re-enable vx and vxe (avoid perf regressions) for existing code by splitting this new code into a new file

6 files changed, 577 insertions, 463 deletions

diff --git a/numpy/core/code_generators/generate_umath.py b/numpy/core/code_generators/generate_umath.py
index f01ac4031..e7e63f06f 100644
--- a/numpy/core/code_generators/generate_umath.py
+++ b/numpy/core/code_generators/generate_umath.py
@@ -921,7 +921,7 @@ defdict = {
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.isnan'),
           'PyUFunc_IsFiniteTypeResolver',
-          TD(noobj, out='?', dispatch=[('loops_unary_fp', inexactvec)]),
+          TD(noobj, out='?', dispatch=[('loops_unary_fp_le', inexactvec)]),
           ),
 'isnat':
     Ufunc(1, 1, None,
@@ -933,19 +933,19 @@ defdict = {
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.isinf'),
           'PyUFunc_IsFiniteTypeResolver',
-          TD(noobj, out='?', dispatch=[('loops_unary_fp', inexactvec)]),
+          TD(noobj, out='?', dispatch=[('loops_unary_fp_le', inexactvec)]),
           ),
 'isfinite':
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.isfinite'),
           'PyUFunc_IsFiniteTypeResolver',
-          TD(noobj, out='?', dispatch=[('loops_unary_fp', inexactvec)]),
+          TD(noobj, out='?', dispatch=[('loops_unary_fp_le', inexactvec)]),
           ),
 'signbit':
     Ufunc(1, 1, None,
           docstrings.get('numpy.core.umath.signbit'),
           None,
-          TD(flts, out='?', dispatch=[('loops_unary_fp', inexactvec)]),
+          TD(flts, out='?', dispatch=[('loops_unary_fp_le', inexactvec)]),
           ),
 'copysign':
     Ufunc(2, 1, None,
diff --git a/numpy/core/setup.py b/numpy/core/setup.py
index a5bd4a056..691c53f16 100644
--- a/numpy/core/setup.py
+++ b/numpy/core/setup.py
@@ -1007,6 +1007,7 @@ def configuration(parent_package='',top_path=None):
             join('src', 'umath', 'loops.c.src'),
             join('src', 'umath', 'loops_unary.dispatch.c.src'),
             join('src', 'umath', 'loops_unary_fp.dispatch.c.src'),
+            join('src', 'umath', 'loops_unary_fp_le.dispatch.c.src'),
             join('src', 'umath', 'loops_arithm_fp.dispatch.c.src'),
             join('src', 'umath', 'loops_arithmetic.dispatch.c.src'),
             join('src', 'umath', 'loops_logical.dispatch.c.src'),
diff --git a/numpy/core/src/umath/loops.h.src b/numpy/core/src/umath/loops.h.src
index c13a6491f..26742946f 100644
--- a/numpy/core/src/umath/loops.h.src
+++ b/numpy/core/src/umath/loops.h.src
@@ -241,8 +241,21 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
  *  #TYPE = FLOAT, DOUBLE#
  */
 /**begin repeat1
- * #kind = rint, floor, trunc, ceil, sqrt, absolute, square, reciprocal,
- *         isnan, isinf, isfinite, signbit#
+ * #kind = rint, floor, trunc, ceil, sqrt, absolute, square, reciprocal#
+ */
+NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
+   (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
+/**end repeat1**/
+/**end repeat**/
+
+#ifndef NPY_DISABLE_OPTIMIZATION
+    #include "loops_unary_fp_le.dispatch.h"
+#endif
+/**begin repeat
+ *  #TYPE = FLOAT, DOUBLE#
+ */
+/**begin repeat1
+ * #kind = isnan, isinf, isfinite, signbit#
  */
 NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@kind@,
    (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)))
diff --git a/numpy/core/src/umath/loops_unary_fp.dispatch.c.src b/numpy/core/src/umath/loops_unary_fp.dispatch.c.src
index 54459d545..c4e7b8929 100644
--- a/numpy/core/src/umath/loops_unary_fp.dispatch.c.src
+++ b/numpy/core/src/umath/loops_unary_fp.dispatch.c.src
@@ -3,13 +3,8 @@
  ** sse2 sse41
  ** vsx2
  ** neon asimd
+ ** vx vxe
  **/
-
-/**
- * We ran into lots of test failures trying to enable this file for
- * VSE and VE on s390x (qemu) so avoiding these targets for now.
-*/
-
 /**
  * Force use SSE only on x86, even if AVX2 or AVX512F are enabled
  * through the baseline, since scatter(AVX512F) and gather very costly
@@ -17,17 +12,10 @@
  * such small operations that this file covers.
 */
 #define NPY_SIMD_FORCE_128
-#define _UMATHMODULE
-#define _MULTIARRAYMODULE
-#define NPY_NO_DEPRECATED_API NPY_API_VERSION
-#include <float.h>
 #include "numpy/npy_math.h"
 #include "simd/simd.h"
 #include "loops_utils.h"
 #include "loops.h"
-#include "lowlevel_strided_loops.h"
-// Provides the various *_LOOP macros
-#include "fast_loop_macros.h"
 /**********************************************************
  ** Scalars
  **********************************************************/
@@ -92,198 +80,6 @@ NPY_FINLINE double c_square_f64(double a)
 #define c_rint_f32 npy_rintf
 #define c_rint_f64 npy_rint
 
-/*******************************************************************************
- ** extra SIMD intrinsics
- ******************************************************************************/
-
-#if NPY_SIMD
-/**begin repeat
- * #TYPE = FLOAT, DOUBLE#
- * #sfx  = f32, f64#
- * #VCHK = NPY_SIMD_F32, NPY_SIMD_F64#
- * #FDMAX = FLT_MAX, DBL_MAX#
- * #fd = f, #
- * #ssfx = 32, 64#
- */
-#if @VCHK@
-
-static NPY_INLINE npyv_u@ssfx@
-npyv_isnan_@sfx@(npyv_@sfx@ v)
-{
-    // (v != v) >> (size - 1)
-#if defined(NPY_HAVE_SSE2) || defined (NPY_HAVE_SSE41)
-    // sse npyv_cmpneq_@sfx@ define includes a cast already
-    npyv_u@ssfx@ r = npyv_cmpneq_@sfx@(v, v);
-#else
-    npyv_u@ssfx@ r = npyv_cvt_u@ssfx@_b@ssfx@(npyv_cmpneq_@sfx@(v, v));
-#endif
-    return npyv_shri_u@ssfx@(r, (sizeof(npyv_lanetype_@sfx@)*8)-1);
-}
-
-static NPY_INLINE npyv_u@ssfx@
-npyv_isinf_@sfx@(npyv_@sfx@ v)
-{
-    // (abs(v) > fltmax) >> (size - 1)
-    const npyv_@sfx@ fltmax = npyv_setall_@sfx@(@FDMAX@);
-#if defined(NPY_HAVE_NEON)
-    npyv_u@ssfx@ r = vcagtq_@sfx@(v, fltmax);
-#else
-    // fabs via masking of sign bit
-    const npyv_@sfx@ signmask = npyv_setall_@sfx@(-0.@fd@);
-    npyv_u8      r_u8 = npyv_andc_u8(npyv_reinterpret_u8_@sfx@(v), npyv_reinterpret_u8_@sfx@(signmask));
- #if defined(NPY_HAVE_SSE2) || defined (NPY_HAVE_SSE41)
-    // return cast already done in npyv_cmpgt_@sfx@
-    npyv_u@ssfx@ r    = npyv_cmpgt_@sfx@(npyv_reinterpret_@sfx@_u8(r_u8), fltmax);
- #else
-    npyv_u@ssfx@ r    = npyv_reinterpret_u@ssfx@_@sfx@(npyv_cmpgt_@sfx@(npyv_reinterpret_@sfx@_u8(r_u8), fltmax));
- #endif
-#endif
-    return npyv_shri_u@ssfx@(r, (sizeof(npyv_lanetype_@sfx@)*8)-1);
-}
-
-static NPY_INLINE npyv_u@ssfx@
-npyv_isfinite_@sfx@(npyv_@sfx@ v)
-{
-    // ((v & signmask) <= fltmax) >> (size-1)
-    const npyv_@sfx@ fltmax = npyv_setall_@sfx@(@FDMAX@);
-#if defined(NPY_HAVE_NEON)
-    npyv_u@ssfx@ r = vcaleq_@sfx@(v, fltmax);
-#else
-    // fabs via masking of sign bit
-    const npyv_@sfx@ signmask = npyv_setall_@sfx@(-0.@fd@);
-    npyv_u8      r_u8 = npyv_andc_u8(npyv_reinterpret_u8_@sfx@(v), npyv_reinterpret_u8_@sfx@(signmask));
- #if defined(NPY_HAVE_SSE2) || defined (NPY_HAVE_SSE41)
-    // return cast already done in npyv_cmpgt_@sfx@
-    npyv_u@ssfx@ r    = npyv_cmple_@sfx@(npyv_reinterpret_@sfx@_u8(r_u8), fltmax);
- #else
-    npyv_u@ssfx@ r    = npyv_reinterpret_u@ssfx@_@sfx@(npyv_cmple_@sfx@(npyv_reinterpret_@sfx@_u8(r_u8), fltmax));
- #endif
-#endif
-    return npyv_shri_u@ssfx@(r, (sizeof(npyv_lanetype_@sfx@)*8)-1);
-}
-
-static NPY_INLINE npyv_u@ssfx@
-npyv_signbit_@sfx@(npyv_@sfx@ v)
-{
-    return npyv_shri_u@ssfx@(npyv_reinterpret_u@ssfx@_@sfx@(v), (sizeof(npyv_lanetype_@sfx@)*8)-1);
-}
-
-#endif // @VCHK@
-/**end repeat**/
-
-// In these functions we use vqtbl4q_u8 which is only available on aarch64
-#if defined(NPY_HAVE_NEON) && defined(__aarch64__)
-    #define PREPACK_ISFINITE 1
-    #define PREPACK_SIGNBIT  1
-
-    #if NPY_SIMD_F32
-
-    static NPY_INLINE npyv_u8
-    npyv_isfinite_4x_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
-    {
-        // F32 exponent is 8-bits, which means we can pack multiple into
-        // a single vector.  We shift out sign bit so that we're left
-        // with only exponent in high byte.  If not all bits are set,
-        // then we've got a finite number.
-        uint8x16x4_t tbl;
-        tbl.val[0] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v0), 1));
-        tbl.val[1] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v1), 1));
-        tbl.val[2] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v2), 1));
-        tbl.val[3] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v3), 1));
-
-        const npyv_u8 permute = {3,7,11,15,  19,23,27,31,  35,39,43,47,  51,55,59,63};
-        npyv_u8 r = vqtbl4q_u8(tbl, permute);
-
-        const npyv_u8 expmask = npyv_setall_u8(0xff);
-        r = npyv_cmpneq_u8(r, expmask);
-        r = vshrq_n_u8(r, 7);
-        return r;
-    }
-
-    static NPY_INLINE npyv_u8
-    npyv_signbit_4x_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
-    {
-        // We only need high byte for signbit, which means we can pack
-        // multiple inputs into a single vector.
-        uint8x16x4_t tbl;
-        tbl.val[0] = npyv_reinterpret_u8_f32(v0);
-        tbl.val[1] = npyv_reinterpret_u8_f32(v1);
-        tbl.val[2] = npyv_reinterpret_u8_f32(v2);
-        tbl.val[3] = npyv_reinterpret_u8_f32(v3);
-
-        const npyv_u8 permute = {3,7,11,15,  19,23,27,31,  35,39,43,47,  51,55,59,63};
-        npyv_u8 r = vqtbl4q_u8(tbl, permute);
-                r = vshrq_n_u8(r, 7);
-        return r;
-    }
-
-    #endif // NPY_SIMD_F32
-
-    #if NPY_SIMD_F64
-
-    static NPY_INLINE npyv_u8
-    npyv_isfinite_8x_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
-                         npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
-    {
-        // F64 exponent is 11-bits, which means we can pack multiple into
-        // a single vector.  We'll need to use u16 to fit all exponent
-        // bits.  If not all bits are set, then we've got a finite number.
-        uint8x16x4_t t0123, t4567;
-        t0123.val[0] = npyv_reinterpret_u8_f64(v0);
-        t0123.val[1] = npyv_reinterpret_u8_f64(v1);
-        t0123.val[2] = npyv_reinterpret_u8_f64(v2);
-        t0123.val[3] = npyv_reinterpret_u8_f64(v3);
-        t4567.val[0] = npyv_reinterpret_u8_f64(v4);
-        t4567.val[1] = npyv_reinterpret_u8_f64(v5);
-        t4567.val[2] = npyv_reinterpret_u8_f64(v6);
-        t4567.val[3] = npyv_reinterpret_u8_f64(v7);
-
-        const npyv_u8 permute = {6,7,14,15,  22,23,30,31,  38,39,46,47,  54,55,62,63};
-        npyv_u16 r0 = npyv_reinterpret_u16_u8(vqtbl4q_u8(t0123, permute));
-        npyv_u16 r1 = npyv_reinterpret_u16_u8(vqtbl4q_u8(t4567, permute));
-
-        const npyv_u16 expmask = npyv_setall_u16(0x7ff0);
-        r0 = npyv_and_u16(r0, expmask);
-        r0 = npyv_cmpneq_u16(r0, expmask);
-        r0 = npyv_shri_u16(r0, 15);
-        r1 = npyv_and_u16(r1, expmask);
-        r1 = npyv_cmpneq_u16(r1, expmask);
-        r1 = npyv_shri_u16(r1, 15);
-
-        npyv_u8 r = npyv_pack_b8_b16(r0, r1);
-        return r;
-    }
-
-    static NPY_INLINE npyv_u8
-    npyv_signbit_8x_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
-                        npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
-    {
-        // We only need high byte for signbit, which means we can pack
-        // multiple inputs into a single vector.
-
-        // vuzp2 faster than vtbl for f64
-        npyv_u32 v01 = vuzp2q_u32(npyv_reinterpret_u32_f64(v0), npyv_reinterpret_u32_f64(v1));
-        npyv_u32 v23 = vuzp2q_u32(npyv_reinterpret_u32_f64(v2), npyv_reinterpret_u32_f64(v3));
-        npyv_u32 v45 = vuzp2q_u32(npyv_reinterpret_u32_f64(v4), npyv_reinterpret_u32_f64(v5));
-        npyv_u32 v67 = vuzp2q_u32(npyv_reinterpret_u32_f64(v6), npyv_reinterpret_u32_f64(v7));
-
-        npyv_u16 v0123 = vuzp2q_u16(npyv_reinterpret_u16_u32(v01), npyv_reinterpret_u16_u32(v23));
-        npyv_u16 v4567 = vuzp2q_u16(npyv_reinterpret_u16_u32(v45), npyv_reinterpret_u16_u32(v67));
-
-        npyv_u8 r = vuzp2q_u8(npyv_reinterpret_u8_u16(v0123), npyv_reinterpret_u8_u16(v4567));
-                r = vshrq_n_u8(r, 7);
-        return r;
-    }
-
-    #endif // NPY_SIMD_F64
-
-#else
-    #define PREPACK_ISFINITE 0
-    #define PREPACK_SIGNBIT  0
-#endif // defined(NPY_HAVE_NEON) && defined(__aarch64__)
-
-#endif // NPY_SIMD
-
 /********************************************************************************
  ** Defining the SIMD kernels
  ********************************************************************************/
@@ -353,9 +149,6 @@ npyv_signbit_@sfx@(npyv_@sfx@ v)
  * #TYPE = FLOAT, DOUBLE#
  * #sfx  = f32, f64#
  * #VCHK = NPY_SIMD_F32, NPY_SIMD_F64#
- * #FDMAX = FLT_MAX, DBL_MAX#
- * #fd = f, #
- * #ssfx = 32, 64#
  */
 #if @VCHK@
 /**begin repeat1
@@ -456,179 +249,10 @@ static void simd_@TYPE@_@kind@_@STYPE@_@DTYPE@
 }
 /**end repeat2**/
 /**end repeat1**/
-
-/**begin repeat1
- * #kind = isnan, isinf, isfinite, signbit#
- * #PREPACK = 0, 0, PREPACK_ISFINITE, PREPACK_SIGNBIT#
- */
-/**begin repeat2
- * #STYPE  = CONTIG, NCONTIG, CONTIG,  NCONTIG#
- * #DTYPE  = CONTIG, CONTIG,  NCONTIG, NCONTIG#
- * #unroll = 1, 1, 1, 1#
- */
-static void simd_unary_@kind@_@TYPE@_@STYPE@_@DTYPE@
-(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
-{
-    const npyv_lanetype_@sfx@ *ip = src;
-    npy_bool *op = dst;
-
-    // How many vectors can be packed into a u8 / bool vector?
-    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_@sfx@)
-    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
-
-    const int vstep = npyv_nlanes_@sfx@;
-    const int wstep = vstep * @unroll@ * PACK_FACTOR;
-
-    // unrolled iterations
-    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
-        /**begin repeat3
-         * #N  = 0, 1, 2, 3#
-         */
-        #if @unroll@ > @N@
-            // Load vectors
-            #if @STYPE@ == CONTIG
-                // contiguous input
-                npyv_@sfx@ v0_@N@ = npyv_load_@sfx@(ip + vstep * (0 + PACK_FACTOR * @N@)); // 2 * (0 + 8 * n)
-                npyv_@sfx@ v1_@N@ = npyv_load_@sfx@(ip + vstep * (1 + PACK_FACTOR * @N@));
-                npyv_@sfx@ v2_@N@ = npyv_load_@sfx@(ip + vstep * (2 + PACK_FACTOR * @N@));
-                npyv_@sfx@ v3_@N@ = npyv_load_@sfx@(ip + vstep * (3 + PACK_FACTOR * @N@));
-                #if PACK_FACTOR == 8
-                npyv_@sfx@ v4_@N@ = npyv_load_@sfx@(ip + vstep * (4 + PACK_FACTOR * @N@));
-                npyv_@sfx@ v5_@N@ = npyv_load_@sfx@(ip + vstep * (5 + PACK_FACTOR * @N@));
-                npyv_@sfx@ v6_@N@ = npyv_load_@sfx@(ip + vstep * (6 + PACK_FACTOR * @N@));
-                npyv_@sfx@ v7_@N@ = npyv_load_@sfx@(ip + vstep * (7 + PACK_FACTOR * @N@)); // 2 * (7 + 8 * n) --> 32 + 7: 39 * 2: 78
-                #endif
-            #else
-                // non-contiguous input
-                npyv_@sfx@ v0_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(0 + PACK_FACTOR * @N@), istride);
-                npyv_@sfx@ v1_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(1 + PACK_FACTOR * @N@), istride);
-                npyv_@sfx@ v2_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(2 + PACK_FACTOR * @N@), istride);
-                npyv_@sfx@ v3_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(3 + PACK_FACTOR * @N@), istride);
-                #if PACK_FACTOR == 8
-                npyv_@sfx@ v4_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(4 + PACK_FACTOR * @N@), istride);
-                npyv_@sfx@ v5_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(5 + PACK_FACTOR * @N@), istride);
-                npyv_@sfx@ v6_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(6 + PACK_FACTOR * @N@), istride);
-                npyv_@sfx@ v7_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(7 + PACK_FACTOR * @N@), istride);
-                #endif
-            #endif
-        #endif // @unroll@ > @N@
-        /**end repeat3**/
-
-        /**begin repeat3
-         * #N  = 0, 1, 2, 3#
-         */
-        #if @unroll@ > @N@
-        #if @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
-            #if @ssfx@ == 32
-            npyv_u8 r_@N@ = npyv_@kind@_4x_@sfx@(v0_@N@, v1_@N@, v2_@N@, v3_@N@);
-            #elif @ssfx@ == 64
-            npyv_u8 r_@N@ = npyv_@kind@_8x_@sfx@(v0_@N@, v1_@N@, v2_@N@, v3_@N@,
-                                                 v4_@N@, v5_@N@, v6_@N@, v7_@N@);
-            #endif
-        #else
-            npyv_b@ssfx@ r0_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v0_@N@));
-            npyv_b@ssfx@ r1_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v1_@N@));
-            npyv_b@ssfx@ r2_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v2_@N@));
-            npyv_b@ssfx@ r3_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v3_@N@));
-            #if PACK_FACTOR == 8
-            npyv_b@ssfx@ r4_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v4_@N@));
-            npyv_b@ssfx@ r5_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v5_@N@));
-            npyv_b@ssfx@ r6_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v6_@N@));
-            npyv_b@ssfx@ r7_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v7_@N@));
-            #endif // PACK_FACTOR == 8
-        #endif // @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
-        #endif // @unroll@ > @N@
-        /**end repeat3**/
-
-        /**begin repeat3
-         * #N  = 0, 1, 2, 3#
-         */
-        #if @unroll@ > @N@
-        #if @DTYPE@ == CONTIG
-            #if @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
-                // Nothing to do, results already packed
-            #else
-                // Pack results
-                #if PACK_FACTOR == 4
-                npyv_u8 r_@N@ = npyv_cvt_u8_b8(npyv_pack_b8_b32(r0_@N@, r1_@N@, r2_@N@, r3_@N@));
-                #elif PACK_FACTOR == 8
-                npyv_u8 r_@N@ = npyv_cvt_u8_b8(npyv_pack_b8_b64(r0_@N@, r1_@N@, r2_@N@, r3_@N@,
-                                                                r4_@N@, r5_@N@, r6_@N@, r7_@N@));
-                #endif // PACK_FACTOR == 8
-            #endif // @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
-
-            npyv_store_u8(op + vstep * PACK_FACTOR * @N@, r_@N@);
-
-        #else // @DTYPE@ == CONTIG
-            #if @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
-                // Results are packed, so we can just loop over them
-                npy_uint8 lane_@N@[npyv_nlanes_u8];
-                npyv_store_u8(lane_@N@, r_@N@);
-                for (int ln=0; (ln * sizeof(npyv_lanetype_@sfx@)) < npyv_nlanes_u8; ++ln){
-                    op[(ln + @N@ * PACK_FACTOR * vstep) * ostride] = lane_@N@[ln * sizeof(npyv_lanetype_@sfx@)];
-                }
-            #else
-                // Results are not packed.  Use template to loop.
-                /**begin repeat4
-                 * #R = 0, 1, 2, 3, 4, 5, 6, 7#
-                 */
-                #if @R@ < PACK_FACTOR
-                npy_uint8 lane@R@_@N@[npyv_nlanes_u8];
-                npyv_store_u8(lane@R@_@N@, npyv_reinterpret_u8_u@ssfx@(r@R@_@N@));
-                op[(0 + (@R@ + @N@ * PACK_FACTOR) * vstep) * ostride] = lane@R@_@N@[0 * sizeof(npyv_lanetype_@sfx@)];
-                op[(1 + (@R@ + @N@ * PACK_FACTOR) * vstep) * ostride] = lane@R@_@N@[1 * sizeof(npyv_lanetype_@sfx@)];
-                #if npyv_nlanes_@sfx@ == 4
-                op[(2 + (@R@ + @N@ * PACK_FACTOR) * vstep) * ostride] = lane@R@_@N@[2 * sizeof(npyv_lanetype_@sfx@)];
-                op[(3 + (@R@ + @N@ * PACK_FACTOR) * vstep) * ostride] = lane@R@_@N@[3 * sizeof(npyv_lanetype_@sfx@)];
-                #endif
-                #endif
-                /**end repeat4**/
-            #endif // @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
-        #endif // @DTYPE@ == CONTIG
-        #endif // @unroll@ > @N@
-        /**end repeat3**/
-    }
-
-    // vector-sized iterations
-    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
-    #if @STYPE@ == CONTIG
-        npyv_@sfx@ v = npyv_load_@sfx@(ip);
-    #else
-        npyv_@sfx@ v = npyv_loadn_@sfx@(ip, istride);
-    #endif
-
-        npyv_u@ssfx@ r = npyv_@kind@_@sfx@(v);
-
-        npy_uint8 lane[npyv_nlanes_u8];
-        npyv_store_u8(lane, npyv_reinterpret_u8_u@ssfx@(r));
-
-        op[0*ostride] = lane[0 * sizeof(npyv_lanetype_@sfx@)];
-        op[1*ostride] = lane[1 * sizeof(npyv_lanetype_@sfx@)];
-        #if npyv_nlanes_@sfx@ == 4
-        op[2*ostride] = lane[2 * sizeof(npyv_lanetype_@sfx@)];
-        op[3*ostride] = lane[3 * sizeof(npyv_lanetype_@sfx@)];
-        #endif
-    }
-
-    #undef PACK_FACTOR
-
-    // Scalar loop to finish off
-    for (; len > 0; --len, ip += istride, op += ostride) {
-        *op = (npy_@kind@(*ip) != 0);
-    }
-
-
-    npyv_cleanup();
-}
-/**end repeat2**/
-/**end repeat1**/
-
 #endif // @VCHK@
 /**end repeat**/
 
 #undef WORKAROUND_CLANG_RECIPROCAL_BUG
-#undef PREPACK_ISFINITE
-#undef PREPACK_SIGNBIT
 
 /********************************************************************************
  ** Defining ufunc inner functions
@@ -692,51 +316,4 @@ clear:;
 #endif
 }
 /**end repeat1**/
-
-/**begin repeat1
- * #kind = isnan, isinf, isfinite, signbit#
- **/
-NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
-(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
-{
-#if @VCHK@
-    const char *ip = args[0];
-    char *op = args[1];
-    const npy_intp istep = steps[0];
-    const npy_intp ostep = steps[1];
-    npy_intp len = dimensions[0];
-    const int ilsize = sizeof(npyv_lanetype_@sfx@);
-    const int olsize = sizeof(npy_bool);
-    const npy_intp istride = istep / ilsize;
-    const npy_intp ostride = ostep / olsize;
-    assert(len <= 1 || ostep % olsize == 0);
-
-    if ((istep % ilsize == 0) &&
-        !is_mem_overlap(ip, istep, op, ostep, len) &&
-        npyv_loadable_stride_@sfx@(istride) &&
-        npyv_storable_stride_@sfx@(ostride))
-    {
-        if (istride == 1 && ostride == 1) {
-            simd_unary_@kind@_@TYPE@_CONTIG_CONTIG(ip, 1, op, 1, len);
-        }
-        else if (ostride == 1) {
-            simd_unary_@kind@_@TYPE@_NCONTIG_CONTIG(ip, istride, op, 1, len);
-        }
-        else if (istride == 1) {
-            simd_unary_@kind@_@TYPE@_CONTIG_NCONTIG(ip, 1, op, ostride, len);
-        } else {
-            simd_unary_@kind@_@TYPE@_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
-        }
-    } else
-#endif // @VCHK@
-    {
-    UNARY_LOOP {
-        const npyv_lanetype_@sfx@ in = *(npyv_lanetype_@sfx@ *)ip1;
-        *((npy_bool *)op1) = (npy_@kind@(in) != 0);
-    }
-    }
-
-    npy_clear_floatstatus_barrier((char*)dimensions);
-}
-/**end repeat1**/
 /**end repeat**/
diff --git a/numpy/core/src/umath/loops_unary_fp_le.dispatch.c.src b/numpy/core/src/umath/loops_unary_fp_le.dispatch.c.src
new file mode 100644
index 000000000..fed8213df
--- /dev/null
+++ b/numpy/core/src/umath/loops_unary_fp_le.dispatch.c.src
@@ -0,0 +1,556 @@
+/*@targets
+ ** $maxopt baseline
+ ** sse2 sse41
+ ** vsx2
+ ** neon asimd
+ **/
+
+/**
+ * This code should really be merged into loops_unary_fp.dispatch.c.src
+ * However there is an issue with enabling the code here for VX and VXE
+ * as the shifts don't behave as expected.
+ * See the code below that references NPY__CPU_TARGET_VX and
+ * NPY_BIG_ENDIAN. Suspect that this is a big endian vector issue.
+ *
+ * Splitting the files out allows us to keep loops_unary_fp.dispatch.c.src
+ * building for VX and VXE so we don't regress performance while adding this
+ * code for other platforms.
+*/
+
+/**
+ * Force use SSE only on x86, even if AVX2 or AVX512F are enabled
+ * through the baseline, since scatter(AVX512F) and gather very costly
+ * to handle non-contiguous memory access comparing with SSE for
+ * such small operations that this file covers.
+*/
+#define NPY_SIMD_FORCE_128
+#define _UMATHMODULE
+#define _MULTIARRAYMODULE
+#define NPY_NO_DEPRECATED_API NPY_API_VERSION
+#include <float.h>
+#include "numpy/npy_math.h"
+#include "simd/simd.h"
+#include "loops_utils.h"
+#include "loops.h"
+#include "lowlevel_strided_loops.h"
+// Provides the various *_LOOP macros
+#include "fast_loop_macros.h"
+
+
+/*******************************************************************************
+ ** extra SIMD intrinsics
+ ******************************************************************************/
+
+#if NPY_SIMD
+/**begin repeat
+ * #TYPE = FLOAT, DOUBLE#
+ * #sfx  = f32, f64#
+ * #VCHK = NPY_SIMD_F32, NPY_SIMD_F64#
+ * #FDMAX = FLT_MAX, DBL_MAX#
+ * #fd = f, #
+ * #ssfx = 32, 64#
+ */
+#if @VCHK@
+
+static NPY_INLINE npyv_u@ssfx@
+npyv_isnan_@sfx@(npyv_@sfx@ v)
+{
+    // (v != v) >> (size - 1)
+#if defined(NPY_HAVE_SSE2) || defined (NPY_HAVE_SSE41)
+    // sse npyv_cmpneq_@sfx@ define includes a cast already
+    npyv_u@ssfx@ r = npyv_cmpneq_@sfx@(v, v);
+#else
+    npyv_u@ssfx@ r = npyv_cvt_u@ssfx@_b@ssfx@(npyv_cmpneq_@sfx@(v, v));
+#endif
+#if defined(NPY__CPU_TARGET_VX) || defined(NPY__CPU_TARGET_VXE)
+    // don't do the shift on s390x
+    return r;
+#else
+    return npyv_shri_u@ssfx@(r, (sizeof(npyv_lanetype_@sfx@)*8)-1);
+#endif
+}
+
+static NPY_INLINE npyv_u@ssfx@
+npyv_isinf_@sfx@(npyv_@sfx@ v)
+{
+    // (abs(v) > fltmax) >> (size - 1)
+    const npyv_@sfx@ fltmax = npyv_setall_@sfx@(@FDMAX@);
+#if defined(NPY_HAVE_NEON)
+    npyv_u@ssfx@ r = vcagtq_@sfx@(v, fltmax);
+#else
+    // fabs via masking of sign bit
+    const npyv_@sfx@ signmask = npyv_setall_@sfx@(-0.@fd@);
+    npyv_u8      r_u8 = npyv_andc_u8(npyv_reinterpret_u8_@sfx@(v), npyv_reinterpret_u8_@sfx@(signmask));
+ #if defined(NPY_HAVE_SSE2) || defined (NPY_HAVE_SSE41)
+    // return cast already done in npyv_cmpgt_@sfx@
+    npyv_u@ssfx@ r    = npyv_cmpgt_@sfx@(npyv_reinterpret_@sfx@_u8(r_u8), fltmax);
+ #else
+    npyv_u@ssfx@ r    = npyv_reinterpret_u@ssfx@_@sfx@(npyv_cmpgt_@sfx@(npyv_reinterpret_@sfx@_u8(r_u8), fltmax));
+ #endif
+#endif
+#if defined(NPY__CPU_TARGET_VX) || defined(NPY__CPU_TARGET_VXE)
+    // don't do the shift on s390x
+    return r;
+#else
+    return npyv_shri_u@ssfx@(r, (sizeof(npyv_lanetype_@sfx@)*8)-1);
+#endif
+}
+
+static NPY_INLINE npyv_u@ssfx@
+npyv_isfinite_@sfx@(npyv_@sfx@ v)
+{
+    // ((v & signmask) <= fltmax) >> (size-1)
+    const npyv_@sfx@ fltmax = npyv_setall_@sfx@(@FDMAX@);
+#if defined(NPY_HAVE_NEON)
+    npyv_u@ssfx@ r = vcaleq_@sfx@(v, fltmax);
+#else
+    // fabs via masking of sign bit
+    const npyv_@sfx@ signmask = npyv_setall_@sfx@(-0.@fd@);
+    npyv_u8      r_u8 = npyv_andc_u8(npyv_reinterpret_u8_@sfx@(v), npyv_reinterpret_u8_@sfx@(signmask));
+ #if defined(NPY_HAVE_SSE2) || defined (NPY_HAVE_SSE41)
+    // return cast already done in npyv_cmpgt_@sfx@
+    npyv_u@ssfx@ r    = npyv_cmple_@sfx@(npyv_reinterpret_@sfx@_u8(r_u8), fltmax);
+ #else
+    npyv_u@ssfx@ r    = npyv_reinterpret_u@ssfx@_@sfx@(npyv_cmple_@sfx@(npyv_reinterpret_@sfx@_u8(r_u8), fltmax));
+ #endif
+#endif
+#if defined(NPY__CPU_TARGET_VX) || defined(NPY__CPU_TARGET_VXE)
+    // don't do the shift on s390x
+    return r;
+#else
+    return npyv_shri_u@ssfx@(r, (sizeof(npyv_lanetype_@sfx@)*8)-1);
+#endif
+}
+
+static NPY_INLINE npyv_u@ssfx@
+npyv_signbit_@sfx@(npyv_@sfx@ v)
+{
+#if NPY_BYTE_ORDER == NPY_BIG_ENDIAN
+    // via masking of sign bit
+    const npyv_@sfx@ signmask = npyv_setall_@sfx@(-0.@fd@);
+    npyv_u@ssfx@ r = npyv_reinterpret_u@ssfx@_@sfx@(npyv_and_@sfx@(v, signmask));
+    return r;
+#else
+    return npyv_shri_u@ssfx@(npyv_reinterpret_u@ssfx@_@sfx@(v), (sizeof(npyv_lanetype_@sfx@)*8)-1);
+#endif
+}
+
+#endif // @VCHK@
+/**end repeat**/
+
+// In these functions we use vqtbl4q_u8 which is only available on aarch64
+#if defined(NPY_HAVE_NEON) && defined(__aarch64__)
+    #define PREPACK_ISFINITE 1
+    #define PREPACK_SIGNBIT  1
+
+    #if NPY_SIMD_F32
+
+    static NPY_INLINE npyv_u8
+    npyv_isfinite_4x_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+    {
+        // F32 exponent is 8-bits, which means we can pack multiple into
+        // a single vector.  We shift out sign bit so that we're left
+        // with only exponent in high byte.  If not all bits are set,
+        // then we've got a finite number.
+        uint8x16x4_t tbl;
+        tbl.val[0] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v0), 1));
+        tbl.val[1] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v1), 1));
+        tbl.val[2] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v2), 1));
+        tbl.val[3] = npyv_reinterpret_u8_u32(npyv_shli_u32(npyv_reinterpret_u32_f32(v3), 1));
+
+        const npyv_u8 permute = {3,7,11,15,  19,23,27,31,  35,39,43,47,  51,55,59,63};
+        npyv_u8 r = vqtbl4q_u8(tbl, permute);
+
+        const npyv_u8 expmask = npyv_setall_u8(0xff);
+        r = npyv_cmpneq_u8(r, expmask);
+        r = vshrq_n_u8(r, 7);
+        return r;
+    }
+
+    static NPY_INLINE npyv_u8
+    npyv_signbit_4x_f32(npyv_f32 v0, npyv_f32 v1, npyv_f32 v2, npyv_f32 v3)
+    {
+        // We only need high byte for signbit, which means we can pack
+        // multiple inputs into a single vector.
+        uint8x16x4_t tbl;
+        tbl.val[0] = npyv_reinterpret_u8_f32(v0);
+        tbl.val[1] = npyv_reinterpret_u8_f32(v1);
+        tbl.val[2] = npyv_reinterpret_u8_f32(v2);
+        tbl.val[3] = npyv_reinterpret_u8_f32(v3);
+
+        const npyv_u8 permute = {3,7,11,15,  19,23,27,31,  35,39,43,47,  51,55,59,63};
+        npyv_u8 r = vqtbl4q_u8(tbl, permute);
+                r = vshrq_n_u8(r, 7);
+        return r;
+    }
+
+    #endif // NPY_SIMD_F32
+
+    #if NPY_SIMD_F64
+
+    static NPY_INLINE npyv_u8
+    npyv_isfinite_8x_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
+                         npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
+    {
+        // F64 exponent is 11-bits, which means we can pack multiple into
+        // a single vector.  We'll need to use u16 to fit all exponent
+        // bits.  If not all bits are set, then we've got a finite number.
+        uint8x16x4_t t0123, t4567;
+        t0123.val[0] = npyv_reinterpret_u8_f64(v0);
+        t0123.val[1] = npyv_reinterpret_u8_f64(v1);
+        t0123.val[2] = npyv_reinterpret_u8_f64(v2);
+        t0123.val[3] = npyv_reinterpret_u8_f64(v3);
+        t4567.val[0] = npyv_reinterpret_u8_f64(v4);
+        t4567.val[1] = npyv_reinterpret_u8_f64(v5);
+        t4567.val[2] = npyv_reinterpret_u8_f64(v6);
+        t4567.val[3] = npyv_reinterpret_u8_f64(v7);
+
+        const npyv_u8 permute = {6,7,14,15,  22,23,30,31,  38,39,46,47,  54,55,62,63};
+        npyv_u16 r0 = npyv_reinterpret_u16_u8(vqtbl4q_u8(t0123, permute));
+        npyv_u16 r1 = npyv_reinterpret_u16_u8(vqtbl4q_u8(t4567, permute));
+
+        const npyv_u16 expmask = npyv_setall_u16(0x7ff0);
+        r0 = npyv_and_u16(r0, expmask);
+        r0 = npyv_cmpneq_u16(r0, expmask);
+        r0 = npyv_shri_u16(r0, 15);
+        r1 = npyv_and_u16(r1, expmask);
+        r1 = npyv_cmpneq_u16(r1, expmask);
+        r1 = npyv_shri_u16(r1, 15);
+
+        npyv_u8 r = npyv_pack_b8_b16(r0, r1);
+        return r;
+    }
+
+    static NPY_INLINE npyv_u8
+    npyv_signbit_8x_f64(npyv_f64 v0, npyv_f64 v1, npyv_f64 v2, npyv_f64 v3,
+                        npyv_f64 v4, npyv_f64 v5, npyv_f64 v6, npyv_f64 v7)
+    {
+        // We only need high byte for signbit, which means we can pack
+        // multiple inputs into a single vector.
+
+        // vuzp2 faster than vtbl for f64
+        npyv_u32 v01 = vuzp2q_u32(npyv_reinterpret_u32_f64(v0), npyv_reinterpret_u32_f64(v1));
+        npyv_u32 v23 = vuzp2q_u32(npyv_reinterpret_u32_f64(v2), npyv_reinterpret_u32_f64(v3));
+        npyv_u32 v45 = vuzp2q_u32(npyv_reinterpret_u32_f64(v4), npyv_reinterpret_u32_f64(v5));
+        npyv_u32 v67 = vuzp2q_u32(npyv_reinterpret_u32_f64(v6), npyv_reinterpret_u32_f64(v7));
+
+        npyv_u16 v0123 = vuzp2q_u16(npyv_reinterpret_u16_u32(v01), npyv_reinterpret_u16_u32(v23));
+        npyv_u16 v4567 = vuzp2q_u16(npyv_reinterpret_u16_u32(v45), npyv_reinterpret_u16_u32(v67));
+
+        npyv_u8 r = vuzp2q_u8(npyv_reinterpret_u8_u16(v0123), npyv_reinterpret_u8_u16(v4567));
+                r = vshrq_n_u8(r, 7);
+        return r;
+    }
+
+    #endif // NPY_SIMD_F64
+
+#else
+    #define PREPACK_ISFINITE 0
+    #define PREPACK_SIGNBIT  0
+#endif // defined(NPY_HAVE_NEON) && defined(__aarch64__)
+
+#endif // NPY_SIMD
+
+/********************************************************************************
+ ** Defining the SIMD kernels
+ ********************************************************************************/
+/** Notes:
+ * - avoid the use of libmath to unify fp/domain errors
+ *   for both scalars and vectors among all compilers/architectures.
+ * - use intrinsic npyv_load_till_* instead of npyv_load_tillz_
+ *   to fill the remind lanes with 1.0 to avoid divide by zero fp
+ *   exception in reciprocal.
+ */
+#define CONTIG  0
+#define NCONTIG 1
+
+/*
+ * clang has a bug that's present at -O1 or greater.  When partially loading a
+ * vector register for a reciprocal operation, the remaining elements are set
+ * to 1 to avoid divide-by-zero.  The partial load is paired with a partial
+ * store after the reciprocal operation.  clang notices that the entire register
+ * is not needed for the store and optimizes out the fill of 1 to the remaining
+ * elements.  This causes either a divide-by-zero or 0/0 with invalid exception
+ * that we were trying to avoid by filling.
+ *
+ * Using a dummy variable marked 'volatile' convinces clang not to ignore
+ * the explicit fill of remaining elements.  If `-ftrapping-math` is
+ * supported, then it'll also avoid the bug.  `-ftrapping-math` is supported
+ * on Apple clang v12+ for x86_64.  It is not currently supported for arm64.
+ * `-ftrapping-math` is set by default of Numpy builds in
+ * numpy/distutils/ccompiler.py.
+ *
+ * Note: Apple clang and clang upstream have different versions that overlap
+ */
+#if defined(__clang__)
+    #if defined(__apple_build_version__)
+    // Apple Clang
+        #if __apple_build_version__ < 12000000
+        // Apple Clang before v12
+        #define WORKAROUND_CLANG_RECIPROCAL_BUG 1
+        #elif defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64)
+        // Apple Clang after v12, targeting i386 or x86_64
+        #define WORKAROUND_CLANG_RECIPROCAL_BUG 0
+        #else
+        // Apple Clang after v12, not targeting i386 or x86_64
+        #define WORKAROUND_CLANG_RECIPROCAL_BUG 1
+        #endif
+    #else
+    // Clang, not Apple Clang
+        #if __clang_major__ < 10
+        // Clang before v10
+        #define WORKAROUND_CLANG_RECIPROCAL_BUG 1
+        #elif defined(_MSC_VER)
+        // clang-cl has the same bug
+        #define WORKAROUND_CLANG_RECIPROCAL_BUG 1
+        #elif defined(NPY_CPU_X86) || defined(NPY_CPU_AMD64)
+        // Clang v10+, targeting i386 or x86_64
+        #define WORKAROUND_CLANG_RECIPROCAL_BUG 0
+        #else
+        // Clang v10+, not targeting i386 or x86_64
+        #define WORKAROUND_CLANG_RECIPROCAL_BUG 1
+        #endif
+    #endif
+#else
+// Not a Clang compiler
+#define WORKAROUND_CLANG_RECIPROCAL_BUG 0
+#endif
+
+/**begin repeat
+ * #TYPE = FLOAT, DOUBLE#
+ * #sfx  = f32, f64#
+ * #VCHK = NPY_SIMD_F32, NPY_SIMD_F64#
+ * #FDMAX = FLT_MAX, DBL_MAX#
+ * #fd = f, #
+ * #ssfx = 32, 64#
+ */
+#if @VCHK@
+/**begin repeat1
+ * #kind = isnan, isinf, isfinite, signbit#
+ * #PREPACK = 0, 0, PREPACK_ISFINITE, PREPACK_SIGNBIT#
+ */
+/**begin repeat2
+ * #STYPE  = CONTIG, NCONTIG, CONTIG,  NCONTIG#
+ * #DTYPE  = CONTIG, CONTIG,  NCONTIG, NCONTIG#
+ * #unroll = 1, 1, 1, 1#
+ */
+static void simd_unary_@kind@_@TYPE@_@STYPE@_@DTYPE@
+(const void *src, npy_intp istride, void *dst, npy_intp ostride, npy_intp len)
+{
+    const npyv_lanetype_@sfx@ *ip = src;
+    npy_bool *op = dst;
+
+    // How many vectors can be packed into a u8 / bool vector?
+    #define PACK_FACTOR (NPY_SIMD_WIDTH / npyv_nlanes_@sfx@)
+    assert(PACK_FACTOR == 4 || PACK_FACTOR == 8);
+
+    const int vstep = npyv_nlanes_@sfx@;
+    const int wstep = vstep * @unroll@ * PACK_FACTOR;
+
+    // unrolled iterations
+    for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
+        /**begin repeat3
+         * #N  = 0, 1, 2, 3#
+         */
+        #if @unroll@ > @N@
+            // Load vectors
+            #if @STYPE@ == CONTIG
+                // contiguous input
+                npyv_@sfx@ v0_@N@ = npyv_load_@sfx@(ip + vstep * (0 + PACK_FACTOR * @N@)); // 2 * (0 + 8 * n)
+                npyv_@sfx@ v1_@N@ = npyv_load_@sfx@(ip + vstep * (1 + PACK_FACTOR * @N@));
+                npyv_@sfx@ v2_@N@ = npyv_load_@sfx@(ip + vstep * (2 + PACK_FACTOR * @N@));
+                npyv_@sfx@ v3_@N@ = npyv_load_@sfx@(ip + vstep * (3 + PACK_FACTOR * @N@));
+                #if PACK_FACTOR == 8
+                npyv_@sfx@ v4_@N@ = npyv_load_@sfx@(ip + vstep * (4 + PACK_FACTOR * @N@));
+                npyv_@sfx@ v5_@N@ = npyv_load_@sfx@(ip + vstep * (5 + PACK_FACTOR * @N@));
+                npyv_@sfx@ v6_@N@ = npyv_load_@sfx@(ip + vstep * (6 + PACK_FACTOR * @N@));
+                npyv_@sfx@ v7_@N@ = npyv_load_@sfx@(ip + vstep * (7 + PACK_FACTOR * @N@)); // 2 * (7 + 8 * n) --> 32 + 7: 39 * 2: 78
+                #endif
+            #else
+                // non-contiguous input
+                npyv_@sfx@ v0_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(0 + PACK_FACTOR * @N@), istride);
+                npyv_@sfx@ v1_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(1 + PACK_FACTOR * @N@), istride);
+                npyv_@sfx@ v2_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(2 + PACK_FACTOR * @N@), istride);
+                npyv_@sfx@ v3_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(3 + PACK_FACTOR * @N@), istride);
+                #if PACK_FACTOR == 8
+                npyv_@sfx@ v4_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(4 + PACK_FACTOR * @N@), istride);
+                npyv_@sfx@ v5_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(5 + PACK_FACTOR * @N@), istride);
+                npyv_@sfx@ v6_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(6 + PACK_FACTOR * @N@), istride);
+                npyv_@sfx@ v7_@N@ = npyv_loadn_@sfx@(ip + istride*vstep*(7 + PACK_FACTOR * @N@), istride);
+                #endif
+            #endif
+        #endif // @unroll@ > @N@
+        /**end repeat3**/
+
+        /**begin repeat3
+         * #N  = 0, 1, 2, 3#
+         */
+        #if @unroll@ > @N@
+        #if @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
+            #if @ssfx@ == 32
+            npyv_u8 r_@N@ = npyv_@kind@_4x_@sfx@(v0_@N@, v1_@N@, v2_@N@, v3_@N@);
+            #elif @ssfx@ == 64
+            npyv_u8 r_@N@ = npyv_@kind@_8x_@sfx@(v0_@N@, v1_@N@, v2_@N@, v3_@N@,
+                                                 v4_@N@, v5_@N@, v6_@N@, v7_@N@);
+            #endif
+        #else
+            npyv_b@ssfx@ r0_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v0_@N@));
+            npyv_b@ssfx@ r1_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v1_@N@));
+            npyv_b@ssfx@ r2_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v2_@N@));
+            npyv_b@ssfx@ r3_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v3_@N@));
+            #if PACK_FACTOR == 8
+            npyv_b@ssfx@ r4_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v4_@N@));
+            npyv_b@ssfx@ r5_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v5_@N@));
+            npyv_b@ssfx@ r6_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v6_@N@));
+            npyv_b@ssfx@ r7_@N@ = npyv_cvt_b@ssfx@_u@ssfx@(npyv_@kind@_@sfx@(v7_@N@));
+            #endif // PACK_FACTOR == 8
+        #endif // @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
+        #endif // @unroll@ > @N@
+        /**end repeat3**/
+
+        /**begin repeat3
+         * #N  = 0, 1, 2, 3#
+         */
+        #if @unroll@ > @N@
+        #if @DTYPE@ == CONTIG
+            #if @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
+                // Nothing to do, results already packed
+            #else
+                // Pack results
+                #if PACK_FACTOR == 4
+                npyv_u8 r_@N@ = npyv_cvt_u8_b8(npyv_pack_b8_b32(r0_@N@, r1_@N@, r2_@N@, r3_@N@));
+                #elif PACK_FACTOR == 8
+                npyv_u8 r_@N@ = npyv_cvt_u8_b8(npyv_pack_b8_b64(r0_@N@, r1_@N@, r2_@N@, r3_@N@,
+                                                                r4_@N@, r5_@N@, r6_@N@, r7_@N@));
+                #endif // PACK_FACTOR == 8
+            #endif // @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
+
+            npyv_store_u8(op + vstep * PACK_FACTOR * @N@, r_@N@);
+
+        #else // @DTYPE@ == CONTIG
+            #if @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
+                // Results are packed, so we can just loop over them
+                npy_uint8 lane_@N@[npyv_nlanes_u8];
+                npyv_store_u8(lane_@N@, r_@N@);
+                for (int ln=0; (ln * sizeof(npyv_lanetype_@sfx@)) < npyv_nlanes_u8; ++ln){
+                    op[(ln + @N@ * PACK_FACTOR * vstep) * ostride] = lane_@N@[ln * sizeof(npyv_lanetype_@sfx@)];
+                }
+            #else
+                // Results are not packed.  Use template to loop.
+                /**begin repeat4
+                 * #R = 0, 1, 2, 3, 4, 5, 6, 7#
+                 */
+                #if @R@ < PACK_FACTOR
+                npy_uint8 lane@R@_@N@[npyv_nlanes_u8];
+                npyv_store_u8(lane@R@_@N@, npyv_reinterpret_u8_u@ssfx@(r@R@_@N@));
+                op[(0 + (@R@ + @N@ * PACK_FACTOR) * vstep) * ostride] = lane@R@_@N@[0 * sizeof(npyv_lanetype_@sfx@)];
+                op[(1 + (@R@ + @N@ * PACK_FACTOR) * vstep) * ostride] = lane@R@_@N@[1 * sizeof(npyv_lanetype_@sfx@)];
+                #if npyv_nlanes_@sfx@ == 4
+                op[(2 + (@R@ + @N@ * PACK_FACTOR) * vstep) * ostride] = lane@R@_@N@[2 * sizeof(npyv_lanetype_@sfx@)];
+                op[(3 + (@R@ + @N@ * PACK_FACTOR) * vstep) * ostride] = lane@R@_@N@[3 * sizeof(npyv_lanetype_@sfx@)];
+                #endif
+                #endif
+                /**end repeat4**/
+            #endif // @PREPACK@ && (@ssfx@ == 32 || @ssfx@ == 64)
+        #endif // @DTYPE@ == CONTIG
+        #endif // @unroll@ > @N@
+        /**end repeat3**/
+    }
+
+    // vector-sized iterations
+    for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
+    #if @STYPE@ == CONTIG
+        npyv_@sfx@ v = npyv_load_@sfx@(ip);
+    #else
+        npyv_@sfx@ v = npyv_loadn_@sfx@(ip, istride);
+    #endif
+
+        npyv_u@ssfx@ r = npyv_@kind@_@sfx@(v);
+
+        npy_uint8 lane[npyv_nlanes_u8];
+        npyv_store_u8(lane, npyv_reinterpret_u8_u@ssfx@(r));
+
+        op[0*ostride] = lane[0 * sizeof(npyv_lanetype_@sfx@)];
+        op[1*ostride] = lane[1 * sizeof(npyv_lanetype_@sfx@)];
+        #if npyv_nlanes_@sfx@ == 4
+        op[2*ostride] = lane[2 * sizeof(npyv_lanetype_@sfx@)];
+        op[3*ostride] = lane[3 * sizeof(npyv_lanetype_@sfx@)];
+        #endif
+    }
+
+    #undef PACK_FACTOR
+
+    // Scalar loop to finish off
+    for (; len > 0; --len, ip += istride, op += ostride) {
+        *op = (npy_@kind@(*ip) != 0);
+    }
+
+
+    npyv_cleanup();
+}
+/**end repeat2**/
+/**end repeat1**/
+
+#endif // @VCHK@
+/**end repeat**/
+
+#undef WORKAROUND_CLANG_RECIPROCAL_BUG
+#undef PREPACK_ISFINITE
+#undef PREPACK_SIGNBIT
+
+/********************************************************************************
+ ** Defining ufunc inner functions
+ ********************************************************************************/
+/**begin repeat
+ * #TYPE = FLOAT, DOUBLE#
+ * #sfx  = f32, f64#
+ * #VCHK = NPY_SIMD_F32, NPY_SIMD_F64#
+ */
+
+/**begin repeat1
+ * #kind = isnan, isinf, isfinite, signbit#
+ **/
+NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
+(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
+{
+#if @VCHK@
+    const char *ip = args[0];
+    char *op = args[1];
+    const npy_intp istep = steps[0];
+    const npy_intp ostep = steps[1];
+    npy_intp len = dimensions[0];
+    const int ilsize = sizeof(npyv_lanetype_@sfx@);
+    const int olsize = sizeof(npy_bool);
+    const npy_intp istride = istep / ilsize;
+    const npy_intp ostride = ostep / olsize;
+    assert(len <= 1 || ostep % olsize == 0);
+
+    if ((istep % ilsize == 0) &&
+        !is_mem_overlap(ip, istep, op, ostep, len) &&
+        npyv_loadable_stride_@sfx@(istride) &&
+        npyv_storable_stride_@sfx@(ostride))
+    {
+        if (istride == 1 && ostride == 1) {
+            simd_unary_@kind@_@TYPE@_CONTIG_CONTIG(ip, 1, op, 1, len);
+        }
+        else if (ostride == 1) {
+            simd_unary_@kind@_@TYPE@_NCONTIG_CONTIG(ip, istride, op, 1, len);
+        }
+        else if (istride == 1) {
+            simd_unary_@kind@_@TYPE@_CONTIG_NCONTIG(ip, 1, op, ostride, len);
+        } else {
+            simd_unary_@kind@_@TYPE@_NCONTIG_NCONTIG(ip, istride, op, ostride, len);
+        }
+    } else
+#endif // @VCHK@
+    {
+    UNARY_LOOP {
+        const npyv_lanetype_@sfx@ in = *(npyv_lanetype_@sfx@ *)ip1;
+        *((npy_bool *)op1) = (npy_@kind@(in) != 0);
+    }
+    }
+
+    npy_clear_floatstatus_barrier((char*)dimensions);
+}
+/**end repeat1**/
+/**end repeat**/
diff --git a/numpy/core/tests/test_simd.py b/numpy/core/tests/test_simd.py
index 35bf90694..2c16243db 100644
--- a/numpy/core/tests/test_simd.py
+++ b/numpy/core/tests/test_simd.py
@@ -425,39 +425,6 @@ class _SIMD_FP(_Test_Utility):
         square = self.square(vdata)
         assert square == data_square
 
-    def test_isfinite_isinf_isnan_signbit(self):
-        pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
-        assert np.isnan(nan) == True
-        assert np.isfinite(ninf) == False
-        assert np.isfinite(pinf) == False
-        assert np.isinf(ninf) == True
-        assert np.isinf(pinf) == True
-        assert np.signbit(pinf) == False
-        assert np.signbit(ninf) == True
-
-    def test_array_isfinite_isinf_isnan_signbit(self):
-        size = 12
-        for t in [np.float32, np.float64]:
-            arr = np.random.default_rng().random(size, t)
-            assert np.isnan(arr)[2] == False
-            assert np.isfinite(arr)[4] == True
-            assert np.isinf(arr)[6] == False
-            assert np.signbit(arr)[8] == False
-
-        for t in [np.uint8, np.uint16, np.uint32, np.uint64]:
-            arr = np.random.default_rng().integers(0,100,size,dtype=t)
-            assert np.isnan(arr)[2] == False
-            assert np.isfinite(arr)[4] == True
-            assert np.isinf(arr)[6] == False
-            assert np.signbit(arr)[8] == False
-
-        for t in [np.int8, np.int16, np.int32, np.int64]:
-            arr = np.random.default_rng().integers(-100,0,size,dtype=t)
-            assert np.isnan(arr)[2] == False
-            assert np.isfinite(arr)[4] == True
-            assert np.isinf(arr)[6] == False
-            assert np.signbit(arr)[8] == True
-
     @pytest.mark.parametrize("intrin, func", [("ceil", math.ceil),
     ("trunc", math.trunc), ("floor", math.floor), ("rint", round)])
     def test_rounding(self, intrin, func):