1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
|
/*@targets
** $maxopt baseline
** neon asimd
** sse2 avx2 avx512_skx
** vsx2
** vx vxe
**/
#define _UMATHMODULE
#define _MULTIARRAYMODULE
#define NPY_NO_DEPRECATED_API NPY_API_VERSION
#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"
/*******************************************************************************
** Scalar ops
******************************************************************************/
#define scalar_negative(X) (-X)
/*******************************************************************************
** extra SIMD intrinsics
******************************************************************************/
#if NPY_SIMD
/**begin repeat
* #sfx = s8, u8, s16, u16, s32, u32, s64, u64#
* #ssfx = 8, 8, 16, 16, 32, 32, 64, 64#
*/
static NPY_INLINE npyv_@sfx@
npyv_negative_@sfx@(npyv_@sfx@ v)
{
#if defined(NPY_HAVE_NEON) && (defined(__aarch64__) || @ssfx@ < 64)
return npyv_reinterpret_@sfx@_s@ssfx@(vnegq_s@ssfx@(npyv_reinterpret_s@ssfx@_@sfx@(v)));
#else
// (x ^ -1) + 1
const npyv_@sfx@ m1 = npyv_setall_@sfx@((npyv_lanetype_@sfx@)-1);
return npyv_sub_@sfx@(npyv_xor_@sfx@(v, m1), m1);
#endif
}
/**end repeat**/
/**begin repeat
* #sfx = f32, f64#
* #VCHK = NPY_SIMD_F32, NPY_SIMD_F64#
* #fd = f, #
*/
#if @VCHK@
static NPY_INLINE npyv_@sfx@
npyv_negative_@sfx@(npyv_@sfx@ v)
{
#if defined(NPY_HAVE_NEON)
return vnegq_@sfx@(v);
#else
// (v ^ signmask)
const npyv_@sfx@ signmask = npyv_setall_@sfx@(-0.@fd@);
return npyv_xor_@sfx@(v, signmask);
#endif
}
#endif // @VCHK@
/**end repeat**/
#endif // NPY_SIMD
/********************************************************************************
** Defining the SIMD kernels
********************************************************************************/
/**begin repeat
* #sfx = s8, u8, s16, u16, s32, u32, s64, u64, f32, f64#
* #simd_chk = NPY_SIMD*8, NPY_SIMD_F32, NPY_SIMD_F64#
* #is_fp = 0*8, 1*2#
* #supports_ncontig = 0*4,1*6#
*/
/**begin repeat1
* #kind = negative#
* #intrin = negative#
* #unroll = 4#
*/
#if @simd_chk@
#if @unroll@ < 1
#error "Unroll must be at least 1"
#elif NPY_SIMD != 128 && @unroll@ > 2
// Avoid memory bandwidth bottleneck for larger SIMD
#define UNROLL 2
#else
#define UNROLL @unroll@
#endif
// contiguous inputs and output.
static NPY_INLINE void
simd_unary_cc_@intrin@_@sfx@(const npyv_lanetype_@sfx@ *ip,
npyv_lanetype_@sfx@ *op,
npy_intp len)
{
const int vstep = npyv_nlanes_@sfx@;
const int wstep = vstep * UNROLL;
// unrolled vector loop
for (; len >= wstep; len -= wstep, ip += wstep, op += wstep) {
/**begin repeat2
* #U = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
*/
#if UNROLL > @U@
npyv_@sfx@ v_@U@ = npyv_load_@sfx@(ip + @U@ * vstep);
npyv_@sfx@ r_@U@ = npyv_@intrin@_@sfx@(v_@U@);
npyv_store_@sfx@(op + @U@ * vstep, r_@U@);
#endif
/**end repeat2**/
}
// single vector loop
for (; len >= vstep; len -= vstep, ip += vstep, op +=vstep) {
npyv_@sfx@ v = npyv_load_@sfx@(ip);
npyv_@sfx@ r = npyv_@intrin@_@sfx@(v);
npyv_store_@sfx@(op, r);
}
// scalar finish up any remaining iterations
for (; len > 0; --len, ++ip, ++op) {
*op = scalar_@intrin@(*ip);
}
}
#if @supports_ncontig@
// contiguous input, non-contiguous output
static NPY_INLINE void
simd_unary_cn_@intrin@_@sfx@(const npyv_lanetype_@sfx@ *ip,
npyv_lanetype_@sfx@ *op, npy_intp ostride,
npy_intp len)
{
const int vstep = npyv_nlanes_@sfx@;
const int wstep = vstep * UNROLL;
// unrolled vector loop
for (; len >= wstep; len -= wstep, ip += wstep, op += ostride*wstep) {
/**begin repeat2
* #U = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
*/
#if UNROLL > @U@
npyv_@sfx@ v_@U@ = npyv_load_@sfx@(ip + @U@ * vstep);
npyv_@sfx@ r_@U@ = npyv_@intrin@_@sfx@(v_@U@);
npyv_storen_@sfx@(op + @U@ * vstep * ostride, ostride, r_@U@);
#endif
/**end repeat2**/
}
// single vector loop
for (; len >= vstep; len -= vstep, ip += vstep, op += ostride*vstep) {
npyv_@sfx@ v = npyv_load_@sfx@(ip);
npyv_@sfx@ r = npyv_@intrin@_@sfx@(v);
npyv_storen_@sfx@(op, ostride, r);
}
// scalar finish up any remaining iterations
for (; len > 0; --len, ++ip, op += ostride) {
*op = scalar_@intrin@(*ip);
}
}
// non-contiguous input, contiguous output
static NPY_INLINE void
simd_unary_nc_@intrin@_@sfx@(const npyv_lanetype_@sfx@ *ip, npy_intp istride,
npyv_lanetype_@sfx@ *op,
npy_intp len)
{
const int vstep = npyv_nlanes_@sfx@;
const int wstep = vstep * UNROLL;
// unrolled vector loop
for (; len >= wstep; len -= wstep, ip += istride*wstep, op += wstep) {
/**begin repeat2
* #U = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
*/
#if UNROLL > @U@
npyv_@sfx@ v_@U@ = npyv_loadn_@sfx@(ip + @U@ * vstep * istride, istride);
npyv_@sfx@ r_@U@ = npyv_@intrin@_@sfx@(v_@U@);
npyv_store_@sfx@(op + @U@ * vstep, r_@U@);
#endif
/**end repeat2**/
}
// single vector loop
for (; len >= vstep; len -= vstep, ip += istride*vstep, op += vstep) {
npyv_@sfx@ v = npyv_loadn_@sfx@(ip, istride);
npyv_@sfx@ r = npyv_@intrin@_@sfx@(v);
npyv_store_@sfx@(op, r);
}
// scalar finish up any remaining iterations
for (; len > 0; --len, ip += istride, ++op) {
*op = scalar_@intrin@(*ip);
}
}
// non-contiguous input and output
// limit unroll to 2x
#if UNROLL > 2
#undef UNROLL
#define UNROLL 2
#endif
static NPY_INLINE void
simd_unary_nn_@intrin@_@sfx@(const npyv_lanetype_@sfx@ *ip, npy_intp istride,
npyv_lanetype_@sfx@ *op, npy_intp ostride,
npy_intp len)
{
const int vstep = npyv_nlanes_@sfx@;
const int wstep = vstep * UNROLL;
// unrolled vector loop
for (; len >= wstep; len -= wstep, ip += istride*wstep, op += ostride*wstep) {
/**begin repeat2
* #U = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
*/
#if UNROLL > @U@
npyv_@sfx@ v_@U@ = npyv_loadn_@sfx@(ip + @U@ * vstep * istride, istride);
npyv_@sfx@ r_@U@ = npyv_@intrin@_@sfx@(v_@U@);
npyv_storen_@sfx@(op + @U@ * vstep * ostride, ostride, r_@U@);
#endif
/**end repeat2**/
}
// single vector loop
for (; len >= vstep; len -= vstep, ip += istride*vstep, op += ostride*vstep) {
npyv_@sfx@ v = npyv_loadn_@sfx@(ip, istride);
npyv_@sfx@ r = npyv_@intrin@_@sfx@(v);
npyv_storen_@sfx@(op, ostride, r);
}
// scalar finish up any remaining iterations
for (; len > 0; --len, ip += istride, op += ostride) {
*op = scalar_@intrin@(*ip);
}
}
#endif // @supports_ncontig@
#undef UNROLL
#endif // @simd_chk@
/*end repeat1**/
/**end repeat**/
/********************************************************************************
** Defining ufunc inner functions
********************************************************************************/
/**begin repeat
* #TYPE = UBYTE, USHORT, UINT, ULONG, ULONGLONG,
* BYTE, SHORT, INT, LONG, LONGLONG,
* FLOAT, DOUBLE, LONGDOUBLE#
*
* #BTYPE = BYTE, SHORT, INT, LONG, LONGLONG,
* BYTE, SHORT, INT, LONG, LONGLONG,
* FLOAT, DOUBLE, LONGDOUBLE#
* #type = npy_ubyte, npy_ushort, npy_uint, npy_ulong, npy_ulonglong,
* npy_byte, npy_short, npy_int, npy_long, npy_longlong,
* npy_float, npy_double, npy_longdouble#
*
* #is_fp = 0*10, 1*3#
* #is_unsigned = 1*5, 0*5, 0*3#
* #supports_ncontig = 0*2, 1*3, 0*2, 1*3, 1*3#
*/
#undef TO_SIMD_SFX
#if 0
/**begin repeat1
* #len = 8, 16, 32, 64#
*/
#elif NPY_SIMD && NPY_BITSOF_@BTYPE@ == @len@
#if @is_fp@
#define TO_SIMD_SFX(X) X##_f@len@
#if NPY_BITSOF_@BTYPE@ == 32 && !NPY_SIMD_F32
#undef TO_SIMD_SFX
#endif
#if NPY_BITSOF_@BTYPE@ == 64 && !NPY_SIMD_F64
#undef TO_SIMD_SFX
#endif
#elif @is_unsigned@
#define TO_SIMD_SFX(X) X##_u@len@
#else
#define TO_SIMD_SFX(X) X##_s@len@
#endif
/**end repeat1**/
#endif
/**begin repeat1
* #kind = negative#
* #intrin = negative#
*/
NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@kind@)
(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))
{
char *ip = args[0], *op = args[1];
npy_intp istep = steps[0], ostep = steps[1],
len = dimensions[0];
#ifdef TO_SIMD_SFX
#undef STYPE
#define STYPE TO_SIMD_SFX(npyv_lanetype)
if (!is_mem_overlap(ip, istep, op, ostep, len)) {
if (IS_UNARY_CONT(@type@, @type@)) {
// no overlap and operands are contiguous
TO_SIMD_SFX(simd_unary_cc_@intrin@)(
(STYPE*)ip, (STYPE*)op, len
);
goto clear;
}
#if @supports_ncontig@
const npy_intp istride = istep / sizeof(STYPE);
const npy_intp ostride = ostep / sizeof(STYPE);
if (TO_SIMD_SFX(npyv_loadable_stride)(istride) &&
TO_SIMD_SFX(npyv_storable_stride)(ostride))
{
if (istride == 1 && ostride != 1) {
// contiguous input, non-contiguous output
TO_SIMD_SFX(simd_unary_cn_@intrin@)(
(STYPE*)ip, (STYPE*)op, ostride, len
);
goto clear;
}
else if (istride != 1 && ostride == 1) {
// non-contiguous input, contiguous output
TO_SIMD_SFX(simd_unary_nc_@intrin@)(
(STYPE*)ip, istride, (STYPE*)op, len
);
goto clear;
}
// SSE2 does better with unrolled scalar for heavy non-contiguous
#if !defined(NPY_HAVE_SSE2)
else if (istride != 1 && ostride != 1) {
// non-contiguous input and output
TO_SIMD_SFX(simd_unary_nn_@intrin@)(
(STYPE*)ip, istride, (STYPE*)op, ostride, len
);
goto clear;
}
#endif
}
#endif // @supports_ncontig@
}
#endif // TO_SIMD_SFX
#ifndef NPY_DISABLE_OPTIMIZATION
/*
* scalar unrolls
* 8x unroll performed best on
* - Apple M1 Native / arm64
* - Apple M1 Rosetta / SSE42
* - iMacPro / AVX512
*/
#define UNROLL 8
for (; len >= UNROLL; len -= UNROLL, ip += istep*UNROLL, op += ostep*UNROLL) {
/**begin repeat2
* #U = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15#
*/
#if UNROLL > @U@
const @type@ in_@U@ = *((const @type@ *)(ip + @U@ * istep));
*((@type@ *)(op + @U@ * ostep)) = scalar_@intrin@(in_@U@);
#endif
/**end repeat2**/
}
#endif // NPY_DISABLE_OPTIMIZATION
for (; len > 0; --len, ip += istep, op += ostep) {
*((@type@ *)op) = scalar_@intrin@(*(const @type@ *)ip);
}
#ifdef TO_SIMD_SFX
clear:
npyv_cleanup();
#endif
#if @is_fp@
npy_clear_floatstatus_barrier((char*)dimensions);
#endif
}
/**end repeat**/
#undef NEGATIVE_CONTIG_ONLY
|
