_slice.copy_from_slice(result.singular_values.as_slice());
+    out_vh_slice.copy_from_slice(result.v_t.unwrap().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_inv(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_inv", file!(), line!(), column!(), &err_msg);
+    }
+    let dim1 = (*mat1).get_dims();
+
+    if dim1[0] != dim1[1] {
+        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);
+    }
+
+    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 matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
+    if !matrix.is_invertible() {
+        report_error(
+            "LinAlgError",
+            "np_linalg_inv",
+            file!(),
+            line!(),
+            column!(),
+            "no inverse for Singular Matrix",
+        );
+    }
+    let inv = matrix.try_inverse().unwrap();
+    out_slice.copy_from_slice(inv.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_pinv(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_pinv", file!(), line!(), column!(), &err_msg);
+    }
+    let dim1 = (*mat1).get_dims();
+    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 matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
+    let svd = matrix.svd(true, true);
+    let inv = svd.pseudo_inverse(1e-15);
+
+    match inv {
+        Ok(m) => {
+            out_slice.copy_from_slice(m.transpose().as_slice());
+        }
+        Err(err_msg) => {
+            report_error("LinAlgError", "np_linalg_pinv", file!(), line!(), column!(), err_msg);
+        }
+    }
+}
+
+/// # 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
+#[no_mangle]
+pub unsafe extern "C" fn sp_linalg_lu(
+    mat1: *mut InputMatrix,
+    out_l: *mut InputMatrix,
+    out_u: *mut InputMatrix,
+) {
+    let mat1 = mat1.as_mut().unwrap();
+    let out_l = out_l.as_mut().unwrap();
+    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);
+        report_error("ValueError", "sp_linalg_lu", file!(), line!(), column!(), &err_msg);
+    }
+
+    let dim1 = (*mat1).get_dims();
+    let outl_dim = (*out_l).get_dims();
+    let outu_dim = (*out_u).get_dims();
+
+    let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
+    let out_l_slice = unsafe { slice::from_raw_parts_mut(out_l.data, outl_dim[0] * outl_dim[1]) };
+    let out_u_slice = unsafe { slice::from_raw_parts_mut(out_u.data, outu_dim[0] * outu_dim[1]) };
+
+    let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
+    let (_, l, u) = matrix.lu().unpack();
+
+    out_l_slice.copy_from_slice(l.transpose().as_slice());
+    out_u_slice.copy_from_slice(u.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 sp_linalg_schur(
+    mat1: *mut InputMatrix,
+    out_t: *mut InputMatrix,
+    out_z: *mut InputMatrix,
+) {
+    let mat1 = mat1.as_mut().unwrap();
+    let out_t = out_t.as_mut().unwrap();
+    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);
+        report_error("ValueError", "sp_linalg_schur", file!(), line!(), column!(), &err_msg);
+    }
+
+    let dim1 = (*mat1).get_dims();
+
+    if dim1[0] != dim1[1] {
+        let err_msg =
+            format!("last 2 dimensions of the array must be square: {0} != {1}", dim1[0], dim1[1]);
+        report_error("LinAlgError", "np_linalg_schur", file!(), line!(), column!(), &err_msg);
+    }
+
+    let out_t_dim = (*out_t).get_dims();
+    let out_z_dim = (*out_z).get_dims();
+
+    let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
+    let out_t_slice = unsafe { slice::from_raw_parts_mut(out_t.data, out_t_dim[0] * out_t_dim[1]) };
+    let out_z_slice = unsafe { slice::from_raw_parts_mut(out_z.data, out_z_dim[0] * out_z_dim[1]) };
+
+    let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
+    let (z, t) = matrix.schur().unpack();
+
+    out_t_slice.copy_from_slice(t.transpose().as_slice());
+    out_z_slice.copy_from_slice(z.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 sp_linalg_hessenberg(
+    mat1: *mut InputMatrix,
+    out_h: *mut InputMatrix,
+    out_q: *mut InputMatrix,
+) {
+    let mat1 = mat1.as_mut().unwrap();
+    let out_h = out_h.as_mut().unwrap();
+    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);
+        report_error("ValueError", "sp_linalg_hessenberg", file!(), line!(), column!(), &err_msg);
+    }
+
+    let dim1 = (*mat1).get_dims();
+
+    if dim1[0] != dim1[1] {
+        let err_msg =
+            format!("last 2 dimensions of the array must be square: {} != {}", dim1[0], dim1[1]);
+        report_error("LinAlgError", "sp_linalg_hessenberg", file!(), line!(), column!(), &err_msg);
+    }
+
+    let out_h_dim = (*out_h).get_dims();
+    let out_q_dim = (*out_q).get_dims();
+
+    let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
+    let out_h_slice = unsafe { slice::from_raw_parts_mut(out_h.data, out_h_dim[0] * out_h_dim[1]) };
+    let out_q_slice = unsafe { slice::from_raw_parts_mut(out_q.data, out_q_dim[0] * out_q_dim[1]) };
+
+    let matrix = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
+    let (q, h) = matrix.hessenberg().unpack();
+
+    out_h_slice.copy_from_slice(h.transpose().as_slice());
+    out_q_slice.copy_from_slice(q.transpose().as_slice());
+}

nac3standalone/demo/run_demo.sh

@@ -54,15 +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
+  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[@]}"
    
    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
+   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

@@ -1429,26 +1429,50 @@ def test_ndarray_nextafter_broadcast_rhs_scalar():
     output_ndarray_float_2(nextafter_x_zeros)
     output_ndarray_float_2(nextafter_x_ones)
 
-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)
-
-    output_ndarray_float_1(x)
-    output_ndarray_float_1(y)
-    output_float64(z)
-
-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)
-
-    m = np_argmax(z)
+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)
-    output_ndarray_float_2(z)
-    output_int64(m)
+
+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])
+    z1 = np_dot(x1, y1)
+
+    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)
+
+    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)
+
+    z4 = np_dot(2, 3)
+    z5 = np_dot(2., 3.)
+    z6 = np_dot(True, False)
+
+    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]])
@@ -1465,7 +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():
@@ -1482,6 +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)
@@ -1490,7 +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():
@@ -1501,7 +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)
 
 
@@ -1522,7 +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)
 
@@ -1705,14 +1743,17 @@ 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_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()