add new complex vm functions

Author: vtavana

CHANGELOG.md

@@ -8,8 +8,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 * Added mkl implementation for floating point data-types of `exp2`, `log2`, `fabs`, `copysign`, `nextafter`, `fmax`, `fmin` and `remainder` functions [gh-81](https://github.com/IntelPython/mkl_umath/pull/81)
+* Added mkl implementation for complex data-types of `conjugate` and `abs` functions [gh-86](https://github.com/IntelPython/mkl_umath/pull/86)
 
-## [0.2.0] (06/DD/2025)
+## [0.2.0] (06/03/2025)
 This release updates `mkl_umath` to be aligned with both numpy-1.26.x and numpy-2.x.x.
 
 ### Added

mkl_umath/src/mkl_umath_loops.c.src

@@ -129,6 +129,7 @@
  *  when these conditions are not met VML functions may produce incorrect output
  */
 #define DISJOINT_OR_SAME(p1, p2, n, s) (((p1) == (p2)) || ((p2) + (n)*(s) < (p1)) || ((p1) + (n)*(s) < (p2)) )
+#define DISJOINT_OR_SAME_TWO_DTYPES(p1, p2, n, s1, s2) (((p1) == (p2)) || ((p2) + (n)*(s2) < (p1)) || ((p1) + (n)*(s1) < (p2)) )
 
 /*
  * include vectorized functions and dispatchers
@@ -2376,10 +2377,10 @@ mkl_umath_@TYPE@_ldexp_long(char **args, const npy_intp *dimensions, const npy_i
  * complex types
  * #TYPE = CFLOAT, CDOUBLE#
  * #ftype = npy_float, npy_double#
+ * #type = npy_cfloat, npy_cdouble# 
  * #c = f, #
- * #C = F, #
- * #s = s, d#
- * #SUPPORTED_BY_VML = 1, 1#
+ * #C = F, # 
+ * #s = c, z#
  */
 
 /* similar to pairwise sum of real floats */
@@ -2659,33 +2660,39 @@ mkl_umath_@TYPE@__ones_like(char **args, const npy_intp *dimensions, const npy_i
     }
 }
 
-/* TODO: USE MKL */
 void
 mkl_umath_@TYPE@_conjugate(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func)) {
-    UNARY_LOOP {
-        const @ftype@ in1r = ((@ftype@ *)ip1)[0];
-        const @ftype@ in1i = ((@ftype@ *)ip1)[1];
-        ((@ftype@ *)op1)[0] = in1r;
-        ((@ftype@ *)op1)[1] = -in1i;
-    }
+    const int contig = IS_UNARY_CONT(@type@, @type@);
+    const int disjoint_or_same = DISJOINT_OR_SAME(args[0], args[1], dimensions[0], sizeof(@type@));
+    const int can_vectorize = contig && disjoint_or_same;
+
+    if(can_vectorize && dimensions[0] > VML_TRANSCEDENTAL_THRESHOLD) {
+        printf("MKL was used for complex conjugate\n");
+        CHUNKED_VML_CALL2(v@s@Conj, dimensions[0], @type@, args[0], args[1]);
+        /* v@s@Conj(dimensions[0], (@type@*) args[0], (@type@*) args[1]); */
+    } else {
+		UNARY_LOOP {
+			const @ftype@ in1r = ((@ftype@ *)ip1)[0];
+			const @ftype@ in1i = ((@ftype@ *)ip1)[1];
+			((@ftype@ *)op1)[0] = in1r;
+			((@ftype@ *)op1)[1] = -in1i;
+		}
+	}
 }
 
-/* TODO: USE MKL */
 void
 mkl_umath_@TYPE@_absolute(char **args, const npy_intp *dimensions, const npy_intp *steps, void *NPY_UNUSED(func))
 {
     int ignore_fpstatus = 0;
-
-    // FIXME: abs function VML for complex numbers breaks FFT test_basic.py
-    //if(steps[0]/2 == sizeof(@ftype@) && steps[1] == sizeof(@ftype@) && dimensions[0] > VML_TRANSCEDENTAL_THRESHOLD) {
-#if @SUPPORTED_BY_VML@
-    if(0 == 1) {
-        ignore_fpstatus = 1;
-        CHUNKED_VML_CALL2(v@s@Abs, dimensions[0], @ftype@, args[0], args[1]);
-        /* v@s@Abs(dimensions[0], (@ftype@ *) args[0], (@ftype@ *) args[1]); */
-    } else
-#endif
-    {
+    const int contig = IS_UNARY_CONT(@type@, @ftype@);
+    const int disjoint_or_same = DISJOINT_OR_SAME_TWO_DTYPES(args[0], args[1], dimensions[0], sizeof(@type@), sizeof(@ftype@));
+    const int can_vectorize = contig && disjoint_or_same;
+    
+    if(can_vectorize && dimensions[0] > VML_TRANSCEDENTAL_THRESHOLD) {
+        printf("MKL was used for complex absolute\n");
+        CHUNKED_VML_CALL2(v@s@Abs, dimensions[0], @type@, args[0], args[1]);
+        /* v@s@Abs(dimensions[0], (@type@*) args[0], (@type@*) args[1]); */
+    } else {
         UNARY_LOOP {
             const @ftype@ in1r = ((@ftype@ *)ip1)[0];
             const @ftype@ in1i = ((@ftype@ *)ip1)[1];