WIP

.gitignore

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

Cargo.lock

@@ -126,9 +126,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b"
 
 [[package]]
 name = "cc"
-version = "1.1.6"
+version = "1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2aba8f4e9906c7ce3c73463f62a7f0c65183ada1a2d47e397cc8810827f9694f"
+checksum = "eaff6f8ce506b9773fa786672d63fc7a191ffea1be33f72bbd4aeacefca9ffc8"
 
 [[package]]
 name = "cfg-if"
@@ -167,7 +167,7 @@ dependencies = [
  "heck 0.5.0",
  "proc-macro2",
  "quote",
- "syn 2.0.71",
+ "syn 2.0.70",
 ]
 
 [[package]]
@@ -256,12 +256,6 @@ 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"
@@ -320,6 +314,13 @@ dependencies = [
  "windows-sys",
 ]
 
+[[package]]
+name = "externfns"
+version = "0.1.0"
+dependencies = [
+ "nalgebra",
+]
+
 [[package]]
 name = "fastrand"
 version = "2.1.0"
@@ -436,7 +437,7 @@ checksum = "4fa4d8d74483041a882adaa9a29f633253a66dde85055f0495c121620ac484b2"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.71",
+ "syn 2.0.70",
 ]
 
 [[package]]
@@ -528,9 +529,9 @@ checksum = "97b3888a4aecf77e811145cadf6eef5901f4782c53886191b2f693f24761847c"
 
 [[package]]
 name = "libloading"
-version = "0.8.5"
+version = "0.8.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "4979f22fdb869068da03c9f7528f8297c6fd2606bc3a4affe42e6a823fdb8da4"
+checksum = "e310b3a6b5907f99202fcdb4960ff45b93735d7c7d96b760fcff8db2dc0e103d"
 dependencies = [
  "cfg-if",
  "windows-targets",
@@ -552,14 +553,6 @@ dependencies = [
  "libc",
 ]
 
-[[package]]
-name = "linalg_externfns"
-version = "0.1.0"
-dependencies = [
- "cslice",
- "nalgebra",
-]
-
 [[package]]
 name = "linked-hash-map"
 version = "0.5.6"
@@ -687,8 +680,8 @@ name = "nac3standalone"
 version = "0.1.0"
 dependencies = [
  "clap",
+ "externfns",
  "inkwell",
- "linalg_externfns",
  "nac3core",
  "nac3parser",
  "parking_lot",
@@ -837,7 +830,7 @@ dependencies = [
  "phf_shared 0.11.2",
  "proc-macro2",
  "quote",
- "syn 2.0.71",
+ "syn 2.0.70",
 ]
 
 [[package]]
@@ -866,9 +859,9 @@ checksum = "5be167a7af36ee22fe3115051bc51f6e6c7054c9348e28deb4f49bd6f705a315"
 
 [[package]]
 name = "portable-atomic"
-version = "1.7.0"
+version = "1.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "da544ee218f0d287a911e9c99a39a8c9bc8fcad3cb8db5959940044ecfc67265"
+checksum = "7170ef9988bc169ba16dd36a7fa041e5c4cbeb6a35b76d4c03daded371eae7c0"
 
 [[package]]
 name = "ppv-lite86"
@@ -938,7 +931,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-macros-backend",
  "quote",
- "syn 2.0.71",
+ "syn 2.0.70",
 ]
 
 [[package]]
@@ -951,7 +944,7 @@ dependencies = [
  "proc-macro2",
  "pyo3-build-config",
  "quote",
- "syn 2.0.71",
+ "syn 2.0.70",
 ]
 
 [[package]]
@@ -1015,9 +1008,9 @@ dependencies = [
 
 [[package]]
 name = "redox_syscall"
-version = "0.5.3"
+version = "0.5.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2a908a6e00f1fdd0dfd9c0eb08ce85126f6d8bbda50017e74bc4a4b7d4a926a4"
+checksum = "c82cf8cff14456045f55ec4241383baeff27af886adb72ffb2162f99911de0fd"
 dependencies = [
  "bitflags",
 ]
@@ -1132,7 +1125,7 @@ checksum = "e0cd7e117be63d3c3678776753929474f3b04a43a080c744d6b0ae2a8c28e222"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.71",
+ "syn 2.0.70",
 ]
 
 [[package]]
@@ -1234,7 +1227,7 @@ dependencies = [
  "proc-macro2",
  "quote",
  "rustversion",
- "syn 2.0.71",
+ "syn 2.0.70",
 ]
 
 [[package]]
@@ -1250,9 +1243,9 @@ dependencies = [
 
 [[package]]
 name = "syn"
-version = "2.0.71"
+version = "2.0.70"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b146dcf730474b4bcd16c311627b31ede9ab149045db4d6088b3becaea046462"
+checksum = "2f0209b68b3613b093e0ec905354eccaedcfe83b8cb37cbdeae64026c3064c16"
 dependencies = [
  "proc-macro2",
  "quote",
@@ -1303,22 +1296,22 @@ dependencies = [
 
 [[package]]
 name = "thiserror"
-version = "1.0.63"
+version = "1.0.61"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c0342370b38b6a11b6cc11d6a805569958d54cfa061a29969c3b5ce2ea405724"
+checksum = "c546c80d6be4bc6a00c0f01730c08df82eaa7a7a61f11d656526506112cc1709"
 dependencies = [
  "thiserror-impl",
 ]
 
 [[package]]
 name = "thiserror-impl"
-version = "1.0.63"
+version = "1.0.61"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a4558b58466b9ad7ca0f102865eccc95938dca1a74a856f2b57b6629050da261"
+checksum = "46c3384250002a6d5af4d114f2845d37b57521033f30d5c3f46c4d70e1197533"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.71",
+ "syn 2.0.70",
 ]
 
 [[package]]
@@ -1592,5 +1585,5 @@ checksum = "fa4f8080344d4671fb4e831a13ad1e68092748387dfc4f55e356242fae12ce3e"
 dependencies = [
  "proc-macro2",
  "quote",
- "syn 2.0.71",
+ "syn 2.0.70",
 ]

Cargo.toml

@@ -4,8 +4,8 @@ members = [
   "nac3ast",
   "nac3parser",
   "nac3core",
-  "nac3standalone/linalg_externfns",
   "nac3standalone",
+  "nac3standalone/demo/externfns",
   "nac3artiq",
   "runkernel",
 ]

nac3artiq/demo/demo.py

@@ -14,12 +14,23 @@ class Demo:
 
     @kernel
     def run(self):
-        self.core.reset()
+        a = np_array([[1., 2.], [3., 4.]])
-        while True:
+        b = try_invert_to(a)
-            with parallel:
+        if b:
-                self.led0.pulse(100.*ms)
+            # self.core.reset()
-                self.led1.pulse(100.*ms)
+            # while True:
-            self.core.delay(100.*ms)
+            #     with parallel:
+            #         self.led0.pulse(100.*ms)
+            #         self.led1.pulse(100.*ms)
+            #     self.core.delay(100.*ms)
+            v = try_invert_to(np_identity(2))
+            if v:
+                while True:
+                    with parallel:
+                        self.led0.pulse(100.*ms)
+                        self.led1.pulse(100.*ms)
+                    self.core.delay(100.*ms)
+            
 
 
 if __name__ == "__main__":

nac3artiq/demo/list_cmp.py

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

nac3artiq/src/symbol_resolver.rs

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

nac3core/src/codegen/builtin_fns.rs

@@ -1,9 +1,9 @@
 use inkwell::types::BasicTypeEnum;
-use inkwell::values::{BasicValue, BasicValueEnum};
+use inkwell::values::BasicValueEnum;
 use inkwell::{FloatPredicate, IntPredicate, OptimizationLevel};
 use itertools::Itertools;
 
-use crate::codegen::classes::{ArrayLikeValue, NDArrayValue, ProxyValue, UntypedArrayLikeAccessor};
+use crate::codegen::classes::{ArrayLikeValue, NDArrayValue, ProxyValue, TypedArrayLikeAccessor, UntypedArrayLikeAccessor};
 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};
@@ -31,6 +31,7 @@ pub fn call_int32<'ctx, G: CodeGenerator + ?Sized>(
     let llvm_usize = generator.get_size_type(ctx.ctx);
 
     let (n_ty, n) = n;
+
     Ok(match n {
         BasicValueEnum::IntValue(n) if matches!(n.get_type().get_bit_width(), 1 | 8) => {
             debug_assert!(ctx.unifier.unioned(n_ty, ctx.primitives.bool));
@@ -921,6 +922,122 @@ pub fn call_numpy_max_min<'ctx, G: CodeGenerator + ?Sized>(
     })
 }
 
+pub fn call_try_invert_to<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    a: (Type, BasicValueEnum<'ctx>),
+) -> Result<BasicValueEnum<'ctx>, String> {
+    const FN_NAME: &str = "linalg_try_invert_to";
+    let (a_ty, a) = a;
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    
+    match a {
+        BasicValueEnum::PointerValue(n) if a_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
+        {
+            let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, a_ty);
+            let llvm_ndarray_ty = ctx.get_llvm_type(generator, elem_ty);
+            match llvm_ndarray_ty {
+                BasicTypeEnum::FloatType(_) => {},
+                _ => unreachable!("Inverse Operation supported on float type NDArray Values only")
+            };
+
+            let n = NDArrayValue::from_ptr_val(n, llvm_usize, None);
+            let n_sz = irrt::call_ndarray_calc_size(generator, ctx, &n.dim_sizes(), (None, None));
+
+            // Add asserts for dims
+            if cfg!(debug_assertions) {
+                let n_dims = n.load_ndims(ctx);
+
+                // num_dim == 2
+                ctx.make_assert(generator,ctx.builder.build_int_compare(IntPredicate::EQ, n_dims, n_dims.get_type().const_int(2, false), "").unwrap(), 
+                "0:ValueError", format!("Inverse only supported on 2D lists").as_str(), [None, None, None], ctx.current_loc);
+
+                let dim0 = unsafe {n.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)};
+                let dim1 = unsafe {n.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)};
+            
+                // dim0 == dim1
+                ctx.make_assert(generator,ctx.builder.build_int_compare(IntPredicate::EQ, dim0, dim1, "").unwrap(), 
+                "0:ValueError", format!("Dimensions do not match {dim0} is not same as {dim1}").as_str(), [None, None, None], ctx.current_loc);
+
+            }
+
+            if ctx.registry.llvm_options.opt_level == OptimizationLevel::None {
+                let n_sz_eqz = ctx
+                    .builder
+                    .build_int_compare(IntPredicate::NE, n_sz, n_sz.get_type().const_zero(), "")
+                    .unwrap();
+
+                ctx.make_assert(
+                    generator,
+                    n_sz_eqz,
+                    "0:ValueError",
+                    format!("zero-size array to reduction operation {FN_NAME}").as_str(),
+                    [None, None, None],
+                    ctx.current_loc,
+                );
+            }
+
+            // Create a call to linalg_try_invert_to
+            let dim0 = unsafe {n.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)};
+            let dim1 = unsafe {n.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)};
+            
+            Ok(extern_fns::call_linalg_try_invert_to(ctx, dim0, dim1, n.data().base_ptr(ctx, generator), None).into())
+        }
+        _ => unsupported_type(ctx, FN_NAME, &[a_ty]),
+    }
+}
+
+pub fn call_wilkinson_shift<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    a: (Type, BasicValueEnum<'ctx>),
+) -> Result<BasicValueEnum<'ctx>, String> {
+    const FN_NAME: &str = "linalg_wilkinson_shift";
+    let (a_ty, a) = a;
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    
+    match a {
+        BasicValueEnum::PointerValue(n) if a_ty.obj_id(&ctx.unifier).is_some_and(|id| id == PrimDef::NDArray.id()) =>
+        {
+            let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, a_ty);
+            let llvm_ndarray_ty = ctx.get_llvm_type(generator, elem_ty);
+            match llvm_ndarray_ty {
+                BasicTypeEnum::FloatType(_) | BasicTypeEnum::IntType(_) => {},
+                _ => unreachable!("Wilkinson Shift Operation supported on float/integer type NDArray Values only")
+            };
+
+            let n = NDArrayValue::from_ptr_val(n, llvm_usize, None);
+
+            // Add asserts for dims
+            if cfg!(debug_assertions) {
+                let n_dims = n.load_ndims(ctx);
+
+                // num_dim == 2
+                ctx.make_assert(generator,ctx.builder.build_int_compare(IntPredicate::EQ, n_dims, n_dims.get_type().const_int(2, false), "").unwrap(), 
+                "0:ValueError", format!("Wilkinson Shift supported only on 2D lists").as_str(), [None, None, None], ctx.current_loc);
+
+                let dim0 = unsafe {n.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)};
+                let dim1 = unsafe {n.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)};
+            
+                // dim0 == dim1
+                ctx.make_assert(generator,ctx.builder.build_int_compare(IntPredicate::EQ, dim0, dim1, "").unwrap(), 
+                "0:ValueError", format!("Dimensions do not match {dim0} is not same as {dim1}").as_str(), [None, None, None], ctx.current_loc);
+
+                // dimesions should be 2x2
+                ctx.make_assert(generator,ctx.builder.build_int_compare(IntPredicate::EQ, dim0, dim0.get_type().const_int(2, false), "").unwrap(), 
+                "0:ValueError", format!("Wilkinson Shift supported only on 2x2 matrices").as_str(), [None, None, None], ctx.current_loc);
+            }
+
+            // Create a call to linalg_try_invert_to
+            let dim0 = unsafe {n.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_zero(), None)};
+            let dim1 = unsafe {n.dim_sizes().get_typed_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)};
+            
+            Ok(extern_fns::call_linalg_wilkinson_shift(ctx, dim0, dim1, n.data().base_ptr(ctx, generator), None).into())
+        }
+        _ => unsupported_type(ctx, FN_NAME, &[a_ty]),
+    }
+}
+
 /// Invokes the `np_maximum` builtin function.
 pub fn call_numpy_maximum<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
@@ -1834,763 +1951,3 @@ pub fn call_numpy_nextafter<'ctx, G: CodeGenerator + ?Sized>(
         _ => unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty]),
     })
 }
-
-// Linalg Methods
-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;
-
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let one = llvm_usize.const_int(1, false);
-
-    if let (BasicValueEnum::PointerValue(n1), BasicValueEnum::PointerValue(n2)) = (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");
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-        let n2 = NDArrayValue::from_ptr_val(n2, llvm_usize, None);
-
-        // The following constraints must be satisfied:
-        // * Input must be 1D
-        // * Number of elements in two matrices must equal
-        if cfg!(debug_assertions) {
-            let n1_dims = n1.load_ndims(ctx);
-            let n2_dims = n2.load_ndims(ctx);
-
-            let n1_dims_eq1 =
-                ctx.builder.build_int_compare(IntPredicate::EQ, n1_dims, one, "").unwrap();
-            let n2_dims_eq1 =
-                ctx.builder.build_int_compare(IntPredicate::EQ, n2_dims, one, "").unwrap();
-
-            // num_dim = 1
-            ctx.make_assert(
-                generator,
-                n1_dims_eq1,
-                "0:ValueError",
-                format!("{FN_NAME} operates on 1D matrices").as_str(),
-                [None, None, None],
-                ctx.current_loc,
-            );
-
-            ctx.make_assert(
-                generator,
-                n2_dims_eq1,
-                "0:ValueError",
-                format!("{FN_NAME} operates on 1D matrices").as_str(),
-                [None, None, None],
-                ctx.current_loc,
-            );
-
-            // equal number of elements
-            let n1_sz = irrt::call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
-            let n2_sz = irrt::call_ndarray_calc_size(generator, ctx, &n2.dim_sizes(), (None, None));
-
-            let size_eq =
-                ctx.builder.build_int_compare(IntPredicate::EQ, n1_sz, n2_sz, "").unwrap();
-
-            ctx.make_assert(
-                generator,
-                size_eq,
-                "0:ValueError",
-                format!("The operands of {FN_NAME} must have equal length").as_str(),
-                [None, None, None],
-                ctx.current_loc,
-            );
-        }
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-
-        Ok(extern_fns::call_np_dot(
-            ctx,
-            (dim0, one, n1.data().base_ptr(ctx, generator)),
-            (dim0, one, n2.data().base_ptr(ctx, generator)),
-            None,
-        )
-        .into())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty])
-    }
-}
-
-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);
-
-    let one = llvm_usize.const_int(1, false);
-    let two = llvm_usize.const_int(2, false);
-
-    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);
-
-        // The following constraints must be satisfied:
-        // * Input must be 2D
-        // * Number of columns of first matrix should equal number of rows of second
-        if true {
-            let n1_dims = n1.load_ndims(ctx);
-            let n2_dims = n2.load_ndims(ctx);
-
-            let n1_dims_eq2 =
-                ctx.builder.build_int_compare(IntPredicate::EQ, n1_dims, two, "").unwrap();
-            let n2_dims_eq2 =
-                ctx.builder.build_int_compare(IntPredicate::EQ, n2_dims, two, "").unwrap();
-
-            // num_dim = 2
-            ctx.make_assert(
-                generator,
-                n1_dims_eq2,
-                "0:ValueError",
-                format!("{FN_NAME} operates on 2D matrices").as_str(),
-                [None, None, None],
-                ctx.current_loc,
-            );
-
-            ctx.make_assert(
-                generator,
-                n2_dims_eq2,
-                "0:ValueError",
-                format!("{FN_NAME} operates on 2D matrices").as_str(),
-                [None, None, None],
-                ctx.current_loc,
-            );
-
-            // matrix must be compatible for multiplication
-            let n1_col = unsafe {
-                n1.dim_sizes().get_unchecked(ctx, generator, &one, None).into_int_value()
-            };
-            let n2_col = unsafe {
-                n1.dim_sizes()
-                    .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                    .into_int_value()
-            };
-
-            let dim_eq =
-                ctx.builder.build_int_compare(IntPredicate::EQ, n1_col, n2_col, "").unwrap();
-
-            ctx.make_assert(
-                generator,
-                dim_eq,
-                "0:ValueError",
-                format!("Columns of first matrix must equal rows of second for {FN_NAME}").as_str(),
-                [None, None, None],
-                ctx.current_loc,
-            );
-        }
-
-        let out_dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let out_dim1 =
-            unsafe { n2.dim_sizes().get_unchecked(ctx, generator, &one, None).into_int_value() };
-
-        let out = numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[out_dim0, out_dim1])
-            .unwrap();
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let dim1 =
-            unsafe { n1.dim_sizes().get_unchecked(ctx, generator, &one, None).into_int_value() };
-        let dim2 =
-            unsafe { n2.dim_sizes().get_unchecked(ctx, generator, &one, None).into_int_value() };
-
-        // let r = ctx.ctx.const_string(string, null_terminated);
-
-        extern_fns::call_np_linalg_matmul(
-            ctx,
-            (dim0, dim1, n1.data().base_ptr(ctx, generator)),
-            (dim1, dim2, n2.data().base_ptr(ctx, generator)),
-            (dim0, dim2, out.data().base_ptr(ctx, generator)),
-            None,
-        );
-        Ok(out.as_base_value().as_basic_value_enum())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty])
-    }
-}
-
-pub fn call_np_linalg_cholesky<'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_cholesky";
-    let (x1_ty, x1) = x1;
-
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let one = llvm_usize.const_int(1, false);
-    let two = llvm_usize.const_int(2, false);
-
-    if let BasicValueEnum::PointerValue(n1) = 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 {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-
-        // The following constraints must be satisfied:
-        // * Input must be 2D
-        // * Input must be a square matrix (here we assume it is symmetric)
-        if cfg!(debug_assertions) {
-            let n1_dims = n1.load_ndims(ctx);
-
-            let n1_dims_eq2 =
-                ctx.builder.build_int_compare(IntPredicate::EQ, n1_dims, two, "").unwrap();
-
-            // num_dim = 2
-            ctx.make_assert(
-                generator,
-                n1_dims_eq2,
-                "0:ValueError",
-                format!("{FN_NAME} operates on 2D matrices").as_str(),
-                [None, None, None],
-                ctx.current_loc,
-            );
-
-            // Square Matrix
-            let dim0 = unsafe {
-                n1.dim_sizes()
-                    .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                    .into_int_value()
-            };
-            let dim1 = unsafe {
-                n1.dim_sizes().get_unchecked(ctx, generator, &one, None).into_int_value()
-            };
-
-            let dim_match =
-                ctx.builder.build_int_compare(IntPredicate::EQ, dim0, dim1, "").unwrap();
-
-            ctx.make_assert(
-                generator,
-                dim_match,
-                "0:ValueError",
-                format!("Input matrix must be a square matrix {FN_NAME}").as_str(),
-                [None, None, None],
-                ctx.current_loc,
-            );
-        }
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let dim1 =
-            unsafe { n1.dim_sizes().get_unchecked(ctx, generator, &one, None).into_int_value() };
-
-        let out =
-            numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[dim0, dim1]).unwrap();
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-
-        extern_fns::call_np_linalg_cholesky(
-            ctx,
-            (dim0, dim1, n1.data().base_ptr(ctx, generator)),
-            (dim0, dim1, out.data().base_ptr(ctx, generator)),
-            None,
-        );
-        Ok(out.as_base_value().as_basic_value_enum())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty])
-    }
-}
-
-pub fn call_np_linalg_qr<'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_qr";
-    let (x1_ty, x1) = x1;
-
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    let one = llvm_usize.const_int(1, false);
-
-    if let BasicValueEnum::PointerValue(n1) = 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 {
-            unimplemented!("{FN_NAME} operates on float type NdArrays only");
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let dim1 =
-            unsafe { n1.dim_sizes().get_unchecked(ctx, generator, &one, None).into_int_value() };
-        let k = llvm_intrinsics::call_int_smin(ctx, dim0, dim1, None);
-
-        let out_q = numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[dim0, k]).unwrap();
-        let out_r = numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[k, dim1]).unwrap();
-
-        extern_fns::call_np_linalg_qr(
-            ctx,
-            (dim0, dim1, n1.data().base_ptr(ctx, generator)),
-            (dim0, k, out_q.data().base_ptr(ctx, generator)),
-            (k, dim1, out_r.data().base_ptr(ctx, generator)),
-            None,
-        );
-        let out_q = out_q.as_base_value().as_basic_value_enum();
-        let out_r = out_r.as_base_value().as_basic_value_enum();
-        let res_ty = ctx.ctx.struct_type(&[out_q.get_type(), out_r.get_type()], false);
-        let res_ptr = ctx.builder.build_alloca(res_ty, "QR_factorization").unwrap();
-
-        let res_val = [out_q, out_r];
-        for (i, v) in res_val.into_iter().enumerate() {
-            unsafe {
-                let ptr = ctx
-                    .builder
-                    .build_in_bounds_gep(
-                        res_ptr,
-                        &[
-                            ctx.ctx.i32_type().const_zero(),
-                            ctx.ctx.i32_type().const_int(i as u64, false),
-                        ],
-                        "ptr",
-                    )
-                    .unwrap();
-                ctx.builder.build_store(ptr, v).unwrap();
-            }
-        }
-
-        Ok(ctx.builder.build_load(res_ptr, "QR_Factorization_result").map(Into::into).unwrap())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty])
-    }
-}
-
-pub fn call_np_linalg_svd<'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_svd";
-    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_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");
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let dim1 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
-                .into_int_value()
-        };
-        let k = llvm_intrinsics::call_int_smin(ctx, dim0, dim1, None);
-
-        let out_u =
-            numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[dim0, dim0]).unwrap();
-        let out_s = numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[k]).unwrap();
-
-        let out_vh =
-            numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[dim1, dim1]).unwrap();
-
-        extern_fns::call_np_linalg_svd(
-            ctx,
-            (dim0, dim1, n1.data().base_ptr(ctx, generator)),
-            (dim0, dim0, out_u.data().base_ptr(ctx, generator)),
-            (k, llvm_usize.const_int(1, false), out_s.data().base_ptr(ctx, generator)),
-            (dim1, dim1, out_vh.data().base_ptr(ctx, generator)),
-            None,
-        );
-
-        let out_u = out_u.as_base_value().as_basic_value_enum();
-        let out_s = out_s.as_base_value().as_basic_value_enum();
-        let out_vh = out_vh.as_base_value().as_basic_value_enum();
-
-        let res_ty =
-            ctx.ctx.struct_type(&[out_u.get_type(), out_s.get_type(), out_vh.get_type()], false);
-        let res_ptr = ctx.builder.build_alloca(res_ty, "SVD_factorization").unwrap();
-
-        let res_val = [out_u, out_s, out_vh];
-        for (i, v) in res_val.into_iter().enumerate() {
-            unsafe {
-                let ptr = ctx
-                    .builder
-                    .build_in_bounds_gep(
-                        res_ptr,
-                        &[
-                            ctx.ctx.i32_type().const_zero(),
-                            ctx.ctx.i32_type().const_int(i as u64, false),
-                        ],
-                        "ptr",
-                    )
-                    .unwrap();
-                ctx.builder.build_store(ptr, v).unwrap();
-            }
-        }
-
-        Ok(ctx.builder.build_load(res_ptr, "SVD_Factorization_result").map(Into::into).unwrap())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty])
-    }
-}
-
-pub fn call_np_linalg_inv<'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_inv";
-    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_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");
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let dim1 = 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, &[dim0, dim1]).unwrap();
-
-        extern_fns::call_np_linalg_inv(
-            ctx,
-            (dim0, dim1, n1.data().base_ptr(ctx, generator)),
-            (dim0, dim1, out.data().base_ptr(ctx, generator)),
-            None,
-        );
-        Ok(out.as_base_value().as_basic_value_enum())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty])
-    }
-}
-
-pub fn call_np_linalg_pinv<'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_pinv";
-    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_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");
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let dim1 = 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, &[dim1, dim0]).unwrap();
-
-        extern_fns::call_np_linalg_pinv(
-            ctx,
-            (dim0, dim1, n1.data().base_ptr(ctx, generator)),
-            (dim1, dim0, out.data().base_ptr(ctx, generator)),
-            None,
-        );
-        Ok(out.as_base_value().as_basic_value_enum())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty])
-    }
-}
-
-pub fn call_sp_linalg_lu<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    x1: (Type, BasicValueEnum<'ctx>),
-) -> Result<BasicValueEnum<'ctx>, String> {
-    const FN_NAME: &str = "sp_linalg_lu";
-    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_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");
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let dim1 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
-                .into_int_value()
-        };
-        let k = llvm_intrinsics::call_int_smin(ctx, dim0, dim1, None);
-
-        let out_l = numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[dim0, k]).unwrap();
-        let out_u = numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[k, dim1]).unwrap();
-
-        extern_fns::call_sp_linalg_lu(
-            ctx,
-            (dim0, dim1, n1.data().base_ptr(ctx, generator)),
-            (dim0, k, out_l.data().base_ptr(ctx, generator)),
-            (k, dim1, out_u.data().base_ptr(ctx, generator)),
-            None,
-        );
-
-        let out_l = out_l.as_base_value().as_basic_value_enum();
-        let out_u = out_u.as_base_value().as_basic_value_enum();
-
-        let res_ty = ctx.ctx.struct_type(&[out_l.get_type(), out_u.get_type()], false);
-        let res_ptr = ctx.builder.build_alloca(res_ty, "LU_factorization").unwrap();
-
-        let res_val = [out_l, out_u];
-        for (i, v) in res_val.into_iter().enumerate() {
-            unsafe {
-                let ptr = ctx
-                    .builder
-                    .build_in_bounds_gep(
-                        res_ptr,
-                        &[
-                            ctx.ctx.i32_type().const_zero(),
-                            ctx.ctx.i32_type().const_int(i as u64, false),
-                        ],
-                        "ptr",
-                    )
-                    .unwrap();
-                ctx.builder.build_store(ptr, v).unwrap();
-            }
-        }
-
-        Ok(ctx.builder.build_load(res_ptr, "LU_Factorization_result").map(Into::into).unwrap())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty])
-    }
-}
-
-// Must be square (add check later)
-pub fn call_sp_linalg_schur<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    x1: (Type, BasicValueEnum<'ctx>),
-) -> Result<BasicValueEnum<'ctx>, String> {
-    const FN_NAME: &str = "sp_linalg_schur";
-    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_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");
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let dim1 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
-                .into_int_value()
-        };
-        let out_t =
-            numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[dim0, dim0]).unwrap();
-        let out_z =
-            numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[dim0, dim0]).unwrap();
-
-        extern_fns::call_sp_linalg_schur(
-            ctx,
-            (dim0, dim1, n1.data().base_ptr(ctx, generator)),
-            (dim0, dim0, out_t.data().base_ptr(ctx, generator)),
-            (dim0, dim0, out_z.data().base_ptr(ctx, generator)),
-            None,
-        );
-
-        let out_t = out_t.as_base_value().as_basic_value_enum();
-        let out_z = out_z.as_base_value().as_basic_value_enum();
-
-        let res_ty = ctx.ctx.struct_type(&[out_t.get_type(), out_z.get_type()], false);
-
-        let res_ptr = ctx.builder.build_alloca(res_ty, "Schur_factorization").unwrap();
-        let r = ctx
-            .ctx
-            .const_string(ctx.current_loc.file.0.to_string().as_bytes(), true)
-            .as_basic_value_enum()
-            .into_pointer_value();
-        let res_val = [out_t, out_z];
-        for (i, v) in res_val.into_iter().enumerate() {
-            unsafe {
-                let ptr = ctx
-                    .builder
-                    .build_in_bounds_gep(
-                        res_ptr,
-                        &[
-                            ctx.ctx.i32_type().const_zero(),
-                            ctx.ctx.i32_type().const_int(i as u64, false),
-                        ],
-                        "ptr",
-                    )
-                    .unwrap();
-                ctx.builder.build_store(ptr, v).unwrap();
-            }
-        }
-
-        Ok(ctx.builder.build_load(res_ptr, "Schur_Factorization_result").map(Into::into).unwrap())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty])
-    }
-}
-
-// Must be square (add check later)
-pub fn call_sp_linalg_hessenberg<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    x1: (Type, BasicValueEnum<'ctx>),
-) -> Result<BasicValueEnum<'ctx>, String> {
-    const FN_NAME: &str = "sp_linalg_hessenberg";
-    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_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");
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-
-        let dim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let dim1 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
-                .into_int_value()
-        };
-
-        // Check if matrix is square
-        // ctx.builder.build_select(
-        //     ctx.builder.build_int_compare(IntPredicate::EQ, dim0, dim1, "").unwrap(),
-        //     {
-        //         let func =
-        //     }, else_, name)
-        // ;
-
-        // ctx.builder.build_call(
-        //     ctx.module.get_function("__nac3_raise"),
-        //     &[]
-
-        // )
-        // let err_msg = ctx.gen_string(generator, "{FN_NAME} requires square matrix");
-        // ctx.raise_exn(generator, "0:ValueError", err_msg, [None, None, None], ctx.current_loc);
-
-        let out_h =
-            numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[dim0, dim0]).unwrap();
-
-        extern_fns::call_sp_linalg_hessenberg(
-            ctx,
-            (dim0, dim1, n1.data().base_ptr(ctx, generator)),
-            (dim0, dim0, out_h.data().base_ptr(ctx, generator)),
-            None,
-        );
-
-        Ok(out_h.as_base_value().as_basic_value_enum())
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty])
-    }
-}

nac3core/src/codegen/expr.rs

@@ -951,9 +951,9 @@ pub fn destructure_range<'ctx>(
 /// Allocates a List structure with the given [type][ty] and [length]. The name of the resulting
 /// LLVM value is `{name}.addr`, or `list.addr` if [name] is not specified.
 ///
-/// Setting `ty` to [`None`] implies that the list is empty **and** does not have a known element
+/// Setting `ty` to [`None`] implies that the list does not have a known element type, which is only
-/// type, and will therefore set the `list.data` type as `size_t*`. It is undefined behavior to
+/// valid for empty lists. It is undefined behavior to generate a sized list with an unknown element
-/// generate a sized list with an unknown element type.
+/// type.
 pub fn allocate_list<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,

nac3core/src/codegen/extern_fns.rs

@@ -131,153 +131,81 @@ pub fn call_ldexp<'ctx>(
         .unwrap()
 }
 
-/// Macro to generate np_linalg external functions
+/// Invokes the [`try_invert_to`](https://docs.rs/nalgebra/latest/nalgebra/linalg/fn.try_invert_to.html) function
-macro_rules! generate_np_linalg_extern_fn {
+pub fn call_linalg_try_invert_to<'ctx>(
-    ($fn_name:ident, $ret_ty:ident, $extern_ret_ty:ident, $map_fn:expr, $extern_fn:literal, 1) => {
+    ctx: &CodeGenContext<'ctx, '_>,
-        generate_np_linalg_extern_fn!($fn_name, $ret_ty, $extern_ret_ty, $map_fn, $extern_fn, mat1);
+    dim0: IntValue<'ctx>,
-    };
+    dim1: IntValue<'ctx>,
-    ($fn_name:ident, $ret_ty:ident, $extern_ret_ty:ident, $map_fn:expr, $extern_fn:literal, 2) => {
+    data: PointerValue<'ctx>,
-        generate_np_linalg_extern_fn!($fn_name, $ret_ty, $extern_ret_ty, $map_fn, $extern_fn, mat1, mat2);
+    name: Option<&str>,
-    };
+) -> IntValue<'ctx> {
-    ($fn_name:ident, $ret_ty:ident, $extern_ret_ty:ident, $map_fn:expr, $extern_fn:literal, 3) => {
+    const FN_NAME: &str = "linalg_try_invert_to";
-        generate_np_linalg_extern_fn!($fn_name, $ret_ty, $extern_ret_ty, $map_fn, $extern_fn, mat1, mat2, mat3);
-    };
-    ($fn_name:ident, $ret_ty:ident, $extern_ret_ty:ident, $map_fn:expr, $extern_fn:literal, 4) => {
-        generate_np_linalg_extern_fn!($fn_name, $ret_ty, $extern_ret_ty, $map_fn, $extern_fn, mat1, mat2, mat3, mat4);
-    };
-    ($fn_name:ident, $ret_ty:ident, $extern_ret_ty:ident, $map_fn:expr, $extern_fn:literal $(,$input_matrix:ident)*) => {
-        #[doc = concat!("Invokes the numpy `", stringify!($extern_fn), " function." )]
-        pub fn $fn_name<'ctx>(
-            ctx: &mut CodeGenContext<'ctx, '_>
-            $(,$input_matrix: (IntValue<'ctx>, IntValue<'ctx>, PointerValue<'ctx>))*,
-            name: Option<&str>,
-        ) -> $ret_ty<'ctx> {
-            const FN_NAME: &str = $extern_fn;
 
-            let llvm_f64 = ctx.ctx.f64_type();
+    let llvm_f64 = ctx.ctx.f64_type();
-            let allowed_index_types = [ctx.ctx.i32_type(), ctx.ctx.i64_type()];
+    let allowed_indices = [ctx.ctx.i32_type(), ctx.ctx.i64_type()];
+    
+    let allowed_dim0 = allowed_indices.iter().any(|p| *p == dim0.get_type());
+    let allowed_dim1 = allowed_indices.iter().any(|p| *p == dim1.get_type());
 
-            $(
+    debug_assert!(allowed_dim0);
-                debug_assert!(allowed_index_types.iter().any(|p| *p == $input_matrix.0.get_type()));
+    debug_assert!(allowed_dim1);
-                debug_assert!(allowed_index_types.iter().any(|p| *p == $input_matrix.1.get_type()));
+    debug_assert_eq!(data.get_type().get_element_type().into_float_type(), llvm_f64);
-                debug_assert_eq!($input_matrix.2.get_type().get_element_type().into_float_type(), llvm_f64);
-            )*
 
-            // let row = ctx.ctx.i32_type().const_int(ctx.current_loc.row.try_into().unwrap(), false);
+    let extern_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
-            // let col = ctx.ctx.i32_type().const_int(ctx.current_loc.column.try_into().unwrap(), false);
+        let fn_type = ctx.ctx.i8_type().fn_type(&[dim0.get_type().into(), dim0.get_type().into(), data.get_type().into()], false);
-            // let file_name = ctx.current_loc.file.0;
+        let func = ctx.module.add_function(FN_NAME, fn_type, None);
-            // let name_len = ctx.ctx.i32_type().const_int(file_name.to_string().len().try_into().unwrap(), false);
+        for attr in ["mustprogress", "nofree", "nounwind", "willreturn"] {
-            // let file_name = ctx.ctx.const_string(&ctx.current_loc.file.0.to_string().into_bytes(), true);
+                func.add_attribute(
+                    AttributeLoc::Function,
+                    ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
+                );
+            }
 
-            let extern_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
+        func
-                // let fn_type = ctx.ctx.$extern_ret_ty().fn_type(&[row.get_type().into(), col.get_type().into(), file_name.get_type().into(), $($input_matrix.0.get_type().into(), $input_matrix.1.get_type().into(), $input_matrix.2.get_type().into()),*], false);
+    });
-                // let fn_type = ctx.ctx.$extern_ret_ty().fn_type(&[row.get_type().into(), col.get_type().into(), file_name.get_type().into(), name_len.get_type().into(), $($input_matrix.0.get_type().into(), $input_matrix.1.get_type().into(), $input_matrix.2.get_type().into()),*], false);
+    
-                let fn_type = ctx.ctx.$extern_ret_ty().fn_type(&[$($input_matrix.0.get_type().into(), $input_matrix.1.get_type().into(), $input_matrix.2.get_type().into()),*], false);
+    ctx.builder
-
+    .build_call(extern_fn, &[dim0.into(), dim1.into(), data.into()], name.unwrap_or_default())
-                let func = ctx.module.add_function(FN_NAME, fn_type, None);
+    .map(CallSiteValue::try_as_basic_value)
-                for attr in ["mustprogress", "nofree", "nounwind", "willreturn", "writeonly"] {
+    .map(|v| v.map_left(BasicValueEnum::into_int_value))
-                    func.add_attribute(
+    .map(Either::unwrap_left)
-                        AttributeLoc::Function,
+    .unwrap().into()
-                        ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
-                    );
-                }
-                func
-            });
-
-            ctx.builder
-                // .build_call(extern_fn, &[row.into(), col.into(), file_name.into(), $($input_matrix.0.into(), $input_matrix.1.into(), $input_matrix.2.into(),)*], name.unwrap_or_default())
-                // .build_call(extern_fn, &[name_len.into(), col.into(), file_name.into(), row.into(), $($input_matrix.0.into(), $input_matrix.1.into(), $input_matrix.2.into(),)*], name.unwrap_or_default())
-                .build_call(extern_fn, &[$($input_matrix.0.into(), $input_matrix.1.into(), $input_matrix.2.into(),)*], name.unwrap_or_default())
-                .map(CallSiteValue::try_as_basic_value)
-                .map(|v| v.map_left($map_fn))
-                .map(Either::unwrap_left)
-                .unwrap()
-        }
-    };
 }
 
-generate_np_linalg_extern_fn!(
+/// Invokes the [`wilkinson_shift`](https://docs.rs/nalgebra/latest/nalgebra/linalg/fn.wilkinson_shift.html) function
-    call_np_dot,
+pub fn call_linalg_wilkinson_shift<'ctx>(
-    FloatValue,
+    ctx: &CodeGenContext<'ctx, '_>,
-    f64_type,
+    dim0: IntValue<'ctx>,
-    BasicValueEnum::into_float_value,
+    dim1: IntValue<'ctx>,
-    "np_dot",
+    data: PointerValue<'ctx>,
-    2
+    name: Option<&str>,
-);
+) -> FloatValue<'ctx> {
-generate_np_linalg_extern_fn!(
+    const FN_NAME: &str = "linalg_wilkinson_shift";
-    call_np_linalg_matmul,
+    let allowed_index_types = [ctx.ctx.i32_type(), ctx.ctx.i64_type()];
-    IntValue,
+    
-    i8_type,
+    let allowed_dim0 = allowed_index_types.iter().any(|p| *p == dim0.get_type());
-    BasicValueEnum::into_int_value,
+    let allowed_dim1 = allowed_index_types.iter().any(|p| *p == dim1.get_type());
-    "np_linalg_matmul",
-    3
-);
-generate_np_linalg_extern_fn!(
-    call_np_linalg_cholesky,
-    IntValue,
-    i8_type,
-    BasicValueEnum::into_int_value,
-    "np_linalg_cholesky",
-    2
-);
-generate_np_linalg_extern_fn!(
-    call_np_linalg_qr,
-    IntValue,
-    i8_type,
-    BasicValueEnum::into_int_value,
-    "np_linalg_qr",
-    3
-);
-generate_np_linalg_extern_fn!(
-    call_np_linalg_svd,
-    IntValue,
-    i8_type,
-    BasicValueEnum::into_int_value,
-    "np_linalg_svd",
-    4
-);
 
-generate_np_linalg_extern_fn!(
+    debug_assert!(allowed_dim0);
-    call_np_linalg_inv,
+    debug_assert!(allowed_dim1);
-    IntValue,
-    i8_type,
-    BasicValueEnum::into_int_value,
-    "np_linalg_inv",
-    2
-);
 
-generate_np_linalg_extern_fn!(
+    let extern_fn = ctx.module.get_function(FN_NAME).unwrap_or_else(|| {
-    call_np_linalg_pinv,
+        let fn_type = ctx.ctx.f64_type().fn_type(&[dim0.get_type().into(), dim0.get_type().into(), data.get_type().into()], false);
-    IntValue,
+        let func = ctx.module.add_function(FN_NAME, fn_type, None);
-    i8_type,
+        for attr in ["mustprogress", "nofree", "nounwind", "willreturn"] {
-    BasicValueEnum::into_int_value,
+                func.add_attribute(
-    "np_linalg_pinv",
+                    AttributeLoc::Function,
-    2
+                    ctx.ctx.create_enum_attribute(Attribute::get_named_enum_kind_id(attr), 0),
-);
+                );
+            }
 
-generate_np_linalg_extern_fn!(
+        func
-    call_sp_linalg_lu,
+    });
-    IntValue,
+    
-    i8_type,
+    ctx.builder
-    BasicValueEnum::into_int_value,
+    .build_call(extern_fn, &[dim0.into(), dim1.into(), data.into()], name.unwrap_or_default())
-    "sp_linalg_lu",
+    .map(CallSiteValue::try_as_basic_value)
-    3
+    .map(|v| v.map_left(BasicValueEnum::into_float_value))
-);
+    .map(Either::unwrap_left)
-
+    .unwrap().into()
-generate_np_linalg_extern_fn!(
+}
-    call_sp_linalg_schur,
-    IntValue,
-    i8_type,
-    BasicValueEnum::into_int_value,
-    "sp_linalg_schur",
-    3
-);
-
-generate_np_linalg_extern_fn!(
-    call_sp_linalg_hessenberg,
-    IntValue,
-    i8_type,
-    BasicValueEnum::into_int_value,
-    "sp_linalg_hessenberg",
-    2
-);

nac3core/src/codegen/numpy.rs

@@ -61,7 +61,7 @@ fn create_ndarray_uninitialized<'ctx, G: CodeGenerator + ?Sized>(
 /// * `shape` - The shape of the `NDArray`.
 /// * `shape_len_fn` - A function that retrieves the number of dimensions from `shape`.
 /// * `shape_data_fn` - A function that retrieves the size of a dimension from `shape`.
-pub fn create_ndarray_dyn_shape<'ctx, 'a, G, V, LenFn, DataFn>(
+fn create_ndarray_dyn_shape<'ctx, 'a, G, V, LenFn, DataFn>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, 'a>,
     elem_ty: Type,
@@ -157,7 +157,7 @@ where
 ///
 /// * `elem_ty` - The element type of the `NDArray`.
 /// * `shape` - The shape of the `NDArray`, represented am array of [`IntValue`]s.
-pub fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
+fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     elem_ty: Type,

nac3core/src/codegen/stmt.rs

@@ -1637,7 +1637,7 @@ pub fn gen_stmt<G: CodeGenerator>(
             };
             ctx.make_assert_impl(
                 generator,
-                generator.bool_to_i1(ctx, test.into_int_value()),
+                test.into_int_value(),
                 "0:AssertionError",
                 err_msg,
                 [None, None, None],

nac3core/src/toplevel/builtins.rs

@@ -557,18 +557,8 @@ impl<'a> BuiltinBuilder<'a> {
             | PrimDef::FunNpHypot
             | PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
 
-            PrimDef::FunNpDot
+            PrimDef::FunTryInvertTo => self.build_linalg_try_invert_to(prim),  // Inplace invert
-            | PrimDef::FunNpLinalgMatmul
+            PrimDef::FunWilkinsonShift => self.build_linalg_wilkinson_shift(prim),
-            | PrimDef::FunNpLinalgCholesky
-            | PrimDef::FunNpLinalgQr
-            | PrimDef::FunNpLinalgSvd
-            | PrimDef::FunNpLinalgInv
-            | PrimDef::FunNpLinalgPinv
-            | PrimDef::FunSpLinalgLu
-            | PrimDef::FunSpLinalgSchur
-            | PrimDef::FunSpLinalgHessenberg => self.build_np_linalg_methods(prim),
-            // PrimDef::FunNpDot | PrimDef::FunNpLinalgMatmul =>  self.build_np_linalg_binary_methods(prim),
-            // PrimDef::FunNpLinalgCholesky | PrimDef::FunNpLinalgQr => self.build_np_linalg_unary_methods(prim),
         };
 
         if cfg!(debug_assertions) {
@@ -577,7 +567,7 @@ impl<'a> BuiltinBuilder<'a> {
             match (&tld, prim.details()) {
                 (
                     TopLevelDef::Class { name, object_id, .. },
-                    PrimDefDetails::PrimClass { name: exp_name, .. },
+                    PrimDefDetails::PrimClass { name: exp_name },
                 ) => {
                     let exp_object_id = prim.id();
                     assert_eq!(name, &exp_name.into());
@@ -1887,140 +1877,62 @@ impl<'a> BuiltinBuilder<'a> {
         }
     }
 
-    fn build_np_linalg_methods(&mut self, prim: PrimDef) -> TopLevelDef {
+    fn build_linalg_try_invert_to(&mut self, prim: PrimDef) -> TopLevelDef {
         debug_assert_prim_is_allowed(
             prim,
             &[
-                PrimDef::FunNpDot,
+                PrimDef::FunTryInvertTo,
-                PrimDef::FunNpLinalgMatmul,
-                PrimDef::FunNpLinalgCholesky,
-                PrimDef::FunNpLinalgQr,
-                PrimDef::FunNpLinalgSvd,
-                PrimDef::FunNpLinalgInv,
-                PrimDef::FunNpLinalgPinv,
-                PrimDef::FunSpLinalgLu,
-                PrimDef::FunSpLinalgSchur,
-                PrimDef::FunSpLinalgHessenberg,
             ],
         );
+        
+        let var_map = self.num_or_ndarray_var_map.clone();
+        create_fn_by_codegen(
+            self.unifier,
+            &var_map,
+            prim.name(),
+            self.primitives.bool,
+            &[(self.ndarray_float_2d, "x")],
+            Box::new(move |ctx, _, fun, args, generator| {
+                let x_ty = fun.0.args[0].ty;
+                let x_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x_ty)?;
 
-        match prim {
+                let func = match prim {
-            PrimDef::FunNpDot => create_fn_by_codegen(
+                    PrimDef::FunTryInvertTo => builtin_fns::call_try_invert_to,
-                self.unifier,
+                    _ => unreachable!(),
-                &self.num_or_ndarray_var_map,
+                };
-                prim.name(),
-                self.primitives.float,
-                &[(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;
-                    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_dot(
+                Ok(Some(func(generator, ctx, (x_ty, x_val))?))
-                        generator,
+            }),
-                        ctx,
+        )
-                        (x1_ty, x1_val),
+    }
-                        (x2_ty, x2_val),
-                    )?))
-                }),
-            ),
 
-            PrimDef::FunNpLinalgMatmul => create_fn_by_codegen(
+    fn build_linalg_wilkinson_shift(&mut self, prim: PrimDef) -> TopLevelDef {
-                self.unifier,
+        debug_assert_prim_is_allowed(
-                &VarMap::new(),
+            prim,
-                prim.name(),
+            &[
-                self.ndarray_float_2d,
+                PrimDef::FunWilkinsonShift,
-                &[(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 var_map = self.num_or_ndarray_var_map.clone();
-                    let x2_ty = fun.0.args[1].ty;
+        create_fn_by_codegen(
-                    let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
+            self.unifier,
+            &var_map,
+            prim.name(),
+            self.primitives.float,
+            &[(self.ndarray_float_2d, "x")],
+            Box::new(move |ctx, _, fun, args, generator| {
+                let x_ty = fun.0.args[0].ty;
+                let x_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x_ty)?;
 
-                    Ok(Some(builtin_fns::call_np_linalg_matmul(
+                let func = match prim {
-                        generator,
+                    PrimDef::FunWilkinsonShift => builtin_fns::call_wilkinson_shift,
-                        ctx,
+                    _ => unreachable!(),
-                        (x1_ty, x1_val),
+                };
-                        (x2_ty, x2_val),
-                    )?))
-                }),
-            ),
 
-            PrimDef::FunNpLinalgCholesky
+                Ok(Some(func(generator, ctx, (x_ty, x_val))?))
-            | PrimDef::FunNpLinalgInv
+            }),
-            | PrimDef::FunNpLinalgPinv
+        )
-            | PrimDef::FunSpLinalgHessenberg => create_fn_by_codegen(
-                self.unifier,
-                &VarMap::new(),
-                prim.name(),
-                self.ndarray_float_2d,
-                &[(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)?;
-
-                    let func = match prim {
-                        PrimDef::FunNpLinalgCholesky => builtin_fns::call_np_linalg_cholesky,
-                        PrimDef::FunNpLinalgInv => builtin_fns::call_np_linalg_inv,
-                        PrimDef::FunNpLinalgPinv => builtin_fns::call_np_linalg_pinv,
-                        PrimDef::FunSpLinalgHessenberg => builtin_fns::call_sp_linalg_hessenberg,
-                        _ => unreachable!(),
-                    };
-                    Ok(Some(func(generator, ctx, (x1_ty, x1_val))?))
-                }),
-            ),
-
-            PrimDef::FunNpLinalgQr | PrimDef::FunSpLinalgLu | PrimDef::FunSpLinalgSchur => {
-                let ret_ty = self.unifier.add_ty(TypeEnum::TTuple {
-                    ty: vec![self.ndarray_float_2d, self.ndarray_float_2d],
-                });
-                create_fn_by_codegen(
-                    self.unifier,
-                    &VarMap::new(),
-                    prim.name(),
-                    ret_ty,
-                    &[(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)?;
-
-                        let func = match prim {
-                            PrimDef::FunNpLinalgQr => builtin_fns::call_np_linalg_qr,
-                            PrimDef::FunSpLinalgLu => builtin_fns::call_sp_linalg_lu,
-                            PrimDef::FunSpLinalgSchur => builtin_fns::call_sp_linalg_schur,
-                            _ => unreachable!(),
-                        };
-                        Ok(Some(func(generator, ctx, (x1_ty, x1_val))?))
-                    }),
-                )
-            }
-
-            PrimDef::FunNpLinalgSvd => {
-                let ret_ty = self.unifier.add_ty(TypeEnum::TTuple {
-                    ty: vec![self.ndarray_float_2d, self.ndarray_float, self.ndarray_float_2d],
-                });
-                create_fn_by_codegen(
-                    self.unifier,
-                    &VarMap::new(),
-                    prim.name(),
-                    ret_ty,
-                    &[(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_svd(generator, ctx, (x1_ty, x1_val))?))
-                    }),
-                )
-            }
-            _ => {
-                println!("{:?}", prim.name());
-                unreachable!()
-            }
-        }
     }
 
     fn create_method(prim: PrimDef, method_ty: Type) -> (StrRef, Type, DefinitionId) {

nac3core/src/toplevel/composer.rs

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

nac3core/src/toplevel/helper.rs

@@ -105,16 +105,8 @@ pub enum PrimDef {
     FunNpLdExp,
     FunNpHypot,
     FunNpNextAfter,
-    FunNpDot,
+    FunTryInvertTo,
-    FunNpLinalgMatmul,
+    FunWilkinsonShift,
-    FunNpLinalgCholesky,
-    FunNpLinalgQr,
-    FunNpLinalgSvd,
-    FunNpLinalgInv,
-    FunNpLinalgPinv,
-    FunSpLinalgLu,
-    FunSpLinalgSchur,
-    FunSpLinalgHessenberg,
 
     // Top-Level Functions
     FunSome,
@@ -123,7 +115,7 @@ pub enum PrimDef {
 /// Associated details of a [`PrimDef`]
 pub enum PrimDefDetails {
     PrimFunction { name: &'static str, simple_name: &'static str },
-    PrimClass { name: &'static str, get_ty_fn: fn(&PrimitiveStore) -> Type },
+    PrimClass { name: &'static str },
 }
 
 impl PrimDef {
@@ -165,17 +157,15 @@ impl PrimDef {
     #[must_use]
     pub fn name(&self) -> &'static str {
         match self.details() {
-            PrimDefDetails::PrimFunction { name, .. } | PrimDefDetails::PrimClass { name, .. } => {
+            PrimDefDetails::PrimFunction { name, .. } | PrimDefDetails::PrimClass { name } => name,
-                name
-            }
         }
     }
 
     /// Get the associated details of this [`PrimDef`]
     #[must_use]
     pub fn details(self) -> PrimDefDetails {
-        fn class(name: &'static str, get_ty_fn: fn(&PrimitiveStore) -> Type) -> PrimDefDetails {
+        fn class(name: &'static str) -> PrimDefDetails {
-            PrimDefDetails::PrimClass { name, get_ty_fn }
+            PrimDefDetails::PrimClass { name }
         }
 
         fn fun(name: &'static str, simple_name: Option<&'static str>) -> PrimDefDetails {
@@ -183,22 +173,22 @@ impl PrimDef {
         }
 
         match self {
-            PrimDef::Int32 => class("int32", |primitives| primitives.int32),
+            PrimDef::Int32 => class("int32"),
-            PrimDef::Int64 => class("int64", |primitives| primitives.int64),
+            PrimDef::Int64 => class("int64"),
-            PrimDef::Float => class("float", |primitives| primitives.float),
+            PrimDef::Float => class("float"),
-            PrimDef::Bool => class("bool", |primitives| primitives.bool),
+            PrimDef::Bool => class("bool"),
-            PrimDef::None => class("none", |primitives| primitives.none),
+            PrimDef::None => class("none"),
-            PrimDef::Range => class("range", |primitives| primitives.range),
+            PrimDef::Range => class("range"),
-            PrimDef::Str => class("str", |primitives| primitives.str),
+            PrimDef::Str => class("str"),
-            PrimDef::Exception => class("Exception", |primitives| primitives.exception),
+            PrimDef::Exception => class("Exception"),
-            PrimDef::UInt32 => class("uint32", |primitives| primitives.uint32),
+            PrimDef::UInt32 => class("uint32"),
-            PrimDef::UInt64 => class("uint64", |primitives| primitives.uint64),
+            PrimDef::UInt64 => class("uint64"),
-            PrimDef::Option => class("Option", |primitives| primitives.option),
+            PrimDef::Option => class("Option"),
             PrimDef::OptionIsSome => fun("Option.is_some", Some("is_some")),
             PrimDef::OptionIsNone => fun("Option.is_none", Some("is_none")),
             PrimDef::OptionUnwrap => fun("Option.unwrap", Some("unwrap")),
-            PrimDef::List => class("list", |primitives| primitives.list),
+            PrimDef::List => class("list"),
-            PrimDef::NDArray => class("ndarray", |primitives| primitives.ndarray),
+            PrimDef::NDArray => class("ndarray"),
             PrimDef::NDArrayCopy => fun("ndarray.copy", Some("copy")),
             PrimDef::NDArrayFill => fun("ndarray.fill", Some("fill")),
             PrimDef::FunInt32 => fun("int32", None),
@@ -273,17 +263,8 @@ impl PrimDef {
             PrimDef::FunNpLdExp => fun("np_ldexp", None),
             PrimDef::FunNpHypot => fun("np_hypot", None),
             PrimDef::FunNpNextAfter => fun("np_nextafter", None),
-            PrimDef::FunNpDot => fun("np_dot", None),
+            PrimDef::FunTryInvertTo => fun("try_invert_to", None),
-            PrimDef::FunNpLinalgMatmul => fun("np_linalg_matmul", None),
+            PrimDef::FunWilkinsonShift => fun("wilkinson_shift", 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::FunSpLinalgLu => fun("sp_linalg_lu", None),
-            PrimDef::FunSpLinalgSchur => fun("sp_linalg_schur", None),
-            PrimDef::FunSpLinalgHessenberg => fun("sp_linalg_hessenberg", None),
-
             PrimDef::FunSome => fun("Some", None),
         }
     }

nac3core/src/toplevel/type_annotation.rs

@@ -1,9 +1,8 @@
 use super::*;
 use crate::symbol_resolver::SymbolValue;
-use crate::toplevel::helper::{PrimDef, PrimDefDetails};
+use crate::toplevel::helper::PrimDef;
 use crate::typecheck::typedef::VarMap;
 use nac3parser::ast::Constant;
-use strum::IntoEnumIterator;
 
 #[derive(Clone, Debug)]
 pub enum TypeAnnotation {
@@ -358,7 +357,6 @@ pub fn parse_ast_to_type_annotation_kinds<T, S: std::hash::BuildHasher + Clone>(
 pub fn get_type_from_type_annotation_kinds(
     top_level_defs: &[Arc<RwLock<TopLevelDef>>],
     unifier: &mut Unifier,
-    primitives: &PrimitiveStore,
     ann: &TypeAnnotation,
     subst_list: &mut Option<Vec<Type>>,
 ) -> Result<Type, HashSet<String>> {
@@ -381,141 +379,100 @@ pub fn get_type_from_type_annotation_kinds(
             let param_ty = params
                 .iter()
                 .map(|x| {
-                    get_type_from_type_annotation_kinds(
+                    get_type_from_type_annotation_kinds(top_level_defs, unifier, x, subst_list)
-                        top_level_defs,
-                        unifier,
-                        primitives,
-                        x,
-                        subst_list,
-                    )
                 })
                 .collect::<Result<Vec<_>, _>>()?;
 
-            let ty = if let Some(prim_def) = PrimDef::iter().find(|prim| prim.id() == *obj_id) {
+            let subst = {
-                // Primitive TopLevelDefs do not contain all fields that are present in their Type
+                // check for compatible range
-                // counterparts, so directly perform subst on the Type instead.
+                // TODO: if allow type var to be applied(now this disallowed in the parse_to_type_annotation), need more check
-
+                let mut result = VarMap::new();
-                let PrimDefDetails::PrimClass { get_ty_fn, .. } = prim_def.details() else {
+                for (tvar, p) in type_vars.iter().zip(param_ty) {
-                    unreachable!()
+                    match unifier.get_ty(*tvar).as_ref() {
-                };
+                        TypeEnum::TVar {
-
+                            id,
-                let base_ty = get_ty_fn(primitives);
+                            range,
-                let params =
+                            fields: None,
-                    if let TypeEnum::TObj { params, .. } = &*unifier.get_ty_immutable(base_ty) {
+                            name,
-                        params.clone()
+                            loc,
-                    } else {
+                            is_const_generic: false,
-                        unreachable!()
+                        } => {
-                    };
+                            let ok: bool = {
-
+                                // create a temp type var and unify to check compatibility
-                unifier
+                                p == *tvar || {
-                    .subst(
+                                    let temp = unifier.get_fresh_var_with_range(
-                        get_ty_fn(primitives),
+                                        range.as_slice(),
-                        &params
+                                        *name,
-                            .iter()
+                                        *loc,
-                            .zip(param_ty)
+                                    );
-                            .map(|(obj_tv, param)| (*obj_tv.0, param))
+                                    unifier.unify(temp.ty, p).is_ok()
-                            .collect(),
-                    )
-                    .unwrap_or(base_ty)
-            } else {
-                let subst = {
-                    // check for compatible range
-                    // TODO: if allow type var to be applied(now this disallowed in the parse_to_type_annotation), need more check
-                    let mut result = VarMap::new();
-                    for (tvar, p) in type_vars.iter().zip(param_ty) {
-                        match unifier.get_ty(*tvar).as_ref() {
-                            TypeEnum::TVar {
-                                id,
-                                range,
-                                fields: None,
-                                name,
-                                loc,
-                                is_const_generic: false,
-                            } => {
-                                let ok: bool = {
-                                    // create a temp type var and unify to check compatibility
-                                    p == *tvar || {
-                                        let temp = unifier.get_fresh_var_with_range(
-                                            range.as_slice(),
-                                            *name,
-                                            *loc,
-                                        );
-                                        unifier.unify(temp.ty, p).is_ok()
-                                    }
-                                };
-                                if ok {
-                                    result.insert(*id, p);
-                                } else {
-                                    return Err(HashSet::from([format!(
-                                        "cannot apply type {} to type variable with id {:?}",
-                                        unifier.internal_stringify(
-                                            p,
-                                            &mut |id| format!("class{id}"),
-                                            &mut |id| format!("typevar{id}"),
-                                            &mut None
-                                        ),
-                                        *id
-                                    )]));
                                 }
+                            };
+                            if ok {
+                                result.insert(*id, p);
+                            } else {
+                                return Err(HashSet::from([format!(
+                                    "cannot apply type {} to type variable with id {:?}",
+                                    unifier.internal_stringify(
+                                        p,
+                                        &mut |id| format!("class{id}"),
+                                        &mut |id| format!("typevar{id}"),
+                                        &mut None
+                                    ),
+                                    *id
+                                )]));
                             }
-
-                            TypeEnum::TVar {
-                                id, range, name, loc, is_const_generic: true, ..
-                            } => {
-                                let ty = range[0];
-                                let ok: bool = {
-                                    // create a temp type var and unify to check compatibility
-                                    p == *tvar || {
-                                        let temp =
-                                            unifier.get_fresh_const_generic_var(ty, *name, *loc);
-                                        unifier.unify(temp.ty, p).is_ok()
-                                    }
-                                };
-                                if ok {
-                                    result.insert(*id, p);
-                                } else {
-                                    return Err(HashSet::from([format!(
-                                        "cannot apply type {} to type variable {}",
-                                        unifier.stringify(p),
-                                        name.unwrap_or_else(|| format!("typevar{id}").into()),
-                                    )]));
-                                }
-                            }
-
-                            _ => unreachable!("must be generic type var"),
                         }
-                    }
-                    result
-                };
-                // Class Attributes keep a copy with Class Definition and are not added to objects
-                let mut tobj_fields = methods
-                    .iter()
-                    .map(|(name, ty, _)| {
-                        let subst_ty = unifier.subst(*ty, &subst).unwrap_or(*ty);
-                        // methods are immutable
-                        (*name, (subst_ty, false))
-                    })
-                    .collect::<HashMap<_, _>>();
-                tobj_fields.extend(fields.iter().map(|(name, ty, mutability)| {
-                    let subst_ty = unifier.subst(*ty, &subst).unwrap_or(*ty);
-                    (*name, (subst_ty, *mutability))
-                }));
-                let need_subst = !subst.is_empty();
-                let ty = unifier.add_ty(TypeEnum::TObj {
-                    obj_id: *obj_id,
-                    fields: tobj_fields,
-                    params: subst,
-                });
 
-                if need_subst {
+                        TypeEnum::TVar { id, range, name, loc, is_const_generic: true, .. } => {
-                    if let Some(wl) = subst_list.as_mut() {
+                            let ty = range[0];
-                        wl.push(ty);
+                            let ok: bool = {
+                                // create a temp type var and unify to check compatibility
+                                p == *tvar || {
+                                    let temp = unifier.get_fresh_const_generic_var(ty, *name, *loc);
+                                    unifier.unify(temp.ty, p).is_ok()
+                                }
+                            };
+                            if ok {
+                                result.insert(*id, p);
+                            } else {
+                                return Err(HashSet::from([format!(
+                                    "cannot apply type {} to type variable {}",
+                                    unifier.stringify(p),
+                                    name.unwrap_or_else(|| format!("typevar{id}").into()),
+                                )]));
+                            }
+                        }
+
+                        _ => unreachable!("must be generic type var"),
                     }
                 }
-
+                result
-                ty
             };
-
+            // Class Attributes keep a copy with Class Definition and are not added to objects
+            let mut tobj_fields = methods
+                .iter()
+                .map(|(name, ty, _)| {
+                    let subst_ty = unifier.subst(*ty, &subst).unwrap_or(*ty);
+                    // methods are immutable
+                    (*name, (subst_ty, false))
+                })
+                .collect::<HashMap<_, _>>();
+            tobj_fields.extend(fields.iter().map(|(name, ty, mutability)| {
+                let subst_ty = unifier.subst(*ty, &subst).unwrap_or(*ty);
+                (*name, (subst_ty, *mutability))
+            }));
+            let need_subst = !subst.is_empty();
+            let ty = unifier.add_ty(TypeEnum::TObj {
+                obj_id: *obj_id,
+                fields: tobj_fields,
+                params: subst,
+            });
+            if need_subst {
+                if let Some(wl) = subst_list.as_mut() {
+                    wl.push(ty);
+                }
+            }
             Ok(ty)
         }
         TypeAnnotation::Primitive(ty) | TypeAnnotation::TypeVar(ty) => Ok(*ty),
@@ -533,7 +490,6 @@ pub fn get_type_from_type_annotation_kinds(
             let ty = get_type_from_type_annotation_kinds(
                 top_level_defs,
                 unifier,
-                primitives,
                 ty.as_ref(),
                 subst_list,
             )?;
@@ -543,13 +499,7 @@ pub fn get_type_from_type_annotation_kinds(
             let tys = tys
                 .iter()
                 .map(|x| {
-                    get_type_from_type_annotation_kinds(
+                    get_type_from_type_annotation_kinds(top_level_defs, unifier, x, subst_list)
-                        top_level_defs,
-                        unifier,
-                        primitives,
-                        x,
-                        subst_list,
-                    )
                 })
                 .collect::<Result<Vec<_>, _>>()?;
             Ok(unifier.add_ty(TypeEnum::TTuple { ty: tys }))

nac3standalone/Cargo.toml

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

nac3standalone/demo/check_demo.sh

@@ -15,7 +15,7 @@ done
 demo="$1"
 
 echo -n "Checking $demo... "
-# ./interpret_demo.py "$demo" > interpreted.log
+./interpret_demo.py "$demo" > interpreted.log
 ./run_demo.sh --out run.log "${nac3args[@]}" "$demo"
 ./run_demo.sh --lli --out run_lli.log "${nac3args[@]}" "$demo"
 diff -Nau interpreted.log run.log

nac3standalone/demo/demo.c

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

nac3standalone/demo/externfns/Cargo.toml

@@ -1,5 +1,5 @@
 [package]
-name = "linalg_externfns"
+name = "externfns"
 version = "0.1.0"
 edition = "2021"
 
@@ -8,4 +8,3 @@ crate-type = ["cdylib"]
 
 [dependencies]
 nalgebra = {version = "0.32.6", default-features = false, features = ["libm", "alloc"]}
-cslice = "0.3.0"

nac3standalone/demo/externfns/src/lib.rs

@@ -0,0 +1,41 @@
+#![deny(
+    future_incompatible,
+    let_underscore,
+    nonstandard_style,
+    rust_2024_compatibility,
+    clippy::all
+)]
+#![warn(clippy::pedantic)]
+#![allow(clippy::semicolon_if_nothing_returned, clippy::uninlined_format_args)]
+
+use core::slice;
+use nalgebra::{DMatrix, linalg};
+
+#[no_mangle]
+/// Provide an interface to `nalgebra::linalg::try_invert_to`
+pub extern "C" fn linalg_try_invert_to(dim0: usize, dim1: usize, data: *mut f64) -> i8 
+{
+    
+    let data_slice = unsafe { slice::from_raw_parts_mut(data, dim0 * dim1) };
+    let matrix = DMatrix::from_row_slice(dim0, dim1, data_slice);
+    let mut inverted_matrix = DMatrix::<f64>::zeros(dim0, dim1);
+
+    if linalg::try_invert_to(matrix, &mut inverted_matrix) {
+        data_slice.copy_from_slice(inverted_matrix.transpose().as_slice());
+        1
+    } else {
+        0
+    }
+}
+
+#[no_mangle]
+/// Provide an interface to `nalgebra::linalg::wilkinson_shift`
+pub extern "C" fn linalg_wilkinson_shift(dim0: usize, dim1: usize, data: *mut f64) -> f64
+{
+    let data_slice = unsafe { slice::from_raw_parts_mut(data, dim0 * dim1) };
+    let matrix = DMatrix::from_row_slice(dim0, dim1, data_slice);
+
+    // Check if matrix is symmetric
+    assert!(matrix[(0, 1)] == matrix[(1, 0)], "Operation Wilkinson Shift expects symmetric matrix");
+    return linalg::wilkinson_shift(matrix[(0, 0)], matrix[(1, 1)], matrix[(0, 1)]);
+}

nac3standalone/demo/interpret_demo.py

@@ -5,7 +5,6 @@ import importlib.util
 import importlib.machinery
 import math
 import numpy as np
-import scipy as sp
 import numpy.typing as npt
 import pathlib
 
@@ -188,7 +187,7 @@ def patch(module):
     module.ceil64 = _ceil
     module.np_ceil = np.ceil
 
-    # NumPy NDArray factory functions
+    # NumPy ndarray functions
     module.ndarray = NDArray
     module.np_ndarray = np.ndarray
     module.np_empty = np.empty
@@ -239,7 +238,7 @@ def patch(module):
     module.np_hypot = np.hypot
     module.np_nextafter = np.nextafter
 
-    # SciPy Math functions
+    # SciPy Math Functions
     module.sp_spec_erf = special.erf
     module.sp_spec_erfc = special.erfc
     module.sp_spec_gamma = special.gamma
@@ -247,21 +246,16 @@ def patch(module):
     module.sp_spec_j0 = special.j0
     module.sp_spec_j1 = special.j1
 
-    # Linalg functions
+    # NumPy NDArray Functions
-    module.np_dot = np.dot
+    module.np_ndarray = np.ndarray
-    module.np_linalg_matmul = np.matmul
+    module.np_empty = np.empty
-    module.np_linalg_cholesky = np.linalg.cholesky
+    module.np_zeros = np.zeros
-    module.np_linalg_qr = np.linalg.qr
+    module.np_ones = np.ones
-    module.np_linalg_svd = np.linalg.svd
+    module.np_full = np.full
-    module.np_linalg_inv = np.linalg.inv
+    module.np_eye = np.eye
-    module.np_linalg_pinv = np.linalg.pinv
+    module.np_identity = np.identity
-    
+    module.try_invert_to = try_invert_to
-    module.sp_linalg_lu = lambda x: sp.linalg.lu(x, True)
+    module.wilkinson_shift = wilkinson_shift
-    module.sp_linalg_schur = sp.linalg.schur
-    # module.sp_linalg_hessenberg = sp.linalg.hessenberg
-    module.sp_linalg_hessenberg = lambda x: x
-
-    
 
 def file_import(filename, prefix="file_import_"):
     filename = pathlib.Path(filename)

nac3standalone/demo/run.log

@@ -1,2 +0,0 @@
-Excepytiopn!!  knfv 0x7fffffff9218
-__nac3_personality(state: 1, exception_object: 1, context: 1381323604)

nac3standalone/demo/run_demo.sh

@@ -42,14 +42,14 @@ done
 
 if [ -n "$debug" ] && [ -e ../../target/debug/nac3standalone ]; then
     nac3standalone=../../target/debug/nac3standalone
-    externfns=../../target/debug/deps/liblinalg_externfns.so
+    externfns=../../target/debug/deps/libexternfns.so
 elif [ -e ../../target/release/nac3standalone ]; then
     nac3standalone=../../target/release/nac3standalone
-    externfns=../../target/release/deps/liblinalg_externfns.so
+    externfns=../../target/release/deps/libexternfns.so
 else
     # used by Nix builds
     nac3standalone=../../target/x86_64-unknown-linux-gnu/release/nac3standalone
-    externfns=../../target/x86_64-unknown-linux-gnu/release/deps/liblinalg_externfns.so
+    externfns=../../target/x86_64-unknown-linux-gnu/release/deps/libexternfns.so
 fi
 
 rm -f ./*.o ./*.bc demo

nac3standalone/demo/run_lli.log

@@ -1,12 +0,0 @@
-8.000000
-10.000000
-12.000000
-4.000000
-5.000000
-6.000000
-1.000000
-2.000000
-3.000000
-4.000000
-5.000000
-6.000000

nac3standalone/demo/src/ndarray.py

@@ -531,12 +531,10 @@ def test_ndarray_ipow_broadcast_scalar():
 
 def test_ndarray_matmul():
     x = np_identity(2)
-    t: ndarray[float, 2] = np_array([[1., 2., 3.,], [4., 5., 6.], [7., 8., 9.], [7., 8., 9.]])
+    y = x @ np_ones([2, 2])
-    y = x @ t
-    y2 = np_linalg_matmul(x, t)
-    output_ndarray_float_2(y)
-    output_ndarray_float_2(y2)
 
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
 
 def test_ndarray_imatmul():
     x = np_identity(2)
@@ -1431,289 +1429,201 @@ 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():
+def test_try_invert():
-    x: ndarray[float, 1] = np_array([5.0, 1.0])
+    x: ndarray[float, 2] = np_array([[1.0, 1.0], [1.0, 5.0]])
-    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)
-
-    output_ndarray_float_2(x)
-    output_ndarray_float_2(y)
-    output_ndarray_float_2(z)
-    output_int64(m)
-
-def test_ndarray_cholesky():
-    x: ndarray[float, 2] = np_array([[5.0, 1.0], [1.0, 4.0]])
-    y = np_linalg_cholesky(x)
-
-    output_ndarray_float_2(x)
-    output_ndarray_float_2(y)
-
-def test_ndarray_qr():
-    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, z  = np_linalg_qr(x)
 
     output_ndarray_float_2(x)
 
-    # QR Factorization in nalgebra and numpy do not give the same result
+    y = try_invert_to(x)
-    # Generating product for printing
-    a = np_linalg_matmul(y, z)
-    output_ndarray_float_2(a)
-
-def test_ndarray_linalg_inv():
-    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_inv(x)
 
     output_ndarray_float_2(x)
-    output_ndarray_float_2(y)
+    output_bool(y)
-
-def test_ndarray_pinv():
-    x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
-    y = np_linalg_pinv(x)
 
+def test_wilkinson_shift():
+    x: ndarray[float, 2] = np_array([[5., 1.], [1., 4.]])
+    y = wilkinson_shift(x)
     output_ndarray_float_2(x)
-    output_ndarray_float_2(y)
+    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)
-
-    output_ndarray_float_2(x)
-    # Same as np_linalg_qr the signs are different in nalgebra and numpy
-    a = np_linalg_matmul(np_linalg_matmul(z, t), np_linalg_inv(z))
-    output_ndarray_float_2(a)
-
-def test_ndarray_hessenberg():
-    x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
-    h = sp_linalg_hessenberg(x)
-
-    output_ndarray_float_2(x)
-    output_ndarray_float_2(h)
-
-
-def test_ndarray_lu():
-    x: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5]])
-    l, u = sp_linalg_lu(x)
-
-    output_ndarray_float_2(x)
-    output_ndarray_float_2(l)
-    output_ndarray_float_2(u)
-
-
-def test_ndarray_svd():
-    w: ndarray[float, 2] = np_array([[-5.0, -1.0, 2.0], [-1.0, 4.0, 7.5], [-1.0, 8.0, -8.5]])
-    x, y, z = np_linalg_svd(w)
-
-    output_ndarray_float_2(w)
-
-    # Same as np_linalg_qr the signs are different in nalgebra and numpy
-    a = np_linalg_matmul(x, z)
-    output_ndarray_float_2(a)
-    output_ndarray_float_1(y)
-
 
 def run() -> int32:
+    test_try_invert()
+    test_wilkinson_shift()
+    test_ndarray_ctor()
+    test_ndarray_empty()
+    test_ndarray_zeros()
+    test_ndarray_ones()
+    test_ndarray_full()
+    test_ndarray_eye()
+    test_ndarray_array()
+    test_ndarray_identity()
+    test_ndarray_fill()
+    test_ndarray_copy()
+
+    test_ndarray_neg_idx()
+    test_ndarray_slices()
+    test_ndarray_nd_idx()
+
+    test_ndarray_add()
+    test_ndarray_add_broadcast()
+    test_ndarray_add_broadcast_lhs_scalar()
+    test_ndarray_add_broadcast_rhs_scalar()
+    test_ndarray_iadd()
+    test_ndarray_iadd_broadcast()
+    test_ndarray_iadd_broadcast_scalar()
+    test_ndarray_sub()
+    test_ndarray_sub_broadcast()
+    test_ndarray_sub_broadcast_lhs_scalar()
+    test_ndarray_sub_broadcast_rhs_scalar()
+    test_ndarray_isub()
+    test_ndarray_isub_broadcast()
+    test_ndarray_isub_broadcast_scalar()
+    test_ndarray_mul()
+    test_ndarray_mul_broadcast()
+    test_ndarray_mul_broadcast_lhs_scalar()
+    test_ndarray_mul_broadcast_rhs_scalar()
+    test_ndarray_imul()
+    test_ndarray_imul_broadcast()
+    test_ndarray_imul_broadcast_scalar()
+    test_ndarray_truediv()
+    test_ndarray_truediv_broadcast()
+    test_ndarray_truediv_broadcast_lhs_scalar()
+    test_ndarray_truediv_broadcast_rhs_scalar()
+    test_ndarray_itruediv()
+    test_ndarray_itruediv_broadcast()
+    test_ndarray_itruediv_broadcast_scalar()
+    test_ndarray_floordiv()
+    test_ndarray_floordiv_broadcast()
+    test_ndarray_floordiv_broadcast_lhs_scalar()
+    test_ndarray_floordiv_broadcast_rhs_scalar()
+    test_ndarray_ifloordiv()
+    test_ndarray_ifloordiv_broadcast()
+    test_ndarray_ifloordiv_broadcast_scalar()
+    test_ndarray_mod()
+    test_ndarray_mod_broadcast()
+    test_ndarray_mod_broadcast_lhs_scalar()
+    test_ndarray_mod_broadcast_rhs_scalar()
+    test_ndarray_imod()
+    test_ndarray_imod_broadcast()
+    test_ndarray_imod_broadcast_scalar()
+    test_ndarray_pow()
+    test_ndarray_pow_broadcast()
+    test_ndarray_pow_broadcast_lhs_scalar()
+    test_ndarray_pow_broadcast_rhs_scalar()
+    test_ndarray_ipow()
+    test_ndarray_ipow_broadcast()
+    test_ndarray_ipow_broadcast_scalar()
     test_ndarray_matmul()
-    # test_ndarray_dot()
+    test_ndarray_imatmul()
-    # test_ndarray_linalg_matmul()
+    test_ndarray_pos()
-    # test_ndarray_cholesky()
+    test_ndarray_neg()
-    # test_ndarray_qr()
+    test_ndarray_inv()
-    # test_ndarray_svd()
+    test_ndarray_eq()
-    # test_ndarray_linalg_inv()
+    test_ndarray_eq_broadcast()
-    # test_ndarray_pinv()
+    test_ndarray_eq_broadcast_lhs_scalar()
-    # test_ndarray_lu()
+    test_ndarray_eq_broadcast_rhs_scalar()
-    # test_ndarray_schur()
+    test_ndarray_ne()
-    # test_ndarray_hessenberg()
+    test_ndarray_ne_broadcast()
+    test_ndarray_ne_broadcast_lhs_scalar()
+    test_ndarray_ne_broadcast_rhs_scalar()
+    test_ndarray_lt()
+    test_ndarray_lt_broadcast()
+    test_ndarray_lt_broadcast_lhs_scalar()
+    test_ndarray_lt_broadcast_rhs_scalar()
+    test_ndarray_lt()
+    test_ndarray_le_broadcast()
+    test_ndarray_le_broadcast_lhs_scalar()
+    test_ndarray_le_broadcast_rhs_scalar()
+    test_ndarray_gt()
+    test_ndarray_gt_broadcast()
+    test_ndarray_gt_broadcast_lhs_scalar()
+    test_ndarray_gt_broadcast_rhs_scalar()
+    test_ndarray_gt()
+    test_ndarray_ge_broadcast()
+    test_ndarray_ge_broadcast_lhs_scalar()
+    test_ndarray_ge_broadcast_rhs_scalar()
 
-    # test_ndarray_ctor()
+    test_ndarray_int32()
-    # test_ndarray_empty()
+    test_ndarray_int64()
-    # test_ndarray_zeros()
+    test_ndarray_uint32()
-    # test_ndarray_ones()
+    test_ndarray_uint64()
-    # test_ndarray_full()
+    test_ndarray_float()
-    # test_ndarray_eye()
+    test_ndarray_bool()
-    # test_ndarray_array()
-    # test_ndarray_identity()
-    # test_ndarray_fill()
-    # test_ndarray_copy()
 
-    # test_ndarray_neg_idx()
+    test_ndarray_round()
-    # test_ndarray_slices()
+    test_ndarray_floor()
-    # test_ndarray_nd_idx()
+    test_ndarray_min()
+    test_ndarray_minimum()
+    test_ndarray_minimum_broadcast()
+    test_ndarray_minimum_broadcast_lhs_scalar()
+    test_ndarray_minimum_broadcast_rhs_scalar()
+    test_ndarray_argmin()
+    test_ndarray_max()
+    test_ndarray_maximum()
+    test_ndarray_maximum_broadcast()
+    test_ndarray_maximum_broadcast_lhs_scalar()
+    test_ndarray_maximum_broadcast_rhs_scalar()
+    test_ndarray_argmax()
+    test_ndarray_abs()
+    test_ndarray_isnan()
+    test_ndarray_isinf()
 
-    # test_ndarray_add()
+    test_ndarray_sin()
-    # test_ndarray_add_broadcast()
+    test_ndarray_cos()
-    # test_ndarray_add_broadcast_lhs_scalar()
+    test_ndarray_exp()
-    # test_ndarray_add_broadcast_rhs_scalar()
+    test_ndarray_exp2()
-    # test_ndarray_iadd()
+    test_ndarray_log()
-    # test_ndarray_iadd_broadcast()
+    test_ndarray_log10()
-    # test_ndarray_iadd_broadcast_scalar()
+    test_ndarray_log2()
-    # test_ndarray_sub()
+    test_ndarray_fabs()
-    # test_ndarray_sub_broadcast()
+    test_ndarray_sqrt()
-    # test_ndarray_sub_broadcast_lhs_scalar()
+    test_ndarray_rint()
-    # test_ndarray_sub_broadcast_rhs_scalar()
+    test_ndarray_tan()
-    # test_ndarray_isub()
+    test_ndarray_arcsin()
-    # test_ndarray_isub_broadcast()
+    test_ndarray_arccos()
-    # test_ndarray_isub_broadcast_scalar()
+    test_ndarray_arctan()
-    # test_ndarray_mul()
+    test_ndarray_sinh()
-    # test_ndarray_mul_broadcast()
+    test_ndarray_cosh()
-    # test_ndarray_mul_broadcast_lhs_scalar()
+    test_ndarray_tanh()
-    # test_ndarray_mul_broadcast_rhs_scalar()
+    test_ndarray_arcsinh()
-    # test_ndarray_imul()
+    test_ndarray_arccosh()
-    # test_ndarray_imul_broadcast()
+    test_ndarray_arctanh()
-    # test_ndarray_imul_broadcast_scalar()
+    test_ndarray_expm1()
-    # test_ndarray_truediv()
+    test_ndarray_cbrt()
-    # test_ndarray_truediv_broadcast()
-    # test_ndarray_truediv_broadcast_lhs_scalar()
-    # test_ndarray_truediv_broadcast_rhs_scalar()
-    # test_ndarray_itruediv()
-    # test_ndarray_itruediv_broadcast()
-    # test_ndarray_itruediv_broadcast_scalar()
-    # test_ndarray_floordiv()
-    # test_ndarray_floordiv_broadcast()
-    # test_ndarray_floordiv_broadcast_lhs_scalar()
-    # test_ndarray_floordiv_broadcast_rhs_scalar()
-    # test_ndarray_ifloordiv()
-    # test_ndarray_ifloordiv_broadcast()
-    # test_ndarray_ifloordiv_broadcast_scalar()
-    # test_ndarray_mod()
-    # test_ndarray_mod_broadcast()
-    # test_ndarray_mod_broadcast_lhs_scalar()
-    # test_ndarray_mod_broadcast_rhs_scalar()
-    # test_ndarray_imod()
-    # test_ndarray_imod_broadcast()
-    # test_ndarray_imod_broadcast_scalar()
-    # test_ndarray_pow()
-    # test_ndarray_pow_broadcast()
-    # test_ndarray_pow_broadcast_lhs_scalar()
-    # test_ndarray_pow_broadcast_rhs_scalar()
-    # test_ndarray_ipow()
-    # test_ndarray_ipow_broadcast()
-    # test_ndarray_ipow_broadcast_scalar()
-    # test_ndarray_matmul()
-    # test_ndarray_imatmul()
-    # test_ndarray_pos()
-    # test_ndarray_neg()
-    # test_ndarray_inv()
-    # test_ndarray_eq()
-    # test_ndarray_eq_broadcast()
-    # test_ndarray_eq_broadcast_lhs_scalar()
-    # test_ndarray_eq_broadcast_rhs_scalar()
-    # test_ndarray_ne()
-    # test_ndarray_ne_broadcast()
-    # test_ndarray_ne_broadcast_lhs_scalar()
-    # test_ndarray_ne_broadcast_rhs_scalar()
-    # test_ndarray_lt()
-    # test_ndarray_lt_broadcast()
-    # test_ndarray_lt_broadcast_lhs_scalar()
-    # test_ndarray_lt_broadcast_rhs_scalar()
-    # test_ndarray_lt()
-    # test_ndarray_le_broadcast()
-    # test_ndarray_le_broadcast_lhs_scalar()
-    # test_ndarray_le_broadcast_rhs_scalar()
-    # test_ndarray_gt()
-    # test_ndarray_gt_broadcast()
-    # test_ndarray_gt_broadcast_lhs_scalar()
-    # test_ndarray_gt_broadcast_rhs_scalar()
-    # test_ndarray_gt()
-    # test_ndarray_ge_broadcast()
-    # test_ndarray_ge_broadcast_lhs_scalar()
-    # test_ndarray_ge_broadcast_rhs_scalar()
 
-    # test_ndarray_int32()
+    test_ndarray_erf()
-    # test_ndarray_int64()
+    test_ndarray_erfc()
-    # test_ndarray_uint32()
+    test_ndarray_gamma()
-    # test_ndarray_uint64()
+    test_ndarray_gammaln()
-    # test_ndarray_float()
+    test_ndarray_j0()
-    # test_ndarray_bool()
+    test_ndarray_j1()
 
-    # test_ndarray_round()
+    test_ndarray_arctan2()
-    # test_ndarray_floor()
+    test_ndarray_arctan2_broadcast()
-    # test_ndarray_min()
+    test_ndarray_arctan2_broadcast_lhs_scalar()
-    # test_ndarray_minimum()
+    test_ndarray_arctan2_broadcast_rhs_scalar()
-    # test_ndarray_minimum_broadcast()
+    test_ndarray_copysign()
-    # test_ndarray_minimum_broadcast_lhs_scalar()
+    test_ndarray_copysign_broadcast()
-    # test_ndarray_minimum_broadcast_rhs_scalar()
+    test_ndarray_copysign_broadcast_lhs_scalar()
-    # test_ndarray_argmin()
+    test_ndarray_copysign_broadcast_rhs_scalar()
-    # test_ndarray_max()
+    test_ndarray_fmax()
-    # test_ndarray_maximum()
+    test_ndarray_fmax_broadcast()
-    # test_ndarray_maximum_broadcast()
+    test_ndarray_fmax_broadcast_lhs_scalar()
-    # test_ndarray_maximum_broadcast_lhs_scalar()
+    test_ndarray_fmax_broadcast_rhs_scalar()
-    # test_ndarray_maximum_broadcast_rhs_scalar()
+    test_ndarray_fmin()
-    # test_ndarray_argmax()
+    test_ndarray_fmin_broadcast()
-    # test_ndarray_abs()
+    test_ndarray_fmin_broadcast_lhs_scalar()
-    # test_ndarray_isnan()
+    test_ndarray_fmin_broadcast_rhs_scalar()
-    # test_ndarray_isinf()
+    test_ndarray_ldexp()
-
+    test_ndarray_ldexp_broadcast()
-    # test_ndarray_sin()
+    test_ndarray_ldexp_broadcast_lhs_scalar()
-    # test_ndarray_cos()
+    test_ndarray_ldexp_broadcast_rhs_scalar()
-    # test_ndarray_exp()
+    test_ndarray_hypot()
-    # test_ndarray_exp2()
+    test_ndarray_hypot_broadcast()
-    # test_ndarray_log()
+    test_ndarray_hypot_broadcast_lhs_scalar()
-    # test_ndarray_log10()
+    test_ndarray_hypot_broadcast_rhs_scalar()
-    # test_ndarray_log2()
+    test_ndarray_nextafter()
-    # test_ndarray_fabs()
+    test_ndarray_nextafter_broadcast()
-    # test_ndarray_sqrt()
+    test_ndarray_nextafter_broadcast_lhs_scalar()
-    # test_ndarray_rint()
+    test_ndarray_nextafter_broadcast_rhs_scalar()
-    # test_ndarray_tan()
-    # test_ndarray_arcsin()
-    # test_ndarray_arccos()
-    # test_ndarray_arctan()
-    # test_ndarray_sinh()
-    # test_ndarray_cosh()
-    # test_ndarray_tanh()
-    # test_ndarray_arcsinh()
-    # test_ndarray_arccosh()
-    # test_ndarray_arctanh()
-    # test_ndarray_expm1()
-    # test_ndarray_cbrt()
-
-    # test_ndarray_erf()
-    # test_ndarray_erfc()
-    # test_ndarray_gamma()
-    # test_ndarray_gammaln()
-    # test_ndarray_j0()
-    # test_ndarray_j1()
-
-    # test_ndarray_arctan2()
-    # test_ndarray_arctan2_broadcast()
-    # test_ndarray_arctan2_broadcast_lhs_scalar()
-    # test_ndarray_arctan2_broadcast_rhs_scalar()
-    # test_ndarray_copysign()
-    # test_ndarray_copysign_broadcast()
-    # test_ndarray_copysign_broadcast_lhs_scalar()
-    # test_ndarray_copysign_broadcast_rhs_scalar()
-    # test_ndarray_fmax()
-    # test_ndarray_fmax_broadcast()
-    # test_ndarray_fmax_broadcast_lhs_scalar()
-    # test_ndarray_fmax_broadcast_rhs_scalar()
-    # test_ndarray_fmin()
-    # test_ndarray_fmin_broadcast()
-    # test_ndarray_fmin_broadcast_lhs_scalar()
-    # test_ndarray_fmin_broadcast_rhs_scalar()
-    # test_ndarray_ldexp()
-    # test_ndarray_ldexp_broadcast()
-    # test_ndarray_ldexp_broadcast_lhs_scalar()
-    # test_ndarray_ldexp_broadcast_rhs_scalar()
-    # test_ndarray_hypot()
-    # test_ndarray_hypot_broadcast()
-    # test_ndarray_hypot_broadcast_lhs_scalar()
-    # test_ndarray_hypot_broadcast_rhs_scalar()
-    # test_ndarray_nextafter()
-    # test_ndarray_nextafter_broadcast()
-    # test_ndarray_nextafter_broadcast_lhs_scalar()
-    # test_ndarray_nextafter_broadcast_rhs_scalar()
-
-    # test_try_invert()
-    # test_wilkinson_shift()
 
     return 0

nac3standalone/linalg_externfns/src/lib.rs

@@ -1,346 +0,0 @@
-mod runtime_exception;
-use core::slice;
-use nalgebra::{linalg, DMatrix};
-
-macro_rules! raise_exn {
-    ($name:expr, $message:expr, $param0:expr, $param1:expr, $param2:expr) => {{
-        use cslice::AsCSlice;
-        let name_id = $crate::runtime_exception::get_exception_id($name);
-        let exn = $crate::runtime_exception::Exception {
-            id: name_id,
-            file: file!().as_c_slice(),
-            line: line!(),
-            column: column!(),
-            // https://github.com/rust-lang/rfcs/pull/1719
-            function: "(Rust function)".as_c_slice(),
-            message: $message.as_c_slice(),
-            param: [$param0, $param1, $param2],
-        };
-        #[allow(unused_unsafe)]
-        unsafe {
-            $crate::runtime_exception::raise(&exn)
-        }
-    }};
-    ($name:expr, $message:expr) => {{
-        raise_exn!($name, $message, 0, 0, 0)
-    }};
-}
-
-/// # Safety
-///
-/// `data` must point to an array with `dim0`x`dim1` elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn linalg_try_invert_to(dim0: usize, dim1: usize, data: *mut f64) -> i8 {
-    let data_slice = unsafe { slice::from_raw_parts_mut(data, dim0 * dim1) };
-    let matrix = DMatrix::from_row_slice(dim0, dim1, data_slice);
-    let mut inverted_matrix = DMatrix::<f64>::zeros(dim0, dim1);
-
-    if linalg::try_invert_to(matrix, &mut inverted_matrix) {
-        data_slice.copy_from_slice(inverted_matrix.transpose().as_slice());
-        1
-    } else {
-        0
-    }
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn linalg_wilkinson_shift(dim0: usize, dim1: usize, data: *mut f64) -> f64 {
-    let data_slice = unsafe { slice::from_raw_parts_mut(data, dim0 * dim1) };
-    let matrix = DMatrix::from_row_slice(dim0, dim1, data_slice);
-
-    linalg::wilkinson_shift(matrix[(0, 0)], matrix[(1, 1)], matrix[(0, 1)])
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn np_dot(
-    dim0: usize,
-    dim1: usize,
-    x1: *mut f64,
-    _: usize,
-    _: usize,
-    x2: *mut f64,
-) -> f64 {
-    let data_slice1 = unsafe { slice::from_raw_parts_mut(x1, dim0 * dim1) };
-    let data_slice2 = unsafe { slice::from_raw_parts_mut(x2, dim0 * dim1) };
-
-    let matrix1 = DMatrix::from_row_slice(dim0, dim1, data_slice1);
-    let matrix2 = DMatrix::from_row_slice(dim0, dim1, data_slice2);
-
-    matrix1.dot(&matrix2)
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn np_linalg_matmul(
-    dim1_0: usize,
-    dim1_1: usize,
-    x1: *mut f64,
-    dim2_0: usize,
-    dim2_1: usize,
-    x2: *mut f64,
-    dim3_0: usize,
-    dim3_1: usize,
-    out: *mut f64,
-) -> i8 {
-    // let name = unsafe {slice::from_raw_parts_mut(n, l)};
-    // let fne = name.as_c_slice();
-    raise_exn!("ZeroDivisionError", "Divide by Zero");
-    let data_slice1 = unsafe { slice::from_raw_parts_mut(x1, dim1_0 * dim1_1) };
-    let data_slice2 = unsafe { slice::from_raw_parts_mut(x2, dim2_0 * dim2_1) };
-    let out_slice = unsafe { slice::from_raw_parts_mut(out, dim3_0 * dim3_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(dim3_0, dim3_1);
-
-    matrix1.mul_to(&matrix2, &mut result);
-    out_slice.copy_from_slice(result.transpose().as_slice());
-    // raise_exn!("ZeroDivisionError", "Divide by Zero", r, c, n);
-
-    1
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn np_linalg_cholesky(
-    dim1_0: usize,
-    dim1_1: usize,
-    x1: *mut f64,
-    dim2_0: usize,
-    dim2_1: usize,
-    out: *mut f64,
-) -> i8 {
-    let data_slice1 = unsafe { slice::from_raw_parts_mut(x1, dim1_0 * dim1_1) };
-    let out_slice = unsafe { slice::from_raw_parts_mut(out, dim2_0 * dim2_1) };
-
-    let matrix1 = DMatrix::from_row_slice(dim1_0, dim1_1, data_slice1);
-
-    let res = matrix1.cholesky();
-    match res {
-        None => 0,
-        Some(c) => {
-            out_slice.copy_from_slice(c.unpack().transpose().as_slice());
-            1
-        }
-    }
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn np_linalg_qr(
-    dim1_0: usize,
-    dim1_1: usize,
-    x1: *mut f64,
-    dim2_0: usize,
-    dim2_1: usize,
-    out_q: *mut f64,
-    dim3_0: usize,
-    dim3_1: usize,
-    out_r: *mut f64,
-) -> i8 {
-    let data_slice1 = unsafe { slice::from_raw_parts_mut(x1, dim1_0 * dim1_1) };
-    let out_q_slice = unsafe { slice::from_raw_parts_mut(out_q, dim2_0 * dim2_1) };
-    let out_r_slice = unsafe { slice::from_raw_parts_mut(out_r, dim3_0 * dim3_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());
-    1
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn np_linalg_svd(
-    dim1_0: usize,
-    dim1_1: usize,
-    x1: *mut f64,
-    dim2_0: usize,
-    dim2_1: usize,
-    out_u: *mut f64,
-    dim3_0: usize,
-    dim3_1: usize,
-    out_s: *mut f64,
-    dim4_0: usize,
-    dim4_1: usize,
-    out_vh: *mut f64,
-) -> i8 {
-    let data_slice = unsafe { slice::from_raw_parts_mut(x1, dim1_0 * dim1_1) };
-    let out_u_slice = unsafe { slice::from_raw_parts_mut(out_u, dim2_0 * dim2_1) };
-    let out_s_slice = unsafe { slice::from_raw_parts_mut(out_s, dim3_0 * dim3_1) };
-    let out_vh_slice = unsafe { slice::from_raw_parts_mut(out_vh, dim4_0 * dim4_1) };
-
-    let matrix = DMatrix::from_row_slice(dim1_0, dim1_1, data_slice);
-    let res = matrix.svd(true, true);
-
-    out_u_slice.copy_from_slice(res.u.unwrap().transpose().as_slice());
-    out_s_slice.copy_from_slice(res.singular_values.as_slice());
-    out_vh_slice.copy_from_slice(res.v_t.unwrap().transpose().as_slice());
-
-    1
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn np_linalg_inv(
-    dim1_0: usize,
-    dim1_1: usize,
-    x1: *mut f64,
-    dim2_0: usize,
-    dim2_1: usize,
-    out: *mut f64,
-) -> i8 {
-    let data_slice = unsafe { slice::from_raw_parts_mut(x1, dim1_0 * dim1_1) };
-    let out_slice = unsafe { slice::from_raw_parts_mut(out, dim2_0 * dim2_1) };
-
-    let matrix = DMatrix::from_row_slice(dim1_0, dim1_1, data_slice);
-    if !matrix.is_invertible() {
-        // raise error
-        return 0;
-    }
-    let inv = matrix.try_inverse().unwrap();
-
-    out_slice.copy_from_slice(inv.transpose().as_slice());
-    1
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn np_linalg_pinv(
-    dim1_0: usize,
-    dim1_1: usize,
-    x1: *mut f64,
-    dim2_0: usize,
-    dim2_1: usize,
-    out: *mut f64,
-) -> i8 {
-    let data_slice = unsafe { slice::from_raw_parts_mut(x1, dim1_0 * dim1_1) };
-    let out_slice = unsafe { slice::from_raw_parts_mut(out, dim2_0 * dim2_1) };
-
-    let matrix = DMatrix::from_row_slice(dim1_0, dim1_1, data_slice);
-    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());
-            1
-        }
-        Err(e) => {
-            // raise exception here
-            assert!(false, "{e}");
-            0
-        }
-    }
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn sp_linalg_lu(
-    dim1_0: usize,
-    dim1_1: usize,
-    x1: *mut f64,
-    dim2_0: usize,
-    dim2_1: usize,
-    out_l: *mut f64,
-    dim3_0: usize,
-    dim3_1: usize,
-    out_u: *mut f64,
-) -> i8 {
-    let data_slice = unsafe { slice::from_raw_parts_mut(x1, dim1_0 * dim1_1) };
-    let out_l_slice = unsafe { slice::from_raw_parts_mut(out_l, dim2_0 * dim2_1) };
-    let out_u_slice = unsafe { slice::from_raw_parts_mut(out_u, dim3_0 * dim3_1) };
-
-    let matrix = DMatrix::from_row_slice(dim1_0, dim1_1, data_slice);
-    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());
-
-    1
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn sp_linalg_schur(
-    dim1_0: usize,
-    dim1_1: usize,
-    x1: *mut f64,
-    dim2_0: usize,
-    dim2_1: usize,
-    out_t: *mut f64,
-    dim3_0: usize,
-    dim3_1: usize,
-    out_z: *mut f64,
-) -> i8 {
-    let data_slice = unsafe { slice::from_raw_parts_mut(x1, dim1_0 * dim1_1) };
-    let out_t_slice = unsafe { slice::from_raw_parts_mut(out_t, dim2_0 * dim2_1) };
-    let out_z_slice = unsafe { slice::from_raw_parts_mut(out_z, dim3_0 * dim3_1) };
-
-    let matrix = DMatrix::from_row_slice(dim1_0, dim1_1, data_slice);
-    if !matrix.is_square() {
-        // Throw error here
-        return 0;
-    }
-    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());
-
-    1
-}
-
-/// # Safety
-///
-/// `data` must point to an array of 4 elements in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn sp_linalg_hessenberg(
-    dim1_0: usize,
-    dim1_1: usize,
-    x1: *mut f64,
-    dim2_0: usize,
-    dim2_1: usize,
-    out_h: *mut f64,
-) -> i8 {
-    let data_slice = unsafe { slice::from_raw_parts_mut(x1, dim1_0 * dim1_1) };
-    let out_h_slice = unsafe { slice::from_raw_parts_mut(out_h, dim2_0 * dim2_1) };
-
-    let matrix = DMatrix::from_row_slice(dim1_0, dim1_1, data_slice);
-    if !matrix.is_square() {
-        // Throw error here
-
-        return 0;
-    }
-    let (_, h) = matrix.hessenberg().unpack();
-
-    out_h_slice.copy_from_slice(h.transpose().as_slice());
-
-    1
-}

nac3standalone/linalg_externfns/src/runtime_exception.rs

@@ -1,80 +0,0 @@
-#![allow(non_camel_case_types)]
-#![allow(unused)]
-
-// ARTIQ Exception struct declaration
-use cslice::CSlice;
-
-// Note: CSlice within an exception may not be actual cslice, they may be strings that exist only
-// in the host. If the length == usize:MAX, the pointer is actually a string key in the host.
-#[repr(C)]
-#[derive(Clone)]
-pub struct Exception<'a> {
-    pub id: u32,
-    pub file: CSlice<'a, u8>,
-    pub line: u32,
-    pub column: u32,
-    pub function: CSlice<'a, u8>,
-    pub message: CSlice<'a, u8>,
-    pub param: [i64; 3],
-}
-
-fn str_err(_: core::str::Utf8Error) -> core::fmt::Error {
-    core::fmt::Error
-}
-
-fn exception_str<'a>(s: &'a CSlice<'a, u8>) -> Result<&'a str, core::str::Utf8Error> {
-    if s.len() == usize::MAX {
-        Ok("<host string>")
-    } else {
-        core::str::from_utf8(s.as_ref())
-    }
-}
-
-impl<'a> core::fmt::Debug for Exception<'a> {
-    fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result {
-        write!(
-            f,
-            "Exception {} from {} in {}:{}:{}, message: {}",
-            self.id,
-            exception_str(&self.function).map_err(str_err)?,
-            exception_str(&self.file).map_err(str_err)?,
-            self.line,
-            self.column,
-            exception_str(&self.message).map_err(str_err)?
-        )
-    }
-}
-
-pub unsafe fn raise(exception: *const Exception) -> ! {
-    println!("Excepytiopn!!  knfv {:?}", exception);
-    let e = &*exception;
-    let f1 = exception_str(&e.function).map_err(str_err).unwrap();
-    let f2 = exception_str(&e.file).map_err(str_err).unwrap();
-    let f3 = exception_str(&e.message).map_err(str_err).unwrap();
-
-    panic!("Exception {} from {} in {}:{}:{}, message: {}", e.id, f1, f2, e.line, e.column, f3);
-}
-
-static EXCEPTION_ID_LOOKUP: [(&str, u32); 12] = [
-    ("RuntimeError", 0),
-    ("RTIOUnderflow", 1),
-    ("RTIOOverflow", 2),
-    ("RTIODestinationUnreachable", 3),
-    ("DMAError", 4),
-    ("I2CError", 5),
-    ("CacheError", 6),
-    ("SPIError", 7),
-    ("ZeroDivisionError", 8),
-    ("IndexError", 9),
-    ("UnwrapNoneError", 10),
-    ("Value", 11),
-];
-
-pub fn get_exception_id(name: &str) -> u32 {
-    for (n, id) in EXCEPTION_ID_LOOKUP.iter() {
-        if *n == name {
-            return *id;
-        }
-    }
-    unimplemented!("unallocated internal exception id")
-}

nac3standalone/src/main.rs

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