8000 ENH: Use AVX for float32 implementation of np.sin & np.cos by r-devulap · Pull Request #13368 · numpy/numpy · GitHub
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ENH: Use AVX for float32 implementation of np.sin & np.cos #13368

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Merged
merged 11 commits into from
Aug 18, 2019
Merged

ENH: Use AVX for float32 implementation of np.sin & np.cos #13368

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bd2c82b
ENH: Use AVX for float32 implementation of np.sin & np.cos
r-devulap Mar 20, 2019
e50e725
BUG: fixing NAN handling and adding tests for sin/cos
r-devulap May 1, 2019
d56fbfd
BUG: sin and cos cast float16 to float32
r-devulap May 7, 2019
bca9628
TEST: adding tests to validate AVX sin/cos implementation
r-devulap May 20, 2019
9cae09c
BUG: use strides and process strided arrays using AVX
r-devulap May 22, 2019
cd9f1a8
BUG: AVX2 impl of sin/cos requires an FMA
r-devulap May 22, 2019
8589d48
TEST: Rounding max tolerable ulp error to an int
r-devulap May 23, 2019
0f78154
BUG: eliminate unsed variables warning in cpuid
r-devulap May 23, 2019
44d14ab
MAINT: using an enum to switch between sin/cos
r-devulap May 29, 2019
6f34f5f
TEST: improving test coverage for sin/cos for input > 117435.992f
r-devulap May 30, 2019
457b3e6
BUG: rename avx2_scalef_ps to fma_scalef_ps
r-devulap Aug 3, 2019
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BUG: use strides and process strided arrays using AVX
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@r-devulap
r-devulap committed Aug 3, 2019
commit 9cae09cfa46dcb8d4eed07f7df841a36da942b07
21 changes: 8 additions & 13 deletions numpy/core/src/umath/loops.c.src
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Original file line number Diff line number Diff line change
Expand Up @@ -1651,22 +1651,17 @@ FLOAT_@func@_@isa@(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY
NPY_NO_EXPORT NPY_GCC_OPT_3 void
FLOAT_@func@_@isa@(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
{
#if defined @CHK@ && defined NPY_HAVE_SSE2_INTRINSICS
char str[] = "@func@";
@ISA@_sincos_FLOAT((npy_float*)args[1], (npy_float*)args[0], dimensions[0], str);
if (!run_unary_@isa@_sincos_FLOAT(args, dimensions, steps, str)) {
UNARY_LOOP {
#if defined @CHK@ && defined NPY_HAVE_SSE2_INTRINSICS
@ISA@_sincos_FLOAT((npy_float *)op1, (npy_float *)ip1, 1, steps[0], str);
#else
/*
* This is the path it would take if ISA was runtime detected, but not
* compiled for. It fixes the error on clang6.0 which fails to compile
* AVX512F version. Not sure if I like this idea, if during runtime it
* detects AXV512F, it will end up running the scalar version instead
* of AVX2.
*/
UNARY_LOOP {
const npy_float in1 = *(npy_float *)ip1;
*(npy_float *)op1 = @scalarf@(in1);
}
const npy_float in1 = *(npy_float *)ip1;
*(npy_float *)op1 = @scalarf@(in1);
#endif
}
}
}

/**end repeat1**/
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49 changes: 41 additions & 8 deletions numpy/core/src/umath/simd.inc.src
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Expand Up @@ -164,9 +164,23 @@ run_unary_@isa@_@func@_FLOAT(char **args, npy_intp *dimensions, npy_intp *steps)

#if defined HAVE_ATTRIBUTE_TARGET_@ISA@_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
static NPY_INLINE void
@ISA@_sincos_FLOAT(npy_float *, npy_float *, const npy_intp n, char*);
@ISA@_sincos_FLOAT(npy_float *, npy_float *, const npy_intp n, const npy_intp steps, char*);
#endif

static NPY_INLINE int
run_unary_@isa@_sincos_FLOAT(char **args, npy_intp *dimensions, npy_intp *steps, char* mychar)
{
#if defined HAVE_ATTRIBUTE_TARGET_@ISA@_WITH_INTRINSICS && defined NPY_HAVE_SSE2_INTRINSICS
if (IS_OUTPUT_BLOCKABLE_UNARY(sizeof(npy_float), @REGISTER_SIZE@)) {
@ISA@_sincos_FLOAT((npy_float*)args[1], (npy_float*)args[0], dimensions[0], steps[0], mychar);
return 1;
}
else
return 0;
#endif
return 0;
}

/**end repeat**/


Expand Down Expand Up @@ -1473,9 +1487,13 @@ static NPY_INLINE NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ @vtype@

#if defined HAVE_ATTRIBUTE_TARGET_@ISA@_WITH_INTRINSICS
static NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ void
@ISA@_sincos_FLOAT(npy_float * op, npy_float * ip, const npy_intp array_size,
char* operation)
@ISA@_sincos_FLOAT(npy_float * op,
npy_float * ip,
const npy_intp array_size,
const npy_intp steps,
char* operation)
{
const npy_intp stride = steps/sizeof(npy_float);
const npy_int num_lanes = @BYTES@/sizeof(npy_float);
npy_int compute_cos = 1;
npy_float large_number = 71476.0625f;
Expand Down Expand Up @@ -1508,13 +1526,26 @@ static NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ void
@mask@ nan_mask, glibc_mask, sine_mask, negate_mask;
@mask@ load_mask = @isa@_get_full_load_mask();
npy_intp num_remaining_elements = array_size;
npy_int indexarr[16];
for (npy_int ii = 0; ii < 16; ii++) {
indexarr[ii] = ii*stride;
}
@vtype@i vindex = _mm@vsize@_loadu_si@vsize@((@vtype@i*)&indexarr[0]);

while (num_remaining_elements > 0) {

if (num_remaining_elements < num_lanes)
if (num_remaining_elements < num_lanes) {
load_mask = @isa@_get_partial_load_mask(num_remaining_elements,
num_lanes);
@vtype@ x = @isa@_masked_load(load_mask, ip);
}

@vtype@ x;
if (stride == 1) {
x = @isa@_masked_load(load_mask, ip);
}
else {
x = @isa@_masked_gather(zero_f, ip, vindex, load_mask);
}

/*
* For elements outside of this range, Cody-Waite's range reduction
Expand Down Expand Up @@ -1565,19 +1596,21 @@ static NPY_GCC_OPT_3 NPY_GCC_TARGET_@ISA@ void
/* process elements using glibc for large elements */
if (compute_cos) {
for (int ii = 0; iglibc_mask != 0; ii++) {
if (iglibc_mask & 0x01)
if (iglibc_mask & 0x01) {
op[ii] = npy_cosf(ip[ii]);
}
iglibc_mask = iglibc_mask >> 1;
}
}
else {
for (int ii = 0; iglibc_mask != 0; ii++) {
if (iglibc_mask & 0x01)
if (iglibc_mask & 0x01) {
op[ii] = npy_sinf(ip[ii]);
}
iglibc_mask = iglibc_mask >> 1;
}
}
ip += num_lanes;
ip += num_lanes*stride;
op += num_lanes;
num_remaining_elements -= num_lanes;
}
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