diff options
| -rw-r--r-- | benchmarks/benchmarks/bench_ufunc.py | 20 | ||||
| -rw-r--r-- | numpy/core/code_generators/generate_umath.py | 3 | ||||
| -rw-r--r-- | numpy/core/src/common/npy_svml.h | 6 | ||||
| -rw-r--r-- | numpy/core/src/umath/loops.h.src | 13 | ||||
| -rw-r--r-- | numpy/core/src/umath/loops_umath_fp.dispatch.c.src | 80 | ||||
| -rw-r--r-- | numpy/core/tests/test_umath.py | 8 |
6 files changed, 128 insertions, 2 deletions
diff --git a/benchmarks/benchmarks/bench_ufunc.py b/benchmarks/benchmarks/bench_ufunc.py index 858dcccfc..36d8621e8 100644 --- a/benchmarks/benchmarks/bench_ufunc.py +++ b/benchmarks/benchmarks/bench_ufunc.py @@ -294,3 +294,23 @@ class ArgParsingReduce(Benchmark): def time_add_reduce_arg_parsing(self, arg_pack): np.add.reduce(*arg_pack.args, **arg_pack.kwargs) + +class BinaryBench(Benchmark): + def setup(self): + N = 1000000 + self.a32 = np.random.rand(N).astype(np.float32) + self.b32 = np.random.rand(N).astype(np.float32) + self.a64 = np.random.rand(N).astype(np.float64) + self.b64 = np.random.rand(N).astype(np.float64) + + def time_pow_32(self): + np.power(self.a32, self.b32) + + def time_pow_64(self): + np.power(self.a64, self.b64) + + def time_atan2_32(self): + np.arctan2(self.a32, self.b32) + + def time_atan2_64(self): + np.arctan2(self.a64, self.b64) diff --git a/numpy/core/code_generators/generate_umath.py b/numpy/core/code_generators/generate_umath.py index e9a2cc6f5..89320128b 100644 --- a/numpy/core/code_generators/generate_umath.py +++ b/numpy/core/code_generators/generate_umath.py @@ -396,6 +396,8 @@ defdict = { docstrings.get('numpy.core.umath.power'), None, TD(ints), + TD('e', f='pow', astype={'e': 'f'}), + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), TD(inexact, f='pow', astype={'e': 'f'}), TD(O, f='npy_ObjectPower'), ), @@ -877,6 +879,7 @@ defdict = { Ufunc(2, 1, None, docstrings.get('numpy.core.umath.arctan2'), None, + TD('fd', dispatch=[('loops_umath_fp', 'fd')]), TD(flts, f='atan2', astype={'e': 'f'}), TD(P, f='arctan2'), ), diff --git a/numpy/core/src/common/npy_svml.h b/numpy/core/src/common/npy_svml.h index 1111025d7..77e1e1edc 100644 --- a/numpy/core/src/common/npy_svml.h +++ b/numpy/core/src/common/npy_svml.h @@ -13,13 +13,14 @@ extern __m512 __svml_tanf16(__m512 x); extern __m512 __svml_asinf16(__m512 x); extern __m512 __svml_acosf16(__m512 x); extern __m512 __svml_atanf16(__m512 x); -extern __m512 __svml_atan2f16(__m512 x); +extern __m512 __svml_atan2f16(__m512 x, __m512 y); extern __m512 __svml_sinhf16(__m512 x); extern __m512 __svml_coshf16(__m512 x); extern __m512 __svml_tanhf16(__m512 x); extern __m512 __svml_asinhf16(__m512 x); extern __m512 __svml_acoshf16(__m512 x); extern __m512 __svml_atanhf16(__m512 x); +extern __m512 __svml_powf16(__m512 x, __m512 y); extern __m512d __svml_exp8(__m512d x); extern __m512d __svml_exp28(__m512d x); @@ -35,11 +36,12 @@ extern __m512d __svml_tan8(__m512d x); extern __m512d __svml_asin8(__m512d x); extern __m512d __svml_acos8(__m512d x); extern __m512d __svml_atan8(__m512d x); -extern __m512d __svml_atan28(__m512d x); +extern __m512d __svml_atan28(__m512d x, __m512d y); extern __m512d __svml_sinh8(__m512d x); extern __m512d __svml_cosh8(__m512d x); extern __m512d __svml_tanh8(__m512d x); extern __m512d __svml_asinh8(__m512d x); extern __m512d __svml_acosh8(__m512d x); extern __m512d __svml_atanh8(__m512d x); +extern __m512d __svml_pow8(__m512d x, __m512d y); #endif diff --git a/numpy/core/src/umath/loops.h.src b/numpy/core/src/umath/loops.h.src index 5af9f1788..4b363d84b 100644 --- a/numpy/core/src/umath/loops.h.src +++ b/numpy/core/src/umath/loops.h.src @@ -286,6 +286,19 @@ NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void DOUBLE_@func@, * #TYPE = FLOAT, DOUBLE# */ /**begin repeat1 + * #func = power, arctan2# + */ + +NPY_CPU_DISPATCH_DECLARE(NPY_NO_EXPORT void @TYPE@_@func@, + (char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(func))) + +/**end repeat1**/ +/**end repeat**/ + +/**begin repeat + * #TYPE = FLOAT, DOUBLE# + */ +/**begin repeat1 * #func = maximum, minimum# */ NPY_NO_EXPORT void diff --git a/numpy/core/src/umath/loops_umath_fp.dispatch.c.src b/numpy/core/src/umath/loops_umath_fp.dispatch.c.src index 281b4231d..538b5039f 100644 --- a/numpy/core/src/umath/loops_umath_fp.dispatch.c.src +++ b/numpy/core/src/umath/loops_umath_fp.dispatch.c.src @@ -74,6 +74,47 @@ simd_@func@_f64(const double *src, npy_intp ssrc, npyv_cleanup(); } /**end repeat**/ + +/**begin repeat + * #sfx = f32, f64# + * #func_suffix = f16, 8# + */ +/**begin repeat1 + * #func = pow, atan2# + */ + +static void +simd_@func@_@sfx@(const npyv_lanetype_@sfx@ *src1, npy_intp ssrc1, + const npyv_lanetype_@sfx@ *src2, npy_intp ssrc2, + npyv_lanetype_@sfx@ *dst, npy_intp sdst, npy_intp len) +{ + const int vstep = npyv_nlanes_@sfx@; + for (; len > 0; len -= vstep, src1 += ssrc1*vstep, src2 += ssrc2*vstep, dst += sdst*vstep) { + npyv_@sfx@ x1; + if (ssrc1 == 1) { + x1 = npyv_load_till_@sfx@(src1, len, 1); + } else { + x1 = npyv_loadn_till_@sfx@(src1, ssrc1, len, 1); + } + + npyv_@sfx@ x2; + if (ssrc2 == 1) { + x2 = npyv_load_till_@sfx@(src2, len, 1); + } else { + x2 = npyv_loadn_till_@sfx@(src2, ssrc2, len, 1); + } + + npyv_@sfx@ out = __svml_@func@@func_suffix@(x1, x2); + if (sdst == 1) { + npyv_store_till_@sfx@(dst, len, out); + } else { + npyv_storen_till_@sfx@(dst, sdst, len, out); + } + } +} +/**end repeat1**/ +/**end repeat**/ + #endif /**begin repeat @@ -113,6 +154,45 @@ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@func@) /**end repeat**/ /**begin repeat + * #TYPE = DOUBLE, FLOAT# + * #type = npy_double, npy_float# + * #vsub = , f# + * #sfx = f64, f32# + */ +/**begin repeat1 + * #func = power, arctan2# + * #intrin = pow, atan2# + */ +NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(@TYPE@_@func@) +(char **args, npy_intp const *dimensions, npy_intp const *steps, void *NPY_UNUSED(data)) +{ +#if NPY_SIMD && defined(NPY_HAVE_AVX512_SKX) && defined(NPY_CAN_LINK_SVML) + const @type@ *src1 = (@type@*)args[0]; + const @type@ *src2 = (@type@*)args[1]; + @type@ *dst = (@type@*)args[2]; + const int lsize = sizeof(src1[0]); + const npy_intp ssrc1 = steps[0] / lsize; + const npy_intp ssrc2 = steps[1] / lsize; + const npy_intp sdst = steps[2] / lsize; + const npy_intp len = dimensions[0]; + assert(len <= 1 || (steps[0] % lsize == 0 && steps[1] % lsize == 0)); + if (!is_mem_overlap(src1, steps[0], dst, steps[2], len) && !is_mem_overlap(src2, steps[1], dst, steps[2], len) && + npyv_loadable_stride_@sfx@(ssrc1) && npyv_loadable_stride_@sfx@(ssrc2) && + npyv_storable_stride_@sfx@(sdst)) { + simd_@intrin@_@sfx@(src1, ssrc1, src2, ssrc2, dst, sdst, len); + return; + } +#endif + BINARY_LOOP { + const @type@ in1 = *(@type@ *)ip1; + const @type@ in2 = *(@type@ *)ip2; + *(@type@ *)op1 = npy_@intrin@@vsub@(in1, in2); + } +} +/**end repeat1**/ +/**end repeat**/ + +/**begin repeat * #func = sin, cos# */ NPY_NO_EXPORT void NPY_CPU_DISPATCH_CURFX(DOUBLE_@func@) diff --git a/numpy/core/tests/test_umath.py b/numpy/core/tests/test_umath.py index bc8927302..afe42b56a 100644 --- a/numpy/core/tests/test_umath.py +++ b/numpy/core/tests/test_umath.py @@ -1095,6 +1095,14 @@ class TestPower: assert_raises(ValueError, np.power, a, minusone) assert_raises(ValueError, np.power, one, b) assert_raises(ValueError, np.power, one, minusone) + + def test_float_to_inf_power(self): + for dt in [np.float32, np.float64]: + a = np.array([1, 1, 2, 2, -2, -2, np.inf, -np.inf], dt) + b = np.array([np.inf, -np.inf, np.inf, -np.inf, + np.inf, -np.inf, np.inf, -np.inf], dt) + r = np.array([1, 1, np.inf, 0, np.inf, 0, np.inf, 0], dt) + assert_equal(np.power(a, b), r) class TestFloat_power: |