core: remove np_linalg_matmul

nac3core/src/codegen/builtin_fns.rs

@@ -1867,55 +1867,6 @@ fn build_output_struct<'ctx>(
     out_ptr
 }
 
-/// Invokes the `np_linalg_matmul` linalg function
-pub fn call_np_linalg_matmul<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    x1: (Type, BasicValueEnum<'ctx>),
-    x2: (Type, BasicValueEnum<'ctx>),
-) -> Result<BasicValueEnum<'ctx>, String> {
-    const FN_NAME: &str = "np_linalg_matmul";
-    let (x1_ty, x1) = x1;
-    let (x2_ty, x2) = x2;
-
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-    if let (BasicValueEnum::PointerValue(n1), BasicValueEnum::PointerValue(n2)) = (x1, x2) {
-        let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
-        let n1_elem_ty = ctx.get_llvm_type(generator, elem_ty);
-        let (n2_elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x2_ty);
-        let n2_elem_ty = ctx.get_llvm_type(generator, n2_elem_ty);
-
-        let (BasicTypeEnum::FloatType(_), BasicTypeEnum::FloatType(_)) = (n1_elem_ty, n2_elem_ty)
-        else {
-            unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty]);
-        };
-
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-        let n2 = NDArrayValue::from_ptr_val(n2, llvm_usize, None);
-
-        let outdim0 = unsafe {
-            n1.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_zero(), None)
-                .into_int_value()
-        };
-        let outdim1 = unsafe {
-            n2.dim_sizes()
-                .get_unchecked(ctx, generator, &llvm_usize.const_int(1, false), None)
-                .into_int_value()
-        };
-
-        let out = numpy::create_ndarray_const_shape(generator, ctx, elem_ty, &[outdim0, outdim1])
-            .unwrap()
-            .as_base_value()
-            .as_basic_value_enum();
-
-        extern_fns::call_np_linalg_matmul(ctx, x1, x2, out, None);
-        Ok(out)
-    } else {
-        unsupported_type(ctx, FN_NAME, &[x1_ty, x2_ty])
-    }
-}
-
 /// Invokes the `np_linalg_cholesky` linalg function
 pub fn call_np_linalg_cholesky<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,

nac3core/src/codegen/extern_fns.rs

@@ -179,7 +179,6 @@ macro_rules! generate_linalg_extern_fn {
     };
 }
 
-generate_linalg_extern_fn!(call_np_linalg_matmul, "np_linalg_matmul", 3);
 generate_linalg_extern_fn!(call_np_linalg_cholesky, "np_linalg_cholesky", 2);
 generate_linalg_extern_fn!(call_np_linalg_qr, "np_linalg_qr", 3);
 generate_linalg_extern_fn!(call_np_linalg_svd, "np_linalg_svd", 4);

nac3core/src/toplevel/builtins.rs

@@ -562,7 +562,6 @@ impl<'a> BuiltinBuilder<'a> {
             }
 
             PrimDef::FunNpDot
-            | PrimDef::FunNpLinalgMatmul
             | PrimDef::FunNpLinalgCholesky
             | PrimDef::FunNpLinalgQr
             | PrimDef::FunNpLinalgSvd
@@ -1950,7 +1949,6 @@ impl<'a> BuiltinBuilder<'a> {
             prim,
             &[
                 PrimDef::FunNpDot,
-                PrimDef::FunNpLinalgMatmul,
                 PrimDef::FunNpLinalgCholesky,
                 PrimDef::FunNpLinalgQr,
                 PrimDef::FunNpLinalgSvd,
@@ -1981,27 +1979,6 @@ impl<'a> BuiltinBuilder<'a> {
                 }),
             ),
 
-            PrimDef::FunNpLinalgMatmul => create_fn_by_codegen(
-                self.unifier,
-                &VarMap::new(),
-                prim.name(),
-                self.ndarray_float_2d,
-                &[(self.ndarray_float_2d, "x1"), (self.ndarray_float_2d, "x2")],
-                Box::new(move |ctx, _, fun, args, generator| {
-                    let x1_ty = fun.0.args[0].ty;
-                    let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
-                    let x2_ty = fun.0.args[1].ty;
-                    let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
-
-                    Ok(Some(builtin_fns::call_np_linalg_matmul(
-                        generator,
-                        ctx,
-                        (x1_ty, x1_val),
-                        (x2_ty, x2_val),
-                    )?))
-                }),
-            ),
-
             PrimDef::FunNpLinalgCholesky | PrimDef::FunNpLinalgInv | PrimDef::FunNpLinalgPinv => {
                 create_fn_by_codegen(
                     self.unifier,

nac3core/src/toplevel/helper.rs

@@ -104,7 +104,6 @@ pub enum PrimDef {
 
     // Linalg functions
     FunNpDot,
-    FunNpLinalgMatmul,
     FunNpLinalgCholesky,
     FunNpLinalgQr,
     FunNpLinalgSvd,
@@ -291,7 +290,6 @@ impl PrimDef {
 
             // Linalg functions
             PrimDef::FunNpDot => fun("np_dot", None),
-            PrimDef::FunNpLinalgMatmul => fun("np_linalg_matmul", None),
             PrimDef::FunNpLinalgCholesky => fun("np_linalg_cholesky", None),
             PrimDef::FunNpLinalgQr => fun("np_linalg_qr", None),
             PrimDef::FunNpLinalgSvd => fun("np_linalg_svd", None),

nac3standalone/demo/interpret_demo.py

@@ -5,8 +5,8 @@ import importlib.util
 import importlib.machinery
 import math
 import numpy as np
-import scipy as sp
 import numpy.typing as npt
+import scipy as sp
 import pathlib
 
 from numpy import int32, int64, uint32, uint64
@@ -231,7 +231,6 @@ def patch(module):
 
     # Linalg functions
     module.np_dot = np.dot
-    module.np_linalg_matmul = np.matmul
     module.np_linalg_cholesky = np.linalg.cholesky
     module.np_linalg_qr = np.linalg.qr
     module.np_linalg_svd = np.linalg.svd

nac3standalone/demo/linalg/src/lib.rs

@@ -34,51 +34,6 @@ impl InputMatrix {
     }
 }
 
-/// # Safety
-///
-/// `mat1` and `mat2` should point to a valid 2DArray of `f64` floats in row-major order
-#[no_mangle]
-pub unsafe extern "C" fn np_linalg_matmul(
-    mat1: *mut InputMatrix,
-    mat2: *mut InputMatrix,
-    out: *mut InputMatrix,
-) {
-    let mat1 = mat1.as_mut().unwrap();
-    let mat2 = mat2.as_mut().unwrap();
-    let out = out.as_mut().unwrap();
-
-    if !(mat1.ndims == 2 && mat2.ndims == 2) {
-        let err_msg = format!(
-            "expected 2D Vector Input, but received {}D and {}D input",
-            mat1.ndims, mat2.ndims
-        );
-        report_error("ValueError", "np_matmul", file!(), line!(), column!(), &err_msg);
-    }
-
-    let dim1 = (*mat1).get_dims();
-    let dim2 = (*mat2).get_dims();
-
-    if dim1[1] != dim2[0] {
-        let err_msg = format!(
-            "shapes ({},{}) and ({},{}) not aligned: {} (dim 1) != {} (dim 0)",
-            dim1[0], dim1[1], dim2[0], dim2[1], dim1[1], dim2[0]
-        );
-        report_error("ValueError", "np_matmul", file!(), line!(), column!(), &err_msg);
-    }
-
-    let outdim = out.get_dims();
-    let out_slice = unsafe { slice::from_raw_parts_mut(out.data, outdim[0] * outdim[1]) };
-    let data_slice1 = unsafe { slice::from_raw_parts_mut(mat1.data, dim1[0] * dim1[1]) };
-    let data_slice2 = unsafe { slice::from_raw_parts_mut(mat2.data, dim2[0] * dim2[1]) };
-
-    let matrix1 = DMatrix::from_row_slice(dim1[0], dim1[1], data_slice1);
-    let matrix2 = DMatrix::from_row_slice(dim2[0], dim2[1], data_slice2);
-    let mut result = DMatrix::<f64>::zeros(outdim[0], outdim[1]);
-
-    matrix1.mul_to(&matrix2, &mut result);
-    out_slice.copy_from_slice(result.transpose().as_slice());
-}
-
 /// # Safety
 ///
 /// `mat1` should point to a valid 2DArray of `f64` floats in row-major order

nac3standalone/demo/src/ndarray.py

@@ -1474,18 +1474,6 @@ def test_ndarray_dot():
     output_float64(z5)
     output_bool(z6)
 
-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)
@@ -1501,7 +1489,7 @@ def test_ndarray_qr():
 
     # QR Factorization is not unique and gives different results in numpy and nalgebra
     # Reverting the decomposition to compare the initial arrays
-    a = np_linalg_matmul(y, z)
+    a = y @ z
     output_ndarray_float_2(a)
 
 def test_ndarray_linalg_inv():
@@ -1540,7 +1528,7 @@ def test_ndarray_schur():
 
     # Schur Factorization is not unique and gives different results in scipy and nalgebra
     # Reverting the decomposition to compare the initial arrays
-    a = np_linalg_matmul(np_linalg_matmul(z, t), np_linalg_inv(z))
+    a = (z @ t) @ np_linalg_inv(z)
     output_ndarray_float_2(a)
 
 def test_ndarray_hessenberg():
@@ -1551,7 +1539,7 @@ def test_ndarray_hessenberg():
 
     # Hessenberg Factorization is not unique and gives different results in scipy and nalgebra
     # Reverting the decomposition to compare the initial arrays
-    a = np_linalg_matmul(np_linalg_matmul(q, h), np_linalg_inv(q))
+    a = (q @ h) @ np_linalg_inv(q)
     output_ndarray_float_2(a)
 
 
@@ -1572,7 +1560,7 @@ def test_ndarray_svd():
 
     # SVD Factorization is not unique and gives different results in numpy and nalgebra
     # Reverting the decomposition to compare the initial arrays
-    a = np_linalg_matmul(x, z)
+    a = x @ z
     output_ndarray_float_2(a)
     output_ndarray_float_1(y)
 
@@ -1759,7 +1747,6 @@ def run() -> int32:
     test_ndarray_reshape()
     
     test_ndarray_dot()
-    test_ndarray_linalg_matmul()
     test_ndarray_cholesky()
     test_ndarray_qr()
     test_ndarray_svd()