8000 ENH, SIMD: Ditching the old CPU dispatcher(Arithmetic) · numpy/numpy@0985a73 · GitHub
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ENH, SIMD: Ditching the old CPU dispatcher(Arithmetic)
 The first patch in a series of pull-requests aims to facilitate the migration process to our new SIMD interface(NPYV). It is basically a process that focuses on getting rid of the main umath SIMD source `simd.inc`, which contains almost all SIMD kernels, by splitting it into several dispatch-able sources without changing the base code, which facilitates the review process in order to speed up access to the nominal target. In this patch, we have moved the arithmetic operations of real and complex for single/double precision to the new CPU dispatcher. NOTE: previously, the SIMD code of AVX2 and AVX512F for single/double precision wasn't dispatched in runtime before.
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‎.gitignore‎

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Original file line numberDiff line numberDiff line change
@@ -172,6 +172,7 @@ numpy/core/src/umath/simd.inc
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numpy/core/src/umath/struct_ufunc_test.c
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numpy/core/src/umath/test_rational.c
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numpy/core/src/umath/umath_tests.c
175+
numpy/core/src/umath/loops_utils.h
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numpy/distutils/__config__.py
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numpy/linalg/umath_linalg.c
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doc/source/**/generated/
@@ -218,3 +219,4 @@ numpy/core/src/_simd/_simd_data.inc
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numpy/core/src/_simd/_simd_inc.h
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# umath module
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numpy/core/src/umath/loops_unary_fp.dispatch.c
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numpy/core/src/umath/loops_arithm_fp.dispatch.c

‎numpy/core/code_generators/generate_umath.py‎

Lines changed: 17 additions & 14 deletions
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@@ -46,18 +46,19 @@ class TypeDescription:
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If astype['x'] is 'y', uses PyUFunc_x_x_As_y_y/PyUFunc_xx_x_As_yy_y
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instead of PyUFunc_x_x/PyUFunc_xx_x.
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cfunc_alias : str or none, optional
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replaces the suffix of C function name instead of using ufunc_name,
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e.g. "FLOAT_{cfunc_alias}" instead of "FLOAT_{ufunc_name}" (see make_arrays)
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appended to inner loop C function name, e.g. FLOAT_{cfunc_alias} (see make_arrays)
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NOTE: it doesn't support 'astype'
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simd: list
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Available SIMD ufunc loops, dispatched at runtime in specified order
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Currently only supported for simples types (see make_arrays)
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dispatch: str or None, optional
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Dispatch-able source name without its extension '.dispatch.c' that contains the definition of ufunc,
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dispatched at runtime depending on the specified targets of the dispatch-able source.
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Dispatch-able source name without its extension '.dispatch.c' that
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contains the definition of ufunc, dispatched at runtime depending on the
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specified targets of the dispatch-able source.
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NOTE: it doesn't support 'astype'
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"""
60-
def __init__(self, type, f=None, in_=None, out=None, astype=None, cfunc_alias=None, simd=None, dispatch=None):
60+
def __init__(self, type, f=None, in_=None, out=None, astype=None, cfunc_alias=None,
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simd=None, dispatch=None):
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self.type = type
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self.func_data = f
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if astype is None:
@@ -96,7 +97,8 @@ def build_func_data(types, f):
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func_data = [_fdata_map.get(t, '%s') % (f,) for t in types]
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return func_data
9899

99-
def TD(types, f=None, astype=None, in_=None, out=None, cfunc_alias=None, simd=None, dispatch=None):
100+
def TD(types, f=None, astype=None, in_=None, out=None, cfunc_alias=None,
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simd=None, dispatch=None):
100102
if f is not None:
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if isinstance(f, str):
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func_data = build_func_data(types, f)
@@ -132,7 +134,8 @@ def TD(types, f=None, astype=None, in_=None, out=None, cfunc_alias=None, simd=No
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else:
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dispt = None
134136
tds.append(TypeDescription(
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t, f=fd, in_=i, out=o, astype=astype, cfunc_alias=cfunc_alias, simd=simdt, dispatch=dispt
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t, f=fd, in_=i, out=o, astype=astype, cfunc_alias=cfunc_alias,
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simd=simdt, dispatch=dispt
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))
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return tds
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@@ -287,7 +290,7 @@ def english_upper(s):
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Ufunc(2, 1 628C , Zero,
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docstrings.get('numpy.core.umath.add'),
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'PyUFunc_AdditionTypeResolver',
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TD(notimes_or_obj, simd=[('avx512f', cmplxvec),('avx2', ints)]),
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TD(notimes_or_obj, simd=[('avx2', ints)], dispatch=[('loops_arithm_fp', 'fdFD')]),
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[TypeDescription('M', FullTypeDescr, 'Mm', 'M'),
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TypeDescription('m', FullTypeDescr, 'mm', 'm'),
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TypeDescription('M', FullTypeDescr, 'mM', 'M'),
@@ -298,7 +301,7 @@ def english_upper(s):
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Ufunc(2, 1, None, # Zero is only a unit to the right, not the left
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docstrings.get('numpy.core.umath.subtract'),
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'PyUFunc_SubtractionTypeResolver',
301-
TD(ints + inexact, simd=[('avx512f', cmplxvec),('avx2', ints)]),
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TD(ints + inexact, simd=[('avx2', ints)], dispatch=[('loops_arithm_fp', 'fdFD')]),
302305
[TypeDescription('M', FullTypeDescr, 'Mm', 'M'),
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TypeDescription('m', FullTypeDescr, 'mm', 'm'),
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TypeDescription('M', FullTypeDescr, 'MM', 'm'),
@@ -309,7 +312,7 @@ def english_upper(s):
309312
Ufunc(2, 1, One,
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docstrings.get('numpy.core.umath.multiply'),
311314
'PyUFunc_MultiplicationTypeResolver',
312-
TD(notimes_or_obj, simd=[('avx512f', cmplxvec),('avx2', ints)]),
315+
TD(notimes_or_obj, simd=[('avx2', ints)], dispatch=[('loops_arithm_fp', 'fdFD')]),
313316
[TypeDescription('m', FullTypeDescr, 'mq', 'm'),
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TypeDescription('m', FullTypeDescr, 'qm', 'm'),
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TypeDescription('m', FullTypeDescr, 'md', 'm'),
@@ -333,10 +336,10 @@ def english_upper(s):
333336
Ufunc(2, 1, None, # One is only a unit to the right, not the left
334337
docstrings.get('numpy.core.umath.true_divide'),
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'PyUFunc_TrueDivisionTypeResolver',
336-
TD(flts+cmplx),
337-
[TypeDescription('m', FullTypeDescr, 'mq', 'm'),
338-
TypeDescription('m', FullTypeDescr, 'md', 'm'),
339-
TypeDescription('m', FullTypeDescr, 'mm', 'd'),
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TD(flts+cmplx, cfunc_alias='divide', dispatch=[('loops_arithm_fp', 'fd')]),
340+
[TypeDescription('m', FullTypeDescr, 'mq', 'm', cfunc_alias='divide'),
341+
TypeDescription('m', FullTypeDescr, 'md', 'm', cfunc_alias='divide'),
342+
TypeDescription('m', FullTypeDescr, 'mm', 'd', cfunc_alias='divide'),
340343
],
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TD(O, f='PyNumber_TrueDivide'),
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),

‎numpy/core/setup.py‎

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@@ -916,8 +916,10 @@ def generate_umath_c(ext, build_dir):
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join('src', 'umath', 'funcs.inc.src'),
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join('src', 'umath', 'simd.inc.src'),
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join('src', 'umath', 'loops.h.src'),
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join('src', 'umath', 'loops_utils.h.src'),
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join('src', 'umath', 'loops.c.src'),
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join('src', 'umath', 'loops_unary_fp.dispatch.c.src'),
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join('src', 'umath', 'loops_arithm_fp.dispatch.c.src'),
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join('src', 'umath', 'matmul.h.src'),
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join('src', 'umath', 'matmul.c.src'),
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join('src', 'umath', 'clip.h.src'),

‎numpy/core/src/umath/fast_loop_macros.h‎

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@@ -237,5 +237,117 @@ abs_ptrdiff(char *a, char *b)
237237
TYPE io1 = *(TYPE *)iop1; \
238238
BINARY_REDUCE_LOOP_INNER
239239

240+
#define IS_BINARY_STRIDE_ONE(esize, vsize) \
241+
((steps[0] == esize) && \
242+
(steps[1] == esize) && \
243+
(steps[2] == esize) && \
244+
(abs_ptrdiff(args[2], args[0]) >= vsize) && \
245+
(abs_ptrdiff(args[2], args[1]) >= vsize))
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247+
/*
248+
* stride is equal to element size and input and destination are equal or
249+
* don't overlap within one register. The check of the steps against
250+
* esize also quarantees that steps are >= 0.
251+
*/
252+
#define IS_BLOCKABLE_UNARY(esize, vsize) \
253+
(steps[0] == (esize) && steps[0] == steps[1] && \
254+
(npy_is_aligned(args[0], esize) && npy_is_aligned(args[1], esize)) && \
255+
((abs_ptrdiff(args[1], args[0]) >= (vsize)) || \
256+
((abs_ptrdiff(args[1], args[0]) == 0))))
257+
258+
/*
259+
* Avoid using SIMD for very large step sizes for several reasons:
260+
* 1) Supporting large step sizes requires use of i64gather/scatter_ps instructions,
261+
* in which case we need two i64gather instructions and an additional vinsertf32x8
262+
* instruction to load a single zmm register (since one i64gather instruction
263+
* loads into a ymm register). This is not ideal for performance.
264+
* 2) Gather and scatter instructions can be slow when the loads/stores
265+
* cross page boundaries.
266+
*
267+
* We instead rely on i32gather/scatter_ps instructions which use a 32-bit index
268+
* element. The index needs to be < INT_MAX to avoid overflow. MAX_STEP_SIZE
269+
* ensures this. The condition also requires that the input and output arrays
270+
* should have no overlap in memory.
271+
*/
272+
#define IS_BINARY_SMALL_STEPS_AND_NOMEMOVERLAP \
273+
((labs(steps[0]) < MAX_STEP_SIZE) && \
274+
(labs(steps[1]) < MAX_STEP_SIZE) && \
275+
(labs(steps[2]) < MAX_STEP_SIZE) && \
276+
(nomemoverlap(args[0], steps[0] * dimensions[0], args[2], steps[2] * dimensions[0])) && \
277+
(nomemoverlap(args[1], steps[1] * dimensions[0], args[2], steps[2] * dimensions[0])))
278+
279+
#define IS_UNARY_TWO_OUT_SMALL_STEPS_AND_NOMEMOVERLAP \
280+
((labs(steps[0]) < MAX_STEP_SIZE) && \
281+
(labs(steps[1]) < MAX_STEP_SIZE) && \
282+
(labs(steps[2]) < MAX_STEP_SIZE) && \
283+
(nomemoverlap(args[0], steps[0] * dimensions[0], args[2], steps[2] * dimensions[0])) && \
284+
(nomemoverlap(args[0], steps[0] * dimensions[0], args[1], steps[1] * dimensions[0])))
285+
286+
/*
287+
* 1) Output should be contiguous, can handle strided input data
288+
* 2) Input step should be smaller than MAX_STEP_SIZE for performance
289+
* 3) Input and output arrays should have no overlap in memory
290+
*/
291+
#define IS_OUTPUT_BLOCKABLE_UNARY(esizein, esizeout, vsize) \
292+
((steps[0] & (esizein-1)) == 0 && \
293+
steps[1] == (esizeout) && labs(steps[0]) < MAX_STEP_SIZE && \
294+
(nomemoverlap(args[1], steps[1] * dimensions[0], args[0], steps[0] * dimensions[0])))
295+
296+
#define IS_BLOCKABLE_REDUCE(esize, vsize) \
297+
(steps[1] == (esize) && abs_ptrdiff(args[1], args[0]) >= (vsize) && \
298+
npy_is_aligned(args[1], (esize)) && \
299+
npy_is_aligned(args[0], (esize)))
300+
301+
#define IS_BLOCKABLE_BINARY(esize, vsize) \
302+
(steps[0] == steps[1] && steps[1] == steps[2] && steps[2] == (esize) && \
303+
npy_is_aligned(args[2], (esize)) && npy_is_aligned(args[1], (esize)) && \
304+
npy_is_aligned(args[0], (esize)) && \
305+
(abs_ptrdiff(args[2], args[0]) >= (vsize) || \
306+
abs_ptrdiff(args[2], args[0]) == 0) && \
307+
(abs_ptrdiff(args[2], args[1]) >= (vsize) || \
308+
abs_ptrdiff(args[2], args[1]) >= 0))
309+
310+
#define IS_BLOCKABLE_BINARY_SCALAR1(esize, vsize) \
311+
(steps[0] == 0 && steps[1] == steps[2] && steps[2] == (esize) && \
312+
npy_is_aligned(args[2], (esize)) && npy_is_aligned(args[1], (esize)) && \
313+
((abs_ptrdiff(args[2], args[1]) >= (vsize)) || \
314+
(abs_ptrdiff(args[2], args[1]) == 0)) && \
315+
abs_ptrdiff(args[2], args[0]) >= (esize))
316+
317+
#define IS_BLOCKABLE_BINARY_SCALAR2(esize, vsize) \
318+
(steps[1] == 0 && steps[0] == steps[2] && steps[2] == (esize) && \
319+
npy_is_aligned(args[2], (esize)) && npy_is_aligned(args[0], (esize)) && \
320+
((abs_ptrdiff(args[2], args[0]) >= (vsize)) || \
321+
(abs_ptrdiff(args[2], args[0]) == 0)) && \
322+
abs_ptrdiff(args[2], args[1]) >= (esize))
323+
324+
#undef abs_ptrdiff
325+
326+
#define IS_BLOCKABLE_BINARY_BOOL(esize, vsize) \
327+
(steps[0] == (esize) && steps[0] == steps[1] && steps[2] == (1) && \
328+
npy_is_aligned(args[1], (esize)) && \
329+
npy_is_aligned(args[0], (esize)))
330+
331+
#define IS_BLOCKABLE_BINARY_SCALAR1_BOOL(esize, vsize) \
332+
(steps[0] == 0 && steps[1] == (esize) && steps[2] == (1) && \
333+
npy_is_aligned(args[1], (esize)))
334+
335+
#define IS_BLOCKABLE_BINARY_SCALAR2_BOOL(esize, vsize) \
336+
(steps[0] == (esize) && steps[1] == 0 && steps[2] == (1) && \
337+
npy_is_aligned(args[0], (esize)))
338+
339+
/* align var to alignment */
340+
#define LOOP_BLOCK_ALIGN_VAR(var, type, alignment)\
341+
npy_intp i, peel = npy_aligned_block_offset(var, sizeof(type),\
342+
alignment, n);\
343+
for(i = 0; i < peel; i++)
344+
345+
#define LOOP_BLOCKED(type, vsize)\
346+
for(; i < npy_blocked_end(peel, sizeof(type), vsize, n);\
347+
i += (vsize / sizeof(type)))
348+
349+
#define LOOP_BLOCKED_END\
350+
for (; i < n; i++)
351+
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241353
#endif /* _NPY_UMATH_FAST_LOOP_MACROS_H_ */

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