%PDF-
%PDF-
Mini Shell
Mini Shell
# NOTE: Please avoid the use of numpy.testing since NPYV intrinsics
# may be involved in their functionality.
import pytest, math, re
import itertools
import operator
from numpy.core._simd import targets, clear_floatstatus, get_floatstatus
from numpy.core._multiarray_umath import __cpu_baseline__
def check_floatstatus(divbyzero=False, overflow=False,
underflow=False, invalid=False,
all=False):
#define NPY_FPE_DIVIDEBYZERO 1
#define NPY_FPE_OVERFLOW 2
#define NPY_FPE_UNDERFLOW 4
#define NPY_FPE_INVALID 8
err = get_floatstatus()
ret = (all or divbyzero) and (err & 1) != 0
ret |= (all or overflow) and (err & 2) != 0
ret |= (all or underflow) and (err & 4) != 0
ret |= (all or invalid) and (err & 8) != 0
return ret
class _Test_Utility:
# submodule of the desired SIMD extension, e.g. targets["AVX512F"]
npyv = None
# the current data type suffix e.g. 's8'
sfx = None
# target name can be 'baseline' or one or more of CPU features
target_name = None
def __getattr__(self, attr):
"""
To call NPV intrinsics without the attribute 'npyv' and
auto suffixing intrinsics according to class attribute 'sfx'
"""
return getattr(self.npyv, attr + "_" + self.sfx)
def _x2(self, intrin_name):
return getattr(self.npyv, f"{intrin_name}_{self.sfx}x2")
def _data(self, start=None, count=None, reverse=False):
"""
Create list of consecutive numbers according to number of vector's lanes.
"""
if start is None:
start = 1
if count is None:
count = self.nlanes
rng = range(start, start + count)
if reverse:
rng = reversed(rng)
if self._is_fp():
return [x / 1.0 for x in rng]
return list(rng)
def _is_unsigned(self):
return self.sfx[0] == 'u'
def _is_signed(self):
return self.sfx[0] == 's'
def _is_fp(self):
return self.sfx[0] == 'f'
def _scalar_size(self):
return int(self.sfx[1:])
def _int_clip(self, seq):
if self._is_fp():
return seq
max_int = self._int_max()
min_int = self._int_min()
return [min(max(v, min_int), max_int) for v in seq]
def _int_max(self):
if self._is_fp():
return None
max_u = self._to_unsigned(self.setall(-1))[0]
if self._is_signed():
return max_u // 2
return max_u
def _int_min(self):
if self._is_fp():
return None
if self._is_unsigned():
return 0
return -(self._int_max() + 1)
def _true_mask(self):
max_unsig = getattr(self.npyv, "setall_u" + self.sfx[1:])(-1)
return max_unsig[0]
def _to_unsigned(self, vector):
if isinstance(vector, (list, tuple)):
return getattr(self.npyv, "load_u" + self.sfx[1:])(vector)
else:
sfx = vector.__name__.replace("npyv_", "")
if sfx[0] == "b":
cvt_intrin = "cvt_u{0}_b{0}"
else:
cvt_intrin = "reinterpret_u{0}_{1}"
return getattr(self.npyv, cvt_intrin.format(sfx[1:], sfx))(vector)
def _pinfinity(self):
return float("inf")
def _ninfinity(self):
return -float("inf")
def _nan(self):
return float("nan")
def _cpu_features(self):
target = self.target_name
if target == "baseline":
target = __cpu_baseline__
else:
target = target.split('__') # multi-target separator
return ' '.join(target)
class _SIMD_BOOL(_Test_Utility):
"""
To test all boolean vector types at once
"""
def _nlanes(self):
return getattr(self.npyv, "nlanes_u" + self.sfx[1:])
def _data(self, start=None, count=None, reverse=False):
true_mask = self._true_mask()
rng = range(self._nlanes())
if reverse:
rng = reversed(rng)
return [true_mask if x % 2 else 0 for x in rng]
def _load_b(self, data):
len_str = self.sfx[1:]
load = getattr(self.npyv, "load_u" + len_str)
cvt = getattr(self.npyv, f"cvt_b{len_str}_u{len_str}")
return cvt(load(data))
def test_operators_logical(self):
"""
Logical operations for boolean types.
Test intrinsics:
npyv_xor_##SFX, npyv_and_##SFX, npyv_or_##SFX, npyv_not_##SFX,
npyv_andc_b8, npvy_orc_b8, nvpy_xnor_b8
"""
data_a = self._data()
data_b = self._data(reverse=True)
vdata_a = self._load_b(data_a)
vdata_b = self._load_b(data_b)
data_and = [a & b for a, b in zip(data_a, data_b)]
vand = getattr(self, "and")(vdata_a, vdata_b)
assert vand == data_and
data_or = [a | b for a, b in zip(data_a, data_b)]
vor = getattr(self, "or")(vdata_a, vdata_b)
assert vor == data_or
data_xor = [a ^ b for a, b in zip(data_a, data_b)]
vxor = getattr(self, "xor")(vdata_a, vdata_b)
assert vxor == data_xor
vnot = getattr(self, "not")(vdata_a)
assert vnot == data_b
# among the boolean types, andc, orc and xnor only support b8
if self.sfx not in ("b8"):
return
data_andc = [(a & ~b) & 0xFF for a, b in zip(data_a, data_b)]
vandc = getattr(self, "andc")(vdata_a, vdata_b)
assert data_andc == vandc
data_orc = [(a | ~b) & 0xFF for a, b in zip(data_a, data_b)]
vorc = getattr(self, "orc")(vdata_a, vdata_b)
assert data_orc == vorc
data_xnor = [~(a ^ b) & 0xFF for a, b in zip(data_a, data_b)]
vxnor = getattr(self, "xnor")(vdata_a, vdata_b)
assert data_xnor == vxnor
def test_tobits(self):
data2bits = lambda data: sum([int(x != 0) << i for i, x in enumerate(data, 0)])
for data in (self._data(), self._data(reverse=True)):
vdata = self._load_b(data)
data_bits = data2bits(data)
tobits = self.tobits(vdata)
bin_tobits = bin(tobits)
assert bin_tobits == bin(data_bits)
def test_pack(self):
"""
Pack multiple vectors into one
Test intrinsics:
npyv_pack_b8_b16
npyv_pack_b8_b32
npyv_pack_b8_b64
"""
if self.sfx not in ("b16", "b32", "b64"):
return
# create the vectors
data = self._data()
rdata = self._data(reverse=True)
vdata = self._load_b(data)
vrdata = self._load_b(rdata)
pack_simd = getattr(self.npyv, f"pack_b8_{self.sfx}")
# for scalar execution, concatenate the elements of the multiple lists
# into a single list (spack) and then iterate over the elements of
# the created list applying a mask to capture the first byte of them.
if self.sfx == "b16":
spack = [(i & 0xFF) for i in (list(rdata) + list(data))]
vpack = pack_simd(vrdata, vdata)
elif self.sfx == "b32":
spack = [(i & 0xFF) for i in (2*list(rdata) + 2*list(data))]
vpack = pack_simd(vrdata, vrdata, vdata, vdata)
elif self.sfx == "b64":
spack = [(i & 0xFF) for i in (4*list(rdata) + 4*list(data))]
vpack = pack_simd(vrdata, vrdata, vrdata, vrdata,
vdata, vdata, vdata, vdata)
assert vpack == spack
@pytest.mark.parametrize("intrin", ["any", "all"])
@pytest.mark.parametrize("data", (
[-1, 0],
[0, -1],
[-1],
[0]
))
def test_operators_crosstest(self, intrin, data):
"""
Test intrinsics:
npyv_any_##SFX
npyv_all_##SFX
"""
data_a = self._load_b(data * self._nlanes())
func = eval(intrin)
intrin = getattr(self, intrin)
desired = func(data_a)
simd = intrin(data_a)
assert not not simd == desired
class _SIMD_INT(_Test_Utility):
"""
To test all integer vector types at once
"""
def test_operators_shift(self):
if self.sfx in ("u8", "s8"):
return
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
for count in range(self._scalar_size()):
# load to cast
data_shl_a = self.load([a << count for a in data_a])
# left shift
shl = self.shl(vdata_a, count)
assert shl == data_shl_a
# load to cast
data_shr_a = self.load([a >> count for a in data_a])
# right shift
shr = self.shr(vdata_a, count)
assert shr == data_shr_a
# shift by zero or max or out-range immediate constant is not applicable and illogical
for count in range(1, self._scalar_size()):
# load to cast
data_shl_a = self.load([a << count for a in data_a])
# left shift by an immediate constant
shli = self.shli(vdata_a, count)
assert shli == data_shl_a
# load to cast
data_shr_a = self.load([a >> count for a in data_a])
# right shift by an immediate constant
shri = self.shri(vdata_a, count)
assert shri == data_shr_a
def test_arithmetic_subadd_saturated(self):
if self.sfx in ("u32", "s32", "u64", "s64"):
return
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
data_adds = self._int_clip([a + b for a, b in zip(data_a, data_b)])
adds = self.adds(vdata_a, vdata_b)
assert adds == data_adds
data_subs = self._int_clip([a - b for a, b in zip(data_a, data_b)])
subs = self.subs(vdata_a, vdata_b)
assert subs == data_subs
def test_math_max_min(self):
data_a = self._data()
data_b = self._data(self.nlanes)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
data_max = [max(a, b) for a, b in zip(data_a, data_b)]
simd_max = self.max(vdata_a, vdata_b)
assert simd_max == data_max
data_min = [min(a, b) for a, b in zip(data_a, data_b)]
simd_min = self.min(vdata_a, vdata_b)
assert simd_min == data_min
@pytest.mark.parametrize("start", [-100, -10000, 0, 100, 10000])
def test_reduce_max_min(self, start):
"""
Test intrinsics:
npyv_reduce_max_##sfx
npyv_reduce_min_##sfx
"""
vdata_a = self.load(self._data(start))
assert self.reduce_max(vdata_a) == max(vdata_a)
assert self.reduce_min(vdata_a) == min(vdata_a)
class _SIMD_FP32(_Test_Utility):
"""
To only test single precision
"""
def test_conversions(self):
"""
Round to nearest even integer, assume CPU control register is set to rounding.
Test intrinsics:
npyv_round_s32_##SFX
"""
features = self._cpu_features()
if not self.npyv.simd_f64 and re.match(r".*(NEON|ASIMD)", features):
# very costly to emulate nearest even on Armv7
# instead we round halves to up. e.g. 0.5 -> 1, -0.5 -> -1
_round = lambda v: int(v + (0.5 if v >= 0 else -0.5))
else:
_round = round
vdata_a = self.load(self._data())
vdata_a = self.sub(vdata_a, self.setall(0.5))
data_round = [_round(x) for x in vdata_a]
vround = self.round_s32(vdata_a)
assert vround == data_round
class _SIMD_FP64(_Test_Utility):
"""
To only test double precision
"""
def test_conversions(self):
"""
Round to nearest even integer, assume CPU control register is set to rounding.
Test intrinsics:
npyv_round_s32_##SFX
"""
vdata_a = self.load(self._data())
vdata_a = self.sub(vdata_a, self.setall(0.5))
vdata_b = self.mul(vdata_a, self.setall(-1.5))
data_round = [round(x) for x in list(vdata_a) + list(vdata_b)]
vround = self.round_s32(vdata_a, vdata_b)
assert vround == data_round
class _SIMD_FP(_Test_Utility):
"""
To test all float vector types at once
"""
def test_arithmetic_fused(self):
vdata_a, vdata_b, vdata_c = [self.load(self._data())]*3
vdata_cx2 = self.add(vdata_c, vdata_c)
# multiply and add, a*b + c
data_fma = self.load([a * b + c for a, b, c in zip(vdata_a, vdata_b, vdata_c)])
fma = self.muladd(vdata_a, vdata_b, vdata_c)
assert fma == data_fma
# multiply and subtract, a*b - c
fms = self.mulsub(vdata_a, vdata_b, vdata_c)
data_fms = self.sub(data_fma, vdata_cx2)
assert fms == data_fms
# negate multiply and add, -(a*b) + c
nfma = self.nmuladd(vdata_a, vdata_b, vdata_c)
data_nfma = self.sub(vdata_cx2, data_fma)
assert nfma == data_nfma
# negate multiply and subtract, -(a*b) - c
nfms = self.nmulsub(vdata_a, vdata_b, vdata_c)
data_nfms = self.mul(data_fma, self.setall(-1))
assert nfms == data_nfms
# multiply, add for odd elements and subtract even elements.
# (a * b) -+ c
fmas = list(self.muladdsub(vdata_a, vdata_b, vdata_c))
assert fmas[0::2] == list(data_fms)[0::2]
assert fmas[1::2] == list(data_fma)[1::2]
def test_abs(self):
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
data = self._data()
vdata = self.load(self._data())
abs_cases = ((-0, 0), (ninf, pinf), (pinf, pinf), (nan, nan))
for case, desired in abs_cases:
data_abs = [desired]*self.nlanes
vabs = self.abs(self.setall(case))
assert vabs == pytest.approx(data_abs, nan_ok=True)
vabs = self.abs(self.mul(vdata, self.setall(-1)))
assert vabs == data
def test_sqrt(self):
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
data = self._data()
vdata = self.load(self._data())
sqrt_cases = ((-0.0, -0.0), (0.0, 0.0), (-1.0, nan), (ninf, nan), (pinf, pinf))
for case, desired in sqrt_cases:
data_sqrt = [desired]*self.nlanes
sqrt = self.sqrt(self.setall(case))
assert sqrt == pytest.approx(data_sqrt, nan_ok=True)
data_sqrt = self.load([math.sqrt(x) for x in data]) # load to truncate precision
sqrt = self.sqrt(vdata)
assert sqrt == data_sqrt
def test_square(self):
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
data = self._data()
vdata = self.load(self._data())
# square
square_cases = ((nan, nan), (pinf, pinf), (ninf, pinf))
for case, desired in square_cases:
data_square = [desired]*self.nlanes
square = self.square(self.setall(case))
assert square == pytest.approx(data_square, nan_ok=True)
data_square = [x*x for x in data]
square = self.square(vdata)
assert square == data_square
@pytest.mark.parametrize("intrin, func", [("ceil", math.ceil),
("trunc", math.trunc), ("floor", math.floor), ("rint", round)])
def test_rounding(self, intrin, func):
"""
Test intrinsics:
npyv_rint_##SFX
npyv_ceil_##SFX
npyv_trunc_##SFX
npyv_floor##SFX
"""
intrin_name = intrin
intrin = getattr(self, intrin)
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
# special cases
round_cases = ((nan, nan), (pinf, pinf), (ninf, ninf))
for case, desired in round_cases:
data_round = [desired]*self.nlanes
_round = intrin(self.setall(case))
assert _round == pytest.approx(data_round, nan_ok=True)
for x in range(0, 2**20, 256**2):
for w in (-1.05, -1.10, -1.15, 1.05, 1.10, 1.15):
data = self.load([(x+a)*w for a in range(self.nlanes)])
data_round = [func(x) for x in data]
_round = intrin(data)
assert _round == data_round
# test large numbers
for i in (
1.1529215045988576e+18, 4.6116860183954304e+18,
5.902958103546122e+20, 2.3611832414184488e+21
):
x = self.setall(i)
y = intrin(x)
data_round = [func(n) for n in x]
assert y == data_round
# signed zero
if intrin_name == "floor":
data_szero = (-0.0,)
else:
data_szero = (-0.0, -0.25, -0.30, -0.45, -0.5)
for w in data_szero:
_round = self._to_unsigned(intrin(self.setall(w)))
data_round = self._to_unsigned(self.setall(-0.0))
assert _round == data_round
@pytest.mark.parametrize("intrin", [
"max", "maxp", "maxn", "min", "minp", "minn"
])
def test_max_min(self, intrin):
"""
Test intrinsics:
npyv_max_##sfx
npyv_maxp_##sfx
npyv_maxn_##sfx
npyv_min_##sfx
npyv_minp_##sfx
npyv_minn_##sfx
npyv_reduce_max_##sfx
npyv_reduce_maxp_##sfx
npyv_reduce_maxn_##sfx
npyv_reduce_min_##sfx
npyv_reduce_minp_##sfx
npyv_reduce_minn_##sfx
"""
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
chk_nan = {"xp": 1, "np": 1, "nn": 2, "xn": 2}.get(intrin[-2:], 0)
func = eval(intrin[:3])
reduce_intrin = getattr(self, "reduce_" + intrin)
intrin = getattr(self, intrin)
hf_nlanes = self.nlanes//2
cases = (
([0.0, -0.0], [-0.0, 0.0]),
([10, -10], [10, -10]),
([pinf, 10], [10, ninf]),
([10, pinf], [ninf, 10]),
([10, -10], [10, -10]),
([-10, 10], [-10, 10])
)
for op1, op2 in cases:
vdata_a = self.load(op1*hf_nlanes)
vdata_b = self.load(op2*hf_nlanes)
data = func(vdata_a, vdata_b)
simd = intrin(vdata_a, vdata_b)
assert simd == data
data = func(vdata_a)
simd = reduce_intrin(vdata_a)
assert simd == data
if not chk_nan:
return
if chk_nan == 1:
test_nan = lambda a, b: (
b if math.isnan(a) else a if math.isnan(b) else b
)
else:
test_nan = lambda a, b: (
nan if math.isnan(a) or math.isnan(b) else b
)
cases = (
(nan, 10),
(10, nan),
(nan, pinf),
(pinf, nan),
(nan, nan)
)
for op1, op2 in cases:
vdata_ab = self.load([op1, op2]*hf_nlanes)
data = test_nan(op1, op2)
simd = reduce_intrin(vdata_ab)
assert simd == pytest.approx(data, nan_ok=True)
vdata_a = self.setall(op1)
vdata_b = self.setall(op2)
data = [data] * self.nlanes
simd = intrin(vdata_a, vdata_b)
assert simd == pytest.approx(data, nan_ok=True)
def test_reciprocal(self):
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
data = self._data()
vdata = self.load(self._data())
recip_cases = ((nan, nan), (pinf, 0.0), (ninf, -0.0), (0.0, pinf), (-0.0, ninf))
for case, desired in recip_cases:
data_recip = [desired]*self.nlanes
recip = self.recip(self.setall(case))
assert recip == pytest.approx(data_recip, nan_ok=True)
data_recip = self.load([1/x for x in data]) # load to truncate precision
recip = self.recip(vdata)
assert recip == data_recip
def test_special_cases(self):
"""
Compare Not NaN. Test intrinsics:
npyv_notnan_##SFX
"""
nnan = self.notnan(self.setall(self._nan()))
assert nnan == [0]*self.nlanes
@pytest.mark.parametrize("intrin_name", [
"rint", "trunc", "ceil", "floor"
])
def test_unary_invalid_fpexception(self, intrin_name):
intrin = getattr(self, intrin_name)
for d in [float("nan"), float("inf"), -float("inf")]:
v = self.setall(d)
clear_floatstatus()
intrin(v)
assert check_floatstatus(invalid=True) == False
@pytest.mark.parametrize('py_comp,np_comp', [
(operator.lt, "cmplt"),
(operator.le, "cmple"),
(operator.gt, "cmpgt"),
(operator.ge, "cmpge"),
(operator.eq, "cmpeq"),
(operator.ne, "cmpneq")
])
def test_comparison_with_nan(self, py_comp, np_comp):
pinf, ninf, nan = self._pinfinity(), self._ninfinity(), self._nan()
mask_true = self._true_mask()
def to_bool(vector):
return [lane == mask_true for lane in vector]
intrin = getattr(self, np_comp)
cmp_cases = ((0, nan), (nan, 0), (nan, nan), (pinf, nan),
(ninf, nan), (-0.0, +0.0))
for case_operand1, case_operand2 in cmp_cases:
data_a = [case_operand1]*self.nlanes
data_b = [case_operand2]*self.nlanes
vdata_a = self.setall(case_operand1)
vdata_b = self.setall(case_operand2)
vcmp = to_bool(intrin(vdata_a, vdata_b))
data_cmp = [py_comp(a, b) for a, b in zip(data_a, data_b)]
assert vcmp == data_cmp
@pytest.mark.parametrize("intrin", ["any", "all"])
@pytest.mark.parametrize("data", (
[float("nan"), 0],
[0, float("nan")],
[float("nan"), 1],
[1, float("nan")],
[float("nan"), float("nan")],
[0.0, -0.0],
[-0.0, 0.0],
[1.0, -0.0]
))
def test_operators_crosstest(self, intrin, data):
"""
Test intrinsics:
npyv_any_##SFX
npyv_all_##SFX
"""
data_a = self.load(data * self.nlanes)
func = eval(intrin)
intrin = getattr(self, intrin)
desired = func(data_a)
simd = intrin(data_a)
assert not not simd == desired
class _SIMD_ALL(_Test_Utility):
"""
To test all vector types at once
"""
def test_memory_load(self):
data = self._data()
# unaligned load
load_data = self.load(data)
assert load_data == data
# aligned load
loada_data = self.loada(data)
assert loada_data == data
# stream load
loads_data = self.loads(data)
assert loads_data == data
# load lower part
loadl = self.loadl(data)
loadl_half = list(loadl)[:self.nlanes//2]
data_half = data[:self.nlanes//2]
assert loadl_half == data_half
assert loadl != data # detect overflow
def test_memory_store(self):
data = self._data()
vdata = self.load(data)
# unaligned store
store = [0] * self.nlanes
self.store(store, vdata)
assert store == data
# aligned store
store_a = [0] * self.nlanes
self.storea(store_a, vdata)
assert store_a == data
# stream store
store_s = [0] * self.nlanes
self.stores(store_s, vdata)
assert store_s == data
# store lower part
store_l = [0] * self.nlanes
self.storel(store_l, vdata)
assert store_l[:self.nlanes//2] == data[:self.nlanes//2]
assert store_l != vdata # detect overflow
# store higher part
store_h = [0] * self.nlanes
self.storeh(store_h, vdata)
assert store_h[:self.nlanes//2] == data[self.nlanes//2:]
assert store_h != vdata # detect overflow
@pytest.mark.parametrize("intrin, elsizes, scale, fill", [
("self.load_tillz, self.load_till", (32, 64), 1, [0xffff]),
("self.load2_tillz, self.load2_till", (32, 64), 2, [0xffff, 0x7fff]),
])
def test_memory_partial_load(self, intrin, elsizes, scale, fill):
if self._scalar_size() not in elsizes:
return
npyv_load_tillz, npyv_load_till = eval(intrin)
data = self._data()
lanes = list(range(1, self.nlanes + 1))
lanes += [self.nlanes**2, self.nlanes**4] # test out of range
for n in lanes:
load_till = npyv_load_till(data, n, *fill)
load_tillz = npyv_load_tillz(data, n)
n *= scale
data_till = data[:n] + fill * ((self.nlanes-n) // scale)
assert load_till == data_till
data_tillz = data[:n] + [0] * (self.nlanes-n)
assert load_tillz == data_tillz
@pytest.mark.parametrize("intrin, elsizes, scale", [
("self.store_till", (32, 64), 1),
("self.store2_till", (32, 64), 2),
])
def test_memory_partial_store(self, intrin, elsizes, scale):
if self._scalar_size() not in elsizes:
return
npyv_store_till = eval(intrin)
data = self._data()
data_rev = self._data(reverse=True)
vdata = self.load(data)
lanes = list(range(1, self.nlanes + 1))
lanes += [self.nlanes**2, self.nlanes**4]
for n in lanes:
data_till = data_rev.copy()
data_till[:n*scale] = data[:n*scale]
store_till = self._data(reverse=True)
npyv_store_till(store_till, n, vdata)
assert store_till == data_till
@pytest.mark.parametrize("intrin, elsizes, scale", [
("self.loadn", (32, 64), 1),
("self.loadn2", (32, 64), 2),
])
def test_memory_noncont_load(self, intrin, elsizes, scale):
if self._scalar_size() not in elsizes:
return
npyv_loadn = eval(intrin)
for stride in range(-64, 64):
if stride < 0:
data = self._data(stride, -stride*self.nlanes)
data_stride = list(itertools.chain(
*zip(*[data[-i::stride] for i in range(scale, 0, -1)])
))
elif stride == 0:
data = self._data()
data_stride = data[0:scale] * (self.nlanes//scale)
else:
data = self._data(count=stride*self.nlanes)
data_stride = list(itertools.chain(
*zip(*[data[i::stride] for i in range(scale)]))
)
data_stride = self.load(data_stride) # cast unsigned
loadn = npyv_loadn(data, stride)
assert loadn == data_stride
@pytest.mark.parametrize("intrin, elsizes, scale, fill", [
("self.loadn_tillz, self.loadn_till", (32, 64), 1, [0xffff]),
("self.loadn2_tillz, self.loadn2_till", (32, 64), 2, [0xffff, 0x7fff]),
])
def test_memory_noncont_partial_load(self, intrin, elsizes, scale, fill):
if self._scalar_size() not in elsizes:
return
npyv_loadn_tillz, npyv_loadn_till = eval(intrin)
lanes = list(range(1, self.nlanes + 1))
lanes += [self.nlanes**2, self.nlanes**4]
for stride in range(-64, 64):
if stride < 0:
data = self._data(stride, -stride*self.nlanes)
data_stride = list(itertools.chain(
*zip(*[data[-i::stride] for i in range(scale, 0, -1)])
))
elif stride == 0:
data = self._data()
data_stride = data[0:scale] * (self.nlanes//scale)
else:
data = self._data(count=stride*self.nlanes)
data_stride = list(itertools.chain(
*zip(*[data[i::stride] for i in range(scale)])
))
data_stride = list(self.load(data_stride)) # cast unsigned
for n in lanes:
nscale = n * scale
llanes = self.nlanes - nscale
data_stride_till = (
data_stride[:nscale] + fill * (llanes//scale)
)
loadn_till = npyv_loadn_till(data, stride, n, *fill)
assert loadn_till == data_stride_till
data_stride_tillz = data_stride[:nscale] + [0] * llanes
loadn_tillz = npyv_loadn_tillz(data, stride, n)
assert loadn_tillz == data_stride_tillz
@pytest.mark.parametrize("intrin, elsizes, scale", [
("self.storen", (32, 64), 1),
("self.storen2", (32, 64), 2),
])
def test_memory_noncont_store(self, intrin, elsizes, scale):
if self._scalar_size() not in elsizes:
return
npyv_storen = eval(intrin)
data = self._data()
vdata = self.load(data)
hlanes = self.nlanes // scale
for stride in range(1, 64):
data_storen = [0xff] * stride * self.nlanes
for s in range(0, hlanes*stride, stride):
i = (s//stride)*scale
data_storen[s:s+scale] = data[i:i+scale]
storen = [0xff] * stride * self.nlanes
storen += [0x7f]*64
npyv_storen(storen, stride, vdata)
assert storen[:-64] == data_storen
assert storen[-64:] == [0x7f]*64 # detect overflow
for stride in range(-64, 0):
data_storen = [0xff] * -stride * self.nlanes
for s in range(0, hlanes*stride, stride):
i = (s//stride)*scale
data_storen[s-scale:s or None] = data[i:i+scale]
storen = [0x7f]*64
storen += [0xff] * -stride * self.nlanes
npyv_storen(storen, stride, vdata)
assert storen[64:] == data_storen
assert storen[:64] == [0x7f]*64 # detect overflow
# stride 0
data_storen = [0x7f] * self.nlanes
storen = data_storen.copy()
data_storen[0:scale] = data[-scale:]
npyv_storen(storen, 0, vdata)
assert storen == data_storen
@pytest.mark.parametrize("intrin, elsizes, scale", [
("self.storen_till", (32, 64), 1),
("self.storen2_till", (32, 64), 2),
])
def test_memory_noncont_partial_store(self, intrin, elsizes, scale):
if self._scalar_size() not in elsizes:
return
npyv_storen_till = eval(intrin)
data = self._data()
vdata = self.load(data)
lanes = list(range(1, self.nlanes + 1))
lanes += [self.nlanes**2, self.nlanes**4]
hlanes = self.nlanes // scale
for stride in range(1, 64):
for n in lanes:
data_till = [0xff] * stride * self.nlanes
tdata = data[:n*scale] + [0xff] * (self.nlanes-n*scale)
for s in range(0, hlanes*stride, stride)[:n]:
i = (s//stride)*scale
data_till[s:s+scale] = tdata[i:i+scale]
storen_till = [0xff] * stride * self.nlanes
storen_till += [0x7f]*64
npyv_storen_till(storen_till, stride, n, vdata)
assert storen_till[:-64] == data_till
assert storen_till[-64:] == [0x7f]*64 # detect overflow
for stride in range(-64, 0):
for n in lanes:
data_till = [0xff] * -stride * self.nlanes
tdata = data[:n*scale] + [0xff] * (self.nlanes-n*scale)
for s in range(0, hlanes*stride, stride)[:n]:
i = (s//stride)*scale
data_till[s-scale:s or None] = tdata[i:i+scale]
storen_till = [0x7f]*64
storen_till += [0xff] * -stride * self.nlanes
npyv_storen_till(storen_till, stride, n, vdata)
assert storen_till[64:] == data_till
assert storen_till[:64] == [0x7f]*64 # detect overflow
# stride 0
for n in lanes:
data_till = [0x7f] * self.nlanes
storen_till = data_till.copy()
data_till[0:scale] = data[:n*scale][-scale:]
npyv_storen_till(storen_till, 0, n, vdata)
assert storen_till == data_till
@pytest.mark.parametrize("intrin, table_size, elsize", [
("self.lut32", 32, 32),
("self.lut16", 16, 64)
])
def test_lut(self, intrin, table_size, elsize):
"""
Test lookup table intrinsics:
npyv_lut32_##sfx
npyv_lut16_##sfx
"""
if elsize != self._scalar_size():
return
intrin = eval(intrin)
idx_itrin = getattr(self.npyv, f"setall_u{elsize}")
table = range(0, table_size)
for i in table:
broadi = self.setall(i)
idx = idx_itrin(i)
lut = intrin(table, idx)
assert lut == broadi
def test_misc(self):
broadcast_zero = self.zero()
assert broadcast_zero == [0] * self.nlanes
for i in range(1, 10):
broadcasti = self.setall(i)
assert broadcasti == [i] * self.nlanes
data_a, data_b = self._data(), self._data(reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
# py level of npyv_set_* don't support ignoring the extra specified lanes or
# fill non-specified lanes with zero.
vset = self.set(*data_a)
assert vset == data_a
# py level of npyv_setf_* don't support ignoring the extra specified lanes or
# fill non-specified lanes with the specified scalar.
vsetf = self.setf(10, *data_a)
assert vsetf == data_a
# We're testing the sanity of _simd's type-vector,
# reinterpret* intrinsics itself are tested via compiler
# during the build of _simd module
sfxes = ["u8", "s8", "u16", "s16", "u32", "s32", "u64", "s64"]
if self.npyv.simd_f64:
sfxes.append("f64")
if self.npyv.simd_f32:
sfxes.append("f32")
for sfx in sfxes:
vec_name = getattr(self, "reinterpret_" + sfx)(vdata_a).__name__
assert vec_name == "npyv_" + sfx
# select & mask operations
select_a = self.select(self.cmpeq(self.zero(), self.zero()), vdata_a, vdata_b)
assert select_a == data_a
select_b = self.select(self.cmpneq(self.zero(), self.zero()), vdata_a, vdata_b)
assert select_b == data_b
# test extract elements
assert self.extract0(vdata_b) == vdata_b[0]
# cleanup intrinsic is only used with AVX for
# zeroing registers to avoid the AVX-SSE transition penalty,
# so nothing to test here
self.npyv.cleanup()
def test_reorder(self):
data_a, data_b = self._data(), self._data(reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
# lower half part
data_a_lo = data_a[:self.nlanes//2]
data_b_lo = data_b[:self.nlanes//2]
# higher half part
data_a_hi = data_a[self.nlanes//2:]
data_b_hi = data_b[self.nlanes//2:]
# combine two lower parts
combinel = self.combinel(vdata_a, vdata_b)
assert combinel == data_a_lo + data_b_lo
# combine two higher parts
combineh = self.combineh(vdata_a, vdata_b)
assert combineh == data_a_hi + data_b_hi
# combine x2
combine = self.combine(vdata_a, vdata_b)
assert combine == (data_a_lo + data_b_lo, data_a_hi + data_b_hi)
# zip(interleave)
data_zipl = self.load([
v for p in zip(data_a_lo, data_b_lo) for v in p
])
data_ziph = self.load([
v for p in zip(data_a_hi, data_b_hi) for v in p
])
vzip = self.zip(vdata_a, vdata_b)
assert vzip == (data_zipl, data_ziph)
vzip = [0]*self.nlanes*2
self._x2("store")(vzip, (vdata_a, vdata_b))
assert vzip == list(data_zipl) + list(data_ziph)
# unzip(deinterleave)
unzip = self.unzip(data_zipl, data_ziph)
assert unzip == (data_a, data_b)
unzip = self._x2("load")(list(data_zipl) + list(data_ziph))
assert unzip == (data_a, data_b)
def test_reorder_rev64(self):
# Reverse elements of each 64-bit lane
ssize = self._scalar_size()
if ssize == 64:
return
data_rev64 = [
y for x in range(0, self.nlanes, 64//ssize)
for y in reversed(range(x, x + 64//ssize))
]
rev64 = self.rev64(self.load(range(self.nlanes)))
assert rev64 == data_rev64
def test_reorder_permi128(self):
"""
Test permuting elements for each 128-bit lane.
npyv_permi128_##sfx
"""
ssize = self._scalar_size()
if ssize < 32:
return
data = self.load(self._data())
permn = 128//ssize
permd = permn-1
nlane128 = self.nlanes//permn
shfl = [0, 1] if ssize == 64 else [0, 2, 4, 6]
for i in range(permn):
indices = [(i >> shf) & permd for shf in shfl]
vperm = self.permi128(data, *indices)
data_vperm = [
data[j + (e & -permn)]
for e, j in enumerate(indices*nlane128)
]
assert vperm == data_vperm
@pytest.mark.parametrize('func, intrin', [
(operator.lt, "cmplt"),
(operator.le, "cmple"),
(operator.gt, "cmpgt"),
(operator.ge, "cmpge"),
(operator.eq, "cmpeq")
])
def test_operators_comparison(self, func, intrin):
if self._is_fp():
data_a = self._data()
else:
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
intrin = getattr(self, intrin)
mask_true = self._true_mask()
def to_bool(vector):
return [lane == mask_true for lane in vector]
data_cmp = [func(a, b) for a, b in zip(data_a, data_b)]
cmp = to_bool(intrin(vdata_a, vdata_b))
assert cmp == data_cmp
def test_operators_logical(self):
if self._is_fp():
data_a = self._data()
else:
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
if self._is_fp():
data_cast_a = self._to_unsigned(vdata_a)
data_cast_b = self._to_unsigned(vdata_b)
cast, cast_data = self._to_unsigned, self._to_unsigned
else:
data_cast_a, data_cast_b = data_a, data_b
cast, cast_data = lambda a: a, self.load
data_xor = cast_data([a ^ b for a, b in zip(data_cast_a, data_cast_b)])
vxor = cast(self.xor(vdata_a, vdata_b))
assert vxor == data_xor
data_or = cast_data([a | b for a, b in zip(data_cast_a, data_cast_b)])
vor = cast(getattr(self, "or")(vdata_a, vdata_b))
assert vor == data_or
data_and = cast_data([a & b for a, b in zip(data_cast_a, data_cast_b)])
vand = cast(getattr(self, "and")(vdata_a, vdata_b))
assert vand == data_and
data_not = cast_data([~a for a in data_cast_a])
vnot = cast(getattr(self, "not")(vdata_a))
assert vnot == data_not
if self.sfx not in ("u8"):
return
data_andc = [a & ~b for a, b in zip(data_cast_a, data_cast_b)]
vandc = cast(getattr(self, "andc")(vdata_a, vdata_b))
assert vandc == data_andc
@pytest.mark.parametrize("intrin", ["any", "all"])
@pytest.mark.parametrize("data", (
[1, 2, 3, 4],
[-1, -2, -3, -4],
[0, 1, 2, 3, 4],
[0x7f, 0x7fff, 0x7fffffff, 0x7fffffffffffffff],
[0, -1, -2, -3, 4],
[0],
[1],
[-1]
))
def test_operators_crosstest(self, intrin, data):
"""
Test intrinsics:
npyv_any_##SFX
npyv_all_##SFX
"""
data_a = self.load(data * self.nlanes)
func = eval(intrin)
intrin = getattr(self, intrin)
desired = func(data_a)
simd = intrin(data_a)
assert not not simd == desired
def test_conversion_boolean(self):
bsfx = "b" + self.sfx[1:]
to_boolean = getattr(self.npyv, "cvt_%s_%s" % (bsfx, self.sfx))
from_boolean = getattr(self.npyv, "cvt_%s_%s" % (self.sfx, bsfx))
false_vb = to_boolean(self.setall(0))
true_vb = self.cmpeq(self.setall(0), self.setall(0))
assert false_vb != true_vb
false_vsfx = from_boolean(false_vb)
true_vsfx = from_boolean(true_vb)
assert false_vsfx != true_vsfx
def test_conversion_expand(self):
"""
Test expand intrinsics:
npyv_expand_u16_u8
npyv_expand_u32_u16
"""
if self.sfx not in ("u8", "u16"):
return
totype = self.sfx[0]+str(int(self.sfx[1:])*2)
expand = getattr(self.npyv, f"expand_{totype}_{self.sfx}")
# close enough from the edge to detect any deviation
data = self._data(self._int_max() - self.nlanes)
vdata = self.load(data)
edata = expand(vdata)
# lower half part
data_lo = data[:self.nlanes//2]
# higher half part
data_hi = data[self.nlanes//2:]
assert edata == (data_lo, data_hi)
def test_arithmetic_subadd(self):
if self._is_fp():
data_a = self._data()
else:
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
# non-saturated
data_add = self.load([a + b for a, b in zip(data_a, data_b)]) # load to cast
add = self.add(vdata_a, vdata_b)
assert add == data_add
data_sub = self.load([a - b for a, b in zip(data_a, data_b)])
sub = self.sub(vdata_a, vdata_b)
assert sub == data_sub
def test_arithmetic_mul(self):
if self.sfx in ("u64", "s64"):
return
if self._is_fp():
data_a = self._data()
else:
data_a = self._data(self._int_max() - self.nlanes)
data_b = self._data(self._int_min(), reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
data_mul = self.load([a * b for a, b in zip(data_a, data_b)])
mul = self.mul(vdata_a, vdata_b)
assert mul == data_mul
def test_arithmetic_div(self):
if not self._is_fp():
return
data_a, data_b = self._data(), self._data(reverse=True)
vdata_a, vdata_b = self.load(data_a), self.load(data_b)
# load to truncate f64 to precision of f32
data_div = self.load([a / b for a, b in zip(data_a, data_b)])
div = self.div(vdata_a, vdata_b)
assert div == data_div
def test_arithmetic_intdiv(self):
"""
Test integer division intrinsics:
npyv_divisor_##sfx
npyv_divc_##sfx
"""
if self._is_fp():
return
int_min = self._int_min()
def trunc_div(a, d):
"""
Divide towards zero works with large integers > 2^53,
and wrap around overflow similar to what C does.
"""
if d == -1 and a == int_min:
return a
sign_a, sign_d = a < 0, d < 0
if a == 0 or sign_a == sign_d:
return a // d
return (a + sign_d - sign_a) // d + 1
data = [1, -int_min] # to test overflow
data += range(0, 2**8, 2**5)
data += range(0, 2**8, 2**5-1)
bsize = self._scalar_size()
if bsize > 8:
data += range(2**8, 2**16, 2**13)
data += range(2**8, 2**16, 2**13-1)
if bsize > 16:
data += range(2**16, 2**32, 2**29)
data += range(2**16, 2**32, 2**29-1)
if bsize > 32:
data += range(2**32, 2**64, 2**61)
data += range(2**32, 2**64, 2**61-1)
# negate
data += [-x for x in data]
for dividend, divisor in itertools.product(data, data):
divisor = self.setall(divisor)[0] # cast
if divisor == 0:
continue
dividend = self.load(self._data(dividend))
data_divc = [trunc_div(a, divisor) for a in dividend]
divisor_parms = self.divisor(divisor)
divc = self.divc(dividend, divisor_parms)
assert divc == data_divc
def test_arithmetic_reduce_sum(self):
"""
Test reduce sum intrinsics:
npyv_sum_##sfx
"""
if self.sfx not in ("u32", "u64", "f32", "f64"):
return
# reduce sum
data = self._data()
vdata = self.load(data)
data_sum = sum(data)
vsum = self.sum(vdata)
assert vsum == data_sum
def test_arithmetic_reduce_sumup(self):
"""
Test extend reduce sum intrinsics:
npyv_sumup_##sfx
"""
if self.sfx not in ("u8", "u16"):
return
rdata = (0, self.nlanes, self._int_min(), self._int_max()-self.nlanes)
for r in rdata:
data = self._data(r)
vdata = self.load(data)
data_sum = sum(data)
vsum = self.sumup(vdata)
assert vsum == data_sum
def test_mask_conditional(self):
"""
Conditional addition and subtraction for all supported data types.
Test intrinsics:
npyv_ifadd_##SFX, npyv_ifsub_##SFX
"""
vdata_a = self.load(self._data())
vdata_b = self.load(self._data(reverse=True))
true_mask = self.cmpeq(self.zero(), self.zero())
false_mask = self.cmpneq(self.zero(), self.zero())
data_sub = self.sub(vdata_b, vdata_a)
ifsub = self.ifsub(true_mask, vdata_b, vdata_a, vdata_b)
assert ifsub == data_sub
ifsub = self.ifsub(false_mask, vdata_a, vdata_b, vdata_b)
assert ifsub == vdata_b
data_add = self.add(vdata_b, vdata_a)
ifadd = self.ifadd(true_mask, vdata_b, vdata_a, vdata_b)
assert ifadd == data_add
ifadd = self.ifadd(false_mask, vdata_a, vdata_b, vdata_b)
assert ifadd == vdata_b
if not self._is_fp():
return
data_div = self.div(vdata_b, vdata_a)
ifdiv = self.ifdiv(true_mask, vdata_b, vdata_a, vdata_b)
assert ifdiv == data_div
ifdivz = self.ifdivz(true_mask, vdata_b, vdata_a)
assert ifdivz == data_div
ifdiv = self.ifdiv(false_mask, vdata_a, vdata_b, vdata_b)
assert ifdiv == vdata_b
ifdivz = self.ifdivz(false_mask, vdata_a, vdata_b)
assert ifdivz == self.zero()
bool_sfx = ("b8", "b16", "b32", "b64")
int_sfx = ("u8", "s8", "u16", "s16", "u32", "s32", "u64", "s64")
fp_sfx = ("f32", "f64")
all_sfx = int_sfx + fp_sfx
tests_registry = {
bool_sfx: _SIMD_BOOL,
int_sfx : _SIMD_INT,
fp_sfx : _SIMD_FP,
("f32",): _SIMD_FP32,
("f64",): _SIMD_FP64,
all_sfx : _SIMD_ALL
}
for target_name, npyv in targets.items():
simd_width = npyv.simd if npyv else ''
pretty_name = target_name.split('__') # multi-target separator
if len(pretty_name) > 1:
# multi-target
pretty_name = f"({' '.join(pretty_name)})"
else:
pretty_name = pretty_name[0]
skip = ""
skip_sfx = dict()
if not npyv:
skip = f"target '{pretty_name}' isn't supported by current machine"
elif not npyv.simd:
skip = f"target '{pretty_name}' isn't supported by NPYV"
else:
if not npyv.simd_f32:
skip_sfx["f32"] = f"target '{pretty_name}' "\
"doesn't support single-precision"
if not npyv.simd_f64:
skip_sfx["f64"] = f"target '{pretty_name}' doesn't"\
"support double-precision"
for sfxes, cls in tests_registry.items():
for sfx in sfxes:
skip_m = skip_sfx.get(sfx, skip)
inhr = (cls,)
attr = dict(npyv=targets[target_name], sfx=sfx, target_name=target_name)
tcls = type(f"Test{cls.__name__}_{simd_width}_{target_name}_{sfx}", inhr, attr)
if skip_m:
pytest.mark.skip(reason=skip_m)(tcls)
globals()[tcls.__name__] = tcls
Zerion Mini Shell 1.0