From 6a46e0e432136ffd44bc7da88ef4fbd919f56f95 Mon Sep 17 00:00:00 2001 From: vlad-perevezentsev Date: Thu, 20 Jul 2023 23:35:39 +0200 Subject: [PATCH 01/58] Pin to numpy<1.24 in build_sphinx (#1491) --- .github/workflows/build-sphinx.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-sphinx.yml b/.github/workflows/build-sphinx.yml index f58021db135..02118c5d159 100644 --- a/.github/workflows/build-sphinx.yml +++ b/.github/workflows/build-sphinx.yml @@ -79,7 +79,7 @@ jobs: - name: Install dpnp dependencies run: | - conda install dpctl mkl-devel-dpcpp onedpl-devel tbb-devel dpcpp_linux-64 \ + conda install numpy"<1.24" dpctl mkl-devel-dpcpp onedpl-devel tbb-devel dpcpp_linux-64 \ cmake cython"<3" pytest ninja scikit-build sysroot_linux-64">=2.28" ${{ env.CHANNELS }} - name: Install cuPy dependencies From 1bd74b3d9126623daef514b2570ff42fcacec1c3 Mon Sep 17 00:00:00 2001 From: Anton Volkov Date: Fri, 21 Jul 2023 08:15:55 -0500 Subject: [PATCH 02/58] Removed w/a to Klocwork --- conda-recipe/build.sh | 6 ------ 1 file changed, 6 deletions(-) diff --git a/conda-recipe/build.sh b/conda-recipe/build.sh index 09aa0973446..b25baed8ecd 100755 --- a/conda-recipe/build.sh +++ b/conda-recipe/build.sh @@ -1,11 +1,5 @@ #!/bin/bash -# Workaround to Klocwork overwriting LD_LIBRARY_PATH that was modified -# by DPC++ compiler conda packages. Will need to be added to DPC++ compiler -# activation scripts. -export LDFLAGS="$LDFLAGS -Wl,-rpath,$PREFIX/lib" -export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$PREFIX/compiler/lib/intel64_lin:$PREFIX/compiler/lib:$PREFIX/lib" - # Intel LLVM must cooperate with compiler and sysroot from conda echo "--gcc-toolchain=${BUILD_PREFIX} --sysroot=${BUILD_PREFIX}/${HOST}/sysroot -target ${HOST}" > icpx_for_conda.cfg export ICPXCFG="$(pwd)/icpx_for_conda.cfg" From 70e5aa8211ef562fd3550a409268a318d7aab647 Mon Sep 17 00:00:00 2001 From: Alexander Kalistratov Date: Fri, 21 Jul 2023 16:53:08 +0200 Subject: [PATCH 03/58] Use specilized kernel for f-arrays and sum by axis=1. Add keepdims support (#1489) Use specilized kernel for f-arrays and sum by axis=1. Add keepdims support --- dpnp/dpnp_iface_mathematical.py | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/dpnp/dpnp_iface_mathematical.py b/dpnp/dpnp_iface_mathematical.py index 2c45f05e2b9..36e9804618f 100644 --- a/dpnp/dpnp_iface_mathematical.py +++ b/dpnp/dpnp_iface_mathematical.py @@ -1828,12 +1828,18 @@ def sum( elif where is not True: pass else: - if axis == (0,) and len(x.shape) == 2 and not keepdims: + if len(x.shape) == 2 and ( + (axis == (0,) and x.flags.c_contiguous) + or (axis == (1,) and x.flags.f_contiguous) + ): from dpctl.tensor._reduction import _default_reduction_dtype from dpnp.backend.extensions.sycl_ext import _sycl_ext_impl - input = dpnp.get_usm_ndarray(x) + input = x + if axis == (1,): + input = input.T + input = dpnp.get_usm_ndarray(input) queue = input.sycl_queue out_dtype = ( @@ -1850,7 +1856,16 @@ def sum( if sum: sum(input, output, []).wait() - return dpnp_array._create_from_usm_ndarray(output) + result = dpnp_array._create_from_usm_ndarray(output) + + if keepdims: + if axis == (0,): + res_sh = (1,) + output.shape + else: + res_sh = output.shape + (1,) + result = result.reshape(res_sh) + + return result y = dpt.sum( dpnp.get_usm_ndarray(x), axis=axis, dtype=dtype, keepdims=keepdims From e142b5a7203ce8f47e576a02a69ffffb8383a69e Mon Sep 17 00:00:00 2001 From: vlad-perevezentsev Date: Mon, 24 Jul 2023 19:45:21 +0200 Subject: [PATCH 04/58] Update test_statistic.py to run on Iris Xe (#1494) * Update dpnp_median * Update dpnp_cov * Update test_statistics.py * Move get_ret_type_and_func to dpnp_algo_utils * Call get_default_floating_type without <> --- dpnp/backend/kernels/dpnp_krnl_common.cpp | 16 +++---- dpnp/backend/kernels/dpnp_krnl_linalg.cpp | 28 +++++------ dpnp/backend/kernels/dpnp_krnl_statistics.cpp | 16 ++++++- dpnp/dpnp_algo/dpnp_algo_statistics.pxi | 8 ++-- dpnp/dpnp_utils/dpnp_algo_utils.pxd | 8 +++- dpnp/dpnp_utils/dpnp_algo_utils.pyx | 10 ++++ dpnp/dpnp_utils/dpnp_utils_statistics.py | 8 +++- dpnp/linalg/dpnp_algo_linalg.pyx | 20 ++------ tests/test_statistics.py | 46 ++++++++++--------- 9 files changed, 94 insertions(+), 66 deletions(-) diff --git a/dpnp/backend/kernels/dpnp_krnl_common.cpp b/dpnp/backend/kernels/dpnp_krnl_common.cpp index 84107d293ac..bf3102bd7cd 100644 --- a/dpnp/backend/kernels/dpnp_krnl_common.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_common.cpp @@ -1167,17 +1167,17 @@ void func_map_init_linalg(func_map_t &fmap) eft_DBL, (void *)dpnp_eig_default_c}; fmap[DPNPFuncName::DPNP_FN_EIG_EXT][eft_INT][eft_INT] = { - get_default_floating_type<>(), + get_default_floating_type(), (void *)dpnp_eig_ext_c< - int32_t, func_type_map_t::find_type()>>, + int32_t, func_type_map_t::find_type>, get_default_floating_type(), (void *)dpnp_eig_ext_c< int32_t, func_type_map_t::find_type< get_default_floating_type()>>}; fmap[DPNPFuncName::DPNP_FN_EIG_EXT][eft_LNG][eft_LNG] = { - get_default_floating_type<>(), + get_default_floating_type(), (void *)dpnp_eig_ext_c< - int64_t, func_type_map_t::find_type()>>, + int64_t, func_type_map_t::find_type>, get_default_floating_type(), (void *)dpnp_eig_ext_c< int64_t, func_type_map_t::find_type< @@ -1197,17 +1197,17 @@ void func_map_init_linalg(func_map_t &fmap) eft_DBL, (void *)dpnp_eigvals_default_c}; fmap[DPNPFuncName::DPNP_FN_EIGVALS_EXT][eft_INT][eft_INT] = { - get_default_floating_type<>(), + get_default_floating_type(), (void *)dpnp_eigvals_ext_c< - int32_t, func_type_map_t::find_type()>>, + int32_t, func_type_map_t::find_type>, get_default_floating_type(), (void *)dpnp_eigvals_ext_c< int32_t, func_type_map_t::find_type< get_default_floating_type()>>}; fmap[DPNPFuncName::DPNP_FN_EIGVALS_EXT][eft_LNG][eft_LNG] = { - get_default_floating_type<>(), + get_default_floating_type(), (void *)dpnp_eigvals_ext_c< - int64_t, func_type_map_t::find_type()>>, + int64_t, func_type_map_t::find_type>, get_default_floating_type(), (void *)dpnp_eigvals_ext_c< int64_t, func_type_map_t::find_type< diff --git a/dpnp/backend/kernels/dpnp_krnl_linalg.cpp b/dpnp/backend/kernels/dpnp_krnl_linalg.cpp index b83295aa894..8b47f38f9a4 100644 --- a/dpnp/backend/kernels/dpnp_krnl_linalg.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_linalg.cpp @@ -879,17 +879,17 @@ void func_map_init_linalg_func(func_map_t &fmap) eft_DBL, (void *)dpnp_inv_default_c}; fmap[DPNPFuncName::DPNP_FN_INV_EXT][eft_INT][eft_INT] = { - get_default_floating_type<>(), + get_default_floating_type(), (void *)dpnp_inv_ext_c< - int32_t, func_type_map_t::find_type()>>, + int32_t, func_type_map_t::find_type>, get_default_floating_type(), (void *)dpnp_inv_ext_c< int32_t, func_type_map_t::find_type< get_default_floating_type()>>}; fmap[DPNPFuncName::DPNP_FN_INV_EXT][eft_LNG][eft_LNG] = { - get_default_floating_type<>(), + get_default_floating_type(), (void *)dpnp_inv_ext_c< - int64_t, func_type_map_t::find_type()>>, + int64_t, func_type_map_t::find_type>, get_default_floating_type(), (void *)dpnp_inv_ext_c< int64_t, func_type_map_t::find_type< @@ -1051,17 +1051,17 @@ void func_map_init_linalg_func(func_map_t &fmap) // eft_C128, (void*)dpnp_qr_c, std::complex>}; fmap[DPNPFuncName::DPNP_FN_QR_EXT][eft_INT][eft_INT] = { - get_default_floating_type<>(), + get_default_floating_type(), (void *)dpnp_qr_ext_c< - int32_t, func_type_map_t::find_type()>>, + int32_t, func_type_map_t::find_type>, get_default_floating_type(), (void *)dpnp_qr_ext_c< int32_t, func_type_map_t::find_type< get_default_floating_type()>>}; fmap[DPNPFuncName::DPNP_FN_QR_EXT][eft_LNG][eft_LNG] = { - get_default_floating_type<>(), + get_default_floating_type(), (void *)dpnp_qr_ext_c< - int64_t, func_type_map_t::find_type()>>, + int64_t, func_type_map_t::find_type>, get_default_floating_type(), (void *)dpnp_qr_ext_c< int64_t, func_type_map_t::find_type< @@ -1086,10 +1086,10 @@ void func_map_init_linalg_func(func_map_t &fmap) std::complex, double>}; fmap[DPNPFuncName::DPNP_FN_SVD_EXT][eft_INT][eft_INT] = { - get_default_floating_type<>(), + get_default_floating_type(), (void *)dpnp_svd_ext_c< - int32_t, func_type_map_t::find_type()>, - func_type_map_t::find_type()>>, + int32_t, func_type_map_t::find_type, + func_type_map_t::find_type>, get_default_floating_type(), (void *) dpnp_svd_ext_c()>>}; fmap[DPNPFuncName::DPNP_FN_SVD_EXT][eft_LNG][eft_LNG] = { - get_default_floating_type<>(), + get_default_floating_type(), (void *)dpnp_svd_ext_c< - int64_t, func_type_map_t::find_type()>, - func_type_map_t::find_type()>>, + int64_t, func_type_map_t::find_type, + func_type_map_t::find_type>, get_default_floating_type(), (void *) dpnp_svd_ext_c}; fmap[DPNPFuncName::DPNP_FN_MEDIAN_EXT][eft_INT][eft_INT] = { - eft_DBL, (void *)dpnp_median_ext_c}; + get_default_floating_type(), + (void *)dpnp_median_ext_c< + int32_t, func_type_map_t::find_type>, + get_default_floating_type(), + (void *)dpnp_median_ext_c< + int32_t, func_type_map_t::find_type< + get_default_floating_type()>>}; fmap[DPNPFuncName::DPNP_FN_MEDIAN_EXT][eft_LNG][eft_LNG] = { - eft_DBL, (void *)dpnp_median_ext_c}; + get_default_floating_type(), + (void *)dpnp_median_ext_c< + int64_t, func_type_map_t::find_type>, + get_default_floating_type(), + (void *)dpnp_median_ext_c< + int64_t, func_type_map_t::find_type< + get_default_floating_type()>>}; fmap[DPNPFuncName::DPNP_FN_MEDIAN_EXT][eft_FLT][eft_FLT] = { eft_FLT, (void *)dpnp_median_ext_c}; fmap[DPNPFuncName::DPNP_FN_MEDIAN_EXT][eft_DBL][eft_DBL] = { diff --git a/dpnp/dpnp_algo/dpnp_algo_statistics.pxi b/dpnp/dpnp_algo/dpnp_algo_statistics.pxi index 8baa93651ab..3ec3893c3ac 100644 --- a/dpnp/dpnp_algo/dpnp_algo_statistics.pxi +++ b/dpnp/dpnp_algo/dpnp_algo_statistics.pxi @@ -265,8 +265,12 @@ cpdef utils.dpnp_descriptor dpnp_median(utils.dpnp_descriptor array1): array1_obj = array1.get_array() + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(array1_obj, kernel_data) + cdef DPNPFuncType return_type = ret_type_and_func[0] + cdef custom_statistic_1in_1out_func_ptr_t func = < custom_statistic_1in_1out_func_ptr_t > ret_type_and_func[1] + cdef utils.dpnp_descriptor result = utils.create_output_descriptor((1,), - kernel_data.return_type, + return_type, None, device=array1_obj.sycl_device, usm_type=array1_obj.usm_type, @@ -277,8 +281,6 @@ cpdef utils.dpnp_descriptor dpnp_median(utils.dpnp_descriptor array1): cdef c_dpctl.SyclQueue q = result_sycl_queue cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref() - cdef custom_statistic_1in_1out_func_ptr_t func = kernel_data.ptr - # stub for interface support cdef shape_type_c axis cdef Py_ssize_t axis_size = 0 diff --git a/dpnp/dpnp_utils/dpnp_algo_utils.pxd b/dpnp/dpnp_utils/dpnp_algo_utils.pxd index 4a4488ca269..7656d29bb15 100644 --- a/dpnp/dpnp_utils/dpnp_algo_utils.pxd +++ b/dpnp/dpnp_utils/dpnp_algo_utils.pxd @@ -28,7 +28,7 @@ from libcpp cimport bool as cpp_bool from dpnp.dpnp_algo cimport shape_type_c -from dpnp.dpnp_algo.dpnp_algo cimport DPNPFuncName, DPNPFuncType +from dpnp.dpnp_algo.dpnp_algo cimport DPNPFuncData, DPNPFuncName, DPNPFuncType cpdef checker_throw_runtime_error(function_name, message) @@ -162,3 +162,9 @@ cdef tuple get_common_usm_allocation(dpnp_descriptor x1, dpnp_descriptor x2) """ Get common USM allocation in the form of (sycl_device, usm_type, sycl_queue) """ + +cdef (DPNPFuncType, void *) get_ret_type_and_func(x1_obj, DPNPFuncData kernel_data) +""" +Get the corresponding return type and function pointer based on the +capability of the allocated input array device for the integer types. +""" diff --git a/dpnp/dpnp_utils/dpnp_algo_utils.pyx b/dpnp/dpnp_utils/dpnp_algo_utils.pyx index b69bceea9e6..db052be7827 100644 --- a/dpnp/dpnp_utils/dpnp_algo_utils.pyx +++ b/dpnp/dpnp_utils/dpnp_algo_utils.pyx @@ -663,6 +663,16 @@ cdef tuple get_common_usm_allocation(dpnp_descriptor x1, dpnp_descriptor x2): return (common_sycl_queue.sycl_device, common_usm_type, common_sycl_queue) +cdef (DPNPFuncType, void *) get_ret_type_and_func(x1_obj, DPNPFuncData kernel_data): + if dpnp.issubdtype(x1_obj.dtype, dpnp.integer) and not x1_obj.sycl_device.has_aspect_fp64: + return_type = kernel_data.return_type_no_fp64 + func = kernel_data.ptr_no_fp64 + else: + return_type = kernel_data.return_type + func = kernel_data.ptr + return return_type, func + + cdef class dpnp_descriptor: def __init__(self, obj, dpnp_descriptor orig_desc=None): """ Initialze variables """ diff --git a/dpnp/dpnp_utils/dpnp_utils_statistics.py b/dpnp/dpnp_utils/dpnp_utils_statistics.py index 3e8df0aa3cb..e8ccb179468 100644 --- a/dpnp/dpnp_utils/dpnp_utils_statistics.py +++ b/dpnp/dpnp_utils/dpnp_utils_statistics.py @@ -59,7 +59,6 @@ def _get_2dmin_array(x, dtype): It casts to another dtype, if the input array differs from requested one. """ - if x.ndim == 0: x = x.reshape((1, 1)) elif x.ndim == 1: @@ -81,6 +80,13 @@ def _get_2dmin_array(x, dtype): if y is not None: dtypes.append(y.dtype) dtype = dpt.result_type(*dtypes) + # TODO: remove when dpctl.result_type() is fixed + fp64 = queue.sycl_device.has_aspect_fp64 + if not fp64: + if dtype == dpnp.float64: + dtype = dpnp.float32 + elif dtype == dpnp.complex128: + dtype = dpnp.complex64 X = _get_2dmin_array(m, dtype) if y is not None: diff --git a/dpnp/linalg/dpnp_algo_linalg.pyx b/dpnp/linalg/dpnp_algo_linalg.pyx index eeb9bf7f5f8..e6484599c51 100644 --- a/dpnp/linalg/dpnp_algo_linalg.pyx +++ b/dpnp/linalg/dpnp_algo_linalg.pyx @@ -79,16 +79,6 @@ ctypedef c_dpctl.DPCTLSyclEventRef(*custom_linalg_2in_1out_func_ptr_t)(c_dpctl.D const c_dpctl.DPCTLEventVectorRef) -cdef (DPNPFuncType, void *) get_ret_type_and_func(x1_obj, DPNPFuncData kernel_data): - if dpnp.issubdtype(x1_obj.dtype, dpnp.integer) and not x1_obj.sycl_device.has_aspect_fp64: - return_type = kernel_data.return_type_no_fp64 - func = kernel_data.ptr_no_fp64 - else: - return_type = kernel_data.return_type - func = kernel_data.ptr - return return_type, func - - cpdef utils.dpnp_descriptor dpnp_cholesky(utils.dpnp_descriptor input_): size_ = input_.shape[-1] @@ -206,7 +196,7 @@ cpdef tuple dpnp_eig(utils.dpnp_descriptor x1): x1_obj = x1.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = get_ret_type_and_func(x1_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(x1_obj, kernel_data) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_linalg_2in_1out_func_ptr_t func = < custom_linalg_2in_1out_func_ptr_t > ret_type_and_func[1] @@ -252,7 +242,7 @@ cpdef utils.dpnp_descriptor dpnp_eigvals(utils.dpnp_descriptor input): input_obj = input.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = get_ret_type_and_func(input_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(input_obj, kernel_data) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_linalg_1in_1out_with_size_func_ptr_t_ func = < custom_linalg_1in_1out_with_size_func_ptr_t_ > ret_type_and_func[1] @@ -291,7 +281,7 @@ cpdef utils.dpnp_descriptor dpnp_inv(utils.dpnp_descriptor input): input_obj = input.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = get_ret_type_and_func(input_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(input_obj, kernel_data) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_linalg_1in_1out_func_ptr_t func = < custom_linalg_1in_1out_func_ptr_t > ret_type_and_func[1] @@ -472,7 +462,7 @@ cpdef tuple dpnp_qr(utils.dpnp_descriptor x1, str mode): x1_obj = x1.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = get_ret_type_and_func(x1_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(x1_obj, kernel_data) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_linalg_1in_3out_shape_t func = < custom_linalg_1in_3out_shape_t > ret_type_and_func[1] @@ -525,7 +515,7 @@ cpdef tuple dpnp_svd(utils.dpnp_descriptor x1, cpp_bool full_matrices, cpp_bool x1_obj = x1.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = get_ret_type_and_func(x1_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(x1_obj, kernel_data) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_linalg_1in_3out_shape_t func = < custom_linalg_1in_3out_shape_t > ret_type_and_func[1] diff --git a/tests/test_statistics.py b/tests/test_statistics.py index 38346cec90e..4020c6c21d7 100644 --- a/tests/test_statistics.py +++ b/tests/test_statistics.py @@ -1,5 +1,6 @@ import numpy import pytest +from numpy.testing import assert_allclose import dpnp @@ -7,31 +8,32 @@ @pytest.mark.parametrize( - "type", - [numpy.float64, numpy.float32, numpy.int64, numpy.int32], - ids=["float64", "float32", "int64", "int32"], + "dtype", get_all_dtypes(no_none=True, no_bool=True, no_complex=True) ) @pytest.mark.parametrize("size", [2, 4, 8, 16, 3, 9, 27, 81]) -def test_median(type, size): - a = numpy.arange(size, dtype=type) +def test_median(dtype, size): + a = numpy.arange(size, dtype=dtype) ia = dpnp.array(a) np_res = numpy.median(a) dpnp_res = dpnp.median(ia) - numpy.testing.assert_allclose(dpnp_res, np_res) + assert_allclose(dpnp_res, np_res) @pytest.mark.usefixtures("allow_fall_back_on_numpy") @pytest.mark.parametrize("axis", [0, 1, -1, 2, -2, (1, 2), (0, -2)]) -def test_max(axis): - a = numpy.arange(768, dtype=numpy.float64).reshape((4, 4, 6, 8)) +@pytest.mark.parametrize( + "dtype", get_all_dtypes(no_none=True, no_bool=True, no_complex=True) +) +def test_max(axis, dtype): + a = numpy.arange(768, dtype=dtype).reshape((4, 4, 6, 8)) ia = dpnp.array(a) np_res = numpy.max(a, axis=axis) dpnp_res = dpnp.max(ia, axis=axis) - numpy.testing.assert_allclose(dpnp_res, np_res) + assert_allclose(dpnp_res, np_res) @pytest.mark.usefixtures("allow_fall_back_on_numpy") @@ -70,17 +72,21 @@ def test_max(axis): "[[np.nan, np.nan], [np.inf, np.nan]]", ], ) -def test_nanvar(array): - a = numpy.array(array) +@pytest.mark.parametrize( + "dtype", get_all_dtypes(no_none=True, no_bool=True, no_complex=True) +) +def test_nanvar(array, dtype): + dtype = dpnp.default_float_type() + a = numpy.array(array, dtype=dtype) ia = dpnp.array(a) for ddof in range(a.ndim): expected = numpy.nanvar(a, ddof=ddof) result = dpnp.nanvar(ia, ddof=ddof) - numpy.testing.assert_array_equal(expected, result) + assert_allclose(expected, result, rtol=1e-06) expected = numpy.nanvar(a, axis=None, ddof=0) result = dpnp.nanvar(ia, axis=None, ddof=0) - numpy.testing.assert_array_equal(expected, result) + assert_allclose(expected, result, rtol=1e-06) @pytest.mark.usefixtures("allow_fall_back_on_numpy") @@ -99,7 +105,7 @@ def test_bincount_minlength(self, array, minlength): expected = numpy.bincount(np_a, minlength=minlength) result = dpnp.bincount(dpnp_a, minlength=minlength) - numpy.testing.assert_array_equal(expected, result) + assert_allclose(expected, result) @pytest.mark.parametrize( "array", [[1, 2, 2, 1, 2, 4]], ids=["[1, 2, 2, 1, 2, 4]"] @@ -115,7 +121,7 @@ def test_bincount_weights(self, array, weights): expected = numpy.bincount(np_a, weights=weights) result = dpnp.bincount(dpnp_a, weights=weights) - numpy.testing.assert_array_equal(expected, result) + assert_allclose(expected, result) @pytest.mark.parametrize( @@ -124,10 +130,8 @@ def test_bincount_weights(self, array, weights): def test_cov_rowvar(dtype): a = dpnp.array([[0, 2], [1, 1], [2, 0]], dtype=dtype) b = numpy.array([[0, 2], [1, 1], [2, 0]], dtype=dtype) - numpy.testing.assert_array_equal(dpnp.cov(a.T), dpnp.cov(a, rowvar=False)) - numpy.testing.assert_array_equal( - numpy.cov(b, rowvar=False), dpnp.cov(a, rowvar=False) - ) + assert_allclose(dpnp.cov(a.T), dpnp.cov(a, rowvar=False)) + assert_allclose(numpy.cov(b, rowvar=False), dpnp.cov(a, rowvar=False)) @pytest.mark.parametrize( @@ -136,6 +140,4 @@ def test_cov_rowvar(dtype): def test_cov_1D_rowvar(dtype): a = dpnp.array([[0, 1, 2]], dtype=dtype) b = numpy.array([[0, 1, 2]], dtype=dtype) - numpy.testing.assert_array_equal( - numpy.cov(b, rowvar=False), dpnp.cov(a, rowvar=False) - ) + assert_allclose(numpy.cov(b, rowvar=False), dpnp.cov(a, rowvar=False)) From 834f9c4f8d7a6707d1e811ee88ddb160895f9dc2 Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Mon, 24 Jul 2023 22:12:28 +0200 Subject: [PATCH 05/58] Resolve warning and error with cython 3.0.0 (#1495) * Resolved warning and error with cython 3.0.0 * Removed cython pinning --- .github/workflows/build-sphinx.yml | 2 +- .github/workflows/generate_coverage.yaml | 2 +- conda-recipe/meta.yaml | 2 +- dpnp/CMakeLists.txt | 1 + dpnp/dparray.pyx | 70 +++++++++++++++++++----- 5 files changed, 59 insertions(+), 18 deletions(-) diff --git a/.github/workflows/build-sphinx.yml b/.github/workflows/build-sphinx.yml index 02118c5d159..e1ad17217cc 100644 --- a/.github/workflows/build-sphinx.yml +++ b/.github/workflows/build-sphinx.yml @@ -80,7 +80,7 @@ jobs: - name: Install dpnp dependencies run: | conda install numpy"<1.24" dpctl mkl-devel-dpcpp onedpl-devel tbb-devel dpcpp_linux-64 \ - cmake cython"<3" pytest ninja scikit-build sysroot_linux-64">=2.28" ${{ env.CHANNELS }} + cmake cython pytest ninja scikit-build sysroot_linux-64">=2.28" ${{ env.CHANNELS }} - name: Install cuPy dependencies run: conda install cupy cudatoolkit=10.0 diff --git a/.github/workflows/generate_coverage.yaml b/.github/workflows/generate_coverage.yaml index 5091f93a916..413835d336c 100644 --- a/.github/workflows/generate_coverage.yaml +++ b/.github/workflows/generate_coverage.yaml @@ -41,7 +41,7 @@ jobs: sudo apt-get install lcov - name: Install dpnp dependencies run: | - conda install cython"<3" llvm cmake">=3.21" scikit-build ninja pytest pytest-cov coverage[toml] \ + conda install cython llvm cmake">=3.21" scikit-build ninja pytest pytest-cov coverage[toml] \ dpctl dpcpp_linux-64 sysroot_linux-64">=2.28" mkl-devel-dpcpp tbb-devel onedpl-devel ${{ env.CHANNELS }} - name: Conda info run: | diff --git a/conda-recipe/meta.yaml b/conda-recipe/meta.yaml index b2ef34dc648..227fe0e7bb8 100644 --- a/conda-recipe/meta.yaml +++ b/conda-recipe/meta.yaml @@ -9,7 +9,7 @@ requirements: - python - setuptools - numpy >=1.19,<1.25a0 - - cython <3 + - cython - cmake >=3.21 - ninja - git diff --git a/dpnp/CMakeLists.txt b/dpnp/CMakeLists.txt index 5ee84b28153..df4971cfa38 100644 --- a/dpnp/CMakeLists.txt +++ b/dpnp/CMakeLists.txt @@ -9,6 +9,7 @@ function(build_dpnp_cython_ext _trgt _src _dest) if (DPNP_GENERATE_COVERAGE) target_compile_definitions(${_trgt} PRIVATE CYTHON_TRACE=1 CYTHON_TRACE_NOGIL=1) endif() + target_compile_definitions(${_trgt} PRIVATE NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION) # NumPy target_include_directories(${_trgt} PRIVATE ${NumPy_INCLUDE_DIR}) # Dpctl diff --git a/dpnp/dparray.pyx b/dpnp/dparray.pyx index 5ef9ed1c10b..11dcf0784b7 100644 --- a/dpnp/dparray.pyx +++ b/dpnp/dparray.pyx @@ -40,7 +40,13 @@ from libcpp cimport bool as cpp_bool import numpy +from dpnp.dpnp_algo import ( + dpnp_astype, + dpnp_flatten, +) + # to avoid interference with Python internal functions +from dpnp.dpnp_iface import asnumpy from dpnp.dpnp_iface import get_dpnp_descriptor as iface_get_dpnp_descriptor from dpnp.dpnp_iface import prod as iface_prod from dpnp.dpnp_iface import sum as iface_sum @@ -86,29 +92,63 @@ from dpnp.dpnp_iface_arraycreation import ( zeros, zeros_like, ) -from dpnp.dpnp_iface_bitwise import * -from dpnp.dpnp_iface_counting import * -from dpnp.dpnp_iface_indexing import * -from dpnp.dpnp_iface_libmath import * -from dpnp.dpnp_iface_linearalgebra import * -from dpnp.dpnp_iface_logic import * -from dpnp.dpnp_iface_logic import all, any # TODO do the same as for iface_sum -from dpnp.dpnp_iface_manipulation import * -from dpnp.dpnp_iface_mathematical import * -from dpnp.dpnp_iface_searching import * -from dpnp.dpnp_iface_sorting import * -from dpnp.dpnp_iface_statistics import * +from dpnp.dpnp_iface_indexing import ( + choose, + diagonal, + take, +) +from dpnp.dpnp_iface_linearalgebra import matmul +from dpnp.dpnp_iface_logic import ( # TODO do the same as for iface_sum + all, + any, + equal, + greater, + greater_equal, + less, + less_equal, + not_equal, +) +from dpnp.dpnp_iface_manipulation import ( + copyto, + repeat, + squeeze, + transpose, +) +from dpnp.dpnp_iface_mathematical import ( + add, + around, + conjugate, + cumprod, + cumsum, + divide, + multiply, + negative, + power, + remainder, + subtract, +) +from dpnp.dpnp_iface_searching import argmax, argmin +from dpnp.dpnp_iface_sorting import ( + argsort, + partition, + sort, +) from dpnp.dpnp_iface_statistics import ( # TODO do the same as for iface_sum max, + mean, min, + std, + var, ) -from dpnp.dpnp_iface_trigonometric import * -from dpnp.dpnp_iface_types import * +from dpnp.dpnp_iface_types import float64 cimport numpy cimport dpnp.dpnp_utils as utils -from dpnp.dpnp_algo cimport * +from dpnp.dpnp_algo cimport ( + dpnp_memory_alloc_c, + dpnp_memory_free_c, +) # initially copied from original From aa4687e7841bb3aac2ce940ae1f5ee44b34442ee Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Tue, 25 Jul 2023 13:15:23 +0200 Subject: [PATCH 06/58] Pinned DPC++ and OneMKL versions to 2023.2 release (#1496) --- conda-recipe/meta.yaml | 4 +-- dpnp/backend/extensions/vm/common.hpp | 28 ++++--------------- .../kernels/dpnp_krnl_mathematical.cpp | 13 ++------- dpnp/backend/kernels/dpnp_krnl_random.cpp | 4 +-- dpnp/backend/src/dpnp_utils.hpp | 14 ++++++++-- 5 files changed, 24 insertions(+), 39 deletions(-) diff --git a/conda-recipe/meta.yaml b/conda-recipe/meta.yaml index 227fe0e7bb8..5d6d39fd113 100644 --- a/conda-recipe/meta.yaml +++ b/conda-recipe/meta.yaml @@ -14,14 +14,14 @@ requirements: - ninja - git - dpctl >=0.14.5 - - mkl-devel-dpcpp {{ environ.get('MKL_VER', '>=2023.1.0') }} + - mkl-devel-dpcpp {{ environ.get('MKL_VER', '>=2023.2.0') }} - onedpl-devel - tbb-devel - wheel - scikit-build build: - {{ compiler('cxx') }} - - {{ compiler('dpcpp') }} >=2023.1.0 # [not osx] + - {{ compiler('dpcpp') }} >=2023.2.0 # [not osx] - sysroot_linux-64 >=2.28 # [linux] run: - python diff --git a/dpnp/backend/extensions/vm/common.hpp b/dpnp/backend/extensions/vm/common.hpp index 787e9cd47bd..db68df60967 100644 --- a/dpnp/backend/extensions/vm/common.hpp +++ b/dpnp/backend/extensions/vm/common.hpp @@ -37,6 +37,11 @@ #include "utils/type_dispatch.hpp" #include "utils/type_utils.hpp" +#include "dpnp_utils.hpp" + +static_assert(INTEL_MKL_VERSION >= __INTEL_MKL_2023_2_0_VERSION_REQUIRED, + "OneMKL does not meet minimum version requirement"); + // OneMKL namespace with VM functions namespace mkl_vm = oneapi::mkl::vm; @@ -272,7 +277,6 @@ bool need_to_call_unary_ufunc(sycl::queue exec_q, dpctl::tensor::usm_ndarray dst, const dispatchT &dispatch_vector) { -#if INTEL_MKL_VERSION >= 20230002 // check type_nums int src_typenum = src.get_typenum(); int dst_typenum = dst.get_typenum(); @@ -356,16 +360,6 @@ bool need_to_call_unary_ufunc(sycl::queue exec_q, return false; } return true; -#else - // In OneMKL 2023.1.0 the call of oneapi::mkl::vm::div() is going to dead - // lock inside ~usm_wrapper_to_host()->{...; q_->wait_and_throw(); ...} - - (void)exec_q; - (void)src; - (void)dst; - (void)dispatch_vector; - return false; -#endif // INTEL_MKL_VERSION >= 20230002 } template @@ -375,7 +369,6 @@ bool need_to_call_binary_ufunc(sycl::queue exec_q, dpctl::tensor::usm_ndarray dst, const dispatchT &dispatch_vector) { -#if INTEL_MKL_VERSION >= 20230002 // check type_nums int src1_typenum = src1.get_typenum(); int src2_typenum = src2.get_typenum(); @@ -465,17 +458,6 @@ bool need_to_call_binary_ufunc(sycl::queue exec_q, return false; } return true; -#else - // In OneMKL 2023.1.0 the call of oneapi::mkl::vm::div() is going to dead - // lock inside ~usm_wrapper_to_host()->{...; q_->wait_and_throw(); ...} - - (void)exec_q; - (void)src1; - (void)src2; - (void)dst; - (void)dispatch_vector; - return false; -#endif // INTEL_MKL_VERSION >= 20230002 } template = __SYCL_COMPILER_VECTOR_ABS_CHANGED, + "SYCL DPC++ compiler does not meet minimum version requirement"); + template class dpnp_around_c_kernel; @@ -180,18 +183,8 @@ DPCTLSyclEventRef sycl::vec<_DataType_input, vec_sz> data_vec = sg.load(input_ptrT(&array1[start])); -#if (__SYCL_COMPILER_VERSION < __SYCL_COMPILER_VECTOR_ABS_CHANGED) - // sycl::abs() returns unsigned integers only, so explicit - // casting to signed ones is required - using result_absT = typename cl::sycl::detail::make_unsigned< - _DataType_output>::type; - sycl::vec<_DataType_output, vec_sz> res_vec = - dpnp_vec_cast<_DataType_output, result_absT, vec_sz>( - sycl::abs(data_vec)); -#else sycl::vec<_DataType_output, vec_sz> res_vec = sycl::abs(data_vec); -#endif sg.store(result_ptrT(&result[start]), res_vec); } diff --git a/dpnp/backend/kernels/dpnp_krnl_random.cpp b/dpnp/backend/kernels/dpnp_krnl_random.cpp index ea4336880fb..a6ed5794c24 100644 --- a/dpnp/backend/kernels/dpnp_krnl_random.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_random.cpp @@ -37,8 +37,8 @@ #include "dpnpc_memory_adapter.hpp" #include "queue_sycl.hpp" -static_assert(INTEL_MKL_VERSION >= __INTEL_MKL_2023_VERSION_REQUIRED, - "MKL does not meet minimum version requirement"); +static_assert(INTEL_MKL_VERSION >= __INTEL_MKL_2023_0_0_VERSION_REQUIRED, + "OneMKL does not meet minimum version requirement"); namespace mkl_blas = oneapi::mkl::blas; namespace mkl_rng = oneapi::mkl::rng; diff --git a/dpnp/backend/src/dpnp_utils.hpp b/dpnp/backend/src/dpnp_utils.hpp index 2276c2f3823..34547f76f76 100644 --- a/dpnp/backend/src/dpnp_utils.hpp +++ b/dpnp/backend/src/dpnp_utils.hpp @@ -64,8 +64,18 @@ /** * Version of Intel MKL at which transition to OneMKL release 2023.0.0 occurs. */ -#ifndef __INTEL_MKL_2023_VERSION_REQUIRED -#define __INTEL_MKL_2023_VERSION_REQUIRED 20230000 +#ifndef __INTEL_MKL_2023_0_0_VERSION_REQUIRED +#define __INTEL_MKL_2023_0_0_VERSION_REQUIRED 20230000 +#endif + +/** + * Version of Intel MKL at which transition to OneMKL release 2023.2.0 occurs. + * + * @note with OneMKL=2023.1.0 the call of oneapi::mkl::vm::div() was dead + * locked inside ~usm_wrapper_to_host()->{...; q_->wait_and_throw(); ...} + */ +#ifndef __INTEL_MKL_2023_2_0_VERSION_REQUIRED +#define __INTEL_MKL_2023_2_0_VERSION_REQUIRED 20230002L #endif /** From c88cfe41a28bf648899734196a06395ca12e402f Mon Sep 17 00:00:00 2001 From: Natalia Polina Date: Tue, 25 Jul 2023 10:53:59 -0500 Subject: [PATCH 07/58] Update tests to run on Iris Xe --- dpnp/backend/kernels/dpnp_krnl_random.cpp | 12 ++++ dpnp/random/dpnp_algo_random.pyx | 9 +-- dpnp/random/dpnp_iface_random.py | 6 +- tests/test_logic.py | 5 +- tests/test_manipulation.py | 18 +++-- tests/test_mixins.py | 28 ++++---- tests/test_random.py | 83 +++++++++++++++++++++++ 7 files changed, 132 insertions(+), 29 deletions(-) diff --git a/dpnp/backend/kernels/dpnp_krnl_random.cpp b/dpnp/backend/kernels/dpnp_krnl_random.cpp index a6ed5794c24..a342151c4cc 100644 --- a/dpnp/backend/kernels/dpnp_krnl_random.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_random.cpp @@ -2988,9 +2988,13 @@ void func_map_init_random(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_RNG_CHISQUARE][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_rng_chisquare_default_c}; + fmap[DPNPFuncName::DPNP_FN_RNG_CHISQUARE][eft_FLT][eft_FLT] = { + eft_FLT, (void *)dpnp_rng_chisquare_default_c}; fmap[DPNPFuncName::DPNP_FN_RNG_CHISQUARE_EXT][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_rng_chisquare_ext_c}; + fmap[DPNPFuncName::DPNP_FN_RNG_CHISQUARE_EXT][eft_FLT][eft_FLT] = { + eft_FLT, (void *)dpnp_rng_chisquare_ext_c}; fmap[DPNPFuncName::DPNP_FN_RNG_EXPONENTIAL][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_rng_exponential_default_c}; @@ -3136,15 +3140,23 @@ void func_map_init_random(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_RNG_STANDARD_CAUCHY][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_rng_standard_cauchy_default_c}; + fmap[DPNPFuncName::DPNP_FN_RNG_STANDARD_CAUCHY][eft_FLT][eft_FLT] = { + eft_FLT, (void *)dpnp_rng_standard_cauchy_default_c}; fmap[DPNPFuncName::DPNP_FN_RNG_STANDARD_CAUCHY_EXT][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_rng_standard_cauchy_ext_c}; + fmap[DPNPFuncName::DPNP_FN_RNG_STANDARD_CAUCHY_EXT][eft_FLT][eft_FLT] = { + eft_FLT, (void *)dpnp_rng_standard_cauchy_ext_c}; fmap[DPNPFuncName::DPNP_FN_RNG_STANDARD_EXPONENTIAL][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_rng_standard_exponential_default_c}; + fmap[DPNPFuncName::DPNP_FN_RNG_STANDARD_EXPONENTIAL][eft_FLT][eft_FLT] = { + eft_FLT, (void *)dpnp_rng_standard_exponential_default_c}; fmap[DPNPFuncName::DPNP_FN_RNG_STANDARD_EXPONENTIAL_EXT][eft_DBL][eft_DBL] = {eft_DBL, (void *)dpnp_rng_standard_exponential_ext_c}; + fmap[DPNPFuncName::DPNP_FN_RNG_STANDARD_EXPONENTIAL_EXT][eft_FLT][eft_FLT] = + {eft_FLT, (void *)dpnp_rng_standard_exponential_ext_c}; fmap[DPNPFuncName::DPNP_FN_RNG_STANDARD_GAMMA][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_rng_standard_gamma_default_c}; diff --git a/dpnp/random/dpnp_algo_random.pyx b/dpnp/random/dpnp_algo_random.pyx index b46b4827f7e..e9206bd6682 100644 --- a/dpnp/random/dpnp_algo_random.pyx +++ b/dpnp/random/dpnp_algo_random.pyx @@ -96,7 +96,8 @@ ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_dpnp_rng_beta_c_1out_t)(c_dpctl.DPCTLSy const c_dpctl.DPCTLEventVectorRef) except + ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_dpnp_rng_binomial_c_1out_t)(c_dpctl.DPCTLSyclQueueRef, void * , - const int, const double, + const int, + const double, const size_t, const c_dpctl.DPCTLEventVectorRef) except + ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_dpnp_rng_chisquare_c_1out_t)(c_dpctl.DPCTLSyclQueueRef, @@ -585,7 +586,7 @@ cpdef utils.dpnp_descriptor dpnp_rng_chisquare(int df, size): """ # convert string type names (array.dtype) to C enum DPNPFuncType - cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(dpnp.float64) + cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(dpnp.default_float_type()) # get the FPTR data structure cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_RNG_CHISQUARE_EXT, param1_type, param1_type) @@ -1329,7 +1330,7 @@ cpdef utils.dpnp_descriptor dpnp_rng_standard_cauchy(size): """ # convert string type names (array.dtype) to C enum DPNPFuncType - cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(dpnp.float64) + cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(dpnp.default_float_type()) # get the FPTR data structure cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_RNG_STANDARD_CAUCHY_EXT, param1_type, param1_type) @@ -1364,7 +1365,7 @@ cpdef utils.dpnp_descriptor dpnp_rng_standard_exponential(size): cdef fptr_dpnp_rng_standard_exponential_c_1out_t func # convert string type names (array.dtype) to C enum DPNPFuncType - cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(dpnp.float64) + cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(dpnp.default_float_type()) # get the FPTR data structure cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_RNG_STANDARD_EXPONENTIAL_EXT, param1_type, param1_type) diff --git a/dpnp/random/dpnp_iface_random.py b/dpnp/random/dpnp_iface_random.py index f8d380db2ec..7a7f981f094 100644 --- a/dpnp/random/dpnp_iface_random.py +++ b/dpnp/random/dpnp_iface_random.py @@ -230,7 +230,7 @@ def chisquare(df, size=None): ----------- Parameter ``df`` is supported as a scalar. Otherwise, :obj:`numpy.random.chisquare(df, size)` samples are drawn. - Output array data type is :obj:`dpnp.float64`. + Output array data type is default float type. Examples -------- @@ -1533,7 +1533,7 @@ def standard_cauchy(size=None): Limitations ----------- - Output array data type is :obj:`dpnp.float64`. + Output array data type is default float type. Examples -------- @@ -1562,7 +1562,7 @@ def standard_exponential(size=None): Limitations ----------- - Output array data type is :obj:`dpnp.float64`. + Output array data type is default float type. Examples -------- diff --git a/tests/test_logic.py b/tests/test_logic.py index 982c58942f9..11f358b3e68 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -4,7 +4,7 @@ import dpnp -from .helper import get_all_dtypes +from .helper import get_all_dtypes, has_support_aspect64 @pytest.mark.parametrize("type", get_all_dtypes()) @@ -40,6 +40,9 @@ def test_all(type, shape): assert_allclose(dpnp_res, np_res) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Aborted on Iris Xe: SAT-5988" +) @pytest.mark.parametrize("type", get_all_dtypes(no_bool=True, no_complex=True)) def test_allclose(type): a = numpy.random.rand(10) diff --git a/tests/test_manipulation.py b/tests/test_manipulation.py index e0520d62fd9..fb68d1d6058 100644 --- a/tests/test_manipulation.py +++ b/tests/test_manipulation.py @@ -4,19 +4,23 @@ import dpnp +from .helper import has_support_aspect64 + testdata = [] testdata += [ ([True, False, True], dtype) - for dtype in ["float32", "float64", "int32", "int64", "bool"] -] -testdata += [ - ([1, -1, 0], dtype) for dtype in ["float32", "float64", "int32", "int64"] + for dtype in ["float32", "int32", "int64", "bool"] ] -testdata += [([0.1, 0.0, -0.1], dtype) for dtype in ["float32", "float64"]] -testdata += [([1j, -1j, 1 - 2j], dtype) for dtype in ["complex128"]] +testdata += [([1, -1, 0], dtype) for dtype in ["float32", "int32", "int64"]] +testdata += [([0.1, 0.0, -0.1], dtype) for dtype in ["float32"]] +if has_support_aspect64(): + testdata += [([True, False, True], dtype) for dtype in ["float64"]] + testdata += [([1, -1, 0], dtype) for dtype in ["float64"]] + testdata += [([0.1, 0.0, -0.1], dtype) for dtype in ["float64"]] + testdata += [([1j, -1j, 1 - 2j], dtype) for dtype in ["complex128"]] -@pytest.mark.parametrize("in_obj,out_dtype", testdata) +@pytest.mark.parametrize("in_obj, out_dtype", testdata) def test_copyto_dtype(in_obj, out_dtype): ndarr = numpy.array(in_obj) expected = numpy.empty(ndarr.size, dtype=out_dtype) diff --git a/tests/test_mixins.py b/tests/test_mixins.py index e2ffb26ffd2..06467e67160 100644 --- a/tests/test_mixins.py +++ b/tests/test_mixins.py @@ -12,19 +12,19 @@ def test_matmul(self): # DPNP array1 = inp.reshape( - inp.array(array_data, dtype=inp.float64), (size, size) + inp.array(array_data, dtype=inp.float32), (size, size) ) array2 = inp.reshape( - inp.array(array_data, dtype=inp.float64), (size, size) + inp.array(array_data, dtype=inp.float32), (size, size) ) result = inp.matmul(array1, array2) # print(result) # original - array_1 = numpy.array(array_data, dtype=numpy.float64).reshape( + array_1 = numpy.array(array_data, dtype=numpy.float32).reshape( (size, size) ) - array_2 = numpy.array(array_data, dtype=numpy.float64).reshape( + array_2 = numpy.array(array_data, dtype=numpy.float32).reshape( (size, size) ) expected = numpy.matmul(array_1, array_2) @@ -40,14 +40,14 @@ def test_matmul2(self): array_data2 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] # DPNP - array1 = inp.reshape(inp.array(array_data1, dtype=inp.float64), (3, 2)) - array2 = inp.reshape(inp.array(array_data2, dtype=inp.float64), (2, 4)) + array1 = inp.reshape(inp.array(array_data1, dtype=inp.float32), (3, 2)) + array2 = inp.reshape(inp.array(array_data2, dtype=inp.float32), (2, 4)) result = inp.matmul(array1, array2) # print(result) # original - array_1 = numpy.array(array_data1, dtype=numpy.float64).reshape((3, 2)) - array_2 = numpy.array(array_data2, dtype=numpy.float64).reshape((2, 4)) + array_1 = numpy.array(array_data1, dtype=numpy.float32).reshape((3, 2)) + array_2 = numpy.array(array_data2, dtype=numpy.float32).reshape((2, 4)) expected = numpy.matmul(array_1, array_2) # print(expected) @@ -56,17 +56,17 @@ def test_matmul2(self): def test_matmul3(self): array_data1 = numpy.full((513, 513), 5) array_data2 = numpy.full((513, 513), 2) - out = numpy.empty((513, 513), dtype=numpy.float64) + out = numpy.empty((513, 513), dtype=numpy.float32) # DPNP - array1 = inp.array(array_data1, dtype=inp.float64) - array2 = inp.array(array_data2, dtype=inp.float64) - out1 = inp.array(out, dtype=inp.float64) + array1 = inp.array(array_data1, dtype=inp.float32) + array2 = inp.array(array_data2, dtype=inp.float32) + out1 = inp.array(out, dtype=inp.float32) result = inp.matmul(array1, array2, out=out1) # original - array_1 = numpy.array(array_data1, dtype=numpy.float64) - array_2 = numpy.array(array_data2, dtype=numpy.float64) + array_1 = numpy.array(array_data1, dtype=numpy.float32) + array_2 = numpy.array(array_data2, dtype=numpy.float32) expected = numpy.matmul(array_1, array_2, out=out) numpy.testing.assert_array_equal(expected, result) diff --git a/tests/test_random.py b/tests/test_random.py index fc3777978e7..93026f64b0d 100644 --- a/tests/test_random.py +++ b/tests/test_random.py @@ -7,6 +7,8 @@ import dpnp.random +from .helper import has_support_aspect64 + class TestDistribution(unittest.TestCase): def check_extreme_value(self, dist_name, val, params): @@ -149,6 +151,9 @@ def test_randn_normal_distribution(): assert math.isclose(mean, expected_mean, abs_tol=0.03) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsBeta(TestDistribution): def test_moments(self): @@ -174,6 +179,9 @@ def test_seed(self): self.check_seed("beta", {"a": a, "b": b}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsBinomial(TestDistribution): def test_extreme_value(self): @@ -212,6 +220,9 @@ def test_seed(self): self.check_seed("binomial", {"n": n, "p": p}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsChisquare(TestDistribution): def test_invalid_args(self): @@ -223,6 +234,9 @@ def test_seed(self): self.check_seed("chisquare", {"df": df}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsExponential(TestDistribution): def test_invalid_args(self): @@ -234,6 +248,9 @@ def test_seed(self): self.check_seed("exponential", {"scale": scale}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsF(TestDistribution): def test_moments(self): @@ -267,6 +284,9 @@ def test_seed(self): self.check_seed("f", {"dfnum": dfnum, "dfden": dfden}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsGamma(TestDistribution): def test_moments(self): @@ -317,6 +337,9 @@ def test_seed(self): self.check_seed("geometric", {"p": p}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsGumbel(TestDistribution): def test_extreme_value(self): @@ -348,6 +371,9 @@ def test_seed(self): self.check_seed("gumbel", {"loc": loc, "scale": scale}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-6001" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsHypergeometric(TestDistribution): def test_extreme_value(self): @@ -434,6 +460,9 @@ def test_seed(self): ) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsLaplace(TestDistribution): def test_extreme_value(self): @@ -464,6 +493,9 @@ def test_seed(self): self.check_seed("laplace", {"loc": loc, "scale": scale}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsLogistic(TestDistribution): def test_moments(self): @@ -489,6 +521,9 @@ def test_seed(self): self.check_seed("logistic", {"loc": loc, "scale": scale}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsLognormal(TestDistribution): def test_extreme_value(self): @@ -524,6 +559,9 @@ def test_seed(self): self.check_seed("lognormal", {"mean": mean, "sigma": sigma}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsMultinomial(TestDistribution): def test_extreme_value(self): @@ -620,6 +658,9 @@ def test_seed(self): self.check_seed("multivariate_normal", {"mean": mean, "cov": cov}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsNegativeBinomial(TestDistribution): def test_extreme_value(self): @@ -651,6 +692,9 @@ def test_seed(self): self.check_seed("negative_binomial", {"n": n, "p": p}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) class TestDistributionsNormal(TestDistribution): def test_extreme_value(self): loc = 5 @@ -680,6 +724,9 @@ def test_seed(self): self.check_seed("normal", {"loc": loc, "scale": scale}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsNoncentralChisquare: @pytest.mark.parametrize( @@ -725,6 +772,9 @@ def test_seed(self, df): assert_allclose(a1, a2, rtol=1e-07, atol=0) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsPareto(TestDistribution): def test_moments(self): @@ -743,6 +793,9 @@ def test_seed(self): self.check_seed("pareto", {"a": a}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsPoisson(TestDistribution): def test_extreme_value(self): @@ -764,6 +817,9 @@ def test_seed(self): self.check_seed("poisson", {"lam": lam}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsPower(TestDistribution): def test_moments(self): @@ -783,6 +839,9 @@ def test_seed(self): self.check_seed("power", {"a": a}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsRayleigh(TestDistribution): def test_extreme_value(self): @@ -824,6 +883,9 @@ def test_seed(self): self.check_seed("standard_exponential", {}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsStandardGamma(TestDistribution): def test_extreme_value(self): @@ -855,6 +917,9 @@ def test_seed(self): self.check_seed("standard_normal", {}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsStandardT(TestDistribution): def test_moments(self): @@ -873,6 +938,9 @@ def test_seed(self): self.check_seed("standard_t", {"df": 10.0}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsTriangular(TestDistribution): def test_moments(self): @@ -919,6 +987,9 @@ def test_seed(self): ) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) class TestDistributionsUniform(TestDistribution): def test_extreme_value(self): low = 1.0 @@ -943,6 +1014,9 @@ def test_seed(self): self.check_seed("uniform", {"low": low, "high": high}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsVonmises: @pytest.mark.parametrize( @@ -983,6 +1057,9 @@ def test_seed(self, kappa): assert_allclose(a1, a2, rtol=1e-07, atol=0) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsWald(TestDistribution): def test_moments(self): @@ -1014,6 +1091,9 @@ def test_seed(self): self.check_seed("wald", {"mean": mean, "scale": scale}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsWeibull(TestDistribution): def test_extreme_value(self): @@ -1030,6 +1110,9 @@ def test_seed(self): self.check_seed("weibull", {"a": a}) +@pytest.mark.skipif( + not has_support_aspect64(), reason="Failed on Iris Xe: SAT-5989" +) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestDistributionsZipf(TestDistribution): def test_invalid_args(self): From 713302a147e377306896f51aedd0511a5284e6c9 Mon Sep 17 00:00:00 2001 From: vlad-perevezentsev Date: Wed, 26 Jul 2023 15:06:25 +0200 Subject: [PATCH 08/58] Use get_ret_type_and_func in call_fptr functions (#1500) * Updated get_ret_type_and_func takes cpp_bool has_aspect_fp64 argument * Use the updated get_ret_type_and_func function in call_fptr_1in_1out_strides, call_fptr_2in_1out and call_fptr_2in_1out_strides functions for further test updates to run on Iris Xe. --- dpnp/dpnp_algo/dpnp_algo.pyx | 43 +++++++++++++++--------- dpnp/dpnp_algo/dpnp_algo_statistics.pxi | 3 +- dpnp/dpnp_utils/dpnp_algo_utils.pxd | 5 +-- dpnp/dpnp_utils/dpnp_algo_utils.pyx | 17 +++++++--- dpnp/dpnp_utils/dpnp_utils_statistics.py | 4 +-- dpnp/linalg/dpnp_algo_linalg.pyx | 15 ++++++--- 6 files changed, 57 insertions(+), 30 deletions(-) diff --git a/dpnp/dpnp_algo/dpnp_algo.pyx b/dpnp/dpnp_algo/dpnp_algo.pyx index e60834599a7..89ce50169c8 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pyx +++ b/dpnp/dpnp_algo/dpnp_algo.pyx @@ -348,7 +348,15 @@ cdef utils.dpnp_descriptor call_fptr_1in_1out_strides(DPNPFuncName fptr_name, """ get the FPTR data structure """ cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(fptr_name, param1_type, param1_type) - result_type = dpnp_DPNPFuncType_to_dtype( < size_t > kernel_data.return_type) + x1_obj = x1.get_array() + + # get FPTR function and return type + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + x1_obj.sycl_device.has_aspect_fp64) + cdef DPNPFuncType return_type = ret_type_and_func[0] + cdef fptr_1in_1out_strides_t func = < fptr_1in_1out_strides_t > ret_type_and_func[1] + + result_type = dpnp_DPNPFuncType_to_dtype( < size_t > return_type) cdef shape_type_c x1_shape = x1.shape cdef shape_type_c x1_strides = utils.strides_to_vector(x1.strides, x1_shape) @@ -358,9 +366,8 @@ cdef utils.dpnp_descriptor call_fptr_1in_1out_strides(DPNPFuncName fptr_name, if out is None: """ Create result array with type given by FPTR data """ - x1_obj = x1.get_array() result = utils.create_output_descriptor(result_shape, - kernel_data.return_type, + return_type, None, device=x1_obj.sycl_device, usm_type=x1_obj.usm_type, @@ -383,7 +390,6 @@ cdef utils.dpnp_descriptor call_fptr_1in_1out_strides(DPNPFuncName fptr_name, cdef shape_type_c result_strides = utils.strides_to_vector(result.strides, result_shape) """ Call FPTR function """ - cdef fptr_1in_1out_strides_t func = kernel_data.ptr cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref, result.get_data(), result.size, @@ -419,7 +425,15 @@ cdef utils.dpnp_descriptor call_fptr_2in_1out(DPNPFuncName fptr_name, # get the FPTR data structure cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(fptr_name, x1_c_type, x2_c_type) - result_type = dpnp_DPNPFuncType_to_dtype( < size_t > kernel_data.return_type) + result_sycl_device, result_usm_type, result_sycl_queue = utils.get_common_usm_allocation(x1_obj, x2_obj) + + # get FPTR function and return type + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + result_sycl_device.has_aspect_fp64) + cdef DPNPFuncType return_type = ret_type_and_func[0] + cdef fptr_2in_1out_t func = < fptr_2in_1out_t > ret_type_and_func[1] + + result_type = dpnp_DPNPFuncType_to_dtype( < size_t > return_type) # Create result array cdef shape_type_c x1_shape = x1_obj.shape @@ -427,12 +441,10 @@ cdef utils.dpnp_descriptor call_fptr_2in_1out(DPNPFuncName fptr_name, cdef shape_type_c result_shape = utils.get_common_shape(x1_shape, x2_shape) cdef utils.dpnp_descriptor result - result_sycl_device, result_usm_type, result_sycl_queue = utils.get_common_usm_allocation(x1_obj, x2_obj) - if out is None: """ Create result array with type given by FPTR data """ result = utils.create_output_descriptor(result_shape, - kernel_data.return_type, + return_type, None, device=result_sycl_device, usm_type=result_usm_type, @@ -451,7 +463,6 @@ cdef utils.dpnp_descriptor call_fptr_2in_1out(DPNPFuncName fptr_name, cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref() """ Call FPTR function """ - cdef fptr_2in_1out_t func = kernel_data.ptr cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref, result.get_data(), x1_obj.get_data(), @@ -485,6 +496,14 @@ cdef utils.dpnp_descriptor call_fptr_2in_1out_strides(DPNPFuncName fptr_name, # get the FPTR data structure cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(fptr_name, x1_c_type, x2_c_type) + result_sycl_device, result_usm_type, result_sycl_queue = utils.get_common_usm_allocation(x1_obj, x2_obj) + + # get FPTR function and return type + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + result_sycl_device.has_aspect_fp64) + cdef DPNPFuncType return_type = ret_type_and_func[0] + cdef fptr_2in_1out_strides_t func = < fptr_2in_1out_strides_t > ret_type_and_func[1] + # Create result array cdef shape_type_c x1_shape = x1_obj.shape @@ -495,12 +514,6 @@ cdef utils.dpnp_descriptor call_fptr_2in_1out_strides(DPNPFuncName fptr_name, cdef shape_type_c result_shape = utils.get_common_shape(x1_shape, x2_shape) cdef utils.dpnp_descriptor result - result_sycl_device, result_usm_type, result_sycl_queue = utils.get_common_usm_allocation(x1_obj, x2_obj) - - # get FPTR function and return type - cdef fptr_2in_1out_strides_t func = < fptr_2in_1out_strides_t > kernel_data.ptr - cdef DPNPFuncType return_type = kernel_data.return_type - # check 'out' parameter data if out is not None: if out.shape != result_shape: diff --git a/dpnp/dpnp_algo/dpnp_algo_statistics.pxi b/dpnp/dpnp_algo/dpnp_algo_statistics.pxi index 3ec3893c3ac..1c35cb4ac33 100644 --- a/dpnp/dpnp_algo/dpnp_algo_statistics.pxi +++ b/dpnp/dpnp_algo/dpnp_algo_statistics.pxi @@ -265,7 +265,8 @@ cpdef utils.dpnp_descriptor dpnp_median(utils.dpnp_descriptor array1): array1_obj = array1.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(array1_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + array1_obj.sycl_device.has_aspect_fp64) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_statistic_1in_1out_func_ptr_t func = < custom_statistic_1in_1out_func_ptr_t > ret_type_and_func[1] diff --git a/dpnp/dpnp_utils/dpnp_algo_utils.pxd b/dpnp/dpnp_utils/dpnp_algo_utils.pxd index 7656d29bb15..b7c739dcfbb 100644 --- a/dpnp/dpnp_utils/dpnp_algo_utils.pxd +++ b/dpnp/dpnp_utils/dpnp_algo_utils.pxd @@ -163,8 +163,9 @@ cdef tuple get_common_usm_allocation(dpnp_descriptor x1, dpnp_descriptor x2) Get common USM allocation in the form of (sycl_device, usm_type, sycl_queue) """ -cdef (DPNPFuncType, void *) get_ret_type_and_func(x1_obj, DPNPFuncData kernel_data) +cdef (DPNPFuncType, void *) get_ret_type_and_func(DPNPFuncData kernel_data, + cpp_bool has_aspect_fp64) """ Get the corresponding return type and function pointer based on the -capability of the allocated input array device for the integer types. +capability of the allocated result array device. """ diff --git a/dpnp/dpnp_utils/dpnp_algo_utils.pyx b/dpnp/dpnp_utils/dpnp_algo_utils.pyx index db052be7827..f88b42bde7f 100644 --- a/dpnp/dpnp_utils/dpnp_algo_utils.pyx +++ b/dpnp/dpnp_utils/dpnp_algo_utils.pyx @@ -663,13 +663,20 @@ cdef tuple get_common_usm_allocation(dpnp_descriptor x1, dpnp_descriptor x2): return (common_sycl_queue.sycl_device, common_usm_type, common_sycl_queue) -cdef (DPNPFuncType, void *) get_ret_type_and_func(x1_obj, DPNPFuncData kernel_data): - if dpnp.issubdtype(x1_obj.dtype, dpnp.integer) and not x1_obj.sycl_device.has_aspect_fp64: +cdef (DPNPFuncType, void *) get_ret_type_and_func(DPNPFuncData kernel_data, + cpp_bool has_aspect_fp64): + """ + This function is responsible for determining the appropriate return type + and function pointer based on the capability of the allocated result array device. + """ + return_type = kernel_data.return_type + func = kernel_data.ptr + + if kernel_data.ptr_no_fp64 != NULL and not has_aspect_fp64: + return_type = kernel_data.return_type_no_fp64 func = kernel_data.ptr_no_fp64 - else: - return_type = kernel_data.return_type - func = kernel_data.ptr + return return_type, func diff --git a/dpnp/dpnp_utils/dpnp_utils_statistics.py b/dpnp/dpnp_utils/dpnp_utils_statistics.py index e8ccb179468..9d69dbaa368 100644 --- a/dpnp/dpnp_utils/dpnp_utils_statistics.py +++ b/dpnp/dpnp_utils/dpnp_utils_statistics.py @@ -79,8 +79,8 @@ def _get_2dmin_array(x, dtype): dtypes = [m.dtype, dpnp.default_float_type(sycl_queue=queue)] if y is not None: dtypes.append(y.dtype) - dtype = dpt.result_type(*dtypes) - # TODO: remove when dpctl.result_type() is fixed + dtype = dpnp.result_type(*dtypes) + # TODO: remove when dpctl.result_type() is returned dtype based on fp64 fp64 = queue.sycl_device.has_aspect_fp64 if not fp64: if dtype == dpnp.float64: diff --git a/dpnp/linalg/dpnp_algo_linalg.pyx b/dpnp/linalg/dpnp_algo_linalg.pyx index e6484599c51..39ba81bac59 100644 --- a/dpnp/linalg/dpnp_algo_linalg.pyx +++ b/dpnp/linalg/dpnp_algo_linalg.pyx @@ -196,7 +196,8 @@ cpdef tuple dpnp_eig(utils.dpnp_descriptor x1): x1_obj = x1.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(x1_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + x1_obj.sycl_device.has_aspect_fp64) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_linalg_2in_1out_func_ptr_t func = < custom_linalg_2in_1out_func_ptr_t > ret_type_and_func[1] @@ -242,7 +243,8 @@ cpdef utils.dpnp_descriptor dpnp_eigvals(utils.dpnp_descriptor input): input_obj = input.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(input_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + input_obj.sycl_device.has_aspect_fp64) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_linalg_1in_1out_with_size_func_ptr_t_ func = < custom_linalg_1in_1out_with_size_func_ptr_t_ > ret_type_and_func[1] @@ -281,7 +283,8 @@ cpdef utils.dpnp_descriptor dpnp_inv(utils.dpnp_descriptor input): input_obj = input.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(input_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + input_obj.sycl_device.has_aspect_fp64) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_linalg_1in_1out_func_ptr_t func = < custom_linalg_1in_1out_func_ptr_t > ret_type_and_func[1] @@ -462,7 +465,8 @@ cpdef tuple dpnp_qr(utils.dpnp_descriptor x1, str mode): x1_obj = x1.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(x1_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + x1_obj.sycl_device.has_aspect_fp64) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_linalg_1in_3out_shape_t func = < custom_linalg_1in_3out_shape_t > ret_type_and_func[1] @@ -515,7 +519,8 @@ cpdef tuple dpnp_svd(utils.dpnp_descriptor x1, cpp_bool full_matrices, cpp_bool x1_obj = x1.get_array() - cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(x1_obj, kernel_data) + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + x1_obj.sycl_device.has_aspect_fp64) cdef DPNPFuncType return_type = ret_type_and_func[0] cdef custom_linalg_1in_3out_shape_t func = < custom_linalg_1in_3out_shape_t > ret_type_and_func[1] From cf91bc4f33f3d4498f5be2e823819c5de7dc5e21 Mon Sep 17 00:00:00 2001 From: Natalia Polina Date: Wed, 26 Jul 2023 23:47:14 -0500 Subject: [PATCH 09/58] Added more dtypes for minins tests and manipulation tests. --- tests/test_manipulation.py | 23 ++++++---- tests/test_mixins.py | 94 +++++++++++++++++++------------------- 2 files changed, 62 insertions(+), 55 deletions(-) diff --git a/tests/test_manipulation.py b/tests/test_manipulation.py index fb68d1d6058..c40ec0786ff 100644 --- a/tests/test_manipulation.py +++ b/tests/test_manipulation.py @@ -4,24 +4,29 @@ import dpnp -from .helper import has_support_aspect64 +from .helper import ( + get_all_dtypes, + get_complex_dtypes, + get_float_dtypes, +) testdata = [] testdata += [ ([True, False, True], dtype) - for dtype in ["float32", "int32", "int64", "bool"] + for dtype in get_all_dtypes(no_none=True, no_complex=True) +] +testdata += [ + ([1, -1, 0], dtype) + for dtype in get_all_dtypes(no_none=True, no_bool=True, no_complex=True) ] -testdata += [([1, -1, 0], dtype) for dtype in ["float32", "int32", "int64"]] -testdata += [([0.1, 0.0, -0.1], dtype) for dtype in ["float32"]] -if has_support_aspect64(): - testdata += [([True, False, True], dtype) for dtype in ["float64"]] - testdata += [([1, -1, 0], dtype) for dtype in ["float64"]] - testdata += [([0.1, 0.0, -0.1], dtype) for dtype in ["float64"]] - testdata += [([1j, -1j, 1 - 2j], dtype) for dtype in ["complex128"]] +testdata += [([0.1, 0.0, -0.1], dtype) for dtype in get_float_dtypes()] +testdata += [([1j, -1j, 1 - 2j], dtype) for dtype in get_complex_dtypes()] @pytest.mark.parametrize("in_obj, out_dtype", testdata) def test_copyto_dtype(in_obj, out_dtype): + if out_dtype == dpnp.complex64: + pytest.skip("SAT-6016: dpnp.copyto() do not work with complex64 dtype") ndarr = numpy.array(in_obj) expected = numpy.empty(ndarr.size, dtype=out_dtype) numpy.copyto(expected, ndarr) diff --git a/tests/test_mixins.py b/tests/test_mixins.py index 06467e67160..8b2e9861030 100644 --- a/tests/test_mixins.py +++ b/tests/test_mixins.py @@ -4,72 +4,74 @@ import dpnp as inp +from .helper import get_float_dtypes + class TestMatMul(unittest.TestCase): def test_matmul(self): array_data = [1.0, 2.0, 3.0, 4.0] size = 2 - # DPNP - array1 = inp.reshape( - inp.array(array_data, dtype=inp.float32), (size, size) - ) - array2 = inp.reshape( - inp.array(array_data, dtype=inp.float32), (size, size) - ) - result = inp.matmul(array1, array2) - # print(result) - - # original - array_1 = numpy.array(array_data, dtype=numpy.float32).reshape( - (size, size) - ) - array_2 = numpy.array(array_data, dtype=numpy.float32).reshape( - (size, size) - ) - expected = numpy.matmul(array_1, array_2) - # print(expected) - - # passed - numpy.testing.assert_array_equal(expected, result) - # still failed - # self.assertEqual(expected, result) + for dtype in get_float_dtypes(): + # DPNP + array1 = inp.reshape( + inp.array(array_data, dtype=dtype), (size, size) + ) + array2 = inp.reshape( + inp.array(array_data, dtype=dtype), (size, size) + ) + result = inp.matmul(array1, array2) + # print(result) + + # original + array_1 = numpy.array(array_data, dtype=dtype).reshape((size, size)) + array_2 = numpy.array(array_data, dtype=dtype).reshape((size, size)) + expected = numpy.matmul(array_1, array_2) + # print(expected) + + # passed + numpy.testing.assert_array_equal(expected, result) + # still failed + # self.assertEqual(expected, result) def test_matmul2(self): array_data1 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] array_data2 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] - # DPNP - array1 = inp.reshape(inp.array(array_data1, dtype=inp.float32), (3, 2)) - array2 = inp.reshape(inp.array(array_data2, dtype=inp.float32), (2, 4)) - result = inp.matmul(array1, array2) - # print(result) + for dtype in get_float_dtypes(): + # DPNP + array1 = inp.reshape(inp.array(array_data1, dtype=dtype), (3, 2)) + array2 = inp.reshape(inp.array(array_data2, dtype=dtype), (2, 4)) + result = inp.matmul(array1, array2) + # print(result) - # original - array_1 = numpy.array(array_data1, dtype=numpy.float32).reshape((3, 2)) - array_2 = numpy.array(array_data2, dtype=numpy.float32).reshape((2, 4)) - expected = numpy.matmul(array_1, array_2) - # print(expected) + # original + array_1 = numpy.array(array_data1, dtype=dtype).reshape((3, 2)) + array_2 = numpy.array(array_data2, dtype=dtype).reshape((2, 4)) + expected = numpy.matmul(array_1, array_2) + # print(expected) - numpy.testing.assert_array_equal(expected, result) + numpy.testing.assert_array_equal(expected, result) def test_matmul3(self): array_data1 = numpy.full((513, 513), 5) array_data2 = numpy.full((513, 513), 2) - out = numpy.empty((513, 513), dtype=numpy.float32) - # DPNP - array1 = inp.array(array_data1, dtype=inp.float32) - array2 = inp.array(array_data2, dtype=inp.float32) - out1 = inp.array(out, dtype=inp.float32) - result = inp.matmul(array1, array2, out=out1) + for dtype in get_float_dtypes(): + out = numpy.empty((513, 513), dtype=dtype) + + # DPNP + array1 = inp.array(array_data1, dtype=dtype) + array2 = inp.array(array_data2, dtype=dtype) + out1 = inp.array(out, dtype=dtype) + result = inp.matmul(array1, array2, out=out1) - # original - array_1 = numpy.array(array_data1, dtype=numpy.float32) - array_2 = numpy.array(array_data2, dtype=numpy.float32) - expected = numpy.matmul(array_1, array_2, out=out) + # original + array_1 = numpy.array(array_data1, dtype=dtype) + array_2 = numpy.array(array_data2, dtype=dtype) + expected = numpy.matmul(array_1, array_2, out=out) - numpy.testing.assert_array_equal(expected, result) + numpy.testing.assert_array_equal(expected, result) if __name__ == "__main__": From 815436cecdddf7d311ff861ba3295780660b9270 Mon Sep 17 00:00:00 2001 From: Natalia Polina Date: Thu, 27 Jul 2023 10:16:11 -0700 Subject: [PATCH 10/58] Update test_mixins.py --- tests/test_mixins.py | 88 ++++++++++++++------------------------------ 1 file changed, 27 insertions(+), 61 deletions(-) diff --git a/tests/test_mixins.py b/tests/test_mixins.py index 8b2e9861030..f43b53a2ef3 100644 --- a/tests/test_mixins.py +++ b/tests/test_mixins.py @@ -1,78 +1,44 @@ import unittest -import numpy - -import dpnp as inp - -from .helper import get_float_dtypes +from tests.third_party.cupy import testing class TestMatMul(unittest.TestCase): - def test_matmul(self): - array_data = [1.0, 2.0, 3.0, 4.0] - size = 2 - - for dtype in get_float_dtypes(): - # DPNP - array1 = inp.reshape( - inp.array(array_data, dtype=dtype), (size, size) - ) - array2 = inp.reshape( - inp.array(array_data, dtype=dtype), (size, size) - ) - result = inp.matmul(array1, array2) - # print(result) - - # original - array_1 = numpy.array(array_data, dtype=dtype).reshape((size, size)) - array_2 = numpy.array(array_data, dtype=dtype).reshape((size, size)) - expected = numpy.matmul(array_1, array_2) - # print(expected) - - # passed - numpy.testing.assert_array_equal(expected, result) - # still failed - # self.assertEqual(expected, result) - - def test_matmul2(self): - array_data1 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] - array_data2 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] + @testing.for_float_dtypes() + @testing.numpy_cupy_allclose() + def test_matmul(self, xp, dtype): + data = [1.0, 2.0, 3.0, 4.0] + shape = (2, 2) - for dtype in get_float_dtypes(): - # DPNP - array1 = inp.reshape(inp.array(array_data1, dtype=dtype), (3, 2)) - array2 = inp.reshape(inp.array(array_data2, dtype=dtype), (2, 4)) - result = inp.matmul(array1, array2) - # print(result) + a = xp.array(data, dtype=dtype).reshape(shape) + b = xp.array(data, dtype=dtype).reshape(shape) - # original - array_1 = numpy.array(array_data1, dtype=dtype).reshape((3, 2)) - array_2 = numpy.array(array_data2, dtype=dtype).reshape((2, 4)) - expected = numpy.matmul(array_1, array_2) - # print(expected) + return xp.matmul(a, b) - numpy.testing.assert_array_equal(expected, result) + @testing.for_float_dtypes() + @testing.numpy_cupy_allclose() + def test_matmul2(self, xp, dtype): + data1 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] + data2 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0] - def test_matmul3(self): - array_data1 = numpy.full((513, 513), 5) - array_data2 = numpy.full((513, 513), 2) + a = xp.array(data1, dtype=dtype).reshape(3, 2) + b = xp.array(data2, dtype=dtype).reshape(2, 4) - for dtype in get_float_dtypes(): - out = numpy.empty((513, 513), dtype=dtype) + return xp.matmul(a, b) - # DPNP - array1 = inp.array(array_data1, dtype=dtype) - array2 = inp.array(array_data2, dtype=dtype) - out1 = inp.array(out, dtype=dtype) - result = inp.matmul(array1, array2, out=out1) + @testing.for_float_dtypes() + @testing.numpy_cupy_allclose() + def test_matmul3(self, xp, dtype): + data1 = xp.full((513, 513), 5) + data2 = xp.full((513, 513), 2) + out = xp.empty((513, 513), dtype=dtype) - # original - array_1 = numpy.array(array_data1, dtype=dtype) - array_2 = numpy.array(array_data2, dtype=dtype) - expected = numpy.matmul(array_1, array_2, out=out) + a = xp.array(data1, dtype=dtype) + b = xp.array(data2, dtype=dtype) - numpy.testing.assert_array_equal(expected, result) + xp.matmul(a, b, out=out) + return out if __name__ == "__main__": unittest.main() From f510e164cb242c30deea52839f3f8d38fe427bd3 Mon Sep 17 00:00:00 2001 From: Natalia Polina Date: Thu, 27 Jul 2023 10:29:41 -0700 Subject: [PATCH 11/58] Update test_mixins.py --- tests/test_mixins.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_mixins.py b/tests/test_mixins.py index f43b53a2ef3..01b836b955a 100644 --- a/tests/test_mixins.py +++ b/tests/test_mixins.py @@ -40,5 +40,6 @@ def test_matmul3(self, xp, dtype): return out + if __name__ == "__main__": unittest.main() From 689831a7879db21585ecbaadd240463edf190d20 Mon Sep 17 00:00:00 2001 From: vlad-perevezentsev Date: Fri, 28 Jul 2023 12:55:16 +0200 Subject: [PATCH 12/58] Reuse dpctl.tensort.take for dpnp.take (#1492) * Reuse dpctl.tensort.take for dpnp.take * Add examples and use dpnp.is_supported_array_type * Use dpnp.get_usm_ndarray in take and update examples --------- Co-authored-by: Anton <100830759+antonwolfy@users.noreply.github.com> --- dpnp/backend/include/dpnp_iface_fptr.hpp | 4 +- dpnp/backend/kernels/dpnp_krnl_indexing.cpp | 27 ------- dpnp/dpnp_algo/dpnp_algo.pxd | 2 - dpnp/dpnp_algo/dpnp_algo_indexing.pxi | 44 ----------- dpnp/dpnp_array.py | 6 +- dpnp/dpnp_iface_indexing.py | 71 +++++++++++++---- tests/skipped_tests.tbl | 1 - tests/skipped_tests_gpu.tbl | 2 - tests/test_indexing.py | 78 ++++++++----------- .../cupy/indexing_tests/test_indexing.py | 3 +- 10 files changed, 97 insertions(+), 141 deletions(-) diff --git a/dpnp/backend/include/dpnp_iface_fptr.hpp b/dpnp/backend/include/dpnp_iface_fptr.hpp index 70771130661..96b85885473 100644 --- a/dpnp/backend/include/dpnp_iface_fptr.hpp +++ b/dpnp/backend/include/dpnp_iface_fptr.hpp @@ -487,9 +487,7 @@ enum class DPNPFuncName : size_t DPNP_FN_SVD_EXT, /**< Used in numpy.linalg.svd() impl, requires extra parameters */ DPNP_FN_TAKE, /**< Used in numpy.take() impl */ - DPNP_FN_TAKE_EXT, /**< Used in numpy.take() impl, requires extra parameters - */ - DPNP_FN_TAN, /**< Used in numpy.tan() impl */ + DPNP_FN_TAN, /**< Used in numpy.tan() impl */ DPNP_FN_TAN_EXT, /**< Used in numpy.tan() impl, requires extra parameters */ DPNP_FN_TANH, /**< Used in numpy.tanh() impl */ DPNP_FN_TANH_EXT, /**< Used in numpy.tanh() impl, requires extra parameters diff --git a/dpnp/backend/kernels/dpnp_krnl_indexing.cpp b/dpnp/backend/kernels/dpnp_krnl_indexing.cpp index c2f9e0d9bda..b8fa2179b6f 100644 --- a/dpnp/backend/kernels/dpnp_krnl_indexing.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_indexing.cpp @@ -1059,32 +1059,5 @@ void func_map_init_indexing_func(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_TAKE][eft_C128][eft_LNG] = { eft_C128, (void *)dpnp_take_default_c, int64_t>}; - // TODO: add a handling of other indexes types once DPCtl implementation of - // data copy is ready - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_BLN][eft_INT] = { - eft_BLN, (void *)dpnp_take_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_take_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_LNG][eft_INT] = { - eft_LNG, (void *)dpnp_take_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_FLT][eft_INT] = { - eft_FLT, (void *)dpnp_take_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_DBL][eft_INT] = { - eft_DBL, (void *)dpnp_take_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_C128][eft_INT] = { - eft_C128, (void *)dpnp_take_ext_c, int32_t>}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_BLN][eft_LNG] = { - eft_BLN, (void *)dpnp_take_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_INT][eft_LNG] = { - eft_INT, (void *)dpnp_take_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_take_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_FLT][eft_LNG] = { - eft_FLT, (void *)dpnp_take_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_DBL][eft_LNG] = { - eft_DBL, (void *)dpnp_take_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TAKE_EXT][eft_C128][eft_LNG] = { - eft_C128, (void *)dpnp_take_ext_c, int64_t>}; - return; } diff --git a/dpnp/dpnp_algo/dpnp_algo.pxd b/dpnp/dpnp_algo/dpnp_algo.pxd index b56557f75c8..37d4a7d3694 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pxd +++ b/dpnp/dpnp_algo/dpnp_algo.pxd @@ -295,8 +295,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_SUM_EXT DPNP_FN_SVD DPNP_FN_SVD_EXT - DPNP_FN_TAKE - DPNP_FN_TAKE_EXT DPNP_FN_TAN DPNP_FN_TAN_EXT DPNP_FN_TANH diff --git a/dpnp/dpnp_algo/dpnp_algo_indexing.pxi b/dpnp/dpnp_algo/dpnp_algo_indexing.pxi index e9dc538393c..808961298c2 100644 --- a/dpnp/dpnp_algo/dpnp_algo_indexing.pxi +++ b/dpnp/dpnp_algo/dpnp_algo_indexing.pxi @@ -45,7 +45,6 @@ __all__ += [ "dpnp_put_along_axis", "dpnp_putmask", "dpnp_select", - "dpnp_take", "dpnp_take_along_axis", "dpnp_tril_indices", "dpnp_tril_indices_from", @@ -59,13 +58,6 @@ ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_dpnp_choose_t)(c_dpctl.DPCTLSyclQueueRe ctypedef c_dpctl.DPCTLSyclEventRef(*fptr_dpnp_diag_indices)(c_dpctl.DPCTLSyclQueueRef, void * , size_t, const c_dpctl.DPCTLEventVectorRef) -ctypedef c_dpctl.DPCTLSyclEventRef(*custom_indexing_2in_1out_func_ptr_t)(c_dpctl.DPCTLSyclQueueRef, - void *, - const size_t, - void * , - void * , - size_t, - const c_dpctl.DPCTLEventVectorRef) ctypedef c_dpctl.DPCTLSyclEventRef(*custom_indexing_2in_1out_func_ptr_t_)(c_dpctl.DPCTLSyclQueueRef, void * , const size_t, @@ -417,42 +409,6 @@ cpdef utils.dpnp_descriptor dpnp_select(list condlist, list choicelist, default) return res_array -cpdef utils.dpnp_descriptor dpnp_take(utils.dpnp_descriptor x1, utils.dpnp_descriptor indices): - cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(x1.dtype) - cdef DPNPFuncType param2_type = dpnp_dtype_to_DPNPFuncType(indices.dtype) - - cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_TAKE_EXT, param1_type, param2_type) - - x1_obj = x1.get_array() - - cdef utils.dpnp_descriptor result = utils.create_output_descriptor(indices.shape, - kernel_data.return_type, - None, - device=x1_obj.sycl_device, - usm_type=x1_obj.usm_type, - sycl_queue=x1_obj.sycl_queue) - - result_sycl_queue = result.get_array().sycl_queue - - cdef c_dpctl.SyclQueue q = result_sycl_queue - cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref() - - cdef custom_indexing_2in_1out_func_ptr_t func = kernel_data.ptr - - cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref, - x1.get_data(), - x1.size, - indices.get_data(), - result.get_data(), - indices.size, - NULL) # dep_events_ref - - with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref) - c_dpctl.DPCTLEvent_Delete(event_ref) - - return result - - cpdef object dpnp_take_along_axis(object arr, object indices, int axis): cdef long size_arr = arr.size cdef shape_type_c shape_arr = arr.shape diff --git a/dpnp/dpnp_array.py b/dpnp/dpnp_array.py index 0ff1ca4a8e2..75ae72a31d0 100644 --- a/dpnp/dpnp_array.py +++ b/dpnp/dpnp_array.py @@ -1033,15 +1033,15 @@ def sum( # 'swapaxes', - def take(self, indices, axis=None, out=None, mode="raise"): + def take(self, indices, /, *, axis=None, out=None, mode="wrap"): """ - Take elements from an array. + Take elements from an array along an axis. For full documentation refer to :obj:`numpy.take`. """ - return dpnp.take(self, indices, axis, out, mode) + return dpnp.take(self, indices, axis=axis, out=out, mode=mode) # 'tobytes', # 'tofile', diff --git a/dpnp/dpnp_iface_indexing.py b/dpnp/dpnp_iface_indexing.py index a0b352c4317..0fd3803c917 100644 --- a/dpnp/dpnp_iface_indexing.py +++ b/dpnp/dpnp_iface_indexing.py @@ -539,39 +539,82 @@ def select(condlist, choicelist, default=0): return call_origin(numpy.select, condlist, choicelist, default) -def take(x1, indices, axis=None, out=None, mode="raise"): +def take(x, indices, /, *, axis=None, out=None, mode="wrap"): """ - Take elements from an array. + Take elements from an array along an axis. For full documentation refer to :obj:`numpy.take`. + Returns + ------- + dpnp.ndarray + An array with shape x.shape[:axis] + indices.shape + x.shape[axis + 1:] + filled with elements from `x`. + Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. - Parameters ``axis``, ``out`` and ``mode`` are supported only with default values. - Parameter ``indices`` is supported as :obj:`dpnp.ndarray`. + Parameters `x` and `indices` are supported either as :class:`dpnp.ndarray` + or :class:`dpctl.tensor.usm_ndarray`. + Parameter `indices` is supported as 1-D array of integer data type. + Parameter `out` is supported only with default value. + Parameter `mode` is supported with ``wrap``(default) and ``clip`` mode. + Providing parameter `axis` is optional when `x` is a 1-D array. + Otherwise the function will be executed sequentially on CPU. See Also -------- :obj:`dpnp.compress` : Take elements using a boolean mask. :obj:`take_along_axis` : Take elements by matching the array and the index arrays. + + Notes + ----- + How out-of-bounds indices will be handled. + "wrap" - clamps indices to (-n <= i < n), then wraps negative indices. + "clip" - clips indices to (0 <= i < n) + + Examples + -------- + >>> import dpnp as np + >>> x = np.array([4, 3, 5, 7, 6, 8]) + >>> indices = np.array([0, 1, 4]) + >>> np.take(x, indices) + array([4, 3, 6]) + + In this example "fancy" indexing can be used. + + >>> x[indices] + array([4, 3, 6]) + + >>> indices = dpnp.array([-1, -6, -7, 5, 6]) + >>> np.take(x, indices) + array([8, 4, 4, 8, 8]) + + >>> np.take(x, indices, mode="clip") + array([4, 4, 4, 8, 8]) + """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - indices_desc = dpnp.get_dpnp_descriptor( - indices, copy_when_nondefault_queue=False - ) - if x1_desc and indices_desc: - if axis is not None: + if dpnp.is_supported_array_type(x) and dpnp.is_supported_array_type( + indices + ): + if indices.ndim != 1 or not dpnp.issubdtype( + indices.dtype, dpnp.integer + ): + pass + elif axis is None and x.ndim > 1: pass elif out is not None: pass - elif mode != "raise": + elif mode not in ("clip", "wrap"): pass else: - return dpnp_take(x1_desc, indices_desc).get_pyobj() + dpt_array = dpnp.get_usm_ndarray(x) + dpt_indices = dpnp.get_usm_ndarray(indices) + return dpnp_array._create_from_usm_ndarray( + dpt.take(dpt_array, dpt_indices, axis=axis, mode=mode) + ) - return call_origin(numpy.take, x1, indices, axis, out, mode) + return call_origin(numpy.take, x, indices, axis, out, mode) def take_along_axis(x1, indices, axis): diff --git a/tests/skipped_tests.tbl b/tests/skipped_tests.tbl index d5eab8dc75c..ff5494e5f71 100644 --- a/tests/skipped_tests.tbl +++ b/tests/skipped_tests.tbl @@ -401,7 +401,6 @@ tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_compr tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_compress_empty_1dim_no_axis tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_compress_no_axis tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_compress_no_bool -tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_take_index_range_overflow tests/third_party/cupy/indexing_tests/test_indexing.py::TestSelect::test_select tests/third_party/cupy/indexing_tests/test_indexing.py::TestSelect::test_select_1D_choicelist tests/third_party/cupy/indexing_tests/test_indexing.py::TestSelect::test_select_choicelist_condlist_broadcast diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index 9a44f4cbfce..4fc12eff1ab 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -49,7 +49,6 @@ tests/test_sycl_queue.py::test_modf[level_zero:gpu:0] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-trapz-data19] tests/test_sycl_queue.py::test_1in_1out[opencl:cpu:0-trapz-data19] -tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_take_no_axis tests/third_party/cupy/indexing_tests/test_insert.py::TestDiagIndices_param_0_{n=2, ndim=2}::test_diag_indices tests/third_party/cupy/indexing_tests/test_insert.py::TestDiagIndices_param_1_{n=2, ndim=3}::test_diag_indices tests/third_party/cupy/indexing_tests/test_insert.py::TestDiagIndices_param_2_{n=2, ndim=1}::test_diag_indices @@ -597,7 +596,6 @@ tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_compr tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_compress_empty_1dim_no_axis tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_compress_no_axis tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_compress_no_bool -tests/third_party/cupy/indexing_tests/test_indexing.py::TestIndexing::test_take_index_range_overflow tests/third_party/cupy/indexing_tests/test_indexing.py::TestSelect::test_select tests/third_party/cupy/indexing_tests/test_indexing.py::TestSelect::test_select_1D_choicelist tests/third_party/cupy/indexing_tests/test_indexing.py::TestSelect::test_select_choicelist_condlist_broadcast diff --git a/tests/test_indexing.py b/tests/test_indexing.py index 354cfa06ff1..67600264356 100644 --- a/tests/test_indexing.py +++ b/tests/test_indexing.py @@ -592,61 +592,51 @@ def test_select(): assert_array_equal(expected, result) +@pytest.mark.parametrize("array_type", get_all_dtypes()) @pytest.mark.parametrize( - "array_type", - [ - numpy.bool8, - numpy.int32, - numpy.int64, - numpy.float32, - numpy.float64, - numpy.complex128, - ], - ids=["bool8", "int32", "int64", "float32", "float64", "complex128"], + "indices_type", [numpy.int32, numpy.int64], ids=["int32", "int64"] ) @pytest.mark.parametrize( - "indices_type", [numpy.int32, numpy.int64], ids=["int32", "int64"] + "indices", [[-2, 2], [-5, 4]], ids=["[-2, 2]", "[-5, 4]"] ) +@pytest.mark.parametrize("mode", ["clip", "wrap"], ids=["clip", "wrap"]) +def test_take_1d(indices, array_type, indices_type, mode): + a = numpy.array([-2, -1, 0, 1, 2], dtype=array_type) + ind = numpy.array(indices, dtype=indices_type) + ia = dpnp.array(a) + iind = dpnp.array(ind) + expected = numpy.take(a, ind, mode=mode) + result = dpnp.take(ia, iind, mode=mode) + assert_array_equal(expected, result) + + +@pytest.mark.parametrize("array_type", get_all_dtypes()) @pytest.mark.parametrize( - "indices", - [[[0, 0], [0, 0]], [[1, 2], [1, 2]], [[1, 2], [3, 4]]], - ids=["[[0, 0], [0, 0]]", "[[1, 2], [1, 2]]", "[[1, 2], [3, 4]]"], + "indices_type", [numpy.int32, numpy.int64], ids=["int32", "int64"] ) @pytest.mark.parametrize( - "array", - [ - [[0, 1, 2], [3, 4, 5], [6, 7, 8]], - [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], - [[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]], - [ - [[[1, 2], [3, 4]], [[1, 2], [2, 1]]], - [[[1, 3], [3, 1]], [[0, 1], [1, 3]]], - ], - [ - [[[1, 2, 3], [3, 4, 5]], [[1, 2, 3], [2, 1, 0]]], - [[[1, 3, 5], [3, 1, 0]], [[0, 1, 2], [1, 3, 4]]], - ], - [ - [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]], - [[[13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24]]], - ], - ], - ids=[ - "[[0, 1, 2], [3, 4, 5], [6, 7, 8]]", - "[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]", - "[[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]]", - "[[[[1, 2], [3, 4]], [[1, 2], [2, 1]]], [[[1, 3], [3, 1]], [[0, 1], [1, 3]]]]", - "[[[[1, 2, 3], [3, 4, 5]], [[1, 2, 3], [2, 1, 0]]], [[[1, 3, 5], [3, 1, 0]], [[0, 1, 2], [1, 3, 4]]]]", - "[[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]], [[[13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24]]]]", - ], + "indices", [[-1, 0], [-3, 2]], ids=["[-1, 0]", "[-3, 2]"] ) -def test_take(array, indices, array_type, indices_type): - a = numpy.array(array, dtype=array_type) +@pytest.mark.parametrize("mode", ["clip", "wrap"], ids=["clip", "wrap"]) +@pytest.mark.parametrize("axis", [0, 1], ids=["0", "1"]) +def test_take_2d(indices, array_type, indices_type, axis, mode): + a = numpy.array([[-1, 0, 1], [-2, -3, -4], [2, 3, 4]], dtype=array_type) ind = numpy.array(indices, dtype=indices_type) ia = dpnp.array(a) iind = dpnp.array(ind) - expected = numpy.take(a, ind) - result = dpnp.take(ia, iind) + expected = numpy.take(a, ind, axis=axis, mode=mode) + result = dpnp.take(ia, iind, axis=axis, mode=mode) + assert_array_equal(expected, result) + + +@pytest.mark.parametrize("array_type", get_all_dtypes()) +@pytest.mark.parametrize("indices", [[-5, 5]], ids=["[-5, 5]"]) +@pytest.mark.parametrize("mode", ["clip", "wrap"], ids=["clip", "wrap"]) +def test_take_over_index(indices, array_type, mode): + a = dpnp.array([-2, -1, 0, 1, 2], dtype=array_type) + ind = dpnp.array(indices, dtype=dpnp.int64) + expected = dpnp.array([-2, 2], dtype=a.dtype) + result = dpnp.take(a, ind, mode=mode) assert_array_equal(expected, result) diff --git a/tests/third_party/cupy/indexing_tests/test_indexing.py b/tests/third_party/cupy/indexing_tests/test_indexing.py index ce6b1e86496..9e323990891 100644 --- a/tests/third_party/cupy/indexing_tests/test_indexing.py +++ b/tests/third_party/cupy/indexing_tests/test_indexing.py @@ -28,6 +28,7 @@ def test_take_by_array(self, xp): b = xp.array([[1, 3], [2, 0]]) return a.take(b, axis=1) + @pytest.mark.usefixtures("allow_fall_back_on_numpy") @testing.numpy_cupy_array_equal() def test_take_no_axis(self, xp): a = testing.shaped_arange((2, 3, 4), xp) @@ -46,7 +47,7 @@ def test_take_index_range_overflow(self, xp, dtype): if dtype in (numpy.int32, numpy.uint32): pytest.skip() iinfo = numpy.iinfo(dtype) - a = xp.broadcast_to(xp.ones(1), (iinfo.max + 1,)) + a = xp.broadcast_to(xp.ones(1, dtype=dtype), (iinfo.max + 1,)) b = xp.array([0], dtype=dtype) return a.take(b) From c7cdd419a3708142eb9342620e9f92c010ed3def Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Mon, 31 Jul 2023 16:27:29 +0200 Subject: [PATCH 13/58] Upgraded flake8 and black to newer version (#1505) --- .pre-commit-config.yaml | 4 ++-- dpnp/dpnp_iface_indexing.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f4ea0f1b139..a8823fa7421 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -43,7 +43,7 @@ repos: - id: rst-inline-touching-normal - id: text-unicode-replacement-char - repo: https://github.com/psf/black - rev: 23.3.0 + rev: 23.7.0 hooks: - id: black args: ["--check", "--diff", "--color"] @@ -59,7 +59,7 @@ repos: name: isort (pyi) types: [pyi] - repo: https://github.com/pycqa/flake8 - rev: 6.0.0 + rev: 6.1.0 hooks: - id: flake8 args: ["--config=.flake8"] diff --git a/dpnp/dpnp_iface_indexing.py b/dpnp/dpnp_iface_indexing.py index 0fd3803c917..e378589b863 100644 --- a/dpnp/dpnp_iface_indexing.py +++ b/dpnp/dpnp_iface_indexing.py @@ -435,7 +435,7 @@ def put(x1, ind, v, mode="raise"): if x1_desc: if mode != "raise": pass - elif type(ind) != type(v): + elif type(ind) is not type(v): pass elif ( numpy.max(ind) >= x1_desc.size or numpy.min(ind) + x1_desc.size < 0 From 3c0f24979695ff97c1b366e9fcf6d9dc561cefea Mon Sep 17 00:00:00 2001 From: vlad-perevezentsev Date: Mon, 31 Jul 2023 21:22:56 +0200 Subject: [PATCH 14/58] Update some of the dpnp tests to run on Iris Xe (#1472) * Update tests to test_linalg * Remove skip_dtype_not_supported func * Add support complex64 for dpnp.take * Fix test_sort.py * Fix test_special.py * Use dpnp.default_float_type for test_print_dpnp_nd * A small update skipped_tests * Fix remarks --- dpnp/dpnp_array.py | 2 +- tests/skipped_tests.tbl | 37 --------------- tests/skipped_tests_gpu.tbl | 37 --------------- tests/test_amin_amax.py | 67 +++++++++------------------ tests/test_arraycreation.py | 18 ++++++-- tests/test_dparray.py | 90 ++++++++++++++++++++----------------- tests/test_fft.py | 6 +++ tests/test_histograms.py | 11 ++++- tests/test_sort.py | 29 ++++++------ tests/test_special.py | 7 +-- 10 files changed, 123 insertions(+), 181 deletions(-) diff --git a/dpnp/dpnp_array.py b/dpnp/dpnp_array.py index 75ae72a31d0..17957e15404 100644 --- a/dpnp/dpnp_array.py +++ b/dpnp/dpnp_array.py @@ -62,7 +62,7 @@ class dpnp_array: def __init__( self, shape, - dtype="f8", + dtype=None, buffer=None, offset=0, strides=None, diff --git a/tests/skipped_tests.tbl b/tests/skipped_tests.tbl index ff5494e5f71..cd00837d037 100644 --- a/tests/skipped_tests.tbl +++ b/tests/skipped_tests.tbl @@ -39,43 +39,6 @@ tests/third_party/intel/test_zero_copy_test1.py::test_dpnp_interaction_with_dpct tests/test_arraymanipulation.py::TestHstack::test_generator tests/test_arraymanipulation.py::TestVstack::test_generator -tests/test_dparray.py::test_astype[[]-float64-float64] -tests/test_dparray.py::test_astype[[]-float64-float32] -tests/test_dparray.py::test_astype[[]-float64-int64] -tests/test_dparray.py::test_astype[[]-float64-int32] -tests/test_dparray.py::test_astype[[]-float64-bool] -tests/test_dparray.py::test_astype[[]-float64-complex] -tests/test_dparray.py::test_astype[[]-float32-float64] -tests/test_dparray.py::test_astype[[]-float32-float32] -tests/test_dparray.py::test_astype[[]-float32-int64] -tests/test_dparray.py::test_astype[[]-float32-int32] -tests/test_dparray.py::test_astype[[]-float32-bool] -tests/test_dparray.py::test_astype[[]-float32-complex] -tests/test_dparray.py::test_astype[[]-int64-float64] -tests/test_dparray.py::test_astype[[]-int64-float32] -tests/test_dparray.py::test_astype[[]-int64-int64] -tests/test_dparray.py::test_astype[[]-int64-int32] -tests/test_dparray.py::test_astype[[]-int64-bool] -tests/test_dparray.py::test_astype[[]-int64-complex] -tests/test_dparray.py::test_astype[[]-int32-float64] -tests/test_dparray.py::test_astype[[]-int32-float32] -tests/test_dparray.py::test_astype[[]-int32-int64] -tests/test_dparray.py::test_astype[[]-int32-int32] -tests/test_dparray.py::test_astype[[]-int32-bool] -tests/test_dparray.py::test_astype[[]-int32-complex] -tests/test_dparray.py::test_astype[[]-bool-float64] -tests/test_dparray.py::test_astype[[]-bool-float32] -tests/test_dparray.py::test_astype[[]-bool-int64] -tests/test_dparray.py::test_astype[[]-bool-int32] -tests/test_dparray.py::test_astype[[]-bool-bool] -tests/test_dparray.py::test_astype[[]-bool-complex] -tests/test_dparray.py::test_astype[[]-complex-float64] -tests/test_dparray.py::test_astype[[]-complex-float32] -tests/test_dparray.py::test_astype[[]-complex-int64] -tests/test_dparray.py::test_astype[[]-complex-int32] -tests/test_dparray.py::test_astype[[]-complex-bool] -tests/test_dparray.py::test_astype[[]-complex-complex] - tests/test_linalg.py::test_cond[-1-[[1, 2, 3], [4, 5, 6], [7, 8, 9]]] tests/test_linalg.py::test_cond[1-[[1, 2, 3], [4, 5, 6], [7, 8, 9]]] tests/test_linalg.py::test_cond[-2-[[1, 0, -1], [0, 1, 0], [1, 0, 1]]] diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index 4fc12eff1ab..d0a477666ef 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -239,43 +239,6 @@ tests/third_party/intel/test_zero_copy_test1.py::test_dpnp_interaction_with_dpct tests/test_arraymanipulation.py::TestHstack::test_generator tests/test_arraymanipulation.py::TestVstack::test_generator -tests/test_dparray.py::test_astype[[]-float64-float64] -tests/test_dparray.py::test_astype[[]-float64-float32] -tests/test_dparray.py::test_astype[[]-float64-int64] -tests/test_dparray.py::test_astype[[]-float64-int32] -tests/test_dparray.py::test_astype[[]-float64-bool] -tests/test_dparray.py::test_astype[[]-float64-complex] -tests/test_dparray.py::test_astype[[]-float32-float64] -tests/test_dparray.py::test_astype[[]-float32-float32] -tests/test_dparray.py::test_astype[[]-float32-int64] -tests/test_dparray.py::test_astype[[]-float32-int32] -tests/test_dparray.py::test_astype[[]-float32-bool] -tests/test_dparray.py::test_astype[[]-float32-complex] -tests/test_dparray.py::test_astype[[]-int64-float64] -tests/test_dparray.py::test_astype[[]-int64-float32] -tests/test_dparray.py::test_astype[[]-int64-int64] -tests/test_dparray.py::test_astype[[]-int64-int32] -tests/test_dparray.py::test_astype[[]-int64-bool] -tests/test_dparray.py::test_astype[[]-int64-complex] -tests/test_dparray.py::test_astype[[]-int32-float64] -tests/test_dparray.py::test_astype[[]-int32-float32] -tests/test_dparray.py::test_astype[[]-int32-int64] -tests/test_dparray.py::test_astype[[]-int32-int32] -tests/test_dparray.py::test_astype[[]-int32-bool] -tests/test_dparray.py::test_astype[[]-int32-complex] -tests/test_dparray.py::test_astype[[]-bool-float64] -tests/test_dparray.py::test_astype[[]-bool-float32] -tests/test_dparray.py::test_astype[[]-bool-int64] -tests/test_dparray.py::test_astype[[]-bool-int32] -tests/test_dparray.py::test_astype[[]-bool-bool] -tests/test_dparray.py::test_astype[[]-bool-complex] -tests/test_dparray.py::test_astype[[]-complex-float64] -tests/test_dparray.py::test_astype[[]-complex-float32] -tests/test_dparray.py::test_astype[[]-complex-int64] -tests/test_dparray.py::test_astype[[]-complex-int32] -tests/test_dparray.py::test_astype[[]-complex-bool] -tests/test_dparray.py::test_astype[[]-complex-complex] - tests/test_linalg.py::test_cond[-1-[[1, 2, 3], [4, 5, 6], [7, 8, 9]]] tests/test_linalg.py::test_cond[1-[[1, 2, 3], [4, 5, 6], [7, 8, 9]]] tests/test_linalg.py::test_cond[-2-[[1, 0, -1], [0, 1, 0], [1, 0, 1]]] diff --git a/tests/test_amin_amax.py b/tests/test_amin_amax.py index 832a721d97c..7c5bb8b1b50 100644 --- a/tests/test_amin_amax.py +++ b/tests/test_amin_amax.py @@ -1,61 +1,46 @@ import numpy import pytest +from numpy.testing import assert_allclose, assert_array_equal import dpnp +from .helper import get_all_dtypes -@pytest.mark.parametrize("type", [numpy.float64], ids=["float64"]) -def test_amax_float64(type): + +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) +def test_amax(dtype): a = numpy.array( [ [[-2.0, 3.0], [9.1, 0.2]], [[-2.0, 5.0], [-2, -1.2]], [[1.0, -2.0], [5.0, -1.1]], - ] + ], + dtype=dtype, ) ia = dpnp.array(a) for axis in range(len(a)): result = dpnp.amax(ia, axis=axis) expected = numpy.amax(a, axis=axis) - numpy.testing.assert_array_equal(expected, result) - - -@pytest.mark.parametrize("type", [numpy.int64], ids=["int64"]) -def test_amax_int(type): - a = numpy.array([1, 0, 2, -3, -1, 2, 21, -9]) - ia = dpnp.array(a) + assert_allclose(expected, result) - result = dpnp.amax(ia) - expected = numpy.amax(a) - numpy.testing.assert_array_equal(expected, result) - -@pytest.mark.parametrize("type", [numpy.float64], ids=["float64"]) -def test_amin_float64(type): +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) +def test_amin(dtype): a = numpy.array( [ [[-2.0, 3.0], [9.1, 0.2]], [[-2.0, 5.0], [-2, -1.2]], [[1.0, -2.0], [5.0, -1.1]], - ] + ], + dtype=dtype, ) ia = dpnp.array(a) for axis in range(len(a)): result = dpnp.amin(ia, axis=axis) expected = numpy.amin(a, axis=axis) - numpy.testing.assert_array_equal(expected, result) - - -@pytest.mark.parametrize("type", [numpy.int64], ids=["int64"]) -def test_amin_int(type): - a = numpy.array([1, 0, 2, -3, -1, 2, 21, -9]) - ia = dpnp.array(a) - - result = dpnp.amin(ia) - expected = numpy.amin(a) - numpy.testing.assert_array_equal(expected, result) + assert_allclose(expected, result) def _get_min_max_input(type, shape): @@ -71,22 +56,18 @@ def _get_min_max_input(type, shape): @pytest.mark.usefixtures("allow_fall_back_on_numpy") -@pytest.mark.parametrize( - "type", - [numpy.float64, numpy.float32, numpy.int64, numpy.int32], - ids=["float64", "float32", "int64", "int32"], -) +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) @pytest.mark.parametrize( "shape", [(4,), (2, 3), (4, 5, 6)], ids=["(4,)", "(2,3)", "(4,5,6)"] ) -def test_amax(type, shape): - a = _get_min_max_input(type, shape) +def test_amax_diff_shape(dtype, shape): + a = _get_min_max_input(dtype, shape) ia = dpnp.array(a) np_res = numpy.amax(a) dpnp_res = dpnp.amax(ia) - numpy.testing.assert_array_equal(dpnp_res, np_res) + assert_array_equal(dpnp_res, np_res) np_res = a.max() dpnp_res = ia.max() @@ -94,23 +75,19 @@ def test_amax(type, shape): @pytest.mark.usefixtures("allow_fall_back_on_numpy") -@pytest.mark.parametrize( - "type", - [numpy.float64, numpy.float32, numpy.int64, numpy.int32], - ids=["float64", "float32", "int64", "int32"], -) +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) @pytest.mark.parametrize( "shape", [(4,), (2, 3), (4, 5, 6)], ids=["(4,)", "(2,3)", "(4,5,6)"] ) -def test_amin(type, shape): - a = _get_min_max_input(type, shape) +def test_amin_diff_shape(dtype, shape): + a = _get_min_max_input(dtype, shape) ia = dpnp.array(a) np_res = numpy.amin(a) dpnp_res = dpnp.amin(ia) - numpy.testing.assert_array_equal(dpnp_res, np_res) + assert_array_equal(dpnp_res, np_res) np_res = a.min() dpnp_res = ia.min() - numpy.testing.assert_array_equal(dpnp_res, np_res) + assert_array_equal(dpnp_res, np_res) diff --git a/tests/test_arraycreation.py b/tests/test_arraycreation.py index adde904ca42..f846a74ef38 100644 --- a/tests/test_arraycreation.py +++ b/tests/test_arraycreation.py @@ -14,7 +14,10 @@ import dpnp -from .helper import get_all_dtypes +from .helper import ( + get_all_dtypes, + has_support_aspect64, +) @pytest.mark.parametrize( @@ -115,7 +118,12 @@ def test_eye(N, M, k, dtype, order): @pytest.mark.usefixtures("allow_fall_back_on_numpy") -@pytest.mark.parametrize("dtype", get_all_dtypes(no_float16=False)) +@pytest.mark.parametrize( + "dtype", + get_all_dtypes( + no_float16=False, no_none=False if has_support_aspect64() else True + ), +) def test_frombuffer(dtype): buffer = b"12345678ABCDEF00" func = lambda xp: xp.frombuffer(buffer, dtype=dtype) @@ -607,7 +615,11 @@ def test_linspace(start, stop, num, dtype): if numpy.issubdtype(dtype, dpnp.integer): assert_allclose(func(numpy), func(dpnp), rtol=1) else: - assert_allclose(func(numpy), func(dpnp), atol=numpy.finfo(dtype).eps) + if dtype is None and not has_support_aspect64(): + dtype = dpnp.float32 + assert_allclose( + func(numpy), func(dpnp), rtol=1e-06, atol=numpy.finfo(dtype).eps + ) @pytest.mark.parametrize( diff --git a/tests/test_dparray.py b/tests/test_dparray.py index 051f449b856..47d8c5ca931 100644 --- a/tests/test_dparray.py +++ b/tests/test_dparray.py @@ -1,11 +1,16 @@ import dpctl.tensor as dpt import numpy import pytest -from numpy.testing import assert_array_equal +from numpy.testing import assert_allclose, assert_array_equal import dpnp -from .helper import get_all_dtypes +from .helper import ( + get_all_dtypes, + get_complex_dtypes, + get_float_dtypes, + has_support_aspect64, +) @pytest.mark.parametrize("res_dtype", get_all_dtypes()) @@ -20,7 +25,7 @@ def test_astype(arr, arr_dtype, res_dtype): dpnp_array = dpnp.array(numpy_array) expected = numpy_array.astype(res_dtype) result = dpnp_array.astype(res_dtype) - assert_array_equal(expected, result) + assert_allclose(expected, result) @pytest.mark.parametrize("arr_dtype", get_all_dtypes()) @@ -100,30 +105,27 @@ def test_print_dpnp_int(): assert result == expected -def test_print_dpnp_float(): - result = repr(dpnp.array([1, -1, 21], dtype=float)) +@pytest.mark.parametrize("dtype", get_float_dtypes()) +def test_print_dpnp_float(dtype): + result = repr(dpnp.array([1, -1, 21], dtype=dtype)) expected = "array([ 1., -1., 21.])" - assert result == expected - - result = str(dpnp.array([1, -1, 21], dtype=float)) - expected = "[ 1. -1. 21.]" - assert result == expected - # float32 - result = repr(dpnp.array([1, -1, 21], dtype=dpnp.float32)) - expected = "array([ 1., -1., 21.], dtype=float32)" - assert result == expected + if dtype is dpnp.float32: + expected = expected[:-1] + ", dtype=float32)" - result = str(dpnp.array([1, -1, 21], dtype=dpnp.float32)) + result = str(dpnp.array([1, -1, 21], dtype=dtype)) expected = "[ 1. -1. 21.]" assert result == expected -def test_print_dpnp_complex(): - result = repr(dpnp.array([1, -1, 21], dtype=complex)) +@pytest.mark.parametrize("dtype", get_complex_dtypes()) +def test_print_dpnp_complex(dtype): + result = repr(dpnp.array([1, -1, 21], dtype=dtype)) expected = "array([ 1.+0.j, -1.+0.j, 21.+0.j])" + if dtype is dpnp.complex64: + expected = expected[:-1] + ", dtype=complex64)" assert result == expected - result = str(dpnp.array([1, -1, 21], dtype=complex)) + result = str(dpnp.array([1, -1, 21], dtype=dtype)) expected = "[ 1.+0.j -1.+0.j 21.+0.j]" assert result == expected @@ -138,49 +140,55 @@ def test_print_dpnp_boolean(): assert result == expected -def test_print_dpnp_special_character(): - # NaN - result = repr(dpnp.array([1.0, 0.0, dpnp.nan, 3.0])) - expected = "array([ 1., 0., nan, 3.])" - assert result == expected - - result = str(dpnp.array([1.0, 0.0, dpnp.nan, 3.0])) - expected = "[ 1. 0. nan 3.]" - assert result == expected - # inf - result = repr(dpnp.array([1.0, 0.0, dpnp.inf, 3.0])) - expected = "array([ 1., 0., inf, 3.])" +@pytest.mark.parametrize( + "character", [dpnp.nan, dpnp.inf], ids=["dpnp.nan", "dpnp.inf"] +) +def test_print_dpnp_special_character(character): + result = repr(dpnp.array([1.0, 0.0, character, 3.0])) + expected = f"array([ 1., 0., {character}, 3.])" + if not has_support_aspect64(): + expected = expected[:-1] + ", dtype=float32)" assert result == expected - result = str(dpnp.array([1.0, 0.0, dpnp.inf, 3.0])) - expected = "[ 1. 0. inf 3.]" + result = str(dpnp.array([1.0, 0.0, character, 3.0])) + expected = f"[ 1. 0. {character} 3.]" assert result == expected -def test_print_dpnp_nd(): - # 1D - result = repr(dpnp.arange(10000, dtype="float32")) - expected = "array([0.000e+00, 1.000e+00, 2.000e+00, ..., 9.997e+03, 9.998e+03,\n 9.999e+03], dtype=float32)" +def test_print_dpnp_1d(): + dtype = dpnp.default_float_type() + result = repr(dpnp.arange(10000, dtype=dtype)) + expected = "array([0.000e+00, 1.000e+00, 2.000e+00, ..., 9.997e+03, 9.998e+03,\n 9.999e+03])" + if not has_support_aspect64(): + expected = expected[:-1] + ", dtype=float32)" assert result == expected - result = str(dpnp.arange(10000, dtype="float32")) + result = str(dpnp.arange(10000, dtype=dtype)) expected = ( "[0.000e+00 1.000e+00 2.000e+00 ... 9.997e+03 9.998e+03 9.999e+03]" ) assert result == expected - # 2D - result = repr(dpnp.array([[1, 2], [3, 4]], dtype=float)) + +def test_print_dpnp_2d(): + dtype = dpnp.default_float_type() + result = repr(dpnp.array([[1, 2], [3, 4]], dtype=dtype)) expected = "array([[1., 2.],\n [3., 4.]])" + if not has_support_aspect64(): + expected = expected[:-1] + ", dtype=float32)" assert result == expected result = str(dpnp.array([[1, 2], [3, 4]])) expected = "[[1 2]\n [3 4]]" assert result == expected - # 0 shape + +def test_print_dpnp_zero_shape(): result = repr(dpnp.empty(shape=(0, 0))) - expected = "array([])" + if has_support_aspect64(): + expected = "array([])" + else: + expected = "array([], dtype=float32)" assert result == expected result = str(dpnp.empty(shape=(0, 0))) diff --git a/tests/test_fft.py b/tests/test_fft.py index 2be8e0880c3..38185f3e5f0 100644 --- a/tests/test_fft.py +++ b/tests/test_fft.py @@ -3,6 +3,12 @@ import dpnp +from .helper import has_support_aspect64 + +pytestmark = pytest.mark.skipif( + not has_support_aspect64(), reason="Aborted on Iris Xe: SAT-6028" +) + @pytest.mark.parametrize( "type", ["complex128", "complex64", "float32", "float64", "int32", "int64"] diff --git a/tests/test_histograms.py b/tests/test_histograms.py index b62f653b0c9..2fb4cd71fa6 100644 --- a/tests/test_histograms.py +++ b/tests/test_histograms.py @@ -3,6 +3,8 @@ import dpnp +from .helper import has_support_aspect64 + class TestHistogram: def setup(self): @@ -20,7 +22,14 @@ def test_simple(self): numpy.testing.assert_equal(dpnp.sum(a, axis=0), n) # check that the bin counts are evenly spaced when the data is from # a linear function - a, _ = dpnp.histogram(dpnp.linspace(0, 10, 100)) + a, _ = dpnp.histogram( + numpy.linspace( + 0, + 10, + 100, + dtype="float64" if has_support_aspect64() else "float32", + ) + ) numpy.testing.assert_array_equal(a, 10) @pytest.mark.usefixtures("allow_fall_back_on_numpy") diff --git a/tests/test_sort.py b/tests/test_sort.py index 9bf4d9adf76..7ef6c23f734 100644 --- a/tests/test_sort.py +++ b/tests/test_sort.py @@ -35,9 +35,6 @@ def test_partition(array, dtype, kth): a = dpnp.array(array, dtype) p = dpnp.partition(a, kth) - # TODO: remove once dpnp.less_equal() support complex types - p = p.asnumpy() - assert (p[..., 0:kth] <= p[..., kth : kth + 1]).all() assert (p[..., kth : kth + 1] <= p[..., kth + 1 :]).all() @@ -67,21 +64,27 @@ def test_partition(array, dtype, kth): ], ) @pytest.mark.parametrize( - "dtype", - [numpy.float64, numpy.float32, numpy.int64, numpy.int32], - ids=["float64", "float32", "int64", "int32"], + "dtype", get_all_dtypes(no_none=True, no_bool=True, no_complex=True) ) @pytest.mark.parametrize( "array", - [[1, 2, 3, 4], [-5, -1, 0, 3, 17, 100]], + [ + [1, 2, 3, 4], + [-5, -1, 0, 3, 17, 100], + [1, 0, 3, 0], + [3, 2, 1, 6], + [4, 2, 3, 3, 4, 1], + [1, -3, 3, 0, 5, 2, 0, 1, 1, 0, 0, 1], + [8, 2, 3, 0, 5, 2, 0, 1, 1, 3, 3, 1, 5, 2, 0, 1], + ], ids=[ "[1, 2, 3, 4]", - "[-5, -1, 0, 3, 17, 100]" - # '[1, 0, 3, 0]', - # '[3, 2, 1, 6]', - # '[4, 2, 3, 3, 4, 1]', - # '[1, -3, 3, 0, 5, 2, 0, 1, 1, 0, 0, 1]', - # '[8, 2, 3, 0, 5, 2, 0, 1, 1, 3, 3, 1, 5, 2, 0, 1]' + "[-5, -1, 0, 3, 17, 100]", + "[1, 0, 3, 0]", + "[3, 2, 1, 6]", + "[4, 2, 3, 3, 4, 1]", + "[1, -3, 3, 0, 5, 2, 0, 1, 1, 0, 0, 1]", + "[8, 2, 3, 0, 5, 2, 0, 1, 1, 3, 3, 1, 5, 2, 0, 1]", ], ) def test_searchsorted(array, dtype, v_, side): diff --git a/tests/test_special.py b/tests/test_special.py index c85d0234847..7d2e1b7cb7b 100644 --- a/tests/test_special.py +++ b/tests/test_special.py @@ -1,6 +1,7 @@ import math import numpy +from numpy.testing import assert_allclose import dpnp @@ -9,7 +10,7 @@ def test_erf(): a = numpy.linspace(2.0, 3.0, num=10) ia = dpnp.linspace(2.0, 3.0, num=10) - numpy.testing.assert_allclose(a, ia) + assert_allclose(a, ia) expected = numpy.empty_like(a) for idx, val in enumerate(a): @@ -17,7 +18,7 @@ def test_erf(): result = dpnp.erf(ia) - numpy.testing.assert_array_equal(result, expected) + assert_allclose(result, expected) def test_erf_fallback(): @@ -30,4 +31,4 @@ def test_erf_fallback(): result = dpnp.erf(dpa) - numpy.testing.assert_array_equal(result, expected) + assert_allclose(result, expected) From 70294a35d9b6ef001e1595b4b9af008c92635dec Mon Sep 17 00:00:00 2001 From: Natalia Polina Date: Tue, 1 Aug 2023 05:39:54 -0700 Subject: [PATCH 15/58] Reuse dpctl.tensor.isnan(), dpctl.tensor.isinf(), and dpctl.tensor.isfinite() functions. (#1504) * Reuse dpctl.tensor.isnan(), dpctl.tensor.isinf(), and dpctl.tensor.isfinite() functions. * Add more test for isnan, isinf, and isfinite functions. * Fix pre-commit * Update tests/test_logic.py * Update test_logic.py --------- Co-authored-by: Anton <100830759+antonwolfy@users.noreply.github.com> --- dpnp/dpnp_algo/dpnp_algo_logic.pxi | 48 -------- dpnp/dpnp_algo/dpnp_elementwise_common.py | 109 ++++++++++++++++++ dpnp/dpnp_iface_logic.py | 102 ++++++++-------- tests/test_logic.py | 28 ++++- .../cupy/logic_tests/test_content.py | 3 - 5 files changed, 193 insertions(+), 97 deletions(-) diff --git a/dpnp/dpnp_algo/dpnp_algo_logic.pxi b/dpnp/dpnp_algo/dpnp_algo_logic.pxi index 0d26f1ddeb5..d5cf2aa740d 100644 --- a/dpnp/dpnp_algo/dpnp_algo_logic.pxi +++ b/dpnp/dpnp_algo/dpnp_algo_logic.pxi @@ -40,9 +40,6 @@ __all__ += [ "dpnp_allclose", "dpnp_any", "dpnp_isclose", - "dpnp_isfinite", - "dpnp_isinf", - "dpnp_isnan", ] @@ -174,48 +171,3 @@ cpdef utils.dpnp_descriptor dpnp_isclose(utils.dpnp_descriptor input1, result.get_pyobj()[i] = numpy.isclose(input1.get_pyobj()[i], input2.get_pyobj()[i], rtol, atol, equal_nan) return result - - -cpdef utils.dpnp_descriptor dpnp_isfinite(utils.dpnp_descriptor input1): - input1_obj = input1.get_array() - cdef utils.dpnp_descriptor result = utils_py.create_output_descriptor_py(input1.shape, - dpnp.bool, - None, - device=input1_obj.sycl_device, - usm_type=input1_obj.usm_type, - sycl_queue=input1_obj.sycl_queue) - - for i in range(result.size): - result.get_pyobj()[i] = numpy.isfinite(input1.get_pyobj()[i]) - - return result - - -cpdef utils.dpnp_descriptor dpnp_isinf(utils.dpnp_descriptor input1): - input1_obj = input1.get_array() - cdef utils.dpnp_descriptor result = utils_py.create_output_descriptor_py(input1.shape, - dpnp.bool, - None, - device=input1_obj.sycl_device, - usm_type=input1_obj.usm_type, - sycl_queue=input1_obj.sycl_queue) - - for i in range(result.size): - result.get_pyobj()[i] = numpy.isinf(input1.get_pyobj()[i]) - - return result - - -cpdef utils.dpnp_descriptor dpnp_isnan(utils.dpnp_descriptor input1): - input1_obj = input1.get_array() - cdef utils.dpnp_descriptor result = utils_py.create_output_descriptor_py(input1.shape, - dpnp.bool, - None, - device=input1_obj.sycl_device, - usm_type=input1_obj.usm_type, - sycl_queue=input1_obj.sycl_queue) - - for i in range(result.size): - result.get_pyobj()[i] = numpy.isnan(input1.get_pyobj()[i]) - - return result diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index e7aab3ba490..c97c79ec0ad 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -49,6 +49,9 @@ "dpnp_floor_divide", "dpnp_greater", "dpnp_greater_equal", + "dpnp_isfinite", + "dpnp_isinf", + "dpnp_isnan", "dpnp_less", "dpnp_less_equal", "dpnp_log", @@ -466,6 +469,112 @@ def dpnp_greater_equal(x1, x2, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_isfinite_docstring = """ +isfinite(x, out=None, order="K") + +Checks if each element of input array is a finite number. + +Args: + x (dpnp.ndarray): + Input array, expected to have numeric data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + An array which is True where `x` is not positive infinity, + negative infinity, or NaN, False otherwise. + The data type of the returned array is `bool`. +""" + + +def dpnp_isfinite(x, out=None, order="K"): + """Invokes isfinite() from dpctl.tensor implementation for isfinite() function.""" + + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = UnaryElementwiseFunc( + "isfinite", ti._isfinite_result_type, ti._isfinite, _isfinite_docstring + ) + res_usm = func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + +_isinf_docstring = """ +isinf(x, out=None, order="K") + +Checks if each element of input array is an infinity. + +Args: + x (dpnp.ndarray): + Input array, expected to have numeric data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + An array which is True where `x` is positive or negative infinity, + False otherwise. The data type of the returned array is `bool`. +""" + + +def dpnp_isinf(x, out=None, order="K"): + """Invokes isinf() from dpctl.tensor implementation for isinf() function.""" + + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = UnaryElementwiseFunc( + "isinf", ti._isinf_result_type, ti._isinf, _isinf_docstring + ) + res_usm = func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + +_isnan_docstring = """ +isnan(x, out=None, order="K") + +Checks if each element of an input array is a NaN. + +Args: + x (dpnp.ndarray): + Input array, expected to have numeric data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + An array which is True where x is NaN, False otherwise. + The data type of the returned array is `bool`. +""" + + +def dpnp_isnan(x, out=None, order="K"): + """Invokes isnan() from dpctl.tensor implementation for isnan() function.""" + + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = UnaryElementwiseFunc( + "isnan", ti._isnan_result_type, ti._isnan, _isnan_docstring + ) + res_usm = func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _less_docstring_ = """ less(x1, x2, out=None, order="K") diff --git a/dpnp/dpnp_iface_logic.py b/dpnp/dpnp_iface_logic.py index fdf1dcb3532..f00b61f8ed2 100644 --- a/dpnp/dpnp_iface_logic.py +++ b/dpnp/dpnp_iface_logic.py @@ -51,6 +51,9 @@ dpnp_equal, dpnp_greater, dpnp_greater_equal, + dpnp_isfinite, + dpnp_isinf, + dpnp_isnan, dpnp_less, dpnp_less_equal, dpnp_logical_and, @@ -492,7 +495,9 @@ def isclose(x1, x2, rtol=1e-05, atol=1e-08, equal_nan=False): ) -def isfinite(x1, out=None, **kwargs): +def isfinite( + x, /, out=None, *, where=True, order="K", dtype=None, subok=True, **kwargs +): """ Test element-wise for finiteness (not infinity or not Not a Number). @@ -500,11 +505,10 @@ def isfinite(x1, out=None, **kwargs): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. + Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameters `where`, `dtype` and `subok` are supported with their default values. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. - Parameter ``out`` is supported only with default value ``None``. - Parameter ``where`` is supported only with default value ``True``. See Also -------- @@ -525,23 +529,27 @@ def isfinite(x1, out=None, **kwargs): -------- >>> import dpnp as np >>> x = np.array([-np.inf, 0., np.inf]) - >>> out = np.isfinite(x) - >>> [i for i in out] - [False, True, False] + >>> np.isfinite(x) + array([False, True, False]) """ - # x1_desc = dpnp.get_dpnp_descriptor(x1) - # if x1_desc and kwargs: - # if out is not None: - # pass - # else: - # return dpnp_isfinite(x1_desc).get_pyobj() - - return call_origin(numpy.isfinite, x1, out, **kwargs) + return check_nd_call_func( + numpy.isfinite, + dpnp_isfinite, + x, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, + ) -def isinf(x1, out=None, **kwargs): +def isinf( + x, /, out=None, *, where=True, order="K", dtype=None, subok=True, **kwargs +): """ Test element-wise for positive or negative infinity. @@ -549,11 +557,10 @@ def isinf(x1, out=None, **kwargs): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. + Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameters `where`, `dtype` and `subok` are supported with their default values. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. - Parameter ``out`` is supported only with default value ``None``. - Parameter ``where`` is supported only with default value ``True``. See Also -------- @@ -569,23 +576,27 @@ def isinf(x1, out=None, **kwargs): -------- >>> import dpnp as np >>> x = np.array([-np.inf, 0., np.inf]) - >>> out = np.isinf(x) - >>> [i for i in out] - [True, False, True] + >>> np.isinf(x) + array([ True, False, True]) """ - # x1_desc = dpnp.get_dpnp_descriptor(x1) - # if x1_desc and kwargs: - # if out is not None: - # pass - # else: - # return dpnp_isinf(x1_desc).get_pyobj() - - return call_origin(numpy.isinf, x1, out, **kwargs) + return check_nd_call_func( + numpy.isinf, + dpnp_isinf, + x, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, + ) -def isnan(x1, out=None, **kwargs): +def isnan( + x, /, out=None, *, where=True, order="K", dtype=None, subok=True, **kwargs +): """ Test element-wise for NaN and return result as a boolean array. @@ -593,11 +604,10 @@ def isnan(x1, out=None, **kwargs): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. + Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameters `where`, `dtype` and `subok` are supported with their default values. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. - Parameter ``out`` is supported only with default value ``None``. - Parameter ``where`` is supported only with default value ``True``. See Also -------- @@ -614,20 +624,22 @@ def isnan(x1, out=None, **kwargs): -------- >>> import dpnp as np >>> x = np.array([np.inf, 0., np.nan]) - >>> out = np.isnan(x) - >>> [i for i in out] - [False, False, True] + >>> np.isnan(x) + array([False, False, True]) """ - # x1_desc = dpnp.get_dpnp_descriptor(x1) - # if x1_desc and kwargs: - # if out is not None: - # pass - # else: - # return dpnp_isnan(x1_desc).get_pyobj() - - return call_origin(numpy.isnan, x1, out, **kwargs) + return check_nd_call_func( + numpy.isnan, + dpnp_isnan, + x, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, + ) def less( diff --git a/tests/test_logic.py b/tests/test_logic.py index 11f358b3e68..7be9e6e1ac8 100644 --- a/tests/test_logic.py +++ b/tests/test_logic.py @@ -4,7 +4,11 @@ import dpnp -from .helper import get_all_dtypes, has_support_aspect64 +from .helper import ( + get_all_dtypes, + get_float_complex_dtypes, + has_support_aspect64, +) @pytest.mark.parametrize("type", get_all_dtypes()) @@ -297,3 +301,25 @@ def test_comparison_no_broadcast_with_shapes(op, sh1, sh2): with pytest.raises(ValueError): getattr(dpnp, op)(x1, x2) getattr(numpy, op)(x1.asnumpy(), x2.asnumpy()) + + +@pytest.mark.parametrize( + "op", ["isfinite", "isinf", "isnan"], ids=["isfinite", "isinf", "isnan"] +) +@pytest.mark.parametrize( + "data", + [ + [dpnp.inf, -1, 0, 1, dpnp.nan], + [[dpnp.inf, dpnp.nan], [dpnp.nan, 0], [1, dpnp.inf]], + ], + ids=[ + "[dpnp.inf, -1, 0, 1, dpnp.nan]", + "[[dpnp.inf, dpnp.nan], [dpnp.nan, 0], [1, dpnp.inf]]", + ], +) +@pytest.mark.parametrize("dtype", get_float_complex_dtypes()) +def test_finite(op, data, dtype): + x = dpnp.asarray(data, dtype=dtype) + np_res = getattr(dpnp, op)(x) + dpnp_res = getattr(numpy, op)(x.asnumpy()) + assert_equal(dpnp_res, np_res) diff --git a/tests/third_party/cupy/logic_tests/test_content.py b/tests/third_party/cupy/logic_tests/test_content.py index 6b2eb87a9fa..ac67a4eef4c 100644 --- a/tests/third_party/cupy/logic_tests/test_content.py +++ b/tests/third_party/cupy/logic_tests/test_content.py @@ -22,14 +22,11 @@ def check_unary_nan(self, name, xp, dtype): ) return getattr(xp, name)(a) - @pytest.mark.usefixtures("allow_fall_back_on_numpy") def test_isfinite(self): self.check_unary_inf("isfinite") - @pytest.mark.usefixtures("allow_fall_back_on_numpy") def test_isinf(self): self.check_unary_inf("isinf") - @pytest.mark.usefixtures("allow_fall_back_on_numpy") def test_isnan(self): self.check_unary_nan("isnan") From 6239281200c46fde833050d9aa584bf243be697e Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Tue, 1 Aug 2023 18:42:03 +0200 Subject: [PATCH 16/58] Refresh documentation (#1485) * Updated Overview page * Added Quick Start Guide * Updated developement guides * Applied review comments * Added examples * Keeping existing docs instead of full clean-up --- .github/workflows/build-sphinx.yml | 75 ++++++++++++++- CHANGELOG.md | 13 ++- doc/dpctl.rst | 26 +++-- doc/dpnp_backend_api.rst | 2 +- doc/ext_links.txt | 16 ++++ doc/index.rst | 41 +++----- doc/install.rst | 43 --------- doc/overview.rst | 40 ++++++++ doc/quick_start_guide.rst | 148 +++++++++++++++++++++++++++++ doc/reference/difference.rst | 4 +- doc/reference/index.rst | 2 +- dpnp/backend/doc/Doxyfile | 2 +- dpnp/dpnp_iface.py | 10 +- examples/example1.py | 28 +++--- examples/example10.py | 27 +++--- examples/example2.py | 37 ++------ examples/example6.py | 12 +-- examples/example7.py | 31 +++--- examples/example_bs.py | 14 +-- examples/example_cfd.py | 48 ++++++++++ examples/example_sum.py | 44 +++++++++ 21 files changed, 475 insertions(+), 188 deletions(-) create mode 100644 doc/ext_links.txt delete mode 100644 doc/install.rst create mode 100644 doc/overview.rst create mode 100644 doc/quick_start_guide.rst create mode 100644 examples/example_cfd.py create mode 100644 examples/example_sum.py diff --git a/.github/workflows/build-sphinx.yml b/.github/workflows/build-sphinx.yml index e1ad17217cc..2258287ac0f 100644 --- a/.github/workflows/build-sphinx.yml +++ b/.github/workflows/build-sphinx.yml @@ -4,6 +4,14 @@ on: branches: - master pull_request: + types: [opened, synchronize, reopened, closed] + +env: + PUBLISH_DIR: doc/_build/html/ + +defaults: + run: + shell: bash -l {0} jobs: build-and-deploy: @@ -11,10 +19,6 @@ jobs: runs-on: ubuntu-20.04 - defaults: - run: - shell: bash -l {0} - env: python-ver: '3.9' CHANNELS: '-c dppy/label/dev -c intel -c conda-forge --override-channels' @@ -116,4 +120,65 @@ jobs: uses: peaceiris/actions-gh-pages@v3.9.3 with: github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: doc/_build/html/ + publish_dir: ${{ env.PUBLISH_DIR }} + + - name: Publish pull-request docs + if: | + !github.event.pull_request.head.repo.fork && github.event.pull_request && github.event.action != 'closed' + uses: peaceiris/actions-gh-pages@v3.9.3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: ${{ env.PUBLISH_DIR }} + destination_dir: ./pull/${{ github.event.number }} + allow_empty_commit : true + keep_files: true + commit_message: ${{ github.event.head_commit.message }} + publish_branch: gh-pages + user_name: 'github-actions[bot]' + user_email: 'github-actions[bot]@users.noreply.github.com' + + - name: Comment with URL to published pull-request docs + if: | + !github.event.pull_request.head.repo.fork && github.event.pull_request && github.event.action != 'closed' + env: + PR_NUM: ${{ github.event.number }} + uses: mshick/add-pr-comment@v2.8.1 + with: + message: | + View rendered docs @ https://intelpython.github.io/dpnp/pull/${{ env.PR_NUM }}/index.html + allow-repeats: false + + clean: + if: | + !github.event.pull_request.head.repo.fork && github.event.pull_request && github.event.action == 'closed' + + needs: build-and-deploy + + runs-on: ubuntu-20.04 + + steps: + - uses: actions/checkout@v3.5.2 + with: + fetch-depth: 0 + + - name: Remove docs [PR closed] + env: + PR_NUM: ${{ github.event.number }} + run: | + git remote add tokened_docs https://IntelPython:${{ secrets.GITHUB_TOKEN }}@github.com/IntelPython/dpnp.git + git fetch tokened_docs + git checkout --track tokened_docs/gh-pages + echo `pwd` + [ -d pull/${PR_NUM} ] && git rm -rf pull/${PR_NUM} + git config --global user.name 'github-actions[bot]' + git config --global user.email 'github-actions[bot]@users.noreply.github.com' + git commit -m "Removing docs for closed pull request ${PR_NUM}" + git push tokened_docs gh-pages + + - name: Modify the comment with URL to official documentation + uses: mshick/add-pr-comment@v2.8.1 + with: + find: | + View rendered docs @.+ + replace: | + View rendered docs @ https://intelpython.github.io/dpnp/index.html diff --git a/CHANGELOG.md b/CHANGELOG.md index e212880a1ef..01911ea4911 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,14 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [0.12.2] - TBA + +### Added + +### Changed + +### Fixed + ## [0.12.1] - 07/18/2023 ### Added @@ -23,11 +31,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 * Added a specialized kernel for `dpnp.sum` with `axis=0` as a pybind11 extension [#1479](https://github.com/IntelPython/dpnp/pull/1479) * Redesigned `dpnp.square` function through pybind11 extension of OneMKL call where possible or Leveraging on `dpctl.tensor` implementation [#1473](https://github.com/IntelPython/dpnp/pull/1473) * Redesigned `dpnp.cos` and `dpnp.sin` functions through pybind11 extension of OneMKL calls where possible or Leveraging on `dpctl.tensor` implementation [#1471](https://github.com/IntelPython/dpnp/pull/1471) -* Redesigned `dpnp.sqrt` function through pybind11 extension of OneMKL call where possible or Leveraging on `dpctl.tensor` implementation [#1470](https://github.com/IntelPython/dpnp/pull/1470) -* Redesigned `dpnp.log` function through pybind11 extension of OneMKL call where possible or Leveraging on `dpctl.tensor` implementation [#1469](https://github.com/IntelPython/dpnp/pull/1469) +* Redesigned `dpnp.sqrt` function through pybind11 extension of OneMKL call where possible or leveraging on `dpctl.tensor` implementation [#1470](https://github.com/IntelPython/dpnp/pull/1470) +* Redesigned `dpnp.log` function through pybind11 extension of OneMKL call where possible or leveraging on `dpctl.tensor` implementation [#1469](https://github.com/IntelPython/dpnp/pull/1469) * Leveraged `dpctl.tensor` implementation for logical operations [#1464](https://github.com/IntelPython/dpnp/pull/1464) * Leveraged `dpctl.tensor` implementation for `dpnp.floor_divide` function [#1462](https://github.com/IntelPython/dpnp/pull/1462) * Leveraged `dpctl.tensor` implementation for comparison functions [#1458](https://github.com/IntelPython/dpnp/pull/1458) + ### Fixed * Improved `dpnp.dot` function to support OneMKL calls for input and output arrays with strides [#1477](https://github.com/IntelPython/dpnp/pull/1477) diff --git a/doc/dpctl.rst b/doc/dpctl.rst index 1d6b42b0b82..2df63e0ce49 100644 --- a/doc/dpctl.rst +++ b/doc/dpctl.rst @@ -1,10 +1,19 @@ -DPCtl Usage -=========== +.. _dptcl: +.. include:: ./ext_links.txt -`DPCtl `_ provides API to manage -specific SYCL resources for SYCL-based Python packages. DPNP uses DPCtl as -a global SYCL queue manager. Below code illustrates simple usage of DPNP -in combination with dpCtl. +Interplay with the Data Parallel Control Library +=============================================== + +`Data Parallel Control Library`_ provides API to manage specific +`SYCL*`_ resources for SYCL-based Python packages. + +An example below demonstrates how the Data Parallel Extension for NumPy* can be +easily combined with the device management interface provided by dpctl package. + +Literally, the SYCL* queue manager interface from the dpctl package allows +to set an input queue as the currently usable queue inside the context +manager's scope. This way an array creation function from the dpnp package +which is defined inside the context will allocate the data using that queue. .. code-block:: python :linenos: @@ -16,10 +25,11 @@ in combination with dpCtl. x = np.array([1, 2, 3]) s = np.sum(x) -For more information please refer to `DPCtl's documentation `_. +For more information please refer to `Data Parallel Control Library`_ +documentation. Example -~~~~~~~ +------- .. literalinclude:: ../examples/example10.py :linenos: :language: python diff --git a/doc/dpnp_backend_api.rst b/doc/dpnp_backend_api.rst index e0c78ba9b62..56256c93935 100644 --- a/doc/dpnp_backend_api.rst +++ b/doc/dpnp_backend_api.rst @@ -4,4 +4,4 @@ C++ backend API Reference ************************* -`DPNP C++ backend `_ +`C++ backend of Data Parallel Extension for NumPy* `_ diff --git a/doc/ext_links.txt b/doc/ext_links.txt new file mode 100644 index 00000000000..d7a858723fc --- /dev/null +++ b/doc/ext_links.txt @@ -0,0 +1,16 @@ +.. + ********************************************************** + THESE ARE EXTERNAL PROJECT LINKS USED IN THE DOCUMENTATION + ********************************************************** + +.. _NumPy*: https://numpy.org/ +.. _Python* Array API Standard: https://data-apis.org/array-api/ +.. _OpenCl*: https://www.khronos.org/opencl/ +.. _oneAPI Level Zero: https://spec.oneapi.io/level-zero/latest/index.html +.. _DPC++: https://www.apress.com/gp/book/9781484255735 +.. _SYCL*: https://www.khronos.org/sycl/ +.. _dpctl: https://intelpython.github.io/dpctl/latest/index.html +.. _Data Parallel Control Library: https://intelpython.github.io/dpctl/latest/index.html +.. _Intel oneAPI Base Toolkit: https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html +.. _Intel Distribution for Python*: https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html +.. _Intel AI Analytics Toolkit: https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html diff --git a/doc/index.rst b/doc/index.rst index f6a33e9a58c..b34e2d15ab3 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -1,37 +1,20 @@ -========================================================= -DPNP -- A NumPy-compatible library for SYCL-based devices -========================================================= +.. _index: +.. include:: ./ext_links.txt -.. module:: dpnp - -`DPNP `_ is a NumPy-like library accelerated with SYCL on Intel devices. -It provides Python interfaces for many NumPy functions, and includes a subset of methods of :class:`dpnp.ndarray`. -Under the hood it is based on native C++ and oneMKL based kernels. - -Being drop-in replacement for Numpy its usage is very similar to Numpy: - - >>> import dpnp as np - -The :class:`dpnp.ndarray` class is a compatible alternative of :class:`numpy.ndarray`. - - >>> x = np.array([1, 2, 3]) - -``x`` in the above example is an instance of :class:`dpnp.ndarray` that is created identically to ``NumPy``'s one. -The key difference of :class:`dpnp.ndarray` from :class:`numpy.ndarray` is -that the memory is allocated on Intel GPU when setting up ``DPNP_QUEUE_GPU=1`` in the environment. +Data Parallel Extension for NumPy* +================================== - -Most of the array manipulations are also done in the way similar to NumPy such as: - - >>> s = np.sum(x) - -Please see the :ref:`API Reference ` for the complete list of supported NumPy APIs -along with their limitations. +.. module:: dpnp .. toctree:: :maxdepth: 2 - install + overview + quick_start_guide reference/index + +.. toctree:: + :maxdepth: 1 + :caption: Development information + dpnp_backend_api - dpctl diff --git a/doc/install.rst b/doc/install.rst deleted file mode 100644 index 01dd2e8df02..00000000000 --- a/doc/install.rst +++ /dev/null @@ -1,43 +0,0 @@ -Installation Guide -================== - -Requirements ------------- - -The following Linux distributions are recommended. - -* `Ubuntu `_ 20.04 LTS (x86_64) - -These components must be installed to use DPNP: - -* `Python `_: v3 - -Python Dependencies -~~~~~~~~~~~~~~~~~~~ - -NumPy-compatible API in DPNP is based on `NumPy `_ 1.18+. - - -Installing DPNP from conda-forge --------------------------------- - -You can install DPNP with Conda/Anaconda from the ``intel`` channel:: - - $ conda install -c intel dpnp - -.. _install_dpnp_from_source: - -Installing DPNP from Source ---------------------------- - -You can install the latest development version of DPNP from a cloned Git repository on Linux:: - - $ git clone --recursive https://github.com/IntelPython/dpnp.git - $ cd dpnp - $ python scripts/build_locally.py - -.. note:: - - To build the source tree downloaded from GitHub, you need to install - `Intel oneAPI Toolkit `_ - and Cython (``pip install cython`` or ``conda install cython``). diff --git a/doc/overview.rst b/doc/overview.rst new file mode 100644 index 00000000000..4215566df7e --- /dev/null +++ b/doc/overview.rst @@ -0,0 +1,40 @@ +.. _overview: +.. include:: ./ext_links.txt + +Overview +======== + +.. module:: dpnp + +The Data Parallel Extension for NumPy* (dpnp package) - a library that +implements a subset of `NumPy*`_ that can be executed on any +data parallel device. The subset is a drop-in replacement of core `NumPy*`_ +functions and numerical data types. + +The Data Parallel Extension for NumPy* is being developed as part of +`Intel AI Analytics Toolkit`_ and is distributed with the +`Intel Distribution for Python*`_. The dpnp package is also available +on Anaconda cloud. Please refer the :doc:`quick_start_guide` page to learn more. + +Being drop-in replacement for `NumPy*`_ means that the usage is very similar: + + >>> import dpnp as np + +The :class:`dpnp.ndarray` class is a compatible alternative of +:class:`numpy.ndarray`. + + >>> x = np.array([1, 2, 3]) + +``x`` in the above example is an instance of :class:`dpnp.ndarray` that +is created identically to ``NumPy*``'s one. The key difference of +:class:`dpnp.ndarray` from :class:`numpy.ndarray` is that the memory +is allocated on the default `SYCL*`_ device, which is a ``"gpu"`` on systems +with integrated or discrete GPU (otherwise it is the ``"host"`` device +on systems that do not have GPU). + +Most of the array manipulations are also done in the way similar to `NumPy*`_ such as: + + >>> s = np.sum(x) + +Please see the :ref:`API Reference ` for the complete list of supported `NumPy*`_ APIs +along with their limitations. diff --git a/doc/quick_start_guide.rst b/doc/quick_start_guide.rst new file mode 100644 index 00000000000..43ea5544f7d --- /dev/null +++ b/doc/quick_start_guide.rst @@ -0,0 +1,148 @@ +.. _quick_start_guide: +.. include:: ./ext_links.txt + +.. |copy| unicode:: U+000A9 + +.. |trade| unicode:: U+2122 + +================= +Quick Start Guide +================= + +Device Drivers +================= + +To start programming data parallel devices beyond CPU, you will need +an appropriate hardware. The Data Parallel Extension for NumPy* works fine +on Intel |copy| laptops with integrated graphics. In majority of cases, +your Windows*-based laptop already has all necessary device drivers installed. +But if you want the most up-to-date driver, you can always +`update it to the latest one `_. +Follow device driver installation instructions to complete the step. + + +Python Interpreter +================= + +You will need Python 3.8, 3.9, or 3.10 installed on your system. If you +do not have one yet the easiest way to do that is to install +`Intel Distribution for Python*`_. It installs all essential Python numerical +and machine learning packages optimized for the Intel hardware, including +Data Parallel Extension for NumPy*. +If you have Python installation from another vendor, it is fine too. All you +need is to install Data Parallel Extension for NumPy* manually as shown +in the next installation section. + + +Installation +============ + +Install Package from Anaconda +--------------------- + +It is recommended to use conda packages from the ``anaconda.org/intel`` +channel. You will need one of the commands below: + +* Conda: ``conda install dpnp -c intel -c conda-forge`` + +* Pip: ``pip install -i https://pypi.anaconda.org/intel/simple dpnp`` + +These commands install dpnp package along with its dependencies, including +``dpctl`` package with `Data Parallel Control Library`_ and all required +compiler runtimes and OneMKL. + +.. note:: + Before installing with conda or pip it is strongly advised to update ``conda`` and ``pip`` to latest versions + + +Build and Install Conda Package +------------------------------- + +Alternatively you can create and activate a local conda build environment: + +.. code-block:: bash + + conda create -n build-env conda-build + conda activate build-env + +And to build dpnp package from the sources: + +.. code-block:: bash + + conda build conda-recipe -c intel -c conda-forge + +Finanly, to install the result package: + +.. code-block:: bash + + conda install dpnp -c local + + +Build and Install with scikit-build +----------------------------------- + +Another way to build and install dpnp package from the source is to use Python +``setuptools`` and ``scikit-build``. You will need to create a local conda +build environment by command below depending on hosting OS. + +On Linux: + +.. code-block:: bash + + conda create -n build-env dpctl cython dpcpp_linux-64 mkl-devel-dpcpp tbb-devel onedpl-devel cmake scikit-build ninja pytest -c intel -c conda-forge + conda activate build-env + +On Windows: + +.. code-block:: bash + + conda create -n build-env dpctl cython dpcpp_win-64 mkl-devel-dpcpp tbb-devel onedpl-devel cmake scikit-build ninja pytest -c intel -c conda-forge + conda activate build-env + +To build and install the package on Linux OS, run: + +.. code-block:: bash + + python setup.py install -- -G Ninja -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icpx + +To build and install the package on Windows OS, run: + +.. code-block:: bash + + python setup.py install -- -G Ninja -DCMAKE_C_COMPILER:PATH=icx -DCMAKE_CXX_COMPILER:PATH=icx + +Alternatively, to develop on Linux OS, you can use the driver script: + +.. code-block:: bash + + python scripts/build_locally.py + + +Testing +======= + +If you want to execute the scope of Python test suites which are available +by the source, you will need to run a command as below: + +.. code-block:: bash + + pytest -s tests + +Examples +======== + +The examples below demonstrates a simple usage of the Data Parallel Extension for NumPy* + +.. literalinclude:: ../examples/example_sum.py + :linenos: + :language: python + :lines: 35- + :caption: How to create an array and to sum the elements + +.. literalinclude:: ../examples/example_cfd.py + :linenos: + :language: python + :lines: 34- + :caption: How to create an array on the specific device type and how the next computations follow it + +More examples on how to use ``dpnp`` can be found in ``dpnp/examples``. diff --git a/doc/reference/difference.rst b/doc/reference/difference.rst index ef0eb71a88e..9f2d28f3b97 100644 --- a/doc/reference/difference.rst +++ b/doc/reference/difference.rst @@ -1,5 +1,5 @@ -Difference between DPNP and NumPy -================================= +Difference with NumPy* +====================== .. _Data types: diff --git a/doc/reference/index.rst b/doc/reference/index.rst index 40da7e3df29..2885ac86df3 100644 --- a/doc/reference/index.rst +++ b/doc/reference/index.rst @@ -4,7 +4,7 @@ API Reference ************* -This is the official DPNP API reference. +API reference of the Data Parallel Extension for NumPy* ---- diff --git a/dpnp/backend/doc/Doxyfile b/dpnp/backend/doc/Doxyfile index ec73279f6eb..2d99d18f082 100644 --- a/dpnp/backend/doc/Doxyfile +++ b/dpnp/backend/doc/Doxyfile @@ -44,7 +44,7 @@ PROJECT_NUMBER = 0.12.1 # for a project that appears at the top of each page and should give viewer a # quick idea about the purpose of the project. Keep the description short. -PROJECT_BRIEF = "NumPy-like API accelerated with SYCL" +PROJECT_BRIEF = "Data Parallel Extension for NumPy*" # With the PROJECT_LOGO tag one can specify a logo or an icon that is included # in the documentation. The maximum height of the logo should not exceed 55 diff --git a/dpnp/dpnp_iface.py b/dpnp/dpnp_iface.py index 3c80458f94a..515d3051323 100644 --- a/dpnp/dpnp_iface.py +++ b/dpnp/dpnp_iface.py @@ -334,11 +334,15 @@ def get_dpnp_descriptor( def get_include(): - r"""Return the directory that contains the DPNP C++ backend \*.h header files.""" + r""" + Return the directory that contains \*.h header files of dpnp C++ backend. - dpnp_path = os.path.join(os.path.dirname(__file__), "backend", "include") + An extension module that needs to be compiled against dpnp backend + should use this function to locate the appropriate include directory. - return dpnp_path + """ + + return os.path.join(os.path.dirname(__file__), "backend", "include") def get_normalized_queue_device(obj=None, device=None, sycl_queue=None): diff --git a/examples/example1.py b/examples/example1.py index 7d86c0a15e9..e078a02090d 100644 --- a/examples/example1.py +++ b/examples/example1.py @@ -36,21 +36,13 @@ """ -try: - import dpnp -except ImportError: - import os - import sys - - root_dir = os.path.join(os.path.dirname(__file__), os.pardir) - sys.path.append(root_dir) - - import dpnp - import time +import dpctl import numpy +import dpnp + def run_dgemm(executor, name, size, test_type, repetition): x1 = executor.reshape( @@ -75,15 +67,23 @@ def run_dgemm(executor, name, size, test_type, repetition): return (min_time, med_time, max_time), result.item(5) +def get_dtypes(): + _dtypes_list = [numpy.int32, numpy.int64, numpy.float32] + device = dpctl.select_default_device() + if device.has_aspect_fp64: + _dtypes_list.append(numpy.float64) + return _dtypes_list + + if __name__ == "__main__": test_repetition = 5 - for test_type in [numpy.float64, numpy.float32, numpy.int64, numpy.int32]: + for test_type in get_dtypes(): type_name = numpy.dtype(test_type).name print( f"...Test data type is {test_type}, each test repetitions {test_repetition}" ) - for size in [16, 32, 64, 128]: # , 256, 512, 1024, 2048, 4096]: + for size in [16, 32, 64, 128, 256, 512, 1024]: times_python, result_python = run_dgemm( numpy, "", size, test_type, test_repetition ) @@ -97,6 +97,6 @@ def run_dgemm(executor, name, size, test_type, repetition): msg = f"type:{type_name}:N:{size:4}" msg += f":__NumPy__:{times_python[1]:.3e}:(min:{times_python[0]:.3e}:max:{times_python[2]:.3e})" - msg += f":__SYCL__:{times_sycl[1]:.3e}:(min:{times_sycl[0]:.3e}:max:{times_sycl[2]:.3e})" + msg += f":__DPNP__:{times_sycl[1]:.3e}:(min:{times_sycl[0]:.3e}:max:{times_sycl[2]:.3e})" msg += f":ratio:{times_python[1]/times_sycl[1]:6.2f}:verification:{verification}" print(msg) diff --git a/examples/example10.py b/examples/example10.py index a11c4610f48..e2518423678 100644 --- a/examples/example10.py +++ b/examples/example10.py @@ -28,23 +28,16 @@ """Example 10. This example shows simple usage of the DPNP -in combination with dpCtl. +in combination with dpctl. """ import time -try: - import dpnp -except ImportError: - import os - import sys - - sys.path.insert(0, os.path.abspath(".")) - import dpnp - import numpy +import dpnp + def run(executor, size, test_type, repetition): x = executor.reshape( @@ -61,9 +54,17 @@ def run(executor, size, test_type, repetition): return numpy.median(times), result +def get_dtypes(): + _dtypes_list = [numpy.int32, numpy.int64, numpy.float32] + device = dpctl.select_default_device() + if device.has_aspect_fp64: + _dtypes_list.append(numpy.float64) + return _dtypes_list + + def example(): test_repetition = 5 - for test_type in [numpy.float64, numpy.float32, numpy.int64, numpy.int32]: + for test_type in get_dtypes(): type_name = numpy.dtype(test_type).name print( f"...Test data type is {type_name}, each test repetitions {test_repetition}" @@ -80,7 +81,7 @@ def example(): else: verification = f"({result_dpnp} != {result_numpy})" - msg = f"type:{type_name}:N:{size:4}:NumPy:{time_numpy:.3e}:SYCL:{time_dpnp:.3e}" + msg = f"type:{type_name}:N:{size:4}:NumPy:{time_numpy:.3e}:DPNP:{time_dpnp:.3e}" msg += f":ratio:{time_numpy/time_dpnp:6.2f}:verification:{verification}" print(msg) @@ -90,7 +91,7 @@ def example(): import dpctl with dpctl.device_context("opencl:gpu") as gpu_queue: - gpu_queue.get_sycl_device().dump_device_info() + gpu_queue.get_sycl_device().print_device_info() example() except ImportError: diff --git a/examples/example2.py b/examples/example2.py index f2a4d06a4dc..55408d3df49 100644 --- a/examples/example2.py +++ b/examples/example2.py @@ -36,36 +36,18 @@ """ -try: - import dpnp -except ImportError: - import os - import sys - - root_dir = os.path.join(os.path.dirname(__file__), os.pardir) - sys.path.append(root_dir) - - import dpnp - import time import numpy -common_function_one_input = numpy.sin -""" -Fixed third party function -""" - - -def get_package_specific_input_data_type(input_type, size): - return input_type.arange(size) +import dpnp -def run_third_party_function(input, repetition): +def run_third_party_function(xp, input, repetition): times = [] for _ in range(repetition): start_time = time.time() - result = common_function_one_input(input) + result = xp.sin(input) end_time = time.time() times.append(end_time - start_time) @@ -75,18 +57,19 @@ def run_third_party_function(input, repetition): if __name__ == "__main__": test_repetition = 5 - for input_type in [numpy, dpnp]: - type_name = input_type.__name__ + for xp in [numpy, dpnp]: + type_name = xp.__name__ print( f"...Test data type is {type_name}, each test repetitions {test_repetition}" ) - for size in [2048, 4096, 8192, 16384, 32768, 65536]: - input_data = get_package_specific_input_data_type(input_type, size) + for size in range(20, 25): + size = 2**size + input_data = xp.arange(size) result_time, result = run_third_party_function( - input_data, test_repetition + xp, input_data, test_repetition ) print( - f"type:{type_name}:N:{size:6}:Time:{result_time:.3e}:result:{result:.3e}" + f"type:{type_name}:N:{size:6}:Time:{result_time:.3e}:result:{result}" ) diff --git a/examples/example6.py b/examples/example6.py index 96a494bc437..5cd19651046 100644 --- a/examples/example6.py +++ b/examples/example6.py @@ -32,17 +32,7 @@ dpnp.random.randn """ - -try: - import dpnp -except ImportError: - import os - import sys - - root_dir = os.path.join(os.path.dirname(__file__), os.pardir) - sys.path.append(root_dir) - - import dpnp +import dpnp if __name__ == "__main__": # TODO diff --git a/examples/example7.py b/examples/example7.py index 1e5ee8ce596..a0df2e1eef3 100644 --- a/examples/example7.py +++ b/examples/example7.py @@ -25,7 +25,7 @@ # THE POSSIBILITY OF SUCH DAMAGE. # ***************************************************************************** -"""Example 1. +"""Example 7. This example shows simple usage of the DPNP to calculate square matrix multiplication @@ -35,22 +35,13 @@ """ - -try: - import dpnp -except ImportError: - import os - import sys - - root_dir = os.path.join(os.path.dirname(__file__), os.pardir) - sys.path.append(root_dir) - - import dpnp - import time +import dpctl import numpy +import dpnp + def run_function(executor, name, size, test_type, repetition): x = executor.reshape( @@ -69,15 +60,23 @@ def run_function(executor, name, size, test_type, repetition): return execution_time, result +def get_dtypes(): + _dtypes_list = [numpy.int32, numpy.int64, numpy.float32] + device = dpctl.select_default_device() + if device.has_aspect_fp64: + _dtypes_list.append(numpy.float64) + return _dtypes_list + + if __name__ == "__main__": test_repetition = 5 - for test_type in [numpy.float64, numpy.float32, numpy.int64, numpy.int32]: + for test_type in get_dtypes(): type_name = numpy.dtype(test_type).name print( f"...Test data type is {test_type}, each test repetitions {test_repetition}" ) - for size in [16, 32, 64, 128, 256, 512, 1024, 2048, 4096]: + for size in [256, 512, 1024, 2048, 4096, 8192]: time_python, result_python = run_function( numpy, "", size, test_type, test_repetition ) @@ -90,6 +89,6 @@ def run_function(executor, name, size, test_type, repetition): else: verification = f"({result_mkl} != {result_python})" - msg = f"type:{type_name}:N:{size:4}:NumPy:{time_python:.3e}:SYCL:{time_mkl:.3e}" + msg = f"type:{type_name}:N:{size:4}:NumPy:{time_python:.3e}:DPNP:{time_mkl:.3e}" msg += f":ratio:{time_python/time_mkl:6.2f}:verification:{verification}" print(msg) diff --git a/examples/example_bs.py b/examples/example_bs.py index db920c07ab3..488f495303f 100644 --- a/examples/example_bs.py +++ b/examples/example_bs.py @@ -32,20 +32,10 @@ """ -try: - import dpnp as np -except ImportError: - import os - import sys - - root_dir = os.path.join(os.path.dirname(__file__), os.pardir) - sys.path.append(root_dir) - - import dpnp as np - +import dpnp as np SIZE = 2**8 -DTYPE = np.float64 +DTYPE = np.default_float_type() SEED = 7777777 PL, PH = 10.0, 50.0 diff --git a/examples/example_cfd.py b/examples/example_cfd.py new file mode 100644 index 00000000000..db309065759 --- /dev/null +++ b/examples/example_cfd.py @@ -0,0 +1,48 @@ +# cython: language_level=3 +# -*- coding: utf-8 -*- +# ***************************************************************************** +# Copyright (c) 2023, Intel Corporation +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# - Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# - Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +# THE POSSIBILITY OF SUCH DAMAGE. +# ***************************************************************************** + +"""Example CFD. + +This example shows how DPNP follows compute follows data paradigm + +""" + +import dpnp as np + +x = np.empty(3) +try: + x = np.asarray([1, 2, 3], device="gpu") +except Exception: + print("GPU device is not available") + +print("Array x allocated on the device:", x.device) + +y = np.sum(x) + +print("Result y is located on the device:", y.device) # The same device as x +print("Shape of y is:", y.shape) # 0-dimensional array +print("y=", y) # Expect 6 diff --git a/examples/example_sum.py b/examples/example_sum.py new file mode 100644 index 00000000000..478194ccbe3 --- /dev/null +++ b/examples/example_sum.py @@ -0,0 +1,44 @@ +# cython: language_level=3 +# -*- coding: utf-8 -*- +# ***************************************************************************** +# Copyright (c) 2023, Intel Corporation +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# - Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# - Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +# THE POSSIBILITY OF SUCH DAMAGE. +# ***************************************************************************** + +"""Example sum. + +This example shows simple usage of the DPNP +to calculate sum of an array + +""" + +import dpnp as np + +x = np.asarray([1, 2, 3]) +print("Array x allocated on the device:", x.device) + +y = np.sum(x) + +print("Result y is located on the device:", y.device) # The same device as x +print("Shape of y is:", y.shape) # 0-dimensional array +print("y =", y) # Expect 6 From c3331726f27e511bd9f0734ef11f862cbaf3d8ca Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Wed, 2 Aug 2023 14:24:58 +0200 Subject: [PATCH 17/58] Reorder checks in build docs action (#1506) * Added print event job * Correct checks order for github.event * Dump GitHub context as action step --- .github/workflows/build-sphinx.yml | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build-sphinx.yml b/.github/workflows/build-sphinx.yml index 2258287ac0f..d9e6bad9965 100644 --- a/.github/workflows/build-sphinx.yml +++ b/.github/workflows/build-sphinx.yml @@ -29,6 +29,12 @@ jobs: with: access_token: ${{ github.token }} + - name: Dump GitHub context + env: + GITHUB_CONTEXT: ${{ toJson(github) }} + run: | + echo "$GITHUB_CONTEXT" + - name: Free Disk Space (Ubuntu) uses: jlumbroso/free-disk-space@main with: @@ -115,7 +121,7 @@ jobs: # https://github.com/marketplace/actions/github-pages-action - name: Deploy docs if: | - !github.event.pull_request.head.repo.fork && + github.event.pull_request && !github.event.pull_request.head.repo.fork && (github.ref == 'refs/heads/master' || (startsWith(github.ref, 'refs/heads/release') == true) || github.event_name == 'push' && contains(github.ref, 'refs/tags/')) uses: peaceiris/actions-gh-pages@v3.9.3 with: @@ -124,7 +130,7 @@ jobs: - name: Publish pull-request docs if: | - !github.event.pull_request.head.repo.fork && github.event.pull_request && github.event.action != 'closed' + github.event.pull_request && !github.event.pull_request.head.repo.fork && github.event.action != 'closed' uses: peaceiris/actions-gh-pages@v3.9.3 with: github_token: ${{ secrets.GITHUB_TOKEN }} @@ -139,7 +145,7 @@ jobs: - name: Comment with URL to published pull-request docs if: | - !github.event.pull_request.head.repo.fork && github.event.pull_request && github.event.action != 'closed' + github.event.pull_request && !github.event.pull_request.head.repo.fork && github.event.action != 'closed' env: PR_NUM: ${{ github.event.number }} uses: mshick/add-pr-comment@v2.8.1 @@ -150,7 +156,7 @@ jobs: clean: if: | - !github.event.pull_request.head.repo.fork && github.event.pull_request && github.event.action == 'closed' + github.event.pull_request && !github.event.pull_request.head.repo.fork && github.event.action == 'closed' needs: build-and-deploy From 5c96bf171057436e2b2a557c5e58f6eb65a6299e Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Thu, 3 Aug 2023 00:08:45 +0200 Subject: [PATCH 18/58] Add versioneer script to compute a version number (#1497) * Added versioneer * Setting env variable PROJECT_NUMBER prior building backend docs * Added setup.cfg * Resolve pre-commit faults * Reverted changes in setup.cfg to exclude perf tests * Set env variable to project number --- .flake8 | 2 +- .github/workflows/build-sphinx.yml | 9 + .github/workflows/conda-package.yml | 8 +- doc/conf.py | 8 +- dpnp/__init__.py | 5 +- dpnp/_version.py | 708 +++++++++ dpnp/backend/doc/Doxyfile | 2 +- dpnp/version.py | 34 - setup.cfg | 14 +- setup.py | 12 +- versioneer.py | 2293 +++++++++++++++++++++++++++ 11 files changed, 3046 insertions(+), 49 deletions(-) create mode 100644 dpnp/_version.py delete mode 100755 dpnp/version.py create mode 100644 versioneer.py diff --git a/.flake8 b/.flake8 index abc2ecebefd..e192455f37c 100644 --- a/.flake8 +++ b/.flake8 @@ -57,7 +57,7 @@ exclude = conda.recipe, tests/*.py, tests_external/*.py, - version.py, + versioneer.py, # Print detailed statistic if any issue detected count = True diff --git a/.github/workflows/build-sphinx.yml b/.github/workflows/build-sphinx.yml index d9e6bad9965..62b4eb00fd3 100644 --- a/.github/workflows/build-sphinx.yml +++ b/.github/workflows/build-sphinx.yml @@ -71,6 +71,8 @@ jobs: - name: Checkout repo uses: actions/checkout@v3.5.2 + with: + fetch-depth: 0 # https://github.com/marketplace/actions/setup-miniconda - name: Setup miniconda @@ -109,6 +111,13 @@ jobs: run: make html working-directory: doc + - name: Set a project number to current release + run: | + export PROJECT_NUMBER=$(git describe --tags --abbrev=0) + + echo PROJECT_NUMBER=${PROJECT_NUMBER} + echo "PROJECT_NUMBER=$PROJECT_NUMBER" >> $GITHUB_ENV + # https://github.com/marketplace/actions/doxygen-action - name: Build backend docs uses: mattnotmitt/doxygen-action@v1.9.5 diff --git a/.github/workflows/conda-package.yml b/.github/workflows/conda-package.yml index 2f0956f8cc4..49420a511dd 100644 --- a/.github/workflows/conda-package.yml +++ b/.github/workflows/conda-package.yml @@ -194,7 +194,9 @@ jobs: run: conda list - name: Smoke test - run: python -c "import dpnp, dpctl; dpctl.lsplatform()" + run: | + python -c "import dpnp, dpctl; dpctl.lsplatform()" + python -c "import dpnp; print(dpnp.__version__)" # TODO: run the whole scope once the issues on CPU are resolved - name: Run tests @@ -331,7 +333,9 @@ jobs: Get-Content -Tail 5 -Path $cl_cfg - name: Smoke test - run: python -c "import dpnp, dpctl; dpctl.lsplatform()" + run: | + python -c "import dpnp, dpctl; dpctl.lsplatform()" + python -c "import dpnp; print(dpnp.__version__)" # TODO: run the whole scope once the issues on CPU are resolved - name: Run tests diff --git a/doc/conf.py b/doc/conf.py index f5d7a5db811..f895b345d94 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -23,17 +23,17 @@ with open("reference/comparison_table.rst.inc", "w") as fd: fd.write(comparison_generator.generate()) +import dpnp # -- Project information ----------------------------------------------------- -project = "dpnp" +project = "Data Parallel Extension for NumPy" copyright = "2020-2023, Intel Corporation" author = "Intel" -# The short X.Y version -version = "0.12" +version = dpnp.__version__.strip(".dirty") # The full version, including alpha/beta/rc tags -release = "0.12.1" +release = dpnp.__version__.strip(".dirty") # -- General configuration --------------------------------------------------- diff --git a/dpnp/__init__.py b/dpnp/__init__.py index f38ae1c0b89..370393b654a 100644 --- a/dpnp/__init__.py +++ b/dpnp/__init__.py @@ -54,6 +54,9 @@ from dpnp.dpnp_iface_types import * from dpnp.dpnp_iface import * from dpnp.dpnp_iface import __all__ as _iface__all__ -from dpnp.version import __version__ +from dpnp._version import get_versions __all__ = _iface__all__ + +__version__ = get_versions()["version"] +del get_versions diff --git a/dpnp/_version.py b/dpnp/_version.py new file mode 100644 index 00000000000..ea5379d69f4 --- /dev/null +++ b/dpnp/_version.py @@ -0,0 +1,708 @@ +# This file helps to compute a version number in source trees obtained from +# git-archive tarball (such as those provided by githubs download-from-tag +# feature). Distribution tarballs (built by setup.py sdist) and build +# directories (produced by setup.py build) will contain a much shorter file +# that just contains the computed version number. + +# This file is released into the public domain. +# Generated by versioneer-0.28 +# https://github.com/python-versioneer/python-versioneer + +"""Git implementation of _version.py.""" + +import errno +import functools +import os +import re +import subprocess +import sys +from typing import Callable, Dict + + +def get_keywords(): + """Get the keywords needed to look up the version information.""" + # these strings will be replaced by git during git-archive. + # setup.py/versioneer.py will grep for the variable names, so they must + # each be defined on a line of their own. _version.py will just call + # get_keywords(). + git_refnames = "$Format:%d$" + git_full = "$Format:%H$" + git_date = "$Format:%ci$" + keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} + return keywords + + +class VersioneerConfig: + """Container for Versioneer configuration parameters.""" + + +def get_config(): + """Create, populate and return the VersioneerConfig() object.""" + # these strings are filled in when 'setup.py versioneer' creates + # _version.py + cfg = VersioneerConfig() + cfg.VCS = "git" + cfg.style = "" + cfg.tag_prefix = "" + cfg.parentdir_prefix = "dpnp-" + cfg.versionfile_source = "dpnp/_version.py" + cfg.verbose = False + return cfg + + +class NotThisMethod(Exception): + """Exception raised if a method is not valid for the current scenario.""" + + +LONG_VERSION_PY: Dict[str, str] = {} +HANDLERS: Dict[str, Dict[str, Callable]] = {} + + +def register_vcs_handler(vcs, method): # decorator + """Create decorator to mark a method as the handler of a VCS.""" + + def decorate(f): + """Store f in HANDLERS[vcs][method].""" + if vcs not in HANDLERS: + HANDLERS[vcs] = {} + HANDLERS[vcs][method] = f + return f + + return decorate + + +def run_command( + commands, args, cwd=None, verbose=False, hide_stderr=False, env=None +): + """Call the given command(s).""" + assert isinstance(commands, list) + process = None + + popen_kwargs = {} + if sys.platform == "win32": + # This hides the console window if pythonw.exe is used + startupinfo = subprocess.STARTUPINFO() + startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW + popen_kwargs["startupinfo"] = startupinfo + + for command in commands: + try: + dispcmd = str([command] + args) + # remember shell=False, so use git.cmd on windows, not just git + process = subprocess.Popen( + [command] + args, + cwd=cwd, + env=env, + stdout=subprocess.PIPE, + stderr=(subprocess.PIPE if hide_stderr else None), + **popen_kwargs, + ) + break + except OSError: + e = sys.exc_info()[1] + if e.errno == errno.ENOENT: + continue + if verbose: + print("unable to run %s" % dispcmd) + print(e) + return None, None + else: + if verbose: + print("unable to find command, tried %s" % (commands,)) + return None, None + stdout = process.communicate()[0].strip().decode() + if process.returncode != 0: + if verbose: + print("unable to run %s (error)" % dispcmd) + print("stdout was %s" % stdout) + return None, process.returncode + return stdout, process.returncode + + +def versions_from_parentdir(parentdir_prefix, root, verbose): + """Try to determine the version from the parent directory name. + + Source tarballs conventionally unpack into a directory that includes both + the project name and a version string. We will also support searching up + two directory levels for an appropriately named parent directory + """ + rootdirs = [] + + for _ in range(3): + dirname = os.path.basename(root) + if dirname.startswith(parentdir_prefix): + return { + "version": dirname[len(parentdir_prefix) :], + "full-revisionid": None, + "dirty": False, + "error": None, + "date": None, + } + rootdirs.append(root) + root = os.path.dirname(root) # up a level + + if verbose: + print( + "Tried directories %s but none started with prefix %s" + % (str(rootdirs), parentdir_prefix) + ) + raise NotThisMethod("rootdir doesn't start with parentdir_prefix") + + +@register_vcs_handler("git", "get_keywords") +def git_get_keywords(versionfile_abs): + """Extract version information from the given file.""" + # the code embedded in _version.py can just fetch the value of these + # keywords. When used from setup.py, we don't want to import _version.py, + # so we do it with a regexp instead. This function is not used from + # _version.py. + keywords = {} + try: + with open(versionfile_abs, "r") as fobj: + for line in fobj: + if line.strip().startswith("git_refnames ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["refnames"] = mo.group(1) + if line.strip().startswith("git_full ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["full"] = mo.group(1) + if line.strip().startswith("git_date ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["date"] = mo.group(1) + except OSError: + pass + return keywords + + +@register_vcs_handler("git", "keywords") +def git_versions_from_keywords(keywords, tag_prefix, verbose): + """Get version information from git keywords.""" + if "refnames" not in keywords: + raise NotThisMethod("Short version file found") + date = keywords.get("date") + if date is not None: + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + + # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant + # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 + # -like" string, which we must then edit to make compliant), because + # it's been around since git-1.5.3, and it's too difficult to + # discover which version we're using, or to work around using an + # older one. + date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + refnames = keywords["refnames"].strip() + if refnames.startswith("$Format"): + if verbose: + print("keywords are unexpanded, not using") + raise NotThisMethod("unexpanded keywords, not a git-archive tarball") + refs = {r.strip() for r in refnames.strip("()").split(",")} + # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of + # just "foo-1.0". If we see a "tag: " prefix, prefer those. + TAG = "tag: " + tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)} + if not tags: + # Either we're using git < 1.8.3, or there really are no tags. We use + # a heuristic: assume all version tags have a digit. The old git %d + # expansion behaves like git log --decorate=short and strips out the + # refs/heads/ and refs/tags/ prefixes that would let us distinguish + # between branches and tags. By ignoring refnames without digits, we + # filter out many common branch names like "release" and + # "stabilization", as well as "HEAD" and "master". + tags = {r for r in refs if re.search(r"\d", r)} + if verbose: + print("discarding '%s', no digits" % ",".join(refs - tags)) + if verbose: + print("likely tags: %s" % ",".join(sorted(tags))) + for ref in sorted(tags): + # sorting will prefer e.g. "2.0" over "2.0rc1" + if ref.startswith(tag_prefix): + r = ref[len(tag_prefix) :] + # Filter out refs that exactly match prefix or that don't start + # with a number once the prefix is stripped (mostly a concern + # when prefix is '') + if not re.match(r"\d", r): + continue + if verbose: + print("picking %s" % r) + return { + "version": r, + "full-revisionid": keywords["full"].strip(), + "dirty": False, + "error": None, + "date": date, + } + # no suitable tags, so version is "0+unknown", but full hex is still there + if verbose: + print("no suitable tags, using unknown + full revision id") + return { + "version": "0+unknown", + "full-revisionid": keywords["full"].strip(), + "dirty": False, + "error": "no suitable tags", + "date": None, + } + + +@register_vcs_handler("git", "pieces_from_vcs") +def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): + """Get version from 'git describe' in the root of the source tree. + + This only gets called if the git-archive 'subst' keywords were *not* + expanded, and _version.py hasn't already been rewritten with a short + version string, meaning we're inside a checked out source tree. + """ + GITS = ["git"] + if sys.platform == "win32": + GITS = ["git.cmd", "git.exe"] + + # GIT_DIR can interfere with correct operation of Versioneer. + # It may be intended to be passed to the Versioneer-versioned project, + # but that should not change where we get our version from. + env = os.environ.copy() + env.pop("GIT_DIR", None) + runner = functools.partial(runner, env=env) + + _, rc = runner( + GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=not verbose + ) + if rc != 0: + if verbose: + print("Directory %s not under git control" % root) + raise NotThisMethod("'git rev-parse --git-dir' returned error") + + # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] + # if there isn't one, this yields HEX[-dirty] (no NUM) + describe_out, rc = runner( + GITS, + [ + "describe", + "--tags", + "--dirty", + "--always", + "--long", + "--match", + f"{tag_prefix}[[:digit:]]*", + ], + cwd=root, + ) + # --long was added in git-1.5.5 + if describe_out is None: + raise NotThisMethod("'git describe' failed") + describe_out = describe_out.strip() + full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) + if full_out is None: + raise NotThisMethod("'git rev-parse' failed") + full_out = full_out.strip() + + pieces = {} + pieces["long"] = full_out + pieces["short"] = full_out[:7] # maybe improved later + pieces["error"] = None + + branch_name, rc = runner( + GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root + ) + # --abbrev-ref was added in git-1.6.3 + if rc != 0 or branch_name is None: + raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") + branch_name = branch_name.strip() + + if branch_name == "HEAD": + # If we aren't exactly on a branch, pick a branch which represents + # the current commit. If all else fails, we are on a branchless + # commit. + branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) + # --contains was added in git-1.5.4 + if rc != 0 or branches is None: + raise NotThisMethod("'git branch --contains' returned error") + branches = branches.split("\n") + + # Remove the first line if we're running detached + if "(" in branches[0]: + branches.pop(0) + + # Strip off the leading "* " from the list of branches. + branches = [branch[2:] for branch in branches] + if "master" in branches: + branch_name = "master" + elif not branches: + branch_name = None + else: + # Pick the first branch that is returned. Good or bad. + branch_name = branches[0] + + pieces["branch"] = branch_name + + # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] + # TAG might have hyphens. + git_describe = describe_out + + # look for -dirty suffix + dirty = git_describe.endswith("-dirty") + pieces["dirty"] = dirty + if dirty: + git_describe = git_describe[: git_describe.rindex("-dirty")] + + # now we have TAG-NUM-gHEX or HEX + + if "-" in git_describe: + # TAG-NUM-gHEX + mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe) + if not mo: + # unparsable. Maybe git-describe is misbehaving? + pieces["error"] = ( + "unable to parse git-describe output: '%s'" % describe_out + ) + return pieces + + # tag + full_tag = mo.group(1) + if not full_tag.startswith(tag_prefix): + if verbose: + fmt = "tag '%s' doesn't start with prefix '%s'" + print(fmt % (full_tag, tag_prefix)) + pieces["error"] = "tag '%s' doesn't start with prefix '%s'" % ( + full_tag, + tag_prefix, + ) + return pieces + pieces["closest-tag"] = full_tag[len(tag_prefix) :] + + # distance: number of commits since tag + pieces["distance"] = int(mo.group(2)) + + # commit: short hex revision ID + pieces["short"] = mo.group(3) + + else: + # HEX: no tags + pieces["closest-tag"] = None + out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) + pieces["distance"] = len(out.split()) # total number of commits + + # commit date: see ISO-8601 comment in git_versions_from_keywords() + date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[ + 0 + ].strip() + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + + return pieces + + +def plus_or_dot(pieces): + """Return a + if we don't already have one, else return a .""" + if "+" in pieces.get("closest-tag", ""): + return "." + return "+" + + +def render_pep440(pieces): + """Build up version string, with post-release "local version identifier". + + Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you + get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty + + Exceptions: + 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += plus_or_dot(pieces) + rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_branch(pieces): + """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . + + The ".dev0" means not master branch. Note that .dev0 sorts backwards + (a feature branch will appear "older" than the master branch). + + Exceptions: + 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0" + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def pep440_split_post(ver): + """Split pep440 version string at the post-release segment. + + Returns the release segments before the post-release and the + post-release version number (or -1 if no post-release segment is present). + """ + vc = str.split(ver, ".post") + return vc[0], int(vc[1] or 0) if len(vc) == 2 else None + + +def render_pep440_pre(pieces): + """TAG[.postN.devDISTANCE] -- No -dirty. + + Exceptions: + 1: no tags. 0.post0.devDISTANCE + """ + if pieces["closest-tag"]: + if pieces["distance"]: + # update the post release segment + tag_version, post_version = pep440_split_post(pieces["closest-tag"]) + rendered = tag_version + if post_version is not None: + rendered += ".post%d.dev%d" % ( + post_version + 1, + pieces["distance"], + ) + else: + rendered += ".post0.dev%d" % (pieces["distance"]) + else: + # no commits, use the tag as the version + rendered = pieces["closest-tag"] + else: + # exception #1 + rendered = "0.post0.dev%d" % pieces["distance"] + return rendered + + +def render_pep440_post(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX] . + + The ".dev0" means dirty. Note that .dev0 sorts backwards + (a dirty tree will appear "older" than the corresponding clean one), + but you shouldn't be releasing software with -dirty anyways. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%s" % pieces["short"] + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += "+g%s" % pieces["short"] + return rendered + + +def render_pep440_post_branch(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . + + The ".dev0" means not master branch. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%s" % pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+g%s" % pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_old(pieces): + """TAG[.postDISTANCE[.dev0]] . + + The ".dev0" means dirty. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + return rendered + + +def render_git_describe(pieces): + """TAG[-DISTANCE-gHEX][-dirty]. + + Like 'git describe --tags --dirty --always'. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"]: + rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render_git_describe_long(pieces): + """TAG-DISTANCE-gHEX[-dirty]. + + Like 'git describe --tags --dirty --always -long'. + The distance/hash is unconditional. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render(pieces, style): + """Render the given version pieces into the requested style.""" + if pieces["error"]: + return { + "version": "unknown", + "full-revisionid": pieces.get("long"), + "dirty": None, + "error": pieces["error"], + "date": None, + } + + if not style or style == "default": + style = "pep440" # the default + + if style == "pep440": + rendered = render_pep440(pieces) + elif style == "pep440-branch": + rendered = render_pep440_branch(pieces) + elif style == "pep440-pre": + rendered = render_pep440_pre(pieces) + elif style == "pep440-post": + rendered = render_pep440_post(pieces) + elif style == "pep440-post-branch": + rendered = render_pep440_post_branch(pieces) + elif style == "pep440-old": + rendered = render_pep440_old(pieces) + elif style == "git-describe": + rendered = render_git_describe(pieces) + elif style == "git-describe-long": + rendered = render_git_describe_long(pieces) + else: + raise ValueError("unknown style '%s'" % style) + + return { + "version": rendered, + "full-revisionid": pieces["long"], + "dirty": pieces["dirty"], + "error": None, + "date": pieces.get("date"), + } + + +def get_versions(): + """Get version information or return default if unable to do so.""" + # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have + # __file__, we can work backwards from there to the root. Some + # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which + # case we can only use expanded keywords. + + cfg = get_config() + verbose = cfg.verbose + + try: + return git_versions_from_keywords( + get_keywords(), cfg.tag_prefix, verbose + ) + except NotThisMethod: + pass + + try: + root = os.path.realpath(__file__) + # versionfile_source is the relative path from the top of the source + # tree (where the .git directory might live) to this file. Invert + # this to find the root from __file__. + for _ in cfg.versionfile_source.split("/"): + root = os.path.dirname(root) + except NameError: + return { + "version": "0+unknown", + "full-revisionid": None, + "dirty": None, + "error": "unable to find root of source tree", + "date": None, + } + + try: + pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) + return render(pieces, cfg.style) + except NotThisMethod: + pass + + try: + if cfg.parentdir_prefix: + return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) + except NotThisMethod: + pass + + return { + "version": "0+unknown", + "full-revisionid": None, + "dirty": None, + "error": "unable to compute version", + "date": None, + } diff --git a/dpnp/backend/doc/Doxyfile b/dpnp/backend/doc/Doxyfile index 2d99d18f082..15233d66b33 100644 --- a/dpnp/backend/doc/Doxyfile +++ b/dpnp/backend/doc/Doxyfile @@ -38,7 +38,7 @@ PROJECT_NAME = "DPNP C++ backend kernel library" # could be handy for archiving the generated documentation or if some version # control system is used. -PROJECT_NUMBER = 0.12.1 +PROJECT_NUMBER = $(PROJECT_NUMBER) # Using the PROJECT_BRIEF tag one can provide an optional one line description # for a project that appears at the top of each page and should give viewer a diff --git a/dpnp/version.py b/dpnp/version.py deleted file mode 100755 index 7a7e657e1f1..00000000000 --- a/dpnp/version.py +++ /dev/null @@ -1,34 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# ***************************************************************************** -# Copyright (c) 2016-2023, Intel Corporation -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# - Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# - Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF -# THE POSSIBILITY OF SUCH DAMAGE. -# ***************************************************************************** - -""" -DPNP version module -""" - -__version__: str = "0.12.1" - -version: str = __version__ diff --git a/setup.cfg b/setup.cfg index 592597d12d4..23e56f0c9f7 100644 --- a/setup.cfg +++ b/setup.cfg @@ -2,11 +2,17 @@ max_line_length = 120 ignore = E201 -[flake8] -max_line_length = 120 -ignore = F405 - [tool:pytest] addopts = -p no:warnings --tb=short norecursedirs = tests_perf testpaths = tests + +[versioneer] +VCS = git +versionfile_source = dpnp/_version.py +versionfile_build = dpnp/_version.py +tag_prefix = +parentdir_prefix = dpnp- + +[bdist_wheel] +universal=1 diff --git a/setup.py b/setup.py index 9b56902f359..f95260aeda2 100644 --- a/setup.py +++ b/setup.py @@ -3,14 +3,16 @@ from skbuild import setup +import versioneer + """ Get the project version """ thefile_path = os.path.abspath(os.path.dirname(__file__)) version_mod = imm.SourceFileLoader( - "version", os.path.join(thefile_path, "dpnp", "version.py") + "version", os.path.join(thefile_path, "dpnp", "_version.py") ).load_module() -__version__ = version_mod.__version__ +__version__ = version_mod.get_versions()["version"] """ Set project auxilary data like readme and licence files @@ -18,6 +20,11 @@ with open("README.md") as f: __readme_file__ = f.read() + +def _get_cmdclass(): + return versioneer.get_cmdclass() + + CLASSIFIERS = """\ Development Status :: 4 - Beta Intended Audience :: Science/Research @@ -41,6 +48,7 @@ setup( name="dpnp", version=__version__, + cmdclass=_get_cmdclass(), description="Data Parallel Extension for NumPy", long_description=__readme_file__, long_description_content_type="text/markdown", diff --git a/versioneer.py b/versioneer.py new file mode 100644 index 00000000000..9eb337e1a54 --- /dev/null +++ b/versioneer.py @@ -0,0 +1,2293 @@ +# Version: 0.28 + +"""The Versioneer - like a rocketeer, but for versions. + +The Versioneer +============== + +* like a rocketeer, but for versions! +* https://github.com/python-versioneer/python-versioneer +* Brian Warner +* License: Public Domain (Unlicense) +* Compatible with: Python 3.7, 3.8, 3.9, 3.10 and pypy3 +* [![Latest Version][pypi-image]][pypi-url] +* [![Build Status][travis-image]][travis-url] + +This is a tool for managing a recorded version number in setuptools-based +python projects. The goal is to remove the tedious and error-prone "update +the embedded version string" step from your release process. Making a new +release should be as easy as recording a new tag in your version-control +system, and maybe making new tarballs. + + +## Quick Install + +Versioneer provides two installation modes. The "classic" vendored mode installs +a copy of versioneer into your repository. The experimental build-time dependency mode +is intended to allow you to skip this step and simplify the process of upgrading. + +### Vendored mode + +* `pip install versioneer` to somewhere in your $PATH + * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is + available, so you can also use `conda install -c conda-forge versioneer` +* add a `[tool.versioneer]` section to your `pyproject.toml` or a + `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md)) + * Note that you will need to add `tomli; python_version < "3.11"` to your + build-time dependencies if you use `pyproject.toml` +* run `versioneer install --vendor` in your source tree, commit the results +* verify version information with `python setup.py version` + +### Build-time dependency mode + +* `pip install versioneer` to somewhere in your $PATH + * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is + available, so you can also use `conda install -c conda-forge versioneer` +* add a `[tool.versioneer]` section to your `pyproject.toml` or a + `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md)) +* add `versioneer` (with `[toml]` extra, if configuring in `pyproject.toml`) + to the `requires` key of the `build-system` table in `pyproject.toml`: + ```toml + [build-system] + requires = ["setuptools", "versioneer[toml]"] + build-backend = "setuptools.build_meta" + ``` +* run `versioneer install --no-vendor` in your source tree, commit the results +* verify version information with `python setup.py version` + +## Version Identifiers + +Source trees come from a variety of places: + +* a version-control system checkout (mostly used by developers) +* a nightly tarball, produced by build automation +* a snapshot tarball, produced by a web-based VCS browser, like github's + "tarball from tag" feature +* a release tarball, produced by "setup.py sdist", distributed through PyPI + +Within each source tree, the version identifier (either a string or a number, +this tool is format-agnostic) can come from a variety of places: + +* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows + about recent "tags" and an absolute revision-id +* the name of the directory into which the tarball was unpacked +* an expanded VCS keyword ($Id$, etc) +* a `_version.py` created by some earlier build step + +For released software, the version identifier is closely related to a VCS +tag. Some projects use tag names that include more than just the version +string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool +needs to strip the tag prefix to extract the version identifier. For +unreleased software (between tags), the version identifier should provide +enough information to help developers recreate the same tree, while also +giving them an idea of roughly how old the tree is (after version 1.2, before +version 1.3). Many VCS systems can report a description that captures this, +for example `git describe --tags --dirty --always` reports things like +"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the +0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has +uncommitted changes). + +The version identifier is used for multiple purposes: + +* to allow the module to self-identify its version: `myproject.__version__` +* to choose a name and prefix for a 'setup.py sdist' tarball + +## Theory of Operation + +Versioneer works by adding a special `_version.py` file into your source +tree, where your `__init__.py` can import it. This `_version.py` knows how to +dynamically ask the VCS tool for version information at import time. + +`_version.py` also contains `$Revision$` markers, and the installation +process marks `_version.py` to have this marker rewritten with a tag name +during the `git archive` command. As a result, generated tarballs will +contain enough information to get the proper version. + +To allow `setup.py` to compute a version too, a `versioneer.py` is added to +the top level of your source tree, next to `setup.py` and the `setup.cfg` +that configures it. This overrides several distutils/setuptools commands to +compute the version when invoked, and changes `setup.py build` and `setup.py +sdist` to replace `_version.py` with a small static file that contains just +the generated version data. + +## Installation + +See [INSTALL.md](./INSTALL.md) for detailed installation instructions. + +## Version-String Flavors + +Code which uses Versioneer can learn about its version string at runtime by +importing `_version` from your main `__init__.py` file and running the +`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can +import the top-level `versioneer.py` and run `get_versions()`. + +Both functions return a dictionary with different flavors of version +information: + +* `['version']`: A condensed version string, rendered using the selected + style. This is the most commonly used value for the project's version + string. The default "pep440" style yields strings like `0.11`, + `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section + below for alternative styles. + +* `['full-revisionid']`: detailed revision identifier. For Git, this is the + full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". + +* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the + commit date in ISO 8601 format. This will be None if the date is not + available. + +* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that + this is only accurate if run in a VCS checkout, otherwise it is likely to + be False or None + +* `['error']`: if the version string could not be computed, this will be set + to a string describing the problem, otherwise it will be None. It may be + useful to throw an exception in setup.py if this is set, to avoid e.g. + creating tarballs with a version string of "unknown". + +Some variants are more useful than others. Including `full-revisionid` in a +bug report should allow developers to reconstruct the exact code being tested +(or indicate the presence of local changes that should be shared with the +developers). `version` is suitable for display in an "about" box or a CLI +`--version` output: it can be easily compared against release notes and lists +of bugs fixed in various releases. + +The installer adds the following text to your `__init__.py` to place a basic +version in `YOURPROJECT.__version__`: + + from ._version import get_versions + __version__ = get_versions()['version'] + del get_versions + +## Styles + +The setup.cfg `style=` configuration controls how the VCS information is +rendered into a version string. + +The default style, "pep440", produces a PEP440-compliant string, equal to the +un-prefixed tag name for actual releases, and containing an additional "local +version" section with more detail for in-between builds. For Git, this is +TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags +--dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the +tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and +that this commit is two revisions ("+2") beyond the "0.11" tag. For released +software (exactly equal to a known tag), the identifier will only contain the +stripped tag, e.g. "0.11". + +Other styles are available. See [details.md](details.md) in the Versioneer +source tree for descriptions. + +## Debugging + +Versioneer tries to avoid fatal errors: if something goes wrong, it will tend +to return a version of "0+unknown". To investigate the problem, run `setup.py +version`, which will run the version-lookup code in a verbose mode, and will +display the full contents of `get_versions()` (including the `error` string, +which may help identify what went wrong). + +## Known Limitations + +Some situations are known to cause problems for Versioneer. This details the +most significant ones. More can be found on Github +[issues page](https://github.com/python-versioneer/python-versioneer/issues). + +### Subprojects + +Versioneer has limited support for source trees in which `setup.py` is not in +the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are +two common reasons why `setup.py` might not be in the root: + +* Source trees which contain multiple subprojects, such as + [Buildbot](https://github.com/buildbot/buildbot), which contains both + "master" and "slave" subprojects, each with their own `setup.py`, + `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI + distributions (and upload multiple independently-installable tarballs). +* Source trees whose main purpose is to contain a C library, but which also + provide bindings to Python (and perhaps other languages) in subdirectories. + +Versioneer will look for `.git` in parent directories, and most operations +should get the right version string. However `pip` and `setuptools` have bugs +and implementation details which frequently cause `pip install .` from a +subproject directory to fail to find a correct version string (so it usually +defaults to `0+unknown`). + +`pip install --editable .` should work correctly. `setup.py install` might +work too. + +Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in +some later version. + +[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking +this issue. The discussion in +[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the +issue from the Versioneer side in more detail. +[pip PR#3176](https://github.com/pypa/pip/pull/3176) and +[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve +pip to let Versioneer work correctly. + +Versioneer-0.16 and earlier only looked for a `.git` directory next to the +`setup.cfg`, so subprojects were completely unsupported with those releases. + +### Editable installs with setuptools <= 18.5 + +`setup.py develop` and `pip install --editable .` allow you to install a +project into a virtualenv once, then continue editing the source code (and +test) without re-installing after every change. + +"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a +convenient way to specify executable scripts that should be installed along +with the python package. + +These both work as expected when using modern setuptools. When using +setuptools-18.5 or earlier, however, certain operations will cause +`pkg_resources.DistributionNotFound` errors when running the entrypoint +script, which must be resolved by re-installing the package. This happens +when the install happens with one version, then the egg_info data is +regenerated while a different version is checked out. Many setup.py commands +cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into +a different virtualenv), so this can be surprising. + +[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes +this one, but upgrading to a newer version of setuptools should probably +resolve it. + + +## Updating Versioneer + +To upgrade your project to a new release of Versioneer, do the following: + +* install the new Versioneer (`pip install -U versioneer` or equivalent) +* edit `setup.cfg` and `pyproject.toml`, if necessary, + to include any new configuration settings indicated by the release notes. + See [UPGRADING](./UPGRADING.md) for details. +* re-run `versioneer install --[no-]vendor` in your source tree, to replace + `SRC/_version.py` +* commit any changed files + +## Future Directions + +This tool is designed to make it easily extended to other version-control +systems: all VCS-specific components are in separate directories like +src/git/ . The top-level `versioneer.py` script is assembled from these +components by running make-versioneer.py . In the future, make-versioneer.py +will take a VCS name as an argument, and will construct a version of +`versioneer.py` that is specific to the given VCS. It might also take the +configuration arguments that are currently provided manually during +installation by editing setup.py . Alternatively, it might go the other +direction and include code from all supported VCS systems, reducing the +number of intermediate scripts. + +## Similar projects + +* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time + dependency +* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of + versioneer +* [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools + plugin + +## License + +To make Versioneer easier to embed, all its code is dedicated to the public +domain. The `_version.py` that it creates is also in the public domain. +Specifically, both are released under the "Unlicense", as described in +https://unlicense.org/. + +[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg +[pypi-url]: https://pypi.python.org/pypi/versioneer/ +[travis-image]: +https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg +[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer + +""" +# pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring +# pylint:disable=missing-class-docstring,too-many-branches,too-many-statements +# pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error +# pylint:disable=too-few-public-methods,redefined-outer-name,consider-using-with +# pylint:disable=attribute-defined-outside-init,too-many-arguments + +import configparser +import errno +import functools +import json +import os +import re +import subprocess +import sys +from pathlib import Path +from typing import Callable, Dict + +have_tomllib = True +if sys.version_info >= (3, 11): + import tomllib +else: + try: + import tomli as tomllib + except ImportError: + have_tomllib = False + + +class VersioneerConfig: + """Container for Versioneer configuration parameters.""" + + +def get_root(): + """Get the project root directory. + + We require that all commands are run from the project root, i.e. the + directory that contains setup.py, setup.cfg, and versioneer.py . + """ + root = os.path.realpath(os.path.abspath(os.getcwd())) + setup_py = os.path.join(root, "setup.py") + versioneer_py = os.path.join(root, "versioneer.py") + if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): + # allow 'python path/to/setup.py COMMAND' + root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) + setup_py = os.path.join(root, "setup.py") + versioneer_py = os.path.join(root, "versioneer.py") + if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): + err = ( + "Versioneer was unable to run the project root directory. " + "Versioneer requires setup.py to be executed from " + "its immediate directory (like 'python setup.py COMMAND'), " + "or in a way that lets it use sys.argv[0] to find the root " + "(like 'python path/to/setup.py COMMAND')." + ) + raise VersioneerBadRootError(err) + try: + # Certain runtime workflows (setup.py install/develop in a setuptools + # tree) execute all dependencies in a single python process, so + # "versioneer" may be imported multiple times, and python's shared + # module-import table will cache the first one. So we can't use + # os.path.dirname(__file__), as that will find whichever + # versioneer.py was first imported, even in later projects. + my_path = os.path.realpath(os.path.abspath(__file__)) + me_dir = os.path.normcase(os.path.splitext(my_path)[0]) + vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0]) + if me_dir != vsr_dir and "VERSIONEER_PEP518" not in globals(): + print( + "Warning: build in %s is using versioneer.py from %s" + % (os.path.dirname(my_path), versioneer_py) + ) + except NameError: + pass + return root + + +def get_config_from_root(root): + """Read the project setup.cfg file to determine Versioneer config.""" + # This might raise OSError (if setup.cfg is missing), or + # configparser.NoSectionError (if it lacks a [versioneer] section), or + # configparser.NoOptionError (if it lacks "VCS="). See the docstring at + # the top of versioneer.py for instructions on writing your setup.cfg . + root = Path(root) + pyproject_toml = root / "pyproject.toml" + setup_cfg = root / "setup.cfg" + section = None + if pyproject_toml.exists() and have_tomllib: + try: + with open(pyproject_toml, "rb") as fobj: + pp = tomllib.load(fobj) + section = pp["tool"]["versioneer"] + except (tomllib.TOMLDecodeError, KeyError): + pass + if not section: + parser = configparser.ConfigParser() + with open(setup_cfg) as cfg_file: + parser.read_file(cfg_file) + parser.get("versioneer", "VCS") # raise error if missing + + section = parser["versioneer"] + + cfg = VersioneerConfig() + cfg.VCS = section["VCS"] + cfg.style = section.get("style", "") + cfg.versionfile_source = section.get("versionfile_source") + cfg.versionfile_build = section.get("versionfile_build") + cfg.tag_prefix = section.get("tag_prefix") + if cfg.tag_prefix in ("''", '""', None): + cfg.tag_prefix = "" + cfg.parentdir_prefix = section.get("parentdir_prefix") + cfg.verbose = section.get("verbose") + return cfg + + +class NotThisMethod(Exception): + """Exception raised if a method is not valid for the current scenario.""" + + +# these dictionaries contain VCS-specific tools +LONG_VERSION_PY: Dict[str, str] = {} +HANDLERS: Dict[str, Dict[str, Callable]] = {} + + +def register_vcs_handler(vcs, method): # decorator + """Create decorator to mark a method as the handler of a VCS.""" + + def decorate(f): + """Store f in HANDLERS[vcs][method].""" + HANDLERS.setdefault(vcs, {})[method] = f + return f + + return decorate + + +def run_command( + commands, args, cwd=None, verbose=False, hide_stderr=False, env=None +): + """Call the given command(s).""" + assert isinstance(commands, list) + process = None + + popen_kwargs = {} + if sys.platform == "win32": + # This hides the console window if pythonw.exe is used + startupinfo = subprocess.STARTUPINFO() + startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW + popen_kwargs["startupinfo"] = startupinfo + + for command in commands: + try: + dispcmd = str([command] + args) + # remember shell=False, so use git.cmd on windows, not just git + process = subprocess.Popen( + [command] + args, + cwd=cwd, + env=env, + stdout=subprocess.PIPE, + stderr=(subprocess.PIPE if hide_stderr else None), + **popen_kwargs, + ) + break + except OSError: + e = sys.exc_info()[1] + if e.errno == errno.ENOENT: + continue + if verbose: + print("unable to run %s" % dispcmd) + print(e) + return None, None + else: + if verbose: + print("unable to find command, tried %s" % (commands,)) + return None, None + stdout = process.communicate()[0].strip().decode() + if process.returncode != 0: + if verbose: + print("unable to run %s (error)" % dispcmd) + print("stdout was %s" % stdout) + return None, process.returncode + return stdout, process.returncode + + +LONG_VERSION_PY[ + "git" +] = r''' +# This file helps to compute a version number in source trees obtained from +# git-archive tarball (such as those provided by githubs download-from-tag +# feature). Distribution tarballs (built by setup.py sdist) and build +# directories (produced by setup.py build) will contain a much shorter file +# that just contains the computed version number. + +# This file is released into the public domain. +# Generated by versioneer-0.28 +# https://github.com/python-versioneer/python-versioneer + +"""Git implementation of _version.py.""" + +import errno +import os +import re +import subprocess +import sys +from typing import Callable, Dict +import functools + + +def get_keywords(): + """Get the keywords needed to look up the version information.""" + # these strings will be replaced by git during git-archive. + # setup.py/versioneer.py will grep for the variable names, so they must + # each be defined on a line of their own. _version.py will just call + # get_keywords(). + git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" + git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" + git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s" + keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} + return keywords + + +class VersioneerConfig: + """Container for Versioneer configuration parameters.""" + + +def get_config(): + """Create, populate and return the VersioneerConfig() object.""" + # these strings are filled in when 'setup.py versioneer' creates + # _version.py + cfg = VersioneerConfig() + cfg.VCS = "git" + cfg.style = "%(STYLE)s" + cfg.tag_prefix = "%(TAG_PREFIX)s" + cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" + cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" + cfg.verbose = False + return cfg + + +class NotThisMethod(Exception): + """Exception raised if a method is not valid for the current scenario.""" + + +LONG_VERSION_PY: Dict[str, str] = {} +HANDLERS: Dict[str, Dict[str, Callable]] = {} + + +def register_vcs_handler(vcs, method): # decorator + """Create decorator to mark a method as the handler of a VCS.""" + def decorate(f): + """Store f in HANDLERS[vcs][method].""" + if vcs not in HANDLERS: + HANDLERS[vcs] = {} + HANDLERS[vcs][method] = f + return f + return decorate + + +def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, + env=None): + """Call the given command(s).""" + assert isinstance(commands, list) + process = None + + popen_kwargs = {} + if sys.platform == "win32": + # This hides the console window if pythonw.exe is used + startupinfo = subprocess.STARTUPINFO() + startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW + popen_kwargs["startupinfo"] = startupinfo + + for command in commands: + try: + dispcmd = str([command] + args) + # remember shell=False, so use git.cmd on windows, not just git + process = subprocess.Popen([command] + args, cwd=cwd, env=env, + stdout=subprocess.PIPE, + stderr=(subprocess.PIPE if hide_stderr + else None), **popen_kwargs) + break + except OSError: + e = sys.exc_info()[1] + if e.errno == errno.ENOENT: + continue + if verbose: + print("unable to run %%s" %% dispcmd) + print(e) + return None, None + else: + if verbose: + print("unable to find command, tried %%s" %% (commands,)) + return None, None + stdout = process.communicate()[0].strip().decode() + if process.returncode != 0: + if verbose: + print("unable to run %%s (error)" %% dispcmd) + print("stdout was %%s" %% stdout) + return None, process.returncode + return stdout, process.returncode + + +def versions_from_parentdir(parentdir_prefix, root, verbose): + """Try to determine the version from the parent directory name. + + Source tarballs conventionally unpack into a directory that includes both + the project name and a version string. We will also support searching up + two directory levels for an appropriately named parent directory + """ + rootdirs = [] + + for _ in range(3): + dirname = os.path.basename(root) + if dirname.startswith(parentdir_prefix): + return {"version": dirname[len(parentdir_prefix):], + "full-revisionid": None, + "dirty": False, "error": None, "date": None} + rootdirs.append(root) + root = os.path.dirname(root) # up a level + + if verbose: + print("Tried directories %%s but none started with prefix %%s" %% + (str(rootdirs), parentdir_prefix)) + raise NotThisMethod("rootdir doesn't start with parentdir_prefix") + + +@register_vcs_handler("git", "get_keywords") +def git_get_keywords(versionfile_abs): + """Extract version information from the given file.""" + # the code embedded in _version.py can just fetch the value of these + # keywords. When used from setup.py, we don't want to import _version.py, + # so we do it with a regexp instead. This function is not used from + # _version.py. + keywords = {} + try: + with open(versionfile_abs, "r") as fobj: + for line in fobj: + if line.strip().startswith("git_refnames ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["refnames"] = mo.group(1) + if line.strip().startswith("git_full ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["full"] = mo.group(1) + if line.strip().startswith("git_date ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["date"] = mo.group(1) + except OSError: + pass + return keywords + + +@register_vcs_handler("git", "keywords") +def git_versions_from_keywords(keywords, tag_prefix, verbose): + """Get version information from git keywords.""" + if "refnames" not in keywords: + raise NotThisMethod("Short version file found") + date = keywords.get("date") + if date is not None: + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + + # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant + # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601 + # -like" string, which we must then edit to make compliant), because + # it's been around since git-1.5.3, and it's too difficult to + # discover which version we're using, or to work around using an + # older one. + date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + refnames = keywords["refnames"].strip() + if refnames.startswith("$Format"): + if verbose: + print("keywords are unexpanded, not using") + raise NotThisMethod("unexpanded keywords, not a git-archive tarball") + refs = {r.strip() for r in refnames.strip("()").split(",")} + # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of + # just "foo-1.0". If we see a "tag: " prefix, prefer those. + TAG = "tag: " + tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} + if not tags: + # Either we're using git < 1.8.3, or there really are no tags. We use + # a heuristic: assume all version tags have a digit. The old git %%d + # expansion behaves like git log --decorate=short and strips out the + # refs/heads/ and refs/tags/ prefixes that would let us distinguish + # between branches and tags. By ignoring refnames without digits, we + # filter out many common branch names like "release" and + # "stabilization", as well as "HEAD" and "master". + tags = {r for r in refs if re.search(r'\d', r)} + if verbose: + print("discarding '%%s', no digits" %% ",".join(refs - tags)) + if verbose: + print("likely tags: %%s" %% ",".join(sorted(tags))) + for ref in sorted(tags): + # sorting will prefer e.g. "2.0" over "2.0rc1" + if ref.startswith(tag_prefix): + r = ref[len(tag_prefix):] + # Filter out refs that exactly match prefix or that don't start + # with a number once the prefix is stripped (mostly a concern + # when prefix is '') + if not re.match(r'\d', r): + continue + if verbose: + print("picking %%s" %% r) + return {"version": r, + "full-revisionid": keywords["full"].strip(), + "dirty": False, "error": None, + "date": date} + # no suitable tags, so version is "0+unknown", but full hex is still there + if verbose: + print("no suitable tags, using unknown + full revision id") + return {"version": "0+unknown", + "full-revisionid": keywords["full"].strip(), + "dirty": False, "error": "no suitable tags", "date": None} + + +@register_vcs_handler("git", "pieces_from_vcs") +def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): + """Get version from 'git describe' in the root of the source tree. + + This only gets called if the git-archive 'subst' keywords were *not* + expanded, and _version.py hasn't already been rewritten with a short + version string, meaning we're inside a checked out source tree. + """ + GITS = ["git"] + if sys.platform == "win32": + GITS = ["git.cmd", "git.exe"] + + # GIT_DIR can interfere with correct operation of Versioneer. + # It may be intended to be passed to the Versioneer-versioned project, + # but that should not change where we get our version from. + env = os.environ.copy() + env.pop("GIT_DIR", None) + runner = functools.partial(runner, env=env) + + _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, + hide_stderr=not verbose) + if rc != 0: + if verbose: + print("Directory %%s not under git control" %% root) + raise NotThisMethod("'git rev-parse --git-dir' returned error") + + # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] + # if there isn't one, this yields HEX[-dirty] (no NUM) + describe_out, rc = runner(GITS, [ + "describe", "--tags", "--dirty", "--always", "--long", + "--match", f"{tag_prefix}[[:digit:]]*" + ], cwd=root) + # --long was added in git-1.5.5 + if describe_out is None: + raise NotThisMethod("'git describe' failed") + describe_out = describe_out.strip() + full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) + if full_out is None: + raise NotThisMethod("'git rev-parse' failed") + full_out = full_out.strip() + + pieces = {} + pieces["long"] = full_out + pieces["short"] = full_out[:7] # maybe improved later + pieces["error"] = None + + branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], + cwd=root) + # --abbrev-ref was added in git-1.6.3 + if rc != 0 or branch_name is None: + raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") + branch_name = branch_name.strip() + + if branch_name == "HEAD": + # If we aren't exactly on a branch, pick a branch which represents + # the current commit. If all else fails, we are on a branchless + # commit. + branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) + # --contains was added in git-1.5.4 + if rc != 0 or branches is None: + raise NotThisMethod("'git branch --contains' returned error") + branches = branches.split("\n") + + # Remove the first line if we're running detached + if "(" in branches[0]: + branches.pop(0) + + # Strip off the leading "* " from the list of branches. + branches = [branch[2:] for branch in branches] + if "master" in branches: + branch_name = "master" + elif not branches: + branch_name = None + else: + # Pick the first branch that is returned. Good or bad. + branch_name = branches[0] + + pieces["branch"] = branch_name + + # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] + # TAG might have hyphens. + git_describe = describe_out + + # look for -dirty suffix + dirty = git_describe.endswith("-dirty") + pieces["dirty"] = dirty + if dirty: + git_describe = git_describe[:git_describe.rindex("-dirty")] + + # now we have TAG-NUM-gHEX or HEX + + if "-" in git_describe: + # TAG-NUM-gHEX + mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) + if not mo: + # unparsable. Maybe git-describe is misbehaving? + pieces["error"] = ("unable to parse git-describe output: '%%s'" + %% describe_out) + return pieces + + # tag + full_tag = mo.group(1) + if not full_tag.startswith(tag_prefix): + if verbose: + fmt = "tag '%%s' doesn't start with prefix '%%s'" + print(fmt %% (full_tag, tag_prefix)) + pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" + %% (full_tag, tag_prefix)) + return pieces + pieces["closest-tag"] = full_tag[len(tag_prefix):] + + # distance: number of commits since tag + pieces["distance"] = int(mo.group(2)) + + # commit: short hex revision ID + pieces["short"] = mo.group(3) + + else: + # HEX: no tags + pieces["closest-tag"] = None + out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) + pieces["distance"] = len(out.split()) # total number of commits + + # commit date: see ISO-8601 comment in git_versions_from_keywords() + date = runner(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip() + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + + return pieces + + +def plus_or_dot(pieces): + """Return a + if we don't already have one, else return a .""" + if "+" in pieces.get("closest-tag", ""): + return "." + return "+" + + +def render_pep440(pieces): + """Build up version string, with post-release "local version identifier". + + Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you + get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty + + Exceptions: + 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += plus_or_dot(pieces) + rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], + pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_branch(pieces): + """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . + + The ".dev0" means not master branch. Note that .dev0 sorts backwards + (a feature branch will appear "older" than the master branch). + + Exceptions: + 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0" + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+untagged.%%d.g%%s" %% (pieces["distance"], + pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def pep440_split_post(ver): + """Split pep440 version string at the post-release segment. + + Returns the release segments before the post-release and the + post-release version number (or -1 if no post-release segment is present). + """ + vc = str.split(ver, ".post") + return vc[0], int(vc[1] or 0) if len(vc) == 2 else None + + +def render_pep440_pre(pieces): + """TAG[.postN.devDISTANCE] -- No -dirty. + + Exceptions: + 1: no tags. 0.post0.devDISTANCE + """ + if pieces["closest-tag"]: + if pieces["distance"]: + # update the post release segment + tag_version, post_version = pep440_split_post(pieces["closest-tag"]) + rendered = tag_version + if post_version is not None: + rendered += ".post%%d.dev%%d" %% (post_version + 1, pieces["distance"]) + else: + rendered += ".post0.dev%%d" %% (pieces["distance"]) + else: + # no commits, use the tag as the version + rendered = pieces["closest-tag"] + else: + # exception #1 + rendered = "0.post0.dev%%d" %% pieces["distance"] + return rendered + + +def render_pep440_post(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX] . + + The ".dev0" means dirty. Note that .dev0 sorts backwards + (a dirty tree will appear "older" than the corresponding clean one), + but you shouldn't be releasing software with -dirty anyways. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%%d" %% pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%%s" %% pieces["short"] + else: + # exception #1 + rendered = "0.post%%d" %% pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += "+g%%s" %% pieces["short"] + return rendered + + +def render_pep440_post_branch(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . + + The ".dev0" means not master branch. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%%d" %% pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%%s" %% pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0.post%%d" %% pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+g%%s" %% pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_old(pieces): + """TAG[.postDISTANCE[.dev0]] . + + The ".dev0" means dirty. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%%d" %% pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + else: + # exception #1 + rendered = "0.post%%d" %% pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + return rendered + + +def render_git_describe(pieces): + """TAG[-DISTANCE-gHEX][-dirty]. + + Like 'git describe --tags --dirty --always'. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"]: + rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render_git_describe_long(pieces): + """TAG-DISTANCE-gHEX[-dirty]. + + Like 'git describe --tags --dirty --always -long'. + The distance/hash is unconditional. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render(pieces, style): + """Render the given version pieces into the requested style.""" + if pieces["error"]: + return {"version": "unknown", + "full-revisionid": pieces.get("long"), + "dirty": None, + "error": pieces["error"], + "date": None} + + if not style or style == "default": + style = "pep440" # the default + + if style == "pep440": + rendered = render_pep440(pieces) + elif style == "pep440-branch": + rendered = render_pep440_branch(pieces) + elif style == "pep440-pre": + rendered = render_pep440_pre(pieces) + elif style == "pep440-post": + rendered = render_pep440_post(pieces) + elif style == "pep440-post-branch": + rendered = render_pep440_post_branch(pieces) + elif style == "pep440-old": + rendered = render_pep440_old(pieces) + elif style == "git-describe": + rendered = render_git_describe(pieces) + elif style == "git-describe-long": + rendered = render_git_describe_long(pieces) + else: + raise ValueError("unknown style '%%s'" %% style) + + return {"version": rendered, "full-revisionid": pieces["long"], + "dirty": pieces["dirty"], "error": None, + "date": pieces.get("date")} + + +def get_versions(): + """Get version information or return default if unable to do so.""" + # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have + # __file__, we can work backwards from there to the root. Some + # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which + # case we can only use expanded keywords. + + cfg = get_config() + verbose = cfg.verbose + + try: + return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, + verbose) + except NotThisMethod: + pass + + try: + root = os.path.realpath(__file__) + # versionfile_source is the relative path from the top of the source + # tree (where the .git directory might live) to this file. Invert + # this to find the root from __file__. + for _ in cfg.versionfile_source.split('/'): + root = os.path.dirname(root) + except NameError: + return {"version": "0+unknown", "full-revisionid": None, + "dirty": None, + "error": "unable to find root of source tree", + "date": None} + + try: + pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) + return render(pieces, cfg.style) + except NotThisMethod: + pass + + try: + if cfg.parentdir_prefix: + return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) + except NotThisMethod: + pass + + return {"version": "0+unknown", "full-revisionid": None, + "dirty": None, + "error": "unable to compute version", "date": None} +''' + + +@register_vcs_handler("git", "get_keywords") +def git_get_keywords(versionfile_abs): + """Extract version information from the given file.""" + # the code embedded in _version.py can just fetch the value of these + # keywords. When used from setup.py, we don't want to import _version.py, + # so we do it with a regexp instead. This function is not used from + # _version.py. + keywords = {} + try: + with open(versionfile_abs, "r") as fobj: + for line in fobj: + if line.strip().startswith("git_refnames ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["refnames"] = mo.group(1) + if line.strip().startswith("git_full ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["full"] = mo.group(1) + if line.strip().startswith("git_date ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["date"] = mo.group(1) + except OSError: + pass + return keywords + + +@register_vcs_handler("git", "keywords") +def git_versions_from_keywords(keywords, tag_prefix, verbose): + """Get version information from git keywords.""" + if "refnames" not in keywords: + raise NotThisMethod("Short version file found") + date = keywords.get("date") + if date is not None: + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + + # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant + # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 + # -like" string, which we must then edit to make compliant), because + # it's been around since git-1.5.3, and it's too difficult to + # discover which version we're using, or to work around using an + # older one. + date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + refnames = keywords["refnames"].strip() + if refnames.startswith("$Format"): + if verbose: + print("keywords are unexpanded, not using") + raise NotThisMethod("unexpanded keywords, not a git-archive tarball") + refs = {r.strip() for r in refnames.strip("()").split(",")} + # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of + # just "foo-1.0". If we see a "tag: " prefix, prefer those. + TAG = "tag: " + tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)} + if not tags: + # Either we're using git < 1.8.3, or there really are no tags. We use + # a heuristic: assume all version tags have a digit. The old git %d + # expansion behaves like git log --decorate=short and strips out the + # refs/heads/ and refs/tags/ prefixes that would let us distinguish + # between branches and tags. By ignoring refnames without digits, we + # filter out many common branch names like "release" and + # "stabilization", as well as "HEAD" and "master". + tags = {r for r in refs if re.search(r"\d", r)} + if verbose: + print("discarding '%s', no digits" % ",".join(refs - tags)) + if verbose: + print("likely tags: %s" % ",".join(sorted(tags))) + for ref in sorted(tags): + # sorting will prefer e.g. "2.0" over "2.0rc1" + if ref.startswith(tag_prefix): + r = ref[len(tag_prefix) :] + # Filter out refs that exactly match prefix or that don't start + # with a number once the prefix is stripped (mostly a concern + # when prefix is '') + if not re.match(r"\d", r): + continue + if verbose: + print("picking %s" % r) + return { + "version": r, + "full-revisionid": keywords["full"].strip(), + "dirty": False, + "error": None, + "date": date, + } + # no suitable tags, so version is "0+unknown", but full hex is still there + if verbose: + print("no suitable tags, using unknown + full revision id") + return { + "version": "0+unknown", + "full-revisionid": keywords["full"].strip(), + "dirty": False, + "error": "no suitable tags", + "date": None, + } + + +@register_vcs_handler("git", "pieces_from_vcs") +def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): + """Get version from 'git describe' in the root of the source tree. + + This only gets called if the git-archive 'subst' keywords were *not* + expanded, and _version.py hasn't already been rewritten with a short + version string, meaning we're inside a checked out source tree. + """ + GITS = ["git"] + if sys.platform == "win32": + GITS = ["git.cmd", "git.exe"] + + # GIT_DIR can interfere with correct operation of Versioneer. + # It may be intended to be passed to the Versioneer-versioned project, + # but that should not change where we get our version from. + env = os.environ.copy() + env.pop("GIT_DIR", None) + runner = functools.partial(runner, env=env) + + _, rc = runner( + GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=not verbose + ) + if rc != 0: + if verbose: + print("Directory %s not under git control" % root) + raise NotThisMethod("'git rev-parse --git-dir' returned error") + + # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] + # if there isn't one, this yields HEX[-dirty] (no NUM) + describe_out, rc = runner( + GITS, + [ + "describe", + "--tags", + "--dirty", + "--always", + "--long", + "--match", + f"{tag_prefix}[[:digit:]]*", + ], + cwd=root, + ) + # --long was added in git-1.5.5 + if describe_out is None: + raise NotThisMethod("'git describe' failed") + describe_out = describe_out.strip() + full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) + if full_out is None: + raise NotThisMethod("'git rev-parse' failed") + full_out = full_out.strip() + + pieces = {} + pieces["long"] = full_out + pieces["short"] = full_out[:7] # maybe improved later + pieces["error"] = None + + branch_name, rc = runner( + GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root + ) + # --abbrev-ref was added in git-1.6.3 + if rc != 0 or branch_name is None: + raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") + branch_name = branch_name.strip() + + if branch_name == "HEAD": + # If we aren't exactly on a branch, pick a branch which represents + # the current commit. If all else fails, we are on a branchless + # commit. + branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) + # --contains was added in git-1.5.4 + if rc != 0 or branches is None: + raise NotThisMethod("'git branch --contains' returned error") + branches = branches.split("\n") + + # Remove the first line if we're running detached + if "(" in branches[0]: + branches.pop(0) + + # Strip off the leading "* " from the list of branches. + branches = [branch[2:] for branch in branches] + if "master" in branches: + branch_name = "master" + elif not branches: + branch_name = None + else: + # Pick the first branch that is returned. Good or bad. + branch_name = branches[0] + + pieces["branch"] = branch_name + + # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] + # TAG might have hyphens. + git_describe = describe_out + + # look for -dirty suffix + dirty = git_describe.endswith("-dirty") + pieces["dirty"] = dirty + if dirty: + git_describe = git_describe[: git_describe.rindex("-dirty")] + + # now we have TAG-NUM-gHEX or HEX + + if "-" in git_describe: + # TAG-NUM-gHEX + mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe) + if not mo: + # unparsable. Maybe git-describe is misbehaving? + pieces["error"] = ( + "unable to parse git-describe output: '%s'" % describe_out + ) + return pieces + + # tag + full_tag = mo.group(1) + if not full_tag.startswith(tag_prefix): + if verbose: + fmt = "tag '%s' doesn't start with prefix '%s'" + print(fmt % (full_tag, tag_prefix)) + pieces["error"] = "tag '%s' doesn't start with prefix '%s'" % ( + full_tag, + tag_prefix, + ) + return pieces + pieces["closest-tag"] = full_tag[len(tag_prefix) :] + + # distance: number of commits since tag + pieces["distance"] = int(mo.group(2)) + + # commit: short hex revision ID + pieces["short"] = mo.group(3) + + else: + # HEX: no tags + pieces["closest-tag"] = None + out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) + pieces["distance"] = len(out.split()) # total number of commits + + # commit date: see ISO-8601 comment in git_versions_from_keywords() + date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[ + 0 + ].strip() + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + + return pieces + + +def do_vcs_install(versionfile_source, ipy): + """Git-specific installation logic for Versioneer. + + For Git, this means creating/changing .gitattributes to mark _version.py + for export-subst keyword substitution. + """ + GITS = ["git"] + if sys.platform == "win32": + GITS = ["git.cmd", "git.exe"] + files = [versionfile_source] + if ipy: + files.append(ipy) + if "VERSIONEER_PEP518" not in globals(): + try: + my_path = __file__ + if my_path.endswith((".pyc", ".pyo")): + my_path = os.path.splitext(my_path)[0] + ".py" + versioneer_file = os.path.relpath(my_path) + except NameError: + versioneer_file = "versioneer.py" + files.append(versioneer_file) + present = False + try: + with open(".gitattributes", "r") as fobj: + for line in fobj: + if line.strip().startswith(versionfile_source): + if "export-subst" in line.strip().split()[1:]: + present = True + break + except OSError: + pass + if not present: + with open(".gitattributes", "a+") as fobj: + fobj.write(f"{versionfile_source} export-subst\n") + files.append(".gitattributes") + run_command(GITS, ["add", "--"] + files) + + +def versions_from_parentdir(parentdir_prefix, root, verbose): + """Try to determine the version from the parent directory name. + + Source tarballs conventionally unpack into a directory that includes both + the project name and a version string. We will also support searching up + two directory levels for an appropriately named parent directory + """ + rootdirs = [] + + for _ in range(3): + dirname = os.path.basename(root) + if dirname.startswith(parentdir_prefix): + return { + "version": dirname[len(parentdir_prefix) :], + "full-revisionid": None, + "dirty": False, + "error": None, + "date": None, + } + rootdirs.append(root) + root = os.path.dirname(root) # up a level + + if verbose: + print( + "Tried directories %s but none started with prefix %s" + % (str(rootdirs), parentdir_prefix) + ) + raise NotThisMethod("rootdir doesn't start with parentdir_prefix") + + +SHORT_VERSION_PY = """ +# This file was generated by 'versioneer.py' (0.28) from +# revision-control system data, or from the parent directory name of an +# unpacked source archive. Distribution tarballs contain a pre-generated copy +# of this file. + +import json + +version_json = ''' +%s +''' # END VERSION_JSON + + +def get_versions(): + return json.loads(version_json) +""" + + +def versions_from_file(filename): + """Try to determine the version from _version.py if present.""" + try: + with open(filename) as f: + contents = f.read() + except OSError: + raise NotThisMethod("unable to read _version.py") + mo = re.search( + r"version_json = '''\n(.*)''' # END VERSION_JSON", + contents, + re.M | re.S, + ) + if not mo: + mo = re.search( + r"version_json = '''\r\n(.*)''' # END VERSION_JSON", + contents, + re.M | re.S, + ) + if not mo: + raise NotThisMethod("no version_json in _version.py") + return json.loads(mo.group(1)) + + +def write_to_version_file(filename, versions): + """Write the given version number to the given _version.py file.""" + os.unlink(filename) + contents = json.dumps( + versions, sort_keys=True, indent=1, separators=(",", ": ") + ) + with open(filename, "w") as f: + f.write(SHORT_VERSION_PY % contents) + + print("set %s to '%s'" % (filename, versions["version"])) + + +def plus_or_dot(pieces): + """Return a + if we don't already have one, else return a .""" + if "+" in pieces.get("closest-tag", ""): + return "." + return "+" + + +def render_pep440(pieces): + """Build up version string, with post-release "local version identifier". + + Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you + get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty + + Exceptions: + 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += plus_or_dot(pieces) + rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_branch(pieces): + """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . + + The ".dev0" means not master branch. Note that .dev0 sorts backwards + (a feature branch will appear "older" than the master branch). + + Exceptions: + 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0" + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def pep440_split_post(ver): + """Split pep440 version string at the post-release segment. + + Returns the release segments before the post-release and the + post-release version number (or -1 if no post-release segment is present). + """ + vc = str.split(ver, ".post") + return vc[0], int(vc[1] or 0) if len(vc) == 2 else None + + +def render_pep440_pre(pieces): + """TAG[.postN.devDISTANCE] -- No -dirty. + + Exceptions: + 1: no tags. 0.post0.devDISTANCE + """ + if pieces["closest-tag"]: + if pieces["distance"]: + # update the post release segment + tag_version, post_version = pep440_split_post(pieces["closest-tag"]) + rendered = tag_version + if post_version is not None: + rendered += ".post%d.dev%d" % ( + post_version + 1, + pieces["distance"], + ) + else: + rendered += ".post0.dev%d" % (pieces["distance"]) + else: + # no commits, use the tag as the version + rendered = pieces["closest-tag"] + else: + # exception #1 + rendered = "0.post0.dev%d" % pieces["distance"] + return rendered + + +def render_pep440_post(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX] . + + The ".dev0" means dirty. Note that .dev0 sorts backwards + (a dirty tree will appear "older" than the corresponding clean one), + but you shouldn't be releasing software with -dirty anyways. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%s" % pieces["short"] + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += "+g%s" % pieces["short"] + return rendered + + +def render_pep440_post_branch(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . + + The ".dev0" means not master branch. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%s" % pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+g%s" % pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_old(pieces): + """TAG[.postDISTANCE[.dev0]] . + + The ".dev0" means dirty. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + return rendered + + +def render_git_describe(pieces): + """TAG[-DISTANCE-gHEX][-dirty]. + + Like 'git describe --tags --dirty --always'. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"]: + rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render_git_describe_long(pieces): + """TAG-DISTANCE-gHEX[-dirty]. + + Like 'git describe --tags --dirty --always -long'. + The distance/hash is unconditional. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render(pieces, style): + """Render the given version pieces into the requested style.""" + if pieces["error"]: + return { + "version": "unknown", + "full-revisionid": pieces.get("long"), + "dirty": None, + "error": pieces["error"], + "date": None, + } + + if not style or style == "default": + style = "pep440" # the default + + if style == "pep440": + rendered = render_pep440(pieces) + elif style == "pep440-branch": + rendered = render_pep440_branch(pieces) + elif style == "pep440-pre": + rendered = render_pep440_pre(pieces) + elif style == "pep440-post": + rendered = render_pep440_post(pieces) + elif style == "pep440-post-branch": + rendered = render_pep440_post_branch(pieces) + elif style == "pep440-old": + rendered = render_pep440_old(pieces) + elif style == "git-describe": + rendered = render_git_describe(pieces) + elif style == "git-describe-long": + rendered = render_git_describe_long(pieces) + else: + raise ValueError("unknown style '%s'" % style) + + return { + "version": rendered, + "full-revisionid": pieces["long"], + "dirty": pieces["dirty"], + "error": None, + "date": pieces.get("date"), + } + + +class VersioneerBadRootError(Exception): + """The project root directory is unknown or missing key files.""" + + +def get_versions(verbose=False): + """Get the project version from whatever source is available. + + Returns dict with two keys: 'version' and 'full'. + """ + if "versioneer" in sys.modules: + # see the discussion in cmdclass.py:get_cmdclass() + del sys.modules["versioneer"] + + root = get_root() + cfg = get_config_from_root(root) + + assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" + handlers = HANDLERS.get(cfg.VCS) + assert handlers, "unrecognized VCS '%s'" % cfg.VCS + verbose = verbose or cfg.verbose + assert ( + cfg.versionfile_source is not None + ), "please set versioneer.versionfile_source" + assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" + + versionfile_abs = os.path.join(root, cfg.versionfile_source) + + # extract version from first of: _version.py, VCS command (e.g. 'git + # describe'), parentdir. This is meant to work for developers using a + # source checkout, for users of a tarball created by 'setup.py sdist', + # and for users of a tarball/zipball created by 'git archive' or github's + # download-from-tag feature or the equivalent in other VCSes. + + get_keywords_f = handlers.get("get_keywords") + from_keywords_f = handlers.get("keywords") + if get_keywords_f and from_keywords_f: + try: + keywords = get_keywords_f(versionfile_abs) + ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) + if verbose: + print("got version from expanded keyword %s" % ver) + return ver + except NotThisMethod: + pass + + try: + ver = versions_from_file(versionfile_abs) + if verbose: + print("got version from file %s %s" % (versionfile_abs, ver)) + return ver + except NotThisMethod: + pass + + from_vcs_f = handlers.get("pieces_from_vcs") + if from_vcs_f: + try: + pieces = from_vcs_f(cfg.tag_prefix, root, verbose) + ver = render(pieces, cfg.style) + if verbose: + print("got version from VCS %s" % ver) + return ver + except NotThisMethod: + pass + + try: + if cfg.parentdir_prefix: + ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) + if verbose: + print("got version from parentdir %s" % ver) + return ver + except NotThisMethod: + pass + + if verbose: + print("unable to compute version") + + return { + "version": "0+unknown", + "full-revisionid": None, + "dirty": None, + "error": "unable to compute version", + "date": None, + } + + +def get_version(): + """Get the short version string for this project.""" + return get_versions()["version"] + + +def get_cmdclass(cmdclass=None): + """Get the custom setuptools subclasses used by Versioneer. + + If the package uses a different cmdclass (e.g. one from numpy), it + should be provide as an argument. + """ + if "versioneer" in sys.modules: + del sys.modules["versioneer"] + # this fixes the "python setup.py develop" case (also 'install' and + # 'easy_install .'), in which subdependencies of the main project are + # built (using setup.py bdist_egg) in the same python process. Assume + # a main project A and a dependency B, which use different versions + # of Versioneer. A's setup.py imports A's Versioneer, leaving it in + # sys.modules by the time B's setup.py is executed, causing B to run + # with the wrong versioneer. Setuptools wraps the sub-dep builds in a + # sandbox that restores sys.modules to it's pre-build state, so the + # parent is protected against the child's "import versioneer". By + # removing ourselves from sys.modules here, before the child build + # happens, we protect the child from the parent's versioneer too. + # Also see https://github.com/python-versioneer/python-versioneer/issues/52 + + cmds = {} if cmdclass is None else cmdclass.copy() + + # we add "version" to setuptools + from setuptools import Command + + class cmd_version(Command): + description = "report generated version string" + user_options = [] + boolean_options = [] + + def initialize_options(self): + pass + + def finalize_options(self): + pass + + def run(self): + vers = get_versions(verbose=True) + print("Version: %s" % vers["version"]) + print(" full-revisionid: %s" % vers.get("full-revisionid")) + print(" dirty: %s" % vers.get("dirty")) + print(" date: %s" % vers.get("date")) + if vers["error"]: + print(" error: %s" % vers["error"]) + + cmds["version"] = cmd_version + + # we override "build_py" in setuptools + # + # most invocation pathways end up running build_py: + # distutils/build -> build_py + # distutils/install -> distutils/build ->.. + # setuptools/bdist_wheel -> distutils/install ->.. + # setuptools/bdist_egg -> distutils/install_lib -> build_py + # setuptools/install -> bdist_egg ->.. + # setuptools/develop -> ? + # pip install: + # copies source tree to a tempdir before running egg_info/etc + # if .git isn't copied too, 'git describe' will fail + # then does setup.py bdist_wheel, or sometimes setup.py install + # setup.py egg_info -> ? + + # pip install -e . and setuptool/editable_wheel will invoke build_py + # but the build_py command is not expected to copy any files. + + # we override different "build_py" commands for both environments + if "build_py" in cmds: + _build_py = cmds["build_py"] + else: + from setuptools.command.build_py import build_py as _build_py + + class cmd_build_py(_build_py): + def run(self): + root = get_root() + cfg = get_config_from_root(root) + versions = get_versions() + _build_py.run(self) + if getattr(self, "editable_mode", False): + # During editable installs `.py` and data files are + # not copied to build_lib + return + # now locate _version.py in the new build/ directory and replace + # it with an updated value + if cfg.versionfile_build: + target_versionfile = os.path.join( + self.build_lib, cfg.versionfile_build + ) + print("UPDATING %s" % target_versionfile) + write_to_version_file(target_versionfile, versions) + + cmds["build_py"] = cmd_build_py + + if "build_ext" in cmds: + _build_ext = cmds["build_ext"] + else: + from setuptools.command.build_ext import build_ext as _build_ext + + class cmd_build_ext(_build_ext): + def run(self): + root = get_root() + cfg = get_config_from_root(root) + versions = get_versions() + _build_ext.run(self) + if self.inplace: + # build_ext --inplace will only build extensions in + # build/lib<..> dir with no _version.py to write to. + # As in place builds will already have a _version.py + # in the module dir, we do not need to write one. + return + # now locate _version.py in the new build/ directory and replace + # it with an updated value + if not cfg.versionfile_build: + return + target_versionfile = os.path.join( + self.build_lib, cfg.versionfile_build + ) + if not os.path.exists(target_versionfile): + print( + f"Warning: {target_versionfile} does not exist, skipping " + "version update. This can happen if you are running build_ext " + "without first running build_py." + ) + return + print("UPDATING %s" % target_versionfile) + write_to_version_file(target_versionfile, versions) + + cmds["build_ext"] = cmd_build_ext + + if "cx_Freeze" in sys.modules: # cx_freeze enabled? + from cx_Freeze.dist import build_exe as _build_exe + + # nczeczulin reports that py2exe won't like the pep440-style string + # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g. + # setup(console=[{ + # "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION + # "product_version": versioneer.get_version(), + # ... + + class cmd_build_exe(_build_exe): + def run(self): + root = get_root() + cfg = get_config_from_root(root) + versions = get_versions() + target_versionfile = cfg.versionfile_source + print("UPDATING %s" % target_versionfile) + write_to_version_file(target_versionfile, versions) + + _build_exe.run(self) + os.unlink(target_versionfile) + with open(cfg.versionfile_source, "w") as f: + LONG = LONG_VERSION_PY[cfg.VCS] + f.write( + LONG + % { + "DOLLAR": "$", + "STYLE": cfg.style, + "TAG_PREFIX": cfg.tag_prefix, + "PARENTDIR_PREFIX": cfg.parentdir_prefix, + "VERSIONFILE_SOURCE": cfg.versionfile_source, + } + ) + + cmds["build_exe"] = cmd_build_exe + del cmds["build_py"] + + if "py2exe" in sys.modules: # py2exe enabled? + try: + from py2exe.setuptools_buildexe import py2exe as _py2exe + except ImportError: + from py2exe.distutils_buildexe import py2exe as _py2exe + + class cmd_py2exe(_py2exe): + def run(self): + root = get_root() + cfg = get_config_from_root(root) + versions = get_versions() + target_versionfile = cfg.versionfile_source + print("UPDATING %s" % target_versionfile) + write_to_version_file(target_versionfile, versions) + + _py2exe.run(self) + os.unlink(target_versionfile) + with open(cfg.versionfile_source, "w") as f: + LONG = LONG_VERSION_PY[cfg.VCS] + f.write( + LONG + % { + "DOLLAR": "$", + "STYLE": cfg.style, + "TAG_PREFIX": cfg.tag_prefix, + "PARENTDIR_PREFIX": cfg.parentdir_prefix, + "VERSIONFILE_SOURCE": cfg.versionfile_source, + } + ) + + cmds["py2exe"] = cmd_py2exe + + # sdist farms its file list building out to egg_info + if "egg_info" in cmds: + _egg_info = cmds["egg_info"] + else: + from setuptools.command.egg_info import egg_info as _egg_info + + class cmd_egg_info(_egg_info): + def find_sources(self): + # egg_info.find_sources builds the manifest list and writes it + # in one shot + super().find_sources() + + # Modify the filelist and normalize it + root = get_root() + cfg = get_config_from_root(root) + self.filelist.append("versioneer.py") + if cfg.versionfile_source: + # There are rare cases where versionfile_source might not be + # included by default, so we must be explicit + self.filelist.append(cfg.versionfile_source) + self.filelist.sort() + self.filelist.remove_duplicates() + + # The write method is hidden in the manifest_maker instance that + # generated the filelist and was thrown away + # We will instead replicate their final normalization (to unicode, + # and POSIX-style paths) + from setuptools import unicode_utils + + normalized = [ + unicode_utils.filesys_decode(f).replace(os.sep, "/") + for f in self.filelist.files + ] + + manifest_filename = os.path.join(self.egg_info, "SOURCES.txt") + with open(manifest_filename, "w") as fobj: + fobj.write("\n".join(normalized)) + + cmds["egg_info"] = cmd_egg_info + + # we override different "sdist" commands for both environments + if "sdist" in cmds: + _sdist = cmds["sdist"] + else: + from setuptools.command.sdist import sdist as _sdist + + class cmd_sdist(_sdist): + def run(self): + versions = get_versions() + self._versioneer_generated_versions = versions + # unless we update this, the command will keep using the old + # version + self.distribution.metadata.version = versions["version"] + return _sdist.run(self) + + def make_release_tree(self, base_dir, files): + root = get_root() + cfg = get_config_from_root(root) + _sdist.make_release_tree(self, base_dir, files) + # now locate _version.py in the new base_dir directory + # (remembering that it may be a hardlink) and replace it with an + # updated value + target_versionfile = os.path.join(base_dir, cfg.versionfile_source) + print("UPDATING %s" % target_versionfile) + write_to_version_file( + target_versionfile, self._versioneer_generated_versions + ) + + cmds["sdist"] = cmd_sdist + + return cmds + + +CONFIG_ERROR = """ +setup.cfg is missing the necessary Versioneer configuration. You need +a section like: + + [versioneer] + VCS = git + style = pep440 + versionfile_source = src/myproject/_version.py + versionfile_build = myproject/_version.py + tag_prefix = + parentdir_prefix = myproject- + +You will also need to edit your setup.py to use the results: + + import versioneer + setup(version=versioneer.get_version(), + cmdclass=versioneer.get_cmdclass(), ...) + +Please read the docstring in ./versioneer.py for configuration instructions, +edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. +""" + +SAMPLE_CONFIG = """ +# See the docstring in versioneer.py for instructions. Note that you must +# re-run 'versioneer.py setup' after changing this section, and commit the +# resulting files. + +[versioneer] +#VCS = git +#style = pep440 +#versionfile_source = +#versionfile_build = +#tag_prefix = +#parentdir_prefix = + +""" + +OLD_SNIPPET = """ +from ._version import get_versions +__version__ = get_versions()['version'] +del get_versions +""" + +INIT_PY_SNIPPET = """ +from . import {0} +__version__ = {0}.get_versions()['version'] +""" + + +def do_setup(): + """Do main VCS-independent setup function for installing Versioneer.""" + root = get_root() + try: + cfg = get_config_from_root(root) + except ( + OSError, + configparser.NoSectionError, + configparser.NoOptionError, + ) as e: + if isinstance(e, (OSError, configparser.NoSectionError)): + print( + "Adding sample versioneer config to setup.cfg", file=sys.stderr + ) + with open(os.path.join(root, "setup.cfg"), "a") as f: + f.write(SAMPLE_CONFIG) + print(CONFIG_ERROR, file=sys.stderr) + return 1 + + print(" creating %s" % cfg.versionfile_source) + with open(cfg.versionfile_source, "w") as f: + LONG = LONG_VERSION_PY[cfg.VCS] + f.write( + LONG + % { + "DOLLAR": "$", + "STYLE": cfg.style, + "TAG_PREFIX": cfg.tag_prefix, + "PARENTDIR_PREFIX": cfg.parentdir_prefix, + "VERSIONFILE_SOURCE": cfg.versionfile_source, + } + ) + + ipy = os.path.join(os.path.dirname(cfg.versionfile_source), "__init__.py") + if os.path.exists(ipy): + try: + with open(ipy, "r") as f: + old = f.read() + except OSError: + old = "" + module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0] + snippet = INIT_PY_SNIPPET.format(module) + if OLD_SNIPPET in old: + print(" replacing boilerplate in %s" % ipy) + with open(ipy, "w") as f: + f.write(old.replace(OLD_SNIPPET, snippet)) + elif snippet not in old: + print(" appending to %s" % ipy) + with open(ipy, "a") as f: + f.write(snippet) + else: + print(" %s unmodified" % ipy) + else: + print(" %s doesn't exist, ok" % ipy) + ipy = None + + # Make VCS-specific changes. For git, this means creating/changing + # .gitattributes to mark _version.py for export-subst keyword + # substitution. + do_vcs_install(cfg.versionfile_source, ipy) + return 0 + + +def scan_setup_py(): + """Validate the contents of setup.py against Versioneer's expectations.""" + found = set() + setters = False + errors = 0 + with open("setup.py", "r") as f: + for line in f.readlines(): + if "import versioneer" in line: + found.add("import") + if "versioneer.get_cmdclass()" in line: + found.add("cmdclass") + if "versioneer.get_version()" in line: + found.add("get_version") + if "versioneer.VCS" in line: + setters = True + if "versioneer.versionfile_source" in line: + setters = True + if len(found) != 3: + print("") + print("Your setup.py appears to be missing some important items") + print("(but I might be wrong). Please make sure it has something") + print("roughly like the following:") + print("") + print(" import versioneer") + print(" setup( version=versioneer.get_version(),") + print(" cmdclass=versioneer.get_cmdclass(), ...)") + print("") + errors += 1 + if setters: + print("You should remove lines like 'versioneer.VCS = ' and") + print("'versioneer.versionfile_source = ' . This configuration") + print("now lives in setup.cfg, and should be removed from setup.py") + print("") + errors += 1 + return errors + + +def setup_command(): + """Set up Versioneer and exit with appropriate error code.""" + errors = do_setup() + errors += scan_setup_py() + sys.exit(1 if errors else 0) + + +if __name__ == "__main__": + cmd = sys.argv[1] + if cmd == "setup": + setup_command() From b15aafc6047fd298af7b7f0b00bd20bc3d21a79c Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Sat, 5 Aug 2023 12:35:09 +0200 Subject: [PATCH 19/58] Leverage dpctl.tensor.concat() implementation (#1507) * Leveraged dpctl.tensor.concat() implementation * Increased code coverage --- dpnp/dpnp_iface_manipulation.py | 74 ++- dpnp/dpnp_utils/dpnp_utils_statistics.py | 35 +- tests/test_arraymanipulation.py | 220 +++++-- .../cupy/manipulation_tests/test_join.py | 572 ++++++++++++++++++ tests/third_party/cupy/testing/__init__.py | 1 + tests/third_party/cupy/testing/helper.py | 38 +- 6 files changed, 844 insertions(+), 96 deletions(-) create mode 100644 tests/third_party/cupy/manipulation_tests/test_join.py diff --git a/dpnp/dpnp_iface_manipulation.py b/dpnp/dpnp_iface_manipulation.py index be2c427cf0e..fca218be02f 100644 --- a/dpnp/dpnp_iface_manipulation.py +++ b/dpnp/dpnp_iface_manipulation.py @@ -237,38 +237,76 @@ def broadcast_to(x, /, shape, subok=False): return call_origin(numpy.broadcast_to, x, shape=shape, subok=subok) -def concatenate(arrs, axis=0, out=None, dtype=None, casting="same_kind"): +def concatenate(arrays, *, axis=0, out=None, dtype=None, **kwargs): """ Join a sequence of arrays along an existing axis. For full documentation refer to :obj:`numpy.concatenate`. + Returns + ------- + out : dpnp.ndarray + The concatenated array. + + Limitations + ----------- + Each array in `arrays` is supported as either :class:`dpnp.ndarray` + or :class:`dpctl.tensor.usm_ndarray`. Otherwise ``TypeError`` exeption + will be raised. + Parameter `out` is supported with default value. + Parameter `dtype` is supported with default value. + Keyword argument ``kwargs`` is currently unsupported. + Otherwise the function will be executed sequentially on CPU. + + See Also + -------- + :obj:`dpnp.array_split` : Split an array into multiple sub-arrays of equal or near-equal size. + :obj:`dpnp.split` : Split array into a list of multiple sub-arrays of equal size. + :obj:`dpnp.hsplit` : Split array into multiple sub-arrays horizontally (column wise). + :obj:`dpnp.vsplit` : Split array into multiple sub-arrays vertically (row wise). + :obj:`dpnp.dsplit` : Split array into multiple sub-arrays along the 3rd axis (depth). + :obj:`dpnp.stack` : Stack a sequence of arrays along a new axis. + :obj:`dpnp.block` : Assemble arrays from blocks. + :obj:`dpnp.hstack` : Stack arrays in sequence horizontally (column wise). + :obj:`dpnp.vstack` : Stack arrays in sequence vertically (row wise). + :obj:`dpnp.dstack` : Stack arrays in sequence depth wise (along third dimension). + :obj:`dpnp.column_stack` : Stack 1-D arrays as columns into a 2-D array. + Examples -------- - >>> import dpnp - >>> a = dpnp.array([[1, 2], [3, 4]]) - >>> b = dpnp.array([[5, 6]]) - >>> res = dpnp.concatenate((a, b), axis=0) - >>> print(res) - [[1 2] - [3 4] - [5 6]] - >>> res = dpnp.concatenate((a, b.T), axis=1) - >>> print(res) - [[1 2 5] - [3 4 6]] - >>> res = dpnp.concatenate((a, b), axis=None) - >>> print(res) - [1 2 3 4 5 6] + >>> import dpnp as np + >>> a = np.array([[1, 2], [3, 4]]) + >>> b = np.array([[5, 6]]) + >>> np.concatenate((a, b), axis=0) + array([[1, 2], + [3, 4], + [5, 6]]) + >>> np.concatenate((a, b.T), axis=1) + array([[1, 2, 5], + [3, 4, 6]]) + >>> np.concatenate((a, b), axis=None) + array([1, 2, 3, 4, 5, 6]) """ + + if kwargs: + pass + elif out is not None: + pass + elif dtype is not None: + pass + else: + usm_arrays = [dpnp.get_usm_ndarray(x) for x in arrays] + usm_res = dpt.concat(usm_arrays, axis=axis) + return dpnp_array._create_from_usm_ndarray(usm_res) + return call_origin( numpy.concatenate, - arrs, + arrays, axis=axis, out=out, dtype=dtype, - casting=casting, + **kwargs, ) diff --git a/dpnp/dpnp_utils/dpnp_utils_statistics.py b/dpnp/dpnp_utils/dpnp_utils_statistics.py index 9d69dbaa368..7ed82953541 100644 --- a/dpnp/dpnp_utils/dpnp_utils_statistics.py +++ b/dpnp/dpnp_utils/dpnp_utils_statistics.py @@ -27,12 +27,7 @@ # ***************************************************************************** -import dpctl -import dpctl.tensor as dpt -import dpctl.tensor._tensor_impl as ti - import dpnp -from dpnp.dpnp_array import dpnp_array from dpnp.dpnp_utils import get_usm_allocations __all__ = ["dpnp_cov"] @@ -92,35 +87,7 @@ def _get_2dmin_array(x, dtype): if y is not None: y = _get_2dmin_array(y, dtype) - # TODO: replace with dpnp.concatenate((X, y), axis=0) once dpctl implementation is ready - if X.ndim != y.ndim: - raise ValueError( - "all the input arrays must have same number of dimensions" - ) - - if X.shape[1:] != y.shape[1:]: - raise ValueError( - "all the input array dimensions for the concatenation axis must match exactly" - ) - - res_shape = tuple( - X.shape[i] if i > 0 else (X.shape[i] + y.shape[i]) - for i in range(X.ndim) - ) - res_usm = dpt.empty( - res_shape, dtype=dtype, usm_type=usm_type, sycl_queue=queue - ) - - # concatenate input arrays 'm' and 'y' into single array among 0-axis - hev1, _ = ti._copy_usm_ndarray_into_usm_ndarray( - src=X.get_array(), dst=res_usm[: X.shape[0]], sycl_queue=queue - ) - hev2, _ = ti._copy_usm_ndarray_into_usm_ndarray( - src=y.get_array(), dst=res_usm[X.shape[0] :], sycl_queue=queue - ) - dpctl.SyclEvent.wait_for([hev1, hev2]) - - X = dpnp_array._create_from_usm_ndarray(res_usm) + X = dpnp.concatenate((X, y), axis=0) avg = X.mean(axis=1) diff --git a/tests/test_arraymanipulation.py b/tests/test_arraymanipulation.py index 8d75296cad0..68e8f4af47a 100644 --- a/tests/test_arraymanipulation.py +++ b/tests/test_arraymanipulation.py @@ -128,62 +128,200 @@ def test_broadcast_raise(self, sh1, sh2): func(dpnp, dp_a) -@pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestConcatenate: def test_returns_copy(self): - a = dpnp.array(numpy.eye(3)) + a = dpnp.eye(3) b = dpnp.concatenate([a]) b[0, 0] = 2 assert b[0, 0] != a[0, 0] - def test_large_concatenate_axis_None(self): - x = dpnp.arange(1, 100) - r = dpnp.concatenate(x, None) - assert_array_equal(x, r) - r = dpnp.concatenate(x, 100) - assert_array_equal(x, r) + @pytest.mark.parametrize("ndim", [1, 2, 3]) + def test_axis_exceptions(self, ndim): + dp_a = dpnp.ones((1,) * ndim) + np_a = numpy.ones((1,) * ndim) + + dp_res = dpnp.concatenate((dp_a, dp_a), axis=0) + np_res = numpy.concatenate((np_a, np_a), axis=0) + assert_equal(dp_res.asnumpy(), np_res) + + for axis in [ndim, -(ndim + 1)]: + with pytest.raises(numpy.AxisError): + dpnp.concatenate((dp_a, dp_a), axis=axis) + numpy.concatenate((np_a, np_a), axis=axis) + + def test_scalar_exceptions(self): + assert_raises(TypeError, dpnp.concatenate, (0,)) + assert_raises(ValueError, numpy.concatenate, (0,)) + + for xp in [dpnp, numpy]: + with pytest.raises(ValueError): + xp.concatenate((xp.array(0),)) + + def test_dims_exception(self): + for xp in [dpnp, numpy]: + with pytest.raises(ValueError): + xp.concatenate((xp.zeros(1), xp.zeros((1, 1)))) + + def test_shapes_match_exception(self): + axis = list(range(3)) + np_a = numpy.ones((1, 2, 3)) + np_b = numpy.ones((2, 2, 3)) + + dp_a = dpnp.array(np_a) + dp_b = dpnp.array(np_b) + + for _ in range(3): + # shapes must match except for concatenation axis + np_res = numpy.concatenate((np_a, np_b), axis=axis[0]) + dp_res = dpnp.concatenate((dp_a, dp_b), axis=axis[0]) + assert_equal(dp_res.asnumpy(), np_res) + + for i in range(1, 3): + with pytest.raises(ValueError): + numpy.concatenate((np_a, np_b), axis=axis[i]) + dpnp.concatenate((dp_a, dp_b), axis=axis[i]) + + np_a = numpy.moveaxis(np_a, -1, 0) + dp_a = dpnp.moveaxis(dp_a, -1, 0) + + np_b = numpy.moveaxis(np_b, -1, 0) + dp_b = dpnp.moveaxis(dp_b, -1, 0) + axis.append(axis.pop(0)) + + def test_no_array_exception(self): + with pytest.raises(ValueError): + numpy.concatenate(()) + dpnp.concatenate(()) + + @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True)) + def test_concatenate_axis_None(self, dtype): + stop, sh = (4, (2, 2)) if dtype is not dpnp.bool else (2, (2, 1)) + np_a = numpy.arange(stop, dtype=dtype).reshape(sh) + dp_a = dpnp.arange(stop, dtype=dtype).reshape(sh) + + np_res = numpy.concatenate((np_a, np_a), axis=None) + dp_res = dpnp.concatenate((dp_a, dp_a), axis=None) + assert_equal(dp_res.asnumpy(), np_res) + + @pytest.mark.parametrize( + "dtype", get_all_dtypes(no_bool=True, no_none=True) + ) + def test_large_concatenate_axis_None(self, dtype): + start, stop = (1, 100) + np_a = numpy.arange(start, stop, dtype=dtype) + dp_a = dpnp.arange(start, stop, dtype=dtype) + + np_res = numpy.concatenate(np_a, axis=None) + dp_res = dpnp.concatenate(dp_a, axis=None) + assert_array_equal(dp_res.asnumpy(), np_res) + + # numpy doesn't raise an exception here but probably should + with pytest.raises(numpy.AxisError): + dpnp.concatenate(dp_a, axis=100) - def test_concatenate(self): + @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True)) + def test_concatenate(self, dtype): # Test concatenate function # One sequence returns unmodified (but as array) r4 = list(range(4)) - assert_array_equal(dpnp.concatenate((r4,)), r4) - # Any sequence - assert_array_equal(dpnp.concatenate((tuple(r4),)), r4) - assert_array_equal(dpnp.concatenate((dpnp.array(r4),)), r4) + np_r4 = numpy.array(r4, dtype=dtype) + dp_r4 = dpnp.array(r4, dtype=dtype) + + np_res = numpy.concatenate((np_r4,)) + dp_res = dpnp.concatenate((dp_r4,)) + assert_array_equal(dp_res.asnumpy(), np_res) + # 1D default concatenation r3 = list(range(3)) - assert_array_equal(dpnp.concatenate((r4, r3)), r4 + r3) - # Mixed sequence types - assert_array_equal(dpnp.concatenate((tuple(r4), r3)), r4 + r3) - assert_array_equal(dpnp.concatenate((dpnp.array(r4), r3)), r4 + r3) + np_r3 = numpy.array(r3, dtype=dtype) + dp_r3 = dpnp.array(r3, dtype=dtype) + + np_res = numpy.concatenate((np_r4, np_r3)) + dp_res = dpnp.concatenate((dp_r4, dp_r3)) + assert_array_equal(dp_res.asnumpy(), np_res) + # Explicit axis specification - assert_array_equal(dpnp.concatenate((r4, r3), 0), r4 + r3) + np_res = numpy.concatenate((np_r4, np_r3), axis=0) + dp_res = dpnp.concatenate((dp_r4, dp_r3), axis=0) + assert_array_equal(dp_res.asnumpy(), np_res) + # Including negative - assert_array_equal(dpnp.concatenate((r4, r3), -1), r4 + r3) - # 2D - a23 = dpnp.array([[10, 11, 12], [13, 14, 15]]) - a13 = dpnp.array([[0, 1, 2]]) - res = dpnp.array([[10, 11, 12], [13, 14, 15], [0, 1, 2]]) - assert_array_equal(dpnp.concatenate((a23, a13)), res) - assert_array_equal(dpnp.concatenate((a23, a13), 0), res) - assert_array_equal(dpnp.concatenate((a23.T, a13.T), 1), res.T) - assert_array_equal(dpnp.concatenate((a23.T, a13.T), -1), res.T) + np_res = numpy.concatenate((np_r4, np_r3), axis=-1) + dp_res = dpnp.concatenate((dp_r4, dp_r3), axis=-1) + assert_array_equal(dp_res.asnumpy(), np_res) + + @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True)) + def test_concatenate_2d(self, dtype): + np_a23 = numpy.array([[10, 11, 12], [13, 14, 15]], dtype=dtype) + np_a13 = numpy.array([[0, 1, 2]], dtype=dtype) + + dp_a23 = dpnp.array([[10, 11, 12], [13, 14, 15]], dtype=dtype) + dp_a13 = dpnp.array([[0, 1, 2]], dtype=dtype) + + np_res = numpy.concatenate((np_a23, np_a13)) + dp_res = dpnp.concatenate((dp_a23, dp_a13)) + assert_array_equal(dp_res.asnumpy(), np_res) + + np_res = numpy.concatenate((np_a23, np_a13), axis=0) + dp_res = dpnp.concatenate((dp_a23, dp_a13), axis=0) + assert_array_equal(dp_res.asnumpy(), np_res) + + for axis in [1, -1]: + np_res = numpy.concatenate((np_a23.T, np_a13.T), axis=axis) + dp_res = dpnp.concatenate((dp_a23.T, dp_a13.T), axis=axis) + assert_array_equal(dp_res.asnumpy(), np_res) + # Arrays much match shape - assert_raises(ValueError, dpnp.concatenate, (a23.T, a13.T), 0) - # 3D - res = dpnp.reshape(dpnp.arange(2 * 3 * 7), (2, 3, 7)) - a0 = res[..., :4] - a1 = res[..., 4:6] - a2 = res[..., 6:] - assert_array_equal(dpnp.concatenate((a0, a1, a2), 2), res) - assert_array_equal(dpnp.concatenate((a0, a1, a2), -1), res) - assert_array_equal(dpnp.concatenate((a0.T, a1.T, a2.T), 0), res.T) - - out = dpnp.copy(res) - rout = dpnp.concatenate((a0, a1, a2), 2, out=out) - assert_(out is rout) - assert_equal(res, rout) + with pytest.raises(ValueError): + numpy.concatenate((np_a23.T, np_a13.T), axis=0) + dpnp.concatenate((dp_a23.T, dp_a13.T), axis=0) + + @pytest.mark.parametrize( + "dtype", get_all_dtypes(no_bool=True, no_none=True) + ) + def test_concatenate_3d(self, dtype): + np_a = numpy.arange(2 * 3 * 7, dtype=dtype).reshape((2, 3, 7)) + np_a0 = np_a[..., :4] + np_a1 = np_a[..., 4:6] + np_a2 = np_a[..., 6:] + + dp_a = dpnp.arange(2 * 3 * 7, dtype=dtype).reshape((2, 3, 7)) + dp_a0 = dp_a[..., :4] + dp_a1 = dp_a[..., 4:6] + dp_a2 = dp_a[..., 6:] + + for axis in [2, -1]: + np_res = numpy.concatenate((np_a0, np_a1, np_a2), axis=axis) + dp_res = dpnp.concatenate((dp_a0, dp_a1, dp_a2), axis=axis) + assert_array_equal(dp_res.asnumpy(), np_res) + + np_res = numpy.concatenate((np_a0.T, np_a1.T, np_a2.T), axis=0) + dp_res = dpnp.concatenate((dp_a0.T, dp_a1.T, dp_a2.T), axis=0) + assert_array_equal(dp_res.asnumpy(), np_res) + + @pytest.mark.skip("out keyword is currently unsupported") + @pytest.mark.parametrize( + "dtype", get_all_dtypes(no_bool=True, no_none=True) + ) + def test_concatenate_out(self, dtype): + np_a = numpy.arange(2 * 3 * 7, dtype=dtype).reshape((2, 3, 7)) + np_a0 = np_a[..., :4] + np_a1 = np_a[..., 4:6] + np_a2 = np_a[..., 6:] + np_out = numpy.empty_like(np_a) + + dp_a = dpnp.arange(2 * 3 * 7, dtype=dtype).reshape((2, 3, 7)) + dp_a0 = dp_a[..., :4] + dp_a1 = dp_a[..., 4:6] + dp_a2 = dp_a[..., 6:] + dp_out = dpnp.empty_like(dp_a) + + np_res = numpy.concatenate((np_a0, np_a1, np_a2), axis=2, out=np_out) + dp_res = dpnp.concatenate((dp_a0, dp_a1, dp_a2), axis=2, out=dp_out) + + assert dp_out is dp_res + assert_array_equal(dp_out.asnumpy(), np_out) + assert_array_equal(dp_res.asnumpy(), np_res) class TestHstack: diff --git a/tests/third_party/cupy/manipulation_tests/test_join.py b/tests/third_party/cupy/manipulation_tests/test_join.py new file mode 100644 index 00000000000..e302065ca99 --- /dev/null +++ b/tests/third_party/cupy/manipulation_tests/test_join.py @@ -0,0 +1,572 @@ +import unittest + +import numpy +import pytest + +import dpnp as cupy +from tests.third_party.cupy import testing + + +class TestJoin(unittest.TestCase): + @pytest.mark.skip("dpnp.column_stack() is not implemented yet") + @testing.for_all_dtypes(name="dtype1") + @testing.for_all_dtypes(name="dtype2") + @testing.numpy_cupy_array_equal() + def test_column_stack(self, xp, dtype1, dtype2): + a = testing.shaped_arange((4, 3), xp, dtype1) + b = testing.shaped_arange((4,), xp, dtype2) + c = testing.shaped_arange((4, 2), xp, dtype1) + return xp.column_stack((a, b, c)) + + @pytest.mark.skip("dpnp.column_stack() is not implemented yet") + def test_column_stack_wrong_ndim1(self): + a = cupy.zeros(()) + b = cupy.zeros((3,)) + with pytest.raises(ValueError): + cupy.column_stack((a, b)) + + @pytest.mark.skip("dpnp.column_stack() is not implemented yet") + def test_column_stack_wrong_ndim2(self): + a = cupy.zeros((3, 2, 3)) + b = cupy.zeros((3, 2)) + with pytest.raises(ValueError): + cupy.column_stack((a, b)) + + @pytest.mark.skip("dpnp.column_stack() is not implemented yet") + def test_column_stack_wrong_shape(self): + a = cupy.zeros((3, 2)) + b = cupy.zeros((4, 3)) + with pytest.raises(ValueError): + cupy.column_stack((a, b)) + + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_concatenate1(self, xp, dtype): + a = testing.shaped_arange((2, 3, 4), xp, dtype) + b = testing.shaped_reverse_arange((2, 3, 2), xp, dtype) + c = testing.shaped_arange((2, 3, 3), xp, dtype) + return xp.concatenate((a, b, c), axis=2) + + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_concatenate2(self, xp, dtype): + a = testing.shaped_arange((2, 3, 4), xp, dtype) + b = testing.shaped_reverse_arange((2, 3, 2), xp, dtype) + c = testing.shaped_arange((2, 3, 3), xp, dtype) + return xp.concatenate((a, b, c), axis=-1) + + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_concatenate_axis_none(self, xp, dtype): + a = testing.shaped_arange((2, 3), xp, dtype) + b = testing.shaped_reverse_arange((3, 5, 2), xp, dtype) + c = testing.shaped_arange((7,), xp, dtype) + return xp.concatenate((a, b, c), axis=None) + + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_concatenate_large_2(self, xp, dtype): + a = testing.shaped_arange((2, 3, 4), xp, dtype) + b = testing.shaped_reverse_arange((2, 3, 2), xp, dtype) + c = testing.shaped_arange((2, 3, 3), xp, dtype) + d = testing.shaped_arange((2, 3, 5), xp, dtype) + e = testing.shaped_arange((2, 3, 2), xp, dtype) + return xp.concatenate((a, b, c, d, e) * 2, axis=-1) + + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_concatenate_large_3(self, xp, dtype): + a = testing.shaped_arange((2, 3, 1), xp, dtype) + b = testing.shaped_reverse_arange((2, 3, 1), xp, dtype) + return xp.concatenate((a, b) * 10, axis=-1) + + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_concatenate_large_4(self, xp, dtype): + a = testing.shaped_arange((2, 3, 4), xp, dtype) + b = testing.shaped_reverse_arange((2, 3, 4), xp, dtype) + return xp.concatenate((a, b) * 10, axis=-1) + + @pytest.mark.skip("TODO: remove once dpctl #1325 is resolved") + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_concatenate_large_5(self, xp, dtype): + a = testing.shaped_arange((2, 3, 4), xp, dtype) + b = testing.shaped_reverse_arange((2, 3, 4), xp, "i") + return xp.concatenate((a, b) * 10, axis=-1) + + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_concatenate_f_contiguous(self, xp, dtype): + a = testing.shaped_arange((2, 3, 4), xp, dtype) + b = testing.shaped_arange((2, 3, 2), xp, dtype).T + c = testing.shaped_arange((2, 3, 3), xp, dtype) + return xp.concatenate((a, b, c), axis=-1) + + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_concatenate_large_f_contiguous(self, xp, dtype): + a = testing.shaped_arange((2, 3, 4), xp, dtype) + b = testing.shaped_arange((2, 3, 2), xp, dtype).T + c = testing.shaped_arange((2, 3, 3), xp, dtype) + d = testing.shaped_arange((2, 3, 2), xp, dtype).T + e = testing.shaped_arange((2, 3, 2), xp, dtype) + return xp.concatenate((a, b, c, d, e) * 2, axis=-1) + + @pytest.mark.skip("TODO: remove once dpctl #1325 is resolved") + @testing.numpy_cupy_array_equal() + def test_concatenate_many_multi_dtype(self, xp): + a = testing.shaped_arange((2, 1), xp, "i") + b = testing.shaped_arange((2, 1), xp, "f") + return xp.concatenate((a, b) * 1024, axis=1) + + @pytest.mark.skip("dpnp.int8 is not supported yet") + @testing.slow + def test_concatenate_32bit_boundary(self): + a = cupy.zeros((2**30,), dtype=cupy.int8) + b = cupy.zeros((2**30,), dtype=cupy.int8) + ret = cupy.concatenate([a, b]) + del a + del b + del ret + # Free huge memory for slow test + cupy.get_default_memory_pool().free_all_blocks() + + def test_concatenate_wrong_ndim(self): + a = cupy.empty((2, 3)) + b = cupy.empty((2,)) + with pytest.raises(ValueError): + cupy.concatenate((a, b)) + + def test_concatenate_wrong_shape(self): + a = cupy.empty((2, 3, 4)) + b = cupy.empty((3, 3, 4)) + c = cupy.empty((4, 4, 4)) + with pytest.raises(ValueError): + cupy.concatenate((a, b, c)) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_concatenate_out(self, xp, dtype): + a = testing.shaped_arange((3, 4), xp, dtype) + b = testing.shaped_reverse_arange((3, 4), xp, dtype) + c = testing.shaped_arange((3, 4), xp, dtype) + out = xp.zeros((3, 12), dtype=dtype) + xp.concatenate((a, b, c), axis=1, out=out) + return out + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.numpy_cupy_array_equal() + def test_concatenate_out_same_kind(self, xp): + a = testing.shaped_arange((3, 4), xp, xp.float32) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float32) + c = testing.shaped_arange((3, 4), xp, xp.float32) + out = xp.zeros((3, 12), dtype=xp.float32) + xp.concatenate((a, b, c), axis=1, out=out) + return out + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + def test_concatenate_out_invalid_shape(self): + for xp in (numpy, cupy): + a = testing.shaped_arange((3, 4), xp, xp.float32) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float32) + c = testing.shaped_arange((3, 4), xp, xp.float32) + out = xp.zeros((4, 10), dtype=xp.float32) + with pytest.raises(ValueError): + xp.concatenate((a, b, c), axis=1, out=out) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + def test_concatenate_out_invalid_shape_2(self): + for xp in (numpy, cupy): + a = testing.shaped_arange((3, 4), xp, xp.float32) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float32) + c = testing.shaped_arange((3, 4), xp, xp.float32) + out = xp.zeros((2, 2, 10), dtype=xp.float32) + with pytest.raises(ValueError): + xp.concatenate((a, b, c), axis=1, out=out) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + def test_concatenate_out_invalid_dtype(self): + for xp in (numpy, cupy): + a = testing.shaped_arange((3, 4), xp, xp.float32) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float32) + c = testing.shaped_arange((3, 4), xp, xp.float32) + out = xp.zeros((3, 12), dtype=xp.int64) + with pytest.raises(TypeError): + xp.concatenate((a, b, c), axis=1, out=out) + + @pytest.mark.skip("TODO: remove once dpctl #1325 is resolved") + @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) + @testing.numpy_cupy_array_equal() + def test_concatenate_different_dtype(self, xp, dtype1, dtype2): + a = testing.shaped_arange((3, 4), xp, dtype1) + b = testing.shaped_arange((3, 4), xp, dtype2) + return xp.concatenate((a, b)) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) + @testing.numpy_cupy_array_equal(accept_error=TypeError) + def test_concatenate_out_different_dtype(self, xp, dtype1, dtype2): + a = testing.shaped_arange((3, 4), xp, dtype1) + b = testing.shaped_arange((3, 4), xp, dtype1) + out = xp.zeros((6, 4), dtype=dtype2) + return xp.concatenate((a, b), out=out) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.with_requires("numpy>=1.20.0") + @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) + @testing.numpy_cupy_array_equal(accept_error=TypeError) + def test_concatenate_dtype(self, xp, dtype1, dtype2): + a = testing.shaped_arange((3, 4), xp, dtype1) + b = testing.shaped_arange((3, 4), xp, dtype1) + return xp.concatenate((a, b), dtype=dtype2) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.with_requires("numpy>=1.20.0") + def test_concatenate_dtype_invalid_out(self): + for xp in (numpy, cupy): + a = testing.shaped_arange((3, 4), xp, xp.float32) + b = testing.shaped_arange((3, 4), xp, xp.float32) + out = xp.zeros((6, 4), dtype=xp.int64) + with pytest.raises(TypeError): + xp.concatenate((a, b), out=out, dtype=xp.int64) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.with_requires("numpy>=1.20.0") + @testing.for_castings() + @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) + @testing.numpy_cupy_array_equal( + accept_error=(TypeError, numpy.ComplexWarning) + ) + def test_concatenate_casting(self, xp, dtype1, dtype2, casting): + a = testing.shaped_arange((3, 4), xp, dtype1) + b = testing.shaped_arange((3, 4), xp, dtype1) + return xp.concatenate((a, b), dtype=dtype2, casting=casting) + + @pytest.mark.skip("dpnp.dstack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_dstack(self, xp): + a = testing.shaped_arange((1, 3, 2), xp) + b = testing.shaped_arange((3,), xp) + c = testing.shaped_arange((1, 3), xp) + return xp.dstack((a, b, c)) + + @pytest.mark.skip("dpnp.dstack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_dstack_single_element(self, xp): + a = testing.shaped_arange((1, 2, 3), xp) + return xp.dstack((a,)) + + @pytest.mark.skip("dpnp.dstack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_dstack_single_element_2(self, xp): + a = testing.shaped_arange((1, 2), xp) + return xp.dstack((a,)) + + @pytest.mark.skip("dpnp.dstack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_dstack_single_element_3(self, xp): + a = testing.shaped_arange((1,), xp) + return xp.dstack((a,)) + + @pytest.mark.skip("dpnp.hstack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_hstack_vectors(self, xp): + a = xp.arange(3) + b = xp.arange(2, -1, -1) + return xp.hstack((a, b)) + + @pytest.mark.skip("dpnp.hstack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_hstack_scalars(self, xp): + a = testing.shaped_arange((), xp) + b = testing.shaped_arange((), xp) + c = testing.shaped_arange((), xp) + return xp.hstack((a, b, c)) + + @pytest.mark.skip("dpnp.hstack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_hstack(self, xp): + a = testing.shaped_arange((2, 1), xp) + b = testing.shaped_arange((2, 2), xp) + c = testing.shaped_arange((2, 3), xp) + return xp.hstack((a, b, c)) + + @pytest.mark.skip("dpnp.hstack() is not implemented yet") + @testing.with_requires("numpy>=1.24.0") + @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) + @testing.numpy_cupy_array_equal(accept_error=TypeError) + def test_hstack_dtype(self, xp, dtype1, dtype2): + a = testing.shaped_arange((3, 4), xp, dtype1) + b = testing.shaped_arange((3, 4), xp, dtype1) + return xp.hstack((a, b), dtype=dtype2) + + @pytest.mark.skip("dpnp.hstack() is not implemented yet") + @testing.with_requires("numpy>=1.24.0") + @pytest.mark.parametrize( + "casting", + [ + "no", + "equiv", + "safe", + "same_kind", + "unsafe", + ], + ) + @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) + @testing.numpy_cupy_array_equal( + accept_error=(TypeError, numpy.ComplexWarning) + ) + def test_hstack_casting(self, xp, dtype1, dtype2, casting): + a = testing.shaped_arange((3, 4), xp, dtype1) + b = testing.shaped_arange((3, 4), xp, dtype1) + # may raise TypeError or ComplexWarning + return xp.hstack((a, b), dtype=dtype2, casting=casting) + + @pytest.mark.skip("dpnp.vstack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_vstack_vectors(self, xp): + a = xp.arange(3) + b = xp.arange(2, -1, -1) + return xp.vstack((a, b)) + + @pytest.mark.skip("dpnp.vstack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_vstack_single_element(self, xp): + a = xp.arange(3) + return xp.vstack((a,)) + + @pytest.mark.skip("dpnp.vstack() is not implemented yet") + def test_vstack_wrong_ndim(self): + a = cupy.empty((3,)) + b = cupy.empty((3, 1)) + with pytest.raises(ValueError): + cupy.vstack((a, b)) + + @pytest.mark.skip("dpnp.vstack() is not implemented yet") + @testing.with_requires("numpy>=1.24.0") + @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) + @testing.numpy_cupy_array_equal(accept_error=TypeError) + def test_vstack_dtype(self, xp, dtype1, dtype2): + a = testing.shaped_arange((3, 4), xp, dtype1) + b = testing.shaped_arange((3, 4), xp, dtype1) + return xp.vstack((a, b), dtype=dtype2) + + @pytest.mark.skip("dpnp.vstack() is not implemented yet") + @testing.with_requires("numpy>=1.24.0") + @pytest.mark.parametrize( + "casting", + [ + "no", + "equiv", + "safe", + "same_kind", + "unsafe", + ], + ) + @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) + @testing.numpy_cupy_array_equal( + accept_error=(TypeError, numpy.ComplexWarning) + ) + def test_vstack_casting(self, xp, dtype1, dtype2, casting): + a = testing.shaped_arange((3, 4), xp, dtype1) + b = testing.shaped_arange((3, 4), xp, dtype1) + # may raise TypeError or ComplexWarning + return xp.vstack((a, b), dtype=dtype2, casting=casting) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_stack(self, xp): + a = testing.shaped_arange((2, 3), xp) + b = testing.shaped_arange((2, 3), xp) + c = testing.shaped_arange((2, 3), xp) + return xp.stack((a, b, c)) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + def test_stack_value(self): + a = testing.shaped_arange((2, 3), cupy) + b = testing.shaped_arange((2, 3), cupy) + c = testing.shaped_arange((2, 3), cupy) + s = cupy.stack((a, b, c)) + assert s.shape == (3, 2, 3) + cupy.testing.assert_array_equal(s[0], a) + cupy.testing.assert_array_equal(s[1], b) + cupy.testing.assert_array_equal(s[2], c) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_stack_with_axis1(self, xp): + a = testing.shaped_arange((2, 3), xp) + return xp.stack((a, a), axis=1) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_stack_with_axis2(self, xp): + a = testing.shaped_arange((2, 3), xp) + return xp.stack((a, a), axis=2) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + def test_stack_with_axis_over(self): + for xp in (numpy, cupy): + a = testing.shaped_arange((2, 3), xp) + with pytest.raises(ValueError): + xp.stack((a, a), axis=3) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + def test_stack_with_axis_value(self): + a = testing.shaped_arange((2, 3), cupy) + s = cupy.stack((a, a), axis=1) + + assert s.shape == (2, 2, 3) + cupy.testing.assert_array_equal(s[:, 0, :], a) + cupy.testing.assert_array_equal(s[:, 1, :], a) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_stack_with_negative_axis(self, xp): + a = testing.shaped_arange((2, 3), xp) + return xp.stack((a, a), axis=-1) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + def test_stack_with_negative_axis_value(self): + a = testing.shaped_arange((2, 3), cupy) + s = cupy.stack((a, a), axis=-1) + + assert s.shape == (2, 3, 2) + cupy.testing.assert_array_equal(s[:, :, 0], a) + cupy.testing.assert_array_equal(s[:, :, 1], a) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + def test_stack_different_shape(self): + for xp in (numpy, cupy): + a = testing.shaped_arange((2, 3), xp) + b = testing.shaped_arange((2, 4), xp) + with pytest.raises(ValueError): + xp.stack([a, b]) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + def test_stack_out_of_bounds1(self): + for xp in (numpy, cupy): + a = testing.shaped_arange((2, 3), xp) + with pytest.raises(ValueError): + xp.stack([a, a], axis=3) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + def test_stack_out_of_bounds2(self): + a = testing.shaped_arange((2, 3), cupy) + with pytest.raises(numpy.AxisError): + return cupy.stack([a, a], axis=3) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + @testing.for_all_dtypes(name="dtype") + @testing.numpy_cupy_array_equal() + def test_stack_out(self, xp, dtype): + a = testing.shaped_arange((3, 4), xp, dtype) + b = testing.shaped_reverse_arange((3, 4), xp, dtype) + c = testing.shaped_arange((3, 4), xp, dtype) + out = xp.zeros((3, 3, 4), dtype=dtype) + xp.stack((a, b, c), axis=1, out=out) + return out + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + @testing.numpy_cupy_array_equal() + def test_stack_out_same_kind(self, xp): + a = testing.shaped_arange((3, 4), xp, xp.float64) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float64) + c = testing.shaped_arange((3, 4), xp, xp.float64) + out = xp.zeros((3, 3, 4), dtype=xp.float32) + xp.stack((a, b, c), axis=1, out=out) + return out + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + def test_stack_out_invalid_shape(self): + for xp in (numpy, cupy): + a = testing.shaped_arange((3, 4), xp, xp.float64) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float64) + c = testing.shaped_arange((3, 4), xp, xp.float64) + out = xp.zeros((3, 3, 10), dtype=xp.float64) + with pytest.raises(ValueError): + xp.stack((a, b, c), axis=1, out=out) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + def test_stack_out_invalid_shape_2(self): + for xp in (numpy, cupy): + a = testing.shaped_arange((3, 4), xp, xp.float64) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float64) + c = testing.shaped_arange((3, 4), xp, xp.float64) + out = xp.zeros((3, 3, 3, 10), dtype=xp.float64) + with pytest.raises(ValueError): + xp.stack((a, b, c), axis=1, out=out) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + def test_stack_out_invalid_dtype(self): + for xp in (numpy, cupy): + a = testing.shaped_arange((3, 4), xp, xp.float64) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float64) + c = testing.shaped_arange((3, 4), xp, xp.float64) + out = xp.zeros((3, 3, 4), dtype=xp.int64) + with pytest.raises(TypeError): + xp.stack((a, b, c), axis=1, out=out) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + @testing.with_requires("numpy>=1.24.0") + @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) + @testing.numpy_cupy_array_equal(accept_error=TypeError) + def test_stack_dtype(self, xp, dtype1, dtype2): + a = testing.shaped_arange((3, 4), xp, dtype1) + b = testing.shaped_arange((3, 4), xp, dtype1) + return xp.stack((a, b), dtype=dtype2) + + @pytest.mark.skip("dpnp.stack() is not implemented yet") + @testing.with_requires("numpy>=1.24.0") + @pytest.mark.parametrize( + "casting", + [ + "no", + "equiv", + "safe", + "same_kind", + "unsafe", + ], + ) + @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) + @testing.numpy_cupy_array_equal( + accept_error=(TypeError, numpy.ComplexWarning) + ) + def test_stack_casting(self, xp, dtype1, dtype2, casting): + a = testing.shaped_arange((3, 4), xp, dtype1) + b = testing.shaped_arange((3, 4), xp, dtype1) + # may raise TypeError or ComplexWarning + return xp.stack((a, b), dtype=dtype2, casting=casting) + + @pytest.mark.skip("dpnp.row_stack() is not implemented yet") + @testing.for_all_dtypes(name="dtype1") + @testing.for_all_dtypes(name="dtype2") + @testing.numpy_cupy_array_equal() + def test_row_stack(self, xp, dtype1, dtype2): + a = testing.shaped_arange((4, 3), xp, dtype1) + b = testing.shaped_arange((3,), xp, dtype2) + c = testing.shaped_arange((2, 3), xp, dtype1) + return xp.row_stack((a, b, c)) + + @pytest.mark.skip("dpnp.row_stack() is not implemented yet") + def test_row_stack_wrong_ndim1(self): + a = cupy.zeros(()) + b = cupy.zeros((3,)) + with pytest.raises(ValueError): + cupy.row_stack((a, b)) + + @pytest.mark.skip("dpnp.row_stack() is not implemented yet") + def test_row_stack_wrong_ndim2(self): + a = cupy.zeros((3, 2, 3)) + b = cupy.zeros((3, 2)) + with pytest.raises(ValueError): + cupy.row_stack((a, b)) + + @pytest.mark.skip("dpnp.row_stack() is not implemented yet") + def test_row_stack_wrong_shape(self): + a = cupy.zeros((3, 2)) + b = cupy.zeros((4, 3)) + with pytest.raises(ValueError): + cupy.row_stack((a, b)) diff --git a/tests/third_party/cupy/testing/__init__.py b/tests/third_party/cupy/testing/__init__.py index dfa1f7d2731..701c381e2f3 100644 --- a/tests/third_party/cupy/testing/__init__.py +++ b/tests/third_party/cupy/testing/__init__.py @@ -32,6 +32,7 @@ assert_warns, for_all_dtypes, for_all_dtypes_combination, + for_castings, for_CF_orders, for_complex_dtypes, for_dtypes, diff --git a/tests/third_party/cupy/testing/helper.py b/tests/third_party/cupy/testing/helper.py index ebdf38b8697..c78dc07d999 100644 --- a/tests/third_party/cupy/testing/helper.py +++ b/tests/third_party/cupy/testing/helper.py @@ -1203,12 +1203,12 @@ def for_orders(orders, name="order"): """ def decorator(impl): - @functools.wraps(impl) - def test_func(self, *args, **kw): + @_wraps_partial(impl, name) + def test_func(*args, **kw): for order in orders: try: kw[name] = order - impl(self, *args, **kw) + impl(*args, **kw) except Exception: print(name, "is", order) raise @@ -1230,6 +1230,38 @@ def for_CF_orders(name="order"): return for_orders([None, "C", "c"], name) +def for_castings(castings=None, name="casting"): + """Decorator to parameterize tests with casting. + + Args: + castings(list of casting): casting to be tested. + name(str): Argument name to which the specified casting is passed. + + This decorator adds a keyword argument specified by ``name`` + to the test fixtures. Then, the fixtures run by passing each element of + ``castings`` to the named argument. + + """ + + if castings is None: + castings = ["no", "equiv", "safe", "same_kind", "unsafe"] + + def decorator(impl): + @_wraps_partial(impl, name) + def test_func(*args, **kw): + for casting in castings: + try: + kw[name] = casting + impl(*args, **kw) + except Exception: + print(name, "is", casting) + raise + + return test_func + + return decorator + + def with_requires(*requirements): """Run a test case only when given requirements are satisfied. From 297810e3625944fa3d81dec7286abc16718dd4ca Mon Sep 17 00:00:00 2001 From: vlad-perevezentsev Date: Sat, 5 Aug 2023 17:49:12 +0200 Subject: [PATCH 20/58] Update maths functions to run on Iris Xe (#1502) * Update dpnp.arctan2 * Update dpnp.copysign * Update dpnp.fmod * --amend * Update dpnp.hypot * Update dpnp.maximum * Update dpnp.minimum * Update dpnp.cross * Update test_mathematical * Add a new template function get_res_type * Skip test_remainder on Iris Xe * Fix TestAdd * Fix TestMultiply * Use dispatch_fmod_op in vector implementation * Remove fmap for dpnp_floor_divide_ext * Use dispatch_fmod_vec for vector impl * Rename and update get_res_type func * Fix remarks * Use a common check in dispatch_fmod_op * Update dispatch_fmod_op --------- Co-authored-by: Anton <100830759+antonwolfy@users.noreply.github.com> --- .../include/dpnp_gen_2arg_3type_tbl.hpp | 31 +- dpnp/backend/kernels/dpnp_krnl_elemwise.cpp | 323 +++++++----------- .../kernels/dpnp_krnl_mathematical.cpp | 94 ++--- dpnp/backend/src/dpnp_fptr.hpp | 39 +++ tests/test_mathematical.py | 28 +- 5 files changed, 229 insertions(+), 286 deletions(-) diff --git a/dpnp/backend/include/dpnp_gen_2arg_3type_tbl.hpp b/dpnp/backend/include/dpnp_gen_2arg_3type_tbl.hpp index 62346c2243d..353d1400320 100644 --- a/dpnp/backend/include/dpnp_gen_2arg_3type_tbl.hpp +++ b/dpnp/backend/include/dpnp_gen_2arg_3type_tbl.hpp @@ -114,16 +114,16 @@ MACRO_2ARG_3TYPES_OP(dpnp_add_c, std::complex)) MACRO_2ARG_3TYPES_OP(dpnp_arctan2_c, - sycl::atan2((double)input1_elem, (double)input2_elem), - nullptr, - std::false_type, + sycl::atan2(input1_elem, input2_elem), + sycl::atan2(x1, x2), + MACRO_UNPACK_TYPES(float, double), oneapi::mkl::vm::atan2, MACRO_UNPACK_TYPES(float, double)) MACRO_2ARG_3TYPES_OP(dpnp_copysign_c, - sycl::copysign((double)input1_elem, (double)input2_elem), - nullptr, - std::false_type, + sycl::copysign(input1_elem, input2_elem), + sycl::copysign(x1, x2), + MACRO_UNPACK_TYPES(float, double), oneapi::mkl::vm::copysign, MACRO_UNPACK_TYPES(float, double)) @@ -137,17 +137,18 @@ MACRO_2ARG_3TYPES_OP(dpnp_divide_c, std::complex, std::complex)) -MACRO_2ARG_3TYPES_OP(dpnp_fmod_c, - sycl::fmod((double)input1_elem, (double)input2_elem), - nullptr, - std::false_type, - oneapi::mkl::vm::fmod, - MACRO_UNPACK_TYPES(float, double)) +MACRO_2ARG_3TYPES_OP( + dpnp_fmod_c, + dispatch_fmod_op(input1_elem, input2_elem), + dispatch_fmod_op(x1, x2), + MACRO_UNPACK_TYPES(std::int32_t, std::int64_t, float, double), + oneapi::mkl::vm::fmod, + MACRO_UNPACK_TYPES(float, double)) MACRO_2ARG_3TYPES_OP(dpnp_hypot_c, - sycl::hypot((double)input1_elem, (double)input2_elem), - nullptr, - std::false_type, + sycl::hypot(input1_elem, input2_elem), + sycl::hypot(x1, x2), + MACRO_UNPACK_TYPES(float, double), oneapi::mkl::vm::hypot, MACRO_UNPACK_TYPES(float, double)) diff --git a/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp b/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp index 404657965ff..1bd0d1922e0 100644 --- a/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp @@ -819,6 +819,36 @@ constexpr T dispatch_sign_op(T elem) } } +template +constexpr auto dispatch_fmod_op(T elem1, T elem2) +{ + if constexpr (sycl::detail::is_integral::value) { + if constexpr (sycl::detail::is_vec::value) { + T rem; + using ElemT = typename T::element_type; +#pragma unroll + for (size_t i = 0; i < rem.size(); i++) { + if (elem2[i] == ElemT(0)) { + rem[i] = ElemT(0); + } + else { + rem[i] = elem1[i] % elem2[i]; + } + } + return rem; + } + else { + if (elem2 == T(0)) { + return T(0); + } + return elem1 % elem2; + } + } + else { + return sycl::fmod(elem1, elem2); + } +} + #define MACRO_1ARG_1TYPE_OP(__name__, __operation1__, __operation2__) \ template \ class __name__##_kernel; \ @@ -1644,12 +1674,104 @@ static void func_map_elemwise_2arg_3type_core(func_map_t &fmap) ...); } +template +static void func_map_elemwise_2arg_3type_short_core(func_map_t &fmap) +{ + ((fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][FT1][FTs] = + {get_floating_res_type(), + (void *)dpnp_arctan2_c_ext< + func_type_map_t::find_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>, + get_floating_res_type(), + (void *)dpnp_arctan2_c_ext< + func_type_map_t::find_type< + get_floating_res_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>}), + ...); + ((fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][FT1][FTs] = + {get_floating_res_type(), + (void *)dpnp_copysign_c_ext< + func_type_map_t::find_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>, + get_floating_res_type(), + (void *)dpnp_copysign_c_ext< + func_type_map_t::find_type< + get_floating_res_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>}), + ...); + ((fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][FT1][FTs] = + {get_floating_res_type(), + (void *) + dpnp_fmod_c_ext()>, + func_type_map_t::find_type, + func_type_map_t::find_type>, + get_floating_res_type(), + (void *)dpnp_fmod_c_ext< + func_type_map_t::find_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>}), + ...); + ((fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][FT1][FTs] = + {get_floating_res_type(), + (void *)dpnp_hypot_c_ext< + func_type_map_t::find_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>, + get_floating_res_type(), + (void *)dpnp_hypot_c_ext< + func_type_map_t::find_type< + get_floating_res_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>}), + ...); + ((fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][FT1][FTs] = + {get_floating_res_type(), + (void *)dpnp_maximum_c_ext< + func_type_map_t::find_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>, + get_floating_res_type(), + (void *)dpnp_maximum_c_ext< + func_type_map_t::find_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>}), + ...); + ((fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][FT1][FTs] = + {get_floating_res_type(), + (void *)dpnp_minimum_c_ext< + func_type_map_t::find_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>, + get_floating_res_type(), + (void *)dpnp_minimum_c_ext< + func_type_map_t::find_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>}), + ...); +} + template static void func_map_elemwise_2arg_3type_helper(func_map_t &fmap) { ((func_map_elemwise_2arg_3type_core(fmap)), ...); } +template +static void func_map_elemwise_2arg_3type_short_helper(func_map_t &fmap) +{ + ((func_map_elemwise_2arg_3type_short_core(fmap)), ...); +} + static void func_map_init_elemwise_2arg_3type(func_map_t &fmap) { fmap[DPNPFuncName::DPNP_FN_ADD][eft_INT][eft_INT] = { @@ -1718,39 +1840,6 @@ static void func_map_init_elemwise_2arg_3type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_ARCTAN2][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_arctan2_c_default}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_INT][eft_INT] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_INT][eft_LNG] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_INT][eft_FLT] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_INT][eft_DBL] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_LNG][eft_INT] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_LNG][eft_LNG] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_LNG][eft_FLT] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_LNG][eft_DBL] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_FLT][eft_INT] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_FLT][eft_LNG] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_FLT][eft_DBL] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_DBL][eft_INT] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_DBL][eft_LNG] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_DBL][eft_FLT] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_ARCTAN2_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_arctan2_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN][eft_INT][eft_INT] = { eft_DBL, (void *)dpnp_copysign_c_default}; fmap[DPNPFuncName::DPNP_FN_COPYSIGN][eft_INT][eft_LNG] = { @@ -1784,39 +1873,6 @@ static void func_map_init_elemwise_2arg_3type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_COPYSIGN][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_copysign_c_default}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_INT][eft_INT] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_INT][eft_LNG] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_INT][eft_FLT] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_INT][eft_DBL] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_LNG][eft_INT] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_LNG][eft_LNG] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_LNG][eft_FLT] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_LNG][eft_DBL] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_FLT][eft_INT] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_FLT][eft_LNG] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_FLT][eft_DBL] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_DBL][eft_INT] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_DBL][eft_LNG] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_DBL][eft_FLT] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYSIGN_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_copysign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_DIVIDE][eft_INT][eft_INT] = { eft_DBL, (void *)dpnp_divide_c_default}; fmap[DPNPFuncName::DPNP_FN_DIVIDE][eft_INT][eft_LNG] = { @@ -1883,39 +1939,6 @@ static void func_map_init_elemwise_2arg_3type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_FMOD][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_fmod_c_default}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_INT][eft_LNG] = { - eft_LNG, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_INT][eft_FLT] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_INT][eft_DBL] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_LNG][eft_INT] = { - eft_LNG, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_LNG][eft_FLT] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_LNG][eft_DBL] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_FLT][eft_INT] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_FLT][eft_LNG] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_FLT][eft_DBL] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_DBL][eft_INT] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_DBL][eft_LNG] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_DBL][eft_FLT] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FMOD_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_fmod_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT][eft_INT][eft_INT] = { eft_DBL, (void *)dpnp_hypot_c_default}; fmap[DPNPFuncName::DPNP_FN_HYPOT][eft_INT][eft_LNG] = { @@ -1949,39 +1972,6 @@ static void func_map_init_elemwise_2arg_3type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_HYPOT][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_hypot_c_default}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_INT][eft_INT] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_INT][eft_LNG] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_INT][eft_FLT] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_INT][eft_DBL] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_LNG][eft_INT] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_LNG][eft_LNG] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_LNG][eft_FLT] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_LNG][eft_DBL] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_FLT][eft_INT] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_FLT][eft_LNG] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_FLT][eft_DBL] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_DBL][eft_INT] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_DBL][eft_LNG] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_DBL][eft_FLT] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_HYPOT_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_hypot_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM][eft_INT][eft_INT] = { eft_INT, (void *)dpnp_maximum_c_default}; fmap[DPNPFuncName::DPNP_FN_MAXIMUM][eft_INT][eft_LNG] = { @@ -2015,39 +2005,6 @@ static void func_map_init_elemwise_2arg_3type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_MAXIMUM][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_maximum_c_default}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_INT][eft_LNG] = { - eft_LNG, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_INT][eft_FLT] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_INT][eft_DBL] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_LNG][eft_INT] = { - eft_LNG, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_LNG][eft_FLT] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_LNG][eft_DBL] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_FLT][eft_INT] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_FLT][eft_LNG] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_FLT][eft_DBL] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_DBL][eft_INT] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_DBL][eft_LNG] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_DBL][eft_FLT] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MAXIMUM_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_maximum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM][eft_INT][eft_INT] = { eft_INT, (void *)dpnp_minimum_c_default}; fmap[DPNPFuncName::DPNP_FN_MINIMUM][eft_INT][eft_LNG] = { @@ -2081,39 +2038,6 @@ static void func_map_init_elemwise_2arg_3type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_MINIMUM][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_minimum_c_default}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_INT][eft_LNG] = { - eft_LNG, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_INT][eft_FLT] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_INT][eft_DBL] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_LNG][eft_INT] = { - eft_LNG, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_LNG][eft_FLT] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_LNG][eft_DBL] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_FLT][eft_INT] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_FLT][eft_LNG] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_FLT][eft_DBL] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_DBL][eft_INT] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_DBL][eft_LNG] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_DBL][eft_FLT] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MINIMUM_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_minimum_c_ext}; - fmap[DPNPFuncName::DPNP_FN_MULTIPLY][eft_BLN][eft_BLN] = { eft_BLN, (void *)dpnp_multiply_c_default}; fmap[DPNPFuncName::DPNP_FN_MULTIPLY][eft_BLN][eft_INT] = { @@ -2285,6 +2209,9 @@ static void func_map_init_elemwise_2arg_3type(func_map_t &fmap) func_map_elemwise_2arg_3type_helper(fmap); + func_map_elemwise_2arg_3type_short_helper(fmap); + return; } diff --git a/dpnp/backend/kernels/dpnp_krnl_mathematical.cpp b/dpnp/backend/kernels/dpnp_krnl_mathematical.cpp index 5957cb4a699..32be7b92983 100644 --- a/dpnp/backend/kernels/dpnp_krnl_mathematical.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_mathematical.cpp @@ -1141,6 +1141,31 @@ DPCTLSyclEventRef (*dpnp_trapz_ext_c)(DPCTLSyclQueueRef, const DPCTLEventVectorRef) = dpnp_trapz_c<_DataType_input1, _DataType_input2, _DataType_output>; +template +static void func_map_elemwise_2arg_3type_core(func_map_t &fmap) +{ + ((fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][FT1][FTs] = + {get_floating_res_type(), + (void *)dpnp_cross_ext_c< + func_type_map_t::find_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>, + get_floating_res_type(), + (void *)dpnp_cross_ext_c< + func_type_map_t::find_type()>, + func_type_map_t::find_type, + func_type_map_t::find_type>}), + ...); +} + +template +static void func_map_elemwise_2arg_3type_helper(func_map_t &fmap) +{ + ((func_map_elemwise_2arg_3type_core(fmap)), ...); +} + void func_map_init_mathematical(func_map_t &fmap) { fmap[DPNPFuncName::DPNP_FN_ABSOLUTE][eft_INT][eft_INT] = { @@ -1218,39 +1243,6 @@ void func_map_init_mathematical(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_CROSS][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_cross_default_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_INT][eft_LNG] = { - eft_LNG, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_INT][eft_FLT] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_INT][eft_DBL] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_LNG][eft_INT] = { - eft_LNG, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_LNG][eft_FLT] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_LNG][eft_DBL] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_FLT][eft_INT] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_FLT][eft_LNG] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_FLT][eft_DBL] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_DBL][eft_INT] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_DBL][eft_LNG] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_DBL][eft_FLT] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CROSS_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_cross_ext_c}; - fmap[DPNPFuncName::DPNP_FN_CUMPROD][eft_INT][eft_INT] = { eft_LNG, (void *)dpnp_cumprod_default_c}; fmap[DPNPFuncName::DPNP_FN_CUMPROD][eft_LNG][eft_LNG] = { @@ -1342,39 +1334,6 @@ void func_map_init_mathematical(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_floor_divide_default_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_INT][eft_LNG] = { - eft_LNG, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_INT][eft_FLT] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_INT][eft_DBL] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_LNG][eft_INT] = { - eft_LNG, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_LNG][eft_FLT] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_LNG][eft_DBL] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_FLT][eft_INT] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_FLT][eft_LNG] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_FLT][eft_DBL] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_DBL][eft_INT] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_DBL][eft_LNG] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_DBL][eft_FLT] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_DIVIDE_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_floor_divide_ext_c}; - fmap[DPNPFuncName::DPNP_FN_MODF][eft_INT][eft_INT] = { eft_DBL, (void *)dpnp_modf_default_c}; fmap[DPNPFuncName::DPNP_FN_MODF][eft_LNG][eft_LNG] = { @@ -1525,5 +1484,8 @@ void func_map_init_mathematical(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_TRAPZ_EXT][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_trapz_ext_c}; + func_map_elemwise_2arg_3type_helper( + fmap); + return; } diff --git a/dpnp/backend/src/dpnp_fptr.hpp b/dpnp/backend/src/dpnp_fptr.hpp index 446083204fc..d485711f8f5 100644 --- a/dpnp/backend/src/dpnp_fptr.hpp +++ b/dpnp/backend/src/dpnp_fptr.hpp @@ -271,6 +271,45 @@ static constexpr DPNPFuncType get_default_floating_type() : DPNPFuncType::DPNP_FT_FLOAT; } +/** + * A template function that determines the resulting floating-point type + * based on the value of the template parameter has_fp64. + */ +template +static constexpr DPNPFuncType get_floating_res_type() +{ + constexpr auto widest_type = populate_func_types(); + constexpr auto shortes_type = (widest_type == FT1) ? FT2 : FT1; + + // Return integer result type if save_int is True + if constexpr (keep_int::value) { + if constexpr (widest_type == DPNPFuncType::DPNP_FT_INT || + widest_type == DPNPFuncType::DPNP_FT_LONG) + { + return widest_type; + } + } + + // Check for double + if constexpr (widest_type == DPNPFuncType::DPNP_FT_DOUBLE) { + return widest_type; + } + + // Check for float + else if constexpr (widest_type == DPNPFuncType::DPNP_FT_FLOAT) { + // Check if the shortest type is also float + if constexpr (shortes_type == DPNPFuncType::DPNP_FT_FLOAT) { + return widest_type; + } + } + + // Default case + return get_default_floating_type(); +} + /** * FPTR interface initialization functions */ diff --git a/tests/test_mathematical.py b/tests/test_mathematical.py index 873a134a9c1..fed1928e076 100644 --- a/tests/test_mathematical.py +++ b/tests/test_mathematical.py @@ -13,6 +13,7 @@ from .helper import ( get_all_dtypes, get_float_complex_dtypes, + has_support_aspect64, is_cpu_device, is_win_platform, ) @@ -162,6 +163,16 @@ def test_divide(self, dtype, lhs, rhs): "dtype", get_all_dtypes(no_bool=True, no_complex=True) ) def test_fmod(self, dtype, lhs, rhs): + if dtype == None and rhs == 0.3 and not has_support_aspect64(): + """ + Due to accuracy reason NumPy behaves differently, when: + >>> numpy.fmod(numpy.array([3.9], dtype=numpy.float32), 0.3) + array([0.29999995], dtype=float32) + while numpy with float64 returns something around zero which is aligned with dpnp: + >>> numpy.fmod(numpy.array([3.9], dtype=numpy.float64), 0.3) + array([9.53674318e-08]) + """ + pytest.skip("missaligned between numpy results") self._test_mathematical("fmod", dtype, lhs, rhs) @pytest.mark.parametrize("dtype", get_all_dtypes(no_complex=True)) @@ -197,6 +208,9 @@ def test_minimum(self, dtype, lhs, rhs): def test_multiply(self, dtype, lhs, rhs): self._test_mathematical("multiply", dtype, lhs, rhs) + @pytest.mark.skipif( + not has_support_aspect64(), reason="Aborted on Iris Xe: SAT-6039" + ) @pytest.mark.usefixtures("allow_fall_back_on_numpy") @pytest.mark.parametrize( "dtype", get_all_dtypes(no_bool=True, no_complex=True) @@ -382,7 +396,7 @@ def test_negative(data, dtype): result = dpnp.negative(dpnp_a) expected = numpy.negative(np_a) - assert_array_equal(result, expected) + assert_allclose(result, expected) @pytest.mark.parametrize("val_type", get_all_dtypes(no_none=True)) @@ -768,9 +782,9 @@ def test_inplace_strided_out(self, dtype): "shape", [(0,), (15,), (2, 2)], ids=["(0,)", "(15, )", "(2,2)"] ) def test_invalid_shape(self, shape): - dp_array1 = dpnp.arange(10, dtype=dpnp.float64) - dp_array2 = dpnp.arange(5, 15, dtype=dpnp.float64) - dp_out = dpnp.empty(shape, dtype=dpnp.float64) + dp_array1 = dpnp.arange(10) + dp_array2 = dpnp.arange(5, 15) + dp_out = dpnp.empty(shape) with pytest.raises(TypeError): dpnp.add(dp_array1, dp_array2, out=dp_out) @@ -858,9 +872,9 @@ def test_inplace_strided_out(self, dtype): "shape", [(0,), (15,), (2, 2)], ids=["(0,)", "(15, )", "(2,2)"] ) def test_invalid_shape(self, shape): - dp_array1 = dpnp.arange(10, dtype=dpnp.float64) - dp_array2 = dpnp.arange(5, 15, dtype=dpnp.float64) - dp_out = dpnp.empty(shape, dtype=dpnp.float64) + dp_array1 = dpnp.arange(10) + dp_array2 = dpnp.arange(5, 15) + dp_out = dpnp.empty(shape) with pytest.raises(TypeError): dpnp.multiply(dp_array1, dp_array2, out=dp_out) From 6538397b588470e19eb0ca8d886fc02cb7fdc50f Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Sun, 6 Aug 2023 15:16:36 +0200 Subject: [PATCH 21/58] Add support of python 3.11 for public CI (#1501) * Added support of python 3.11 * Pinned python back to 3.9 in build sphinx * The change to help icpx to find the libraries in * Updated FFT tests to work with numpy from conda-forge * Update tan test to use assert_allclose * Updated remaing FFT tests * Updated test with dpnp.dot() * Updated argsort test * Reduced array size for single precision dtype in dot() test --- .github/workflows/conda-package.yml | 8 +++---- .github/workflows/generate_coverage.yaml | 2 +- .github/workflows/pre-commit.yml | 2 +- conda-recipe/build.sh | 3 +++ setup.py | 1 + tests/helper.py | 22 +++++++++++++++++++ tests/test_dot.py | 8 +++---- tests/test_fft.py | 16 +++++--------- tests/test_sycl_queue.py | 5 ++--- tests/test_umath.py | 2 +- tests/third_party/cupy/fft_tests/test_fft.py | 16 +++++++++++--- .../cupy/sorting_tests/test_sort.py | 3 ++- 12 files changed, 60 insertions(+), 28 deletions(-) diff --git a/.github/workflows/conda-package.yml b/.github/workflows/conda-package.yml index 49420a511dd..096f7a10bbf 100644 --- a/.github/workflows/conda-package.yml +++ b/.github/workflows/conda-package.yml @@ -37,7 +37,7 @@ jobs: strategy: matrix: - python: ['3.8', '3.9', '3.10'] + python: ['3.8', '3.9', '3.10', '3.11'] os: [ubuntu-20.04, windows-latest] runs-on: ${{ matrix.os }} @@ -114,7 +114,7 @@ jobs: strategy: matrix: - python: ['3.8', '3.9', '3.10'] + python: ['3.8', '3.9', '3.10', '3.11'] os: [ubuntu-20.04, ubuntu-latest] experimental: [false] @@ -217,7 +217,7 @@ jobs: strategy: matrix: - python: ['3.8', '3.9', '3.10'] + python: ['3.8', '3.9', '3.10', '3.11'] experimental: [false] continue-on-error: ${{ matrix.experimental }} @@ -350,7 +350,7 @@ jobs: strategy: matrix: - python: ['3.8', '3.9', '3.10'] + python: ['3.8', '3.9', '3.10', '3.11'] os: [ubuntu-20.04, windows-latest] runs-on: ${{ matrix.os }} diff --git a/.github/workflows/generate_coverage.yaml b/.github/workflows/generate_coverage.yaml index 413835d336c..d3a217a03f8 100644 --- a/.github/workflows/generate_coverage.yaml +++ b/.github/workflows/generate_coverage.yaml @@ -14,7 +14,7 @@ jobs: shell: bash -l {0} env: - python-ver: '3.10' + python-ver: '3.11' CHANNELS: '-c dppy/label/dev -c intel -c conda-forge --override-channels' steps: diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 673f6c17243..18668bf10c4 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -19,5 +19,5 @@ jobs: - uses: actions/checkout@v3.5.2 - uses: actions/setup-python@v4.6.1 with: - python-version: '3.10' + python-version: '3.11' - uses: pre-commit/action@v3.0.0 diff --git a/conda-recipe/build.sh b/conda-recipe/build.sh index b25baed8ecd..015a25bbbbd 100755 --- a/conda-recipe/build.sh +++ b/conda-recipe/build.sh @@ -1,5 +1,8 @@ #!/bin/bash +# This is necessary to help DPC++ find Intel libraries such as SVML, IRNG, etc in build prefix +export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:${BUILD_PREFIX}/lib" + # Intel LLVM must cooperate with compiler and sysroot from conda echo "--gcc-toolchain=${BUILD_PREFIX} --sysroot=${BUILD_PREFIX}/${HOST}/sysroot -target ${HOST}" > icpx_for_conda.cfg export ICPXCFG="$(pwd)/icpx_for_conda.cfg" diff --git a/setup.py b/setup.py index f95260aeda2..174851ab770 100644 --- a/setup.py +++ b/setup.py @@ -37,6 +37,7 @@ def _get_cmdclass(): Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 Programming Language :: Python :: 3.10 +Programming Language :: Python :: 3.11 Programming Language :: Python :: Implementation :: CPython Topic :: Software Development Topic :: Scientific/Engineering diff --git a/tests/helper.py b/tests/helper.py index 6971feecb53..42ada294983 100644 --- a/tests/helper.py +++ b/tests/helper.py @@ -1,10 +1,32 @@ from sys import platform import dpctl +import numpy +from numpy.testing import assert_allclose, assert_array_equal import dpnp +def assert_dtype_allclose(dpnp_arr, numpy_arr): + """ + Assert DPNP and NumPy array based on maximum dtype resolution of input arrays + for floating and complex types. + For other dtypes the assertion is based on exact matching of the arrays. + + """ + + is_inexact = lambda x: dpnp.issubdtype(x.dtype, dpnp.inexact) + if is_inexact(dpnp_arr) or is_inexact(numpy_arr): + tol = 8 * max( + numpy.finfo(dpnp_arr.dtype).resolution, + numpy.finfo(numpy_arr.dtype).resolution, + ) + assert_allclose(dpnp_arr.asnumpy(), numpy_arr, atol=tol, rtol=tol) + else: + assert_array_equal(dpnp_arr.asnumpy(), numpy_arr) + assert dpnp_arr.dtype == numpy_arr.dtype + + def get_complex_dtypes(device=None): """ Build a list of complex types supported by DPNP based on device capabilities. diff --git a/tests/test_dot.py b/tests/test_dot.py index 7b4cdd02c92..80da5090e1b 100644 --- a/tests/test_dot.py +++ b/tests/test_dot.py @@ -20,11 +20,11 @@ def test_dot_ones(type): assert_array_equal(expected, result) -@pytest.mark.parametrize("type", get_all_dtypes(no_bool=True, no_complex=True)) -def test_dot_arange(type): +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) +def test_dot_arange(dtype): n = 10**2 - m = 10**3 - a = numpy.hstack((numpy.arange(n, dtype=type),) * m) + m = 10**3 if dtype is not inp.float32 else 10**2 + a = numpy.hstack((numpy.arange(n, dtype=dtype),) * m) b = numpy.flipud(a) ia = inp.array(a) ib = inp.array(b) diff --git a/tests/test_fft.py b/tests/test_fft.py index 38185f3e5f0..ae6835b86ed 100644 --- a/tests/test_fft.py +++ b/tests/test_fft.py @@ -3,7 +3,7 @@ import dpnp -from .helper import has_support_aspect64 +from .helper import assert_dtype_allclose, has_support_aspect64 pytestmark = pytest.mark.skipif( not has_support_aspect64(), reason="Aborted on Iris Xe: SAT-6028" @@ -20,10 +20,9 @@ def test_fft(type, norm): dpnp_data = dpnp.array(data) np_res = numpy.fft.fft(data, norm=norm) - dpnp_res = dpnp.asnumpy(dpnp.fft.fft(dpnp_data, norm=norm)) + dpnp_res = dpnp.fft.fft(dpnp_data, norm=norm) - numpy.testing.assert_allclose(dpnp_res, np_res, rtol=1e-4, atol=1e-7) - assert dpnp_res.dtype == np_res.dtype + assert_dtype_allclose(dpnp_res, np_res) @pytest.mark.parametrize( @@ -38,8 +37,7 @@ def test_fft_ndim(type, shape, norm): np_res = numpy.fft.fft(np_data, norm=norm) dpnp_res = dpnp.fft.fft(dpnp_data, norm=norm) - numpy.testing.assert_allclose(dpnp_res, np_res, rtol=1e-4, atol=1e-7) - assert dpnp_res.dtype == np_res.dtype + assert_dtype_allclose(dpnp_res, np_res) @pytest.mark.parametrize( @@ -56,8 +54,7 @@ def test_fft_ifft(type, shape, norm): np_res = numpy.fft.ifft(np_data, norm=norm) dpnp_res = dpnp.fft.ifft(dpnp_data, norm=norm) - numpy.testing.assert_allclose(dpnp_res, np_res, rtol=1e-4, atol=1e-7) - assert dpnp_res.dtype == np_res.dtype + assert_dtype_allclose(dpnp_res, np_res) @pytest.mark.parametrize("type", ["float32", "float64", "int32", "int64"]) @@ -71,5 +68,4 @@ def test_fft_rfft(type, shape): np_res = numpy.fft.rfft(np_data) dpnp_res = dpnp.fft.rfft(dpnp_data) - numpy.testing.assert_allclose(dpnp_res, np_res, rtol=1e-4, atol=1e-7) - assert dpnp_res.dtype == np_res.dtype + assert_dtype_allclose(dpnp_res, np_res) diff --git a/tests/test_sycl_queue.py b/tests/test_sycl_queue.py index 0de3a1b0ba9..7dc15a6bccf 100644 --- a/tests/test_sycl_queue.py +++ b/tests/test_sycl_queue.py @@ -6,7 +6,7 @@ import dpnp -from .helper import get_all_dtypes, is_win_platform +from .helper import assert_dtype_allclose, get_all_dtypes, is_win_platform list_of_backend_str = [ "host", @@ -670,8 +670,7 @@ def test_fft_rfft(type, shape, device): np_res = numpy.fft.rfft(np_data) dpnp_res = dpnp.fft.rfft(dpnp_data) - numpy.testing.assert_allclose(dpnp_res, np_res, rtol=1e-4, atol=1e-7) - assert dpnp_res.dtype == np_res.dtype + assert_dtype_allclose(dpnp_res, np_res) expected_queue = dpnp_data.get_array().sycl_queue result_queue = dpnp_res.get_array().sycl_queue diff --git a/tests/test_umath.py b/tests/test_umath.py index 7d8bb4acfe5..96ff2a849c8 100644 --- a/tests/test_umath.py +++ b/tests/test_umath.py @@ -480,7 +480,7 @@ def test_tan(self): np_array = numpy.array(array_data, dtype=numpy.float64) expected = numpy.tan(np_array, out=out) - assert_array_equal(expected, result) + assert_allclose(expected, result) @pytest.mark.parametrize( "dtype", diff --git a/tests/third_party/cupy/fft_tests/test_fft.py b/tests/third_party/cupy/fft_tests/test_fft.py index 9214a8515e5..0ddd3cc56c1 100644 --- a/tests/third_party/cupy/fft_tests/test_fft.py +++ b/tests/third_party/cupy/fft_tests/test_fft.py @@ -23,7 +23,11 @@ class TestFft(unittest.TestCase): @testing.for_all_dtypes() @testing.numpy_cupy_allclose( - rtol=1e-4, atol=1e-7, accept_error=ValueError, contiguous_check=False + rtol=1e-4, + atol=1e-7, + accept_error=ValueError, + contiguous_check=False, + type_check=False, ) def test_fft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) @@ -33,7 +37,11 @@ def test_fft(self, xp, dtype): @testing.for_all_dtypes() @testing.numpy_cupy_allclose( - rtol=1e-4, atol=1e-7, accept_error=ValueError, contiguous_check=False + rtol=1e-4, + atol=1e-7, + accept_error=ValueError, + contiguous_check=False, + type_check=False, ) def test_ifft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) @@ -154,7 +162,9 @@ def test_ifftn(self, xp, dtype): @testing.gpu class TestRfft(unittest.TestCase): @testing.for_all_dtypes(no_complex=True) - @testing.numpy_cupy_allclose(rtol=1e-4, atol=1e-7, contiguous_check=False) + @testing.numpy_cupy_allclose( + rtol=1e-4, atol=1e-7, contiguous_check=False, type_check=False + ) def test_rfft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) out = xp.fft.rfft(a, n=self.n, norm=self.norm) diff --git a/tests/third_party/cupy/sorting_tests/test_sort.py b/tests/third_party/cupy/sorting_tests/test_sort.py index d9fb94fc3e0..7ae7911f90e 100644 --- a/tests/third_party/cupy/sorting_tests/test_sort.py +++ b/tests/third_party/cupy/sorting_tests/test_sort.py @@ -317,7 +317,8 @@ def test_argsort_zero_dim(self, xp, dtype): @testing.numpy_cupy_array_equal() def test_argsort_one_dim(self, xp, dtype): a = testing.shaped_random((10,), xp, dtype) - return self.argsort(a) + res = self.argsort(a) + return a[res] @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() From 0a45a540035dfce4ed45b6a8124700ac4c17a864 Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Mon, 7 Aug 2023 19:33:59 +0200 Subject: [PATCH 22/58] Leveraged dpctl.tensor.stack() implementation (#1509) * Leveraged dpctl.tensor.stack() implementation * Relaxed check in a test of SYCL queue to account the error of floating operations --- .github/workflows/conda-package.yml | 2 + dpnp/dpnp_iface_manipulation.py | 71 +++++- tests/conftest.py | 8 + tests/test_arraymanipulation.py | 227 ++++++++++++++++-- tests/test_sycl_queue.py | 40 +-- .../cupy/manipulation_tests/test_join.py | 79 +++--- 6 files changed, 338 insertions(+), 89 deletions(-) diff --git a/.github/workflows/conda-package.yml b/.github/workflows/conda-package.yml index 096f7a10bbf..9ad801cf5fd 100644 --- a/.github/workflows/conda-package.yml +++ b/.github/workflows/conda-package.yml @@ -12,6 +12,7 @@ env: CHANNELS: '-c dppy/label/dev -c intel -c conda-forge --override-channels' TEST_SCOPE: >- test_arraycreation.py + test_arraymanipulation.py test_dot.py test_dparray.py test_fft.py @@ -23,6 +24,7 @@ env: test_umath.py test_usm_type.py third_party/cupy/linalg_tests/test_product.py + third_party/cupy/manipulation_tests/test_join.py third_party/cupy/math_tests/test_explog.py third_party/cupy/math_tests/test_misc.py third_party/cupy/math_tests/test_trigonometric.py diff --git a/dpnp/dpnp_iface_manipulation.py b/dpnp/dpnp_iface_manipulation.py index fca218be02f..5f5dcb3617d 100644 --- a/dpnp/dpnp_iface_manipulation.py +++ b/dpnp/dpnp_iface_manipulation.py @@ -237,7 +237,7 @@ def broadcast_to(x, /, shape, subok=False): return call_origin(numpy.broadcast_to, x, shape=shape, subok=subok) -def concatenate(arrays, *, axis=0, out=None, dtype=None, **kwargs): +def concatenate(arrays, /, *, axis=0, out=None, dtype=None, **kwargs): """ Join a sequence of arrays along an existing axis. @@ -253,8 +253,7 @@ def concatenate(arrays, *, axis=0, out=None, dtype=None, **kwargs): Each array in `arrays` is supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. Otherwise ``TypeError`` exeption will be raised. - Parameter `out` is supported with default value. - Parameter `dtype` is supported with default value. + Parameters `out` and `dtype are supported with default value. Keyword argument ``kwargs`` is currently unsupported. Otherwise the function will be executed sequentially on CPU. @@ -834,15 +833,77 @@ def squeeze(x, /, axis=None): return call_origin(numpy.squeeze, x, axis) -def stack(arrays, axis=0, out=None): +def stack(arrays, /, *, axis=0, out=None, dtype=None, **kwargs): """ Join a sequence of arrays along a new axis. For full documentation refer to :obj:`numpy.stack`. + Returns + ------- + out : dpnp.ndarray + The stacked array which has one more dimension than the input arrays. + + Limitations + ----------- + Each array in `arrays` is supported as either :class:`dpnp.ndarray` + or :class:`dpctl.tensor.usm_ndarray`. Otherwise ``TypeError`` exeption + will be raised. + Parameters `out` and `dtype are supported with default value. + Keyword argument ``kwargs`` is currently unsupported. + Otherwise the function will be executed sequentially on CPU. + + See Also + -------- + :obj:`dpnp.concatenate` : Join a sequence of arrays along an existing axis. + :obj:`dpnp.block` : Assemble an nd-array from nested lists of blocks. + :obj:`dpnp.split` : Split array into a list of multiple sub-arrays of equal size. + + Examples + -------- + >>> import dpnp as np + >>> arrays = [np.random.randn(3, 4) for _ in range(10)] + >>> np.stack(arrays, axis=0).shape + (10, 3, 4) + + >>> np.stack(arrays, axis=1).shape + (3, 10, 4) + + >>> np.stack(arrays, axis=2).shape + (3, 4, 10) + + >>> a = np.array([1, 2, 3]) + >>> b = np.array([4, 5, 6]) + >>> np.stack((a, b)) + array([[1, 2, 3], + [4, 5, 6]]) + + >>> np.stack((a, b), axis=-1) + array([[1, 4], + [2, 5], + [3, 6]]) + """ - return call_origin(numpy.stack, arrays, axis, out) + if kwargs: + pass + elif out is not None: + pass + elif dtype is not None: + pass + else: + usm_arrays = [dpnp.get_usm_ndarray(x) for x in arrays] + usm_res = dpt.stack(usm_arrays, axis=axis) + return dpnp_array._create_from_usm_ndarray(usm_res) + + return call_origin( + numpy.stack, + arrays, + axis=axis, + out=out, + dtype=dtype, + **kwargs, + ) def swapaxes(x1, axis1, axis2): diff --git a/tests/conftest.py b/tests/conftest.py index 49b6692a132..ee37c6a68aa 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -83,6 +83,14 @@ def allow_fall_back_on_numpy(monkeypatch): ) +@pytest.fixture +def suppress_complex_warning(): + sup = numpy.testing.suppress_warnings("always") + sup.filter(numpy.ComplexWarning) + with sup: + yield + + @pytest.fixture def suppress_divide_numpy_warnings(): # divide: treatment for division by zero (infinite result obtained from finite numbers) diff --git a/tests/test_arraymanipulation.py b/tests/test_arraymanipulation.py index 68e8f4af47a..94f54accbc7 100644 --- a/tests/test_arraymanipulation.py +++ b/tests/test_arraymanipulation.py @@ -1,7 +1,6 @@ import numpy import pytest from numpy.testing import ( - assert_, assert_allclose, assert_array_equal, assert_equal, @@ -11,7 +10,7 @@ import dpnp -from .helper import get_all_dtypes +from .helper import get_all_dtypes, get_float_complex_dtypes @pytest.mark.usefixtures("allow_fall_back_on_numpy") @@ -145,9 +144,12 @@ def test_axis_exceptions(self, ndim): assert_equal(dp_res.asnumpy(), np_res) for axis in [ndim, -(ndim + 1)]: - with pytest.raises(numpy.AxisError): - dpnp.concatenate((dp_a, dp_a), axis=axis) - numpy.concatenate((np_a, np_a), axis=axis) + assert_raises( + numpy.AxisError, dpnp.concatenate, (dp_a, dp_a), axis=axis + ) + assert_raises( + numpy.AxisError, numpy.concatenate, (np_a, np_a), axis=axis + ) def test_scalar_exceptions(self): assert_raises(TypeError, dpnp.concatenate, (0,)) @@ -177,9 +179,12 @@ def test_shapes_match_exception(self): assert_equal(dp_res.asnumpy(), np_res) for i in range(1, 3): - with pytest.raises(ValueError): - numpy.concatenate((np_a, np_b), axis=axis[i]) - dpnp.concatenate((dp_a, dp_b), axis=axis[i]) + assert_raises( + ValueError, numpy.concatenate, (np_a, np_b), axis=axis[i] + ) + assert_raises( + ValueError, dpnp.concatenate, (dp_a, dp_b), axis=axis[i] + ) np_a = numpy.moveaxis(np_a, -1, 0) dp_a = dpnp.moveaxis(dp_a, -1, 0) @@ -189,8 +194,7 @@ def test_shapes_match_exception(self): axis.append(axis.pop(0)) def test_no_array_exception(self): - with pytest.raises(ValueError): - numpy.concatenate(()) + with pytest.raises(TypeError): dpnp.concatenate(()) @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True)) @@ -272,9 +276,12 @@ def test_concatenate_2d(self, dtype): assert_array_equal(dp_res.asnumpy(), np_res) # Arrays much match shape - with pytest.raises(ValueError): - numpy.concatenate((np_a23.T, np_a13.T), axis=0) - dpnp.concatenate((dp_a23.T, dp_a13.T), axis=0) + assert_raises( + ValueError, numpy.concatenate, (np_a23.T, np_a13.T), axis=0 + ) + assert_raises( + ValueError, dpnp.concatenate, (dp_a23.T, dp_a13.T), axis=0 + ) @pytest.mark.parametrize( "dtype", get_all_dtypes(no_bool=True, no_none=True) @@ -299,7 +306,7 @@ def test_concatenate_3d(self, dtype): dp_res = dpnp.concatenate((dp_a0.T, dp_a1.T, dp_a2.T), axis=0) assert_array_equal(dp_res.asnumpy(), np_res) - @pytest.mark.skip("out keyword is currently unsupported") + @pytest.mark.usefixtures("allow_fall_back_on_numpy") @pytest.mark.parametrize( "dtype", get_all_dtypes(no_bool=True, no_none=True) ) @@ -363,6 +370,198 @@ def test_generator(self): dpnp.hstack(map(lambda x: x, numpy.ones((3, 2)))) +class TestStack: + def test_non_iterable_input(self): + with pytest.raises(TypeError): + dpnp.stack(1) + + @pytest.mark.parametrize( + "input", [(1, 2, 3), [dpnp.int32(1), dpnp.int32(2), dpnp.int32(3)]] + ) + def test_scalar_input(self, input): + with pytest.raises(TypeError): + dpnp.stack(input) + + @pytest.mark.parametrize("dtype", get_all_dtypes()) + def test_0d_array_input(self, dtype): + np_arrays = [] + dp_arrays = [] + + for x in (1, 2, 3): + np_arrays.append(numpy.array(x, dtype=dtype)) + dp_arrays.append(dpnp.array(x, dtype=dtype)) + + np_res = numpy.stack(np_arrays) + dp_res = dpnp.stack(dp_arrays) + assert_array_equal(dp_res.asnumpy(), np_res) + + @pytest.mark.parametrize("dtype", get_all_dtypes()) + def test_1d_array_input(self, dtype): + np_a = numpy.array([1, 2, 3], dtype=dtype) + np_b = numpy.array([4, 5, 6], dtype=dtype) + dp_a = dpnp.array(np_a, dtype=dtype) + dp_b = dpnp.array(np_b, dtype=dtype) + + np_res = numpy.stack((np_a, np_b)) + dp_res = dpnp.stack((dp_a, dp_b)) + assert_array_equal(dp_res.asnumpy(), np_res) + + np_res = numpy.stack((np_a, np_b), axis=1) + dp_res = dpnp.stack((dp_a, dp_b), axis=1) + assert_array_equal(dp_res.asnumpy(), np_res) + + np_res = numpy.stack(list([np_a, np_b])) + dp_res = dpnp.stack(list([dp_a, dp_b])) + assert_array_equal(dp_res.asnumpy(), np_res) + + np_res = numpy.stack(numpy.array([np_a, np_b])) + dp_res = dpnp.stack(dpnp.array([dp_a, dp_b])) + assert_array_equal(dp_res.asnumpy(), np_res) + + @pytest.mark.parametrize("axis", [0, 1, -1, -2]) + @pytest.mark.parametrize( + "dtype", get_all_dtypes(no_bool=True, no_none=True) + ) + def test_1d_array_axis(self, axis, dtype): + arrays = [numpy.random.randn(3) for _ in range(10)] + np_arrays = numpy.array(arrays, dtype=dtype) + dp_arrays = dpnp.array(arrays, dtype=dtype) + + np_res = numpy.stack(np_arrays, axis=axis) + dp_res = dpnp.stack(dp_arrays, axis=axis) + assert_array_equal(dp_res.asnumpy(), np_res) + + @pytest.mark.parametrize("axis", [2, -3]) + @pytest.mark.parametrize( + "dtype", get_all_dtypes(no_bool=True, no_none=True) + ) + def test_1d_array_invalid_axis(self, axis, dtype): + arrays = [numpy.random.randn(3) for _ in range(10)] + np_arrays = numpy.array(arrays, dtype=dtype) + dp_arrays = dpnp.array(arrays, dtype=dtype) + + assert_raises(numpy.AxisError, numpy.stack, np_arrays, axis=axis) + assert_raises(numpy.AxisError, dpnp.stack, dp_arrays, axis=axis) + + @pytest.mark.parametrize("axis", [0, 1, 2, -1, -2, -3]) + @pytest.mark.parametrize( + "dtype", get_all_dtypes(no_bool=True, no_none=True) + ) + def test_2d_array_axis(self, axis, dtype): + arrays = [numpy.random.randn(3, 4) for _ in range(10)] + np_arrays = numpy.array(arrays, dtype=dtype) + dp_arrays = dpnp.array(arrays, dtype=dtype) + + np_res = numpy.stack(np_arrays, axis=axis) + dp_res = dpnp.stack(dp_arrays, axis=axis) + assert_array_equal(dp_res.asnumpy(), np_res) + + @pytest.mark.parametrize("dtype", get_all_dtypes()) + def test_empty_arrays_input(self, dtype): + arrays = [[], [], []] + np_arrays = numpy.array(arrays, dtype=dtype) + dp_arrays = dpnp.array(arrays, dtype=dtype) + + np_res = numpy.stack(np_arrays) + dp_res = dpnp.stack(dp_arrays) + assert_array_equal(dp_res.asnumpy(), np_res) + + np_res = numpy.stack(np_arrays, axis=1) + dp_res = dpnp.stack(dp_arrays, axis=1) + assert_array_equal(dp_res.asnumpy(), np_res) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @pytest.mark.parametrize("dtype", get_all_dtypes()) + def test_out(self, dtype): + np_a = numpy.array([1, 2, 3], dtype=dtype) + np_b = numpy.array([4, 5, 6], dtype=dtype) + dp_a = dpnp.array(np_a, dtype=dtype) + dp_b = dpnp.array(np_b, dtype=dtype) + + np_out = numpy.empty_like(np_a, shape=(2, 3)) + dp_out = dpnp.empty_like(dp_a, shape=(2, 3)) + + np_res = numpy.stack((np_a, np_b), out=np_out) + dp_res = dpnp.stack((dp_a, dp_b), out=dp_out) + + assert dp_out is dp_res + assert_array_equal(dp_out.asnumpy(), np_out) + assert_array_equal(dp_res.asnumpy(), np_res) + + def test_empty_list_input(self): + with pytest.raises(TypeError): + dpnp.stack([]) + + @pytest.mark.parametrize( + "sh1, sh2", + [ + pytest.param((), (3,), id="()-(3,)"), + pytest.param((3,), (), id="(3,)-()"), + pytest.param((3, 3), (3,), id="(3, 3)-(3,)"), + pytest.param((2,), (3,), id="(2,)-(3,)"), + ], + ) + @pytest.mark.parametrize( + "dtype", get_all_dtypes(no_bool=True, no_none=True) + ) + def test_invalid_shapes_input(self, sh1, sh2, dtype): + np_a = numpy.ones(sh1, dtype=dtype) + np_b = numpy.ones(sh2, dtype=dtype) + dp_a = dpnp.array(np_a, dtype=dtype) + dp_b = dpnp.array(np_b, dtype=dtype) + + assert_raises(ValueError, numpy.stack, [np_a, np_b]) + assert_raises(ValueError, dpnp.stack, [dp_a, dp_b]) + assert_raises(ValueError, numpy.stack, [np_a, np_b], axis=1) + assert_raises(ValueError, dpnp.stack, [dp_a, dp_b], axis=1) + + def test_generator_input(self): + with pytest.raises(TypeError): + dpnp.stack((x for x in range(3))) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @pytest.mark.usefixtures("suppress_complex_warning") + @pytest.mark.parametrize("arr_dtype", get_all_dtypes()) + @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True)) + def test_casting_dtype(self, arr_dtype, dtype): + np_a = numpy.array([1, 2, 3], dtype=arr_dtype) + np_b = numpy.array([2.5, 3.5, 4.5], dtype=arr_dtype) + dp_a = dpnp.array(np_a, dtype=arr_dtype) + dp_b = dpnp.array(np_b, dtype=arr_dtype) + + np_res = numpy.stack( + (np_a, np_b), axis=1, casting="unsafe", dtype=dtype + ) + dp_res = dpnp.stack((dp_a, dp_b), axis=1, casting="unsafe", dtype=dtype) + assert_array_equal(dp_res.asnumpy(), np_res) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @pytest.mark.parametrize("arr_dtype", get_float_complex_dtypes()) + @pytest.mark.parametrize("dtype", [dpnp.bool, dpnp.int32, dpnp.int64]) + def test_invalid_casting_dtype(self, arr_dtype, dtype): + np_a = numpy.array([1, 2, 3], dtype=arr_dtype) + np_b = numpy.array([2.5, 3.5, 4.5], dtype=arr_dtype) + dp_a = dpnp.array(np_a, dtype=arr_dtype) + dp_b = dpnp.array(np_b, dtype=arr_dtype) + + assert_raises( + TypeError, + numpy.stack, + (np_a, np_b), + axis=1, + casting="safe", + dtype=dtype, + ) + assert_raises( + TypeError, + dpnp.stack, + (dp_a, dp_b), + axis=1, + casting="safe", + dtype=dtype, + ) + + class TestVstack: def test_non_iterable(self): assert_raises(TypeError, dpnp.vstack, 1) diff --git a/tests/test_sycl_queue.py b/tests/test_sycl_queue.py index 7dc15a6bccf..6cc3cce50d1 100644 --- a/tests/test_sycl_queue.py +++ b/tests/test_sycl_queue.py @@ -100,7 +100,7 @@ def test_array_creation(func, arg, kwargs, device): dpnp_kwargs["device"] = device dpnp_array = getattr(dpnp, func)(*arg, **dpnp_kwargs) - numpy.testing.assert_array_equal(numpy_array, dpnp_array) + assert_allclose(numpy_array, dpnp_array) assert dpnp_array.sycl_device == device @@ -125,7 +125,7 @@ def test_empty(device): ids=[device.filter_string for device in valid_devices], ) def test_empty_like(device_x, device_y): - x = dpnp.ndarray([1, 2, 3], device=device_x) + x = dpnp.array([1, 2, 3], device=device_x) y = dpnp.empty_like(x) assert_sycl_queue_equal(y.sycl_queue, x.sycl_queue) y = dpnp.empty_like(x, device=device_y) @@ -158,7 +158,7 @@ def test_array_creation_follow_device(func, args, kwargs, device): dpnp_args = [eval(val, {"x0": x}) for val in args] y = getattr(dpnp, func)(*dpnp_args, **kwargs) - numpy.testing.assert_allclose(y_orig, y) + assert_allclose(y_orig, y) assert_sycl_queue_equal(y.sycl_queue, x.sycl_queue) @@ -197,7 +197,7 @@ def test_array_creation_cross_device(func, args, kwargs, device_x, device_y): dpnp_kwargs["device"] = device_y y = getattr(dpnp, func)(*dpnp_args, **dpnp_kwargs) - numpy.testing.assert_allclose(y_orig, y) + assert_allclose(y_orig, y) assert_sycl_queue_equal(y.sycl_queue, x.to_device(device_y).sycl_queue) @@ -382,7 +382,7 @@ def test_broadcasting(func, data1, data2, device): x2 = dpnp.array(data2, device=device) result = getattr(dpnp, func)(x1, x2) - numpy.testing.assert_array_equal(result, expected) + assert_array_equal(result, expected) expected_queue = x1.get_array().sycl_queue result_queue = result.get_array().sycl_queue @@ -623,8 +623,8 @@ def test_modf(device): x = dpnp.array(data, device=device) result1, result2 = dpnp.modf(x) - numpy.testing.assert_array_equal(result1, expected1) - numpy.testing.assert_array_equal(result2, expected2) + assert_array_equal(result1, expected1) + assert_array_equal(result2, expected2) expected_queue = x.get_array().sycl_queue result1_queue = result1.get_array().sycl_queue @@ -648,7 +648,7 @@ def test_fft(type, device): expected = numpy.fft.fft(data) result = dpnp.fft.fft(dpnp_data) - numpy.testing.assert_allclose(result, expected, rtol=1e-4, atol=1e-7) + assert_allclose(result, expected, rtol=1e-4, atol=1e-7) expected_queue = dpnp_data.get_array().sycl_queue result_queue = result.get_array().sycl_queue @@ -690,7 +690,7 @@ def test_cholesky(device): result = dpnp.linalg.cholesky(dpnp_data) expected = numpy.linalg.cholesky(numpy_data) - numpy.testing.assert_array_equal(expected, result) + assert_array_equal(expected, result) expected_queue = dpnp_data.get_array().sycl_queue result_queue = result.get_array().sycl_queue @@ -710,7 +710,7 @@ def test_det(device): result = dpnp.linalg.det(dpnp_data) expected = numpy.linalg.det(numpy_data) - numpy.testing.assert_allclose(expected, result) + assert_allclose(expected, result) expected_queue = dpnp_data.get_array().sycl_queue result_queue = result.get_array().sycl_queue @@ -755,8 +755,8 @@ def test_eig(device): if numpy_vec[0, i] * dpnp_vec[0, i] < 0: numpy_vec[:, i] = -numpy_vec[:, i] - numpy.testing.assert_allclose(dpnp_val, numpy_val, rtol=1e-05, atol=1e-05) - numpy.testing.assert_allclose(dpnp_vec, numpy_vec, rtol=1e-05, atol=1e-05) + assert_allclose(dpnp_val, numpy_val, rtol=1e-05, atol=1e-05) + assert_allclose(dpnp_vec, numpy_vec, rtol=1e-05, atol=1e-05) assert dpnp_val.dtype == numpy_val.dtype assert dpnp_vec.dtype == numpy_vec.dtype @@ -827,7 +827,7 @@ def test_eigvals(device): result = dpnp.linalg.eigvals(dpnp_data) expected = numpy.linalg.eigvals(numpy_data) - numpy.testing.assert_allclose(expected, result, atol=0.5) + assert_allclose(expected, result, atol=0.5) expected_queue = dpnp_data.get_array().sycl_queue result_queue = result.get_array().sycl_queue @@ -847,7 +847,7 @@ def test_inv(device): result = dpnp.linalg.inv(dpnp_data) expected = numpy.linalg.inv(numpy_data) - numpy.testing.assert_allclose(expected, result) + assert_allclose(expected, result) expected_queue = dpnp_data.get_array().sycl_queue result_queue = result.get_array().sycl_queue @@ -867,7 +867,7 @@ def test_matrix_rank(device): result = dpnp.linalg.matrix_rank(dpnp_data) expected = numpy.linalg.matrix_rank(numpy_data) - numpy.testing.assert_array_equal(expected, result) + assert_array_equal(expected, result) @pytest.mark.parametrize( @@ -889,8 +889,8 @@ def test_qr(device): assert dpnp_q.shape == np_q.shape assert dpnp_r.shape == np_r.shape - numpy.testing.assert_allclose(dpnp_q, np_q, rtol=tol, atol=tol) - numpy.testing.assert_allclose(dpnp_r, np_r, rtol=tol, atol=tol) + assert_allclose(dpnp_q, np_q, rtol=tol, atol=tol) + assert_allclose(dpnp_r, np_r, rtol=tol, atol=tol) expected_queue = dpnp_data.get_array().sycl_queue dpnp_q_queue = dpnp_q.get_array().sycl_queue @@ -927,7 +927,7 @@ def test_svd(device): dpnp_diag_s[i, i] = dpnp_s[i] # check decomposition - numpy.testing.assert_allclose( + assert_allclose( dpnp_data, dpnp.dot(dpnp_u, dpnp.dot(dpnp_diag_s, dpnp_vt)), rtol=tol, @@ -941,10 +941,10 @@ def test_svd(device): # compare vectors for non-zero values for i in range(numpy.count_nonzero(np_s > tol)): - numpy.testing.assert_allclose( + assert_allclose( dpnp.asnumpy(dpnp_u)[:, i], np_u[:, i], rtol=tol, atol=tol ) - numpy.testing.assert_allclose( + assert_allclose( dpnp.asnumpy(dpnp_vt)[i, :], np_vt[i, :], rtol=tol, atol=tol ) diff --git a/tests/third_party/cupy/manipulation_tests/test_join.py b/tests/third_party/cupy/manipulation_tests/test_join.py index e302065ca99..054ada455ae 100644 --- a/tests/third_party/cupy/manipulation_tests/test_join.py +++ b/tests/third_party/cupy/manipulation_tests/test_join.py @@ -375,7 +375,6 @@ def test_vstack_casting(self, xp, dtype1, dtype2, casting): # may raise TypeError or ComplexWarning return xp.vstack((a, b), dtype=dtype2, casting=casting) - @pytest.mark.skip("dpnp.stack() is not implemented yet") @testing.numpy_cupy_array_equal() def test_stack(self, xp): a = testing.shaped_arange((2, 3), xp) @@ -383,61 +382,53 @@ def test_stack(self, xp): c = testing.shaped_arange((2, 3), xp) return xp.stack((a, b, c)) - @pytest.mark.skip("dpnp.stack() is not implemented yet") def test_stack_value(self): a = testing.shaped_arange((2, 3), cupy) b = testing.shaped_arange((2, 3), cupy) c = testing.shaped_arange((2, 3), cupy) s = cupy.stack((a, b, c)) assert s.shape == (3, 2, 3) - cupy.testing.assert_array_equal(s[0], a) - cupy.testing.assert_array_equal(s[1], b) - cupy.testing.assert_array_equal(s[2], c) + testing.assert_array_equal(s[0], a) + testing.assert_array_equal(s[1], b) + testing.assert_array_equal(s[2], c) - @pytest.mark.skip("dpnp.stack() is not implemented yet") @testing.numpy_cupy_array_equal() def test_stack_with_axis1(self, xp): a = testing.shaped_arange((2, 3), xp) return xp.stack((a, a), axis=1) - @pytest.mark.skip("dpnp.stack() is not implemented yet") @testing.numpy_cupy_array_equal() def test_stack_with_axis2(self, xp): a = testing.shaped_arange((2, 3), xp) return xp.stack((a, a), axis=2) - @pytest.mark.skip("dpnp.stack() is not implemented yet") def test_stack_with_axis_over(self): for xp in (numpy, cupy): a = testing.shaped_arange((2, 3), xp) with pytest.raises(ValueError): xp.stack((a, a), axis=3) - @pytest.mark.skip("dpnp.stack() is not implemented yet") def test_stack_with_axis_value(self): a = testing.shaped_arange((2, 3), cupy) s = cupy.stack((a, a), axis=1) assert s.shape == (2, 2, 3) - cupy.testing.assert_array_equal(s[:, 0, :], a) - cupy.testing.assert_array_equal(s[:, 1, :], a) + testing.assert_array_equal(s[:, 0, :], a) + testing.assert_array_equal(s[:, 1, :], a) - @pytest.mark.skip("dpnp.stack() is not implemented yet") @testing.numpy_cupy_array_equal() def test_stack_with_negative_axis(self, xp): a = testing.shaped_arange((2, 3), xp) return xp.stack((a, a), axis=-1) - @pytest.mark.skip("dpnp.stack() is not implemented yet") def test_stack_with_negative_axis_value(self): a = testing.shaped_arange((2, 3), cupy) s = cupy.stack((a, a), axis=-1) assert s.shape == (2, 3, 2) - cupy.testing.assert_array_equal(s[:, :, 0], a) - cupy.testing.assert_array_equal(s[:, :, 1], a) + testing.assert_array_equal(s[:, :, 0], a) + testing.assert_array_equal(s[:, :, 1], a) - @pytest.mark.skip("dpnp.stack() is not implemented yet") def test_stack_different_shape(self): for xp in (numpy, cupy): a = testing.shaped_arange((2, 3), xp) @@ -445,20 +436,18 @@ def test_stack_different_shape(self): with pytest.raises(ValueError): xp.stack([a, b]) - @pytest.mark.skip("dpnp.stack() is not implemented yet") def test_stack_out_of_bounds1(self): for xp in (numpy, cupy): a = testing.shaped_arange((2, 3), xp) with pytest.raises(ValueError): xp.stack([a, a], axis=3) - @pytest.mark.skip("dpnp.stack() is not implemented yet") def test_stack_out_of_bounds2(self): a = testing.shaped_arange((2, 3), cupy) with pytest.raises(numpy.AxisError): return cupy.stack([a, a], axis=3) - @pytest.mark.skip("dpnp.stack() is not implemented yet") + @pytest.mark.usefixtures("allow_fall_back_on_numpy") @testing.for_all_dtypes(name="dtype") @testing.numpy_cupy_array_equal() def test_stack_out(self, xp, dtype): @@ -469,47 +458,47 @@ def test_stack_out(self, xp, dtype): xp.stack((a, b, c), axis=1, out=out) return out - @pytest.mark.skip("dpnp.stack() is not implemented yet") + @pytest.mark.usefixtures("allow_fall_back_on_numpy") @testing.numpy_cupy_array_equal() def test_stack_out_same_kind(self, xp): - a = testing.shaped_arange((3, 4), xp, xp.float64) - b = testing.shaped_reverse_arange((3, 4), xp, xp.float64) - c = testing.shaped_arange((3, 4), xp, xp.float64) + a = testing.shaped_arange((3, 4), xp, xp.float32) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float32) + c = testing.shaped_arange((3, 4), xp, xp.float32) out = xp.zeros((3, 3, 4), dtype=xp.float32) xp.stack((a, b, c), axis=1, out=out) return out - @pytest.mark.skip("dpnp.stack() is not implemented yet") + @pytest.mark.usefixtures("allow_fall_back_on_numpy") def test_stack_out_invalid_shape(self): for xp in (numpy, cupy): - a = testing.shaped_arange((3, 4), xp, xp.float64) - b = testing.shaped_reverse_arange((3, 4), xp, xp.float64) - c = testing.shaped_arange((3, 4), xp, xp.float64) - out = xp.zeros((3, 3, 10), dtype=xp.float64) + a = testing.shaped_arange((3, 4), xp, xp.float32) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float32) + c = testing.shaped_arange((3, 4), xp, xp.float32) + out = xp.zeros((3, 3, 10), dtype=xp.float32) with pytest.raises(ValueError): xp.stack((a, b, c), axis=1, out=out) - @pytest.mark.skip("dpnp.stack() is not implemented yet") + @pytest.mark.usefixtures("allow_fall_back_on_numpy") def test_stack_out_invalid_shape_2(self): for xp in (numpy, cupy): - a = testing.shaped_arange((3, 4), xp, xp.float64) - b = testing.shaped_reverse_arange((3, 4), xp, xp.float64) - c = testing.shaped_arange((3, 4), xp, xp.float64) - out = xp.zeros((3, 3, 3, 10), dtype=xp.float64) + a = testing.shaped_arange((3, 4), xp, xp.float32) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float32) + c = testing.shaped_arange((3, 4), xp, xp.float32) + out = xp.zeros((3, 3, 3, 10), dtype=xp.float32) with pytest.raises(ValueError): xp.stack((a, b, c), axis=1, out=out) - @pytest.mark.skip("dpnp.stack() is not implemented yet") + @pytest.mark.usefixtures("allow_fall_back_on_numpy") def test_stack_out_invalid_dtype(self): for xp in (numpy, cupy): - a = testing.shaped_arange((3, 4), xp, xp.float64) - b = testing.shaped_reverse_arange((3, 4), xp, xp.float64) - c = testing.shaped_arange((3, 4), xp, xp.float64) + a = testing.shaped_arange((3, 4), xp, xp.float32) + b = testing.shaped_reverse_arange((3, 4), xp, xp.float32) + c = testing.shaped_arange((3, 4), xp, xp.float32) out = xp.zeros((3, 3, 4), dtype=xp.int64) with pytest.raises(TypeError): xp.stack((a, b, c), axis=1, out=out) - @pytest.mark.skip("dpnp.stack() is not implemented yet") + @pytest.mark.usefixtures("allow_fall_back_on_numpy") @testing.with_requires("numpy>=1.24.0") @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) @testing.numpy_cupy_array_equal(accept_error=TypeError) @@ -518,18 +507,9 @@ def test_stack_dtype(self, xp, dtype1, dtype2): b = testing.shaped_arange((3, 4), xp, dtype1) return xp.stack((a, b), dtype=dtype2) - @pytest.mark.skip("dpnp.stack() is not implemented yet") + @pytest.mark.usefixtures("allow_fall_back_on_numpy") @testing.with_requires("numpy>=1.24.0") - @pytest.mark.parametrize( - "casting", - [ - "no", - "equiv", - "safe", - "same_kind", - "unsafe", - ], - ) + @testing.for_castings() @testing.for_all_dtypes_combination(names=["dtype1", "dtype2"]) @testing.numpy_cupy_array_equal( accept_error=(TypeError, numpy.ComplexWarning) @@ -537,7 +517,6 @@ def test_stack_dtype(self, xp, dtype1, dtype2): def test_stack_casting(self, xp, dtype1, dtype2, casting): a = testing.shaped_arange((3, 4), xp, dtype1) b = testing.shaped_arange((3, 4), xp, dtype1) - # may raise TypeError or ComplexWarning return xp.stack((a, b), dtype=dtype2, casting=casting) @pytest.mark.skip("dpnp.row_stack() is not implemented yet") From 382455151e1c076b93620a3a467e3dc2c7891b41 Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Mon, 7 Aug 2023 23:11:20 +0200 Subject: [PATCH 23/58] Fixed assigning a value to potentially none-valued dictionary (#1511) --- scripts/gen_coverage.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/scripts/gen_coverage.py b/scripts/gen_coverage.py index fa8adcde76e..cdb6188d2a7 100644 --- a/scripts/gen_coverage.py +++ b/scripts/gen_coverage.py @@ -41,13 +41,15 @@ def run( "-DDPNP_GENERATE_COVERAGE=ON", ] - env = None + env = {} if bin_llvm: env = { "PATH": ":".join((os.environ.get("PATH", ""), bin_llvm)), "LLVM_TOOLS_HOME": bin_llvm, } - env.update({k: v for k, v in os.environ.items() if k != "PATH"}) + + # extend with global enviroment variables + env.update({k: v for k, v in os.environ.items() if k != "PATH"}) subprocess.check_call(cmake_args, shell=False, cwd=setup_dir, env=env) From dd7a6ea1e845173e3d724dbd5947257b53deb0e6 Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Tue, 8 Aug 2023 12:28:32 +0200 Subject: [PATCH 24/58] Resolved issue reported by Coverity tool (#1514) --- dpnp/backend/kernels/dpnp_krnl_random.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/dpnp/backend/kernels/dpnp_krnl_random.cpp b/dpnp/backend/kernels/dpnp_krnl_random.cpp index a342151c4cc..97594f1e7a6 100644 --- a/dpnp/backend/kernels/dpnp_krnl_random.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_random.cpp @@ -251,6 +251,7 @@ DPCTLSyclEventRef auto event_out = mkl_rng::generate(distribution, DPNP_RNG_ENGINE, size, result1); event_ref = reinterpret_cast(&event_out); + return DPCTLEvent_Copy(event_ref); } return DPCTLEvent_Copy(event_ref); } From f86749c7386854aef6c2550e88168fc7f3760bd2 Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Tue, 8 Aug 2023 18:32:59 +0200 Subject: [PATCH 25/58] Resolve `Arguments in wrong order` issue reported by Coverity tool (#1513) * Resolved issue reported by Coverity tool * Renamed local variables and updated comments --- dpnp/backend/kernels/dpnp_krnl_common.cpp | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/dpnp/backend/kernels/dpnp_krnl_common.cpp b/dpnp/backend/kernels/dpnp_krnl_common.cpp index bf3102bd7cd..dd6c8119c1b 100644 --- a/dpnp/backend/kernels/dpnp_krnl_common.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_common.cpp @@ -851,17 +851,18 @@ DPCTLSyclEventRef dpnp_matmul_c(DPCTLSyclQueueRef q_ref, std::is_same<_DataType, float>::value) { // using std::max for these ldx variables is required by math library - const std::int64_t lda = - std::max(1UL, size_k); // First dimensions of array_1 - const std::int64_t ldb = + const std::int64_t ld_array_2 = std::max(1UL, size_n); // First dimensions of array_2 - const std::int64_t ldc = + const std::int64_t ld_array_1 = + std::max(1UL, size_k); // First dimensions of array_1 + const std::int64_t ld_result = std::max(1UL, size_n); // Fast dimensions of result event = mkl_blas::gemm(q, oneapi::mkl::transpose::nontrans, oneapi::mkl::transpose::nontrans, size_n, size_m, - size_k, _DataType(1), array_2, ldb, array_1, lda, - _DataType(0), result, ldc, dep_events); + size_k, _DataType(1), array_2, ld_array_2, + array_1, ld_array_1, _DataType(0), result, + ld_result, dep_events); } else { // input1: M x K From 2f235bf47a46c942f4bc49cb030430a5dc704395 Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Tue, 8 Aug 2023 22:45:55 +0200 Subject: [PATCH 26/58] Upgraded pybind11 to the newer version (#1510) --- CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index efa35ac5086..974d19df633 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -45,8 +45,8 @@ include(GNUInstallDirs) include(FetchContent) FetchContent_Declare( pybind11 - URL https://github.com/pybind/pybind11/archive/refs/tags/v2.10.2.tar.gz - URL_HASH SHA256=93bd1e625e43e03028a3ea7389bba5d3f9f2596abc074b068e70f4ef9b1314ae + URL https://github.com/pybind/pybind11/archive/refs/tags/v2.11.1.tar.gz + URL_HASH SHA256=d475978da0cdc2d43b73f30910786759d593a9d8ee05b1b6846d1eb16c6d2e0c ) FetchContent_MakeAvailable(pybind11) From 1f15ccc36a35e9b3dc1b2a90ad928a151d113203 Mon Sep 17 00:00:00 2001 From: Alexander Kalistratov Date: Mon, 14 Aug 2023 09:50:45 +0200 Subject: [PATCH 27/58] Add more tests for dpnp.sum and sum_over_axis_0 extension (#1488) * Add more tests for dpnp.sum and sum_over_axis_0 extension * Add keepdims=True, bool and complex dtypes --------- Co-authored-by: Anton <100830759+antonwolfy@users.noreply.github.com> --- tests/test_extensions.py | 34 +++++++++++++++++-------- tests/test_mathematical.py | 51 +++++++++++++++++++++++++++----------- 2 files changed, 60 insertions(+), 25 deletions(-) diff --git a/tests/test_extensions.py b/tests/test_extensions.py index a020be77637..c0e1ab3ea77 100644 --- a/tests/test_extensions.py +++ b/tests/test_extensions.py @@ -193,8 +193,6 @@ def test_mean_over_axis_0_unsupported_out_types( input = dpt.empty((height, width), dtype=input_type, device=device) output = dpt.empty(width, dtype=output_type, device=device) - if func(input, output): - print(output_type) assert func(input, output) is None @@ -202,7 +200,9 @@ def test_mean_over_axis_0_unsupported_out_types( "func, device, input_type, output_type", product(mean_sum, all_devices, [dpt.float32], [dpt.float32]), ) -def test_mean_over_axis_0_f_contig_input(func, device, input_type, output_type): +def test_mean_sum_over_axis_0_f_contig_input( + func, device, input_type, output_type +): skip_unsupported(device, input_type) skip_unsupported(device, output_type) @@ -212,8 +212,6 @@ def test_mean_over_axis_0_f_contig_input(func, device, input_type, output_type): input = dpt.empty((height, width), dtype=input_type, device=device).T output = dpt.empty(width, dtype=output_type, device=device) - if func(input, output): - print(output_type) assert func(input, output) is None @@ -221,7 +219,7 @@ def test_mean_over_axis_0_f_contig_input(func, device, input_type, output_type): "func, device, input_type, output_type", product(mean_sum, all_devices, [dpt.float32], [dpt.float32]), ) -def test_mean_over_axis_0_f_contig_output( +def test_mean_sum_over_axis_0_f_contig_output( func, device, input_type, output_type ): skip_unsupported(device, input_type) @@ -230,9 +228,25 @@ def test_mean_over_axis_0_f_contig_output( height = 1 width = 10 - input = dpt.empty((height, 10), dtype=input_type, device=device) - output = dpt.empty(20, dtype=output_type, device=device)[::2] + input = dpt.empty((height, width), dtype=input_type, device=device) + output = dpt.empty(width * 2, dtype=output_type, device=device)[::2] + + assert func(input, output) is None + + +@pytest.mark.parametrize( + "func, device, input_type, output_type", + product(mean_sum, all_devices, [dpt.float32], [dpt.float32, dpt.float64]), +) +def test_mean_sum_over_axis_0_big_output(func, device, input_type, output_type): + skip_unsupported(device, input_type) + skip_unsupported(device, output_type) + + local_mem_size = device.local_mem_size + height = 1 + width = 1 + local_mem_size // output_type.itemsize + + input = dpt.empty((height, width), dtype=input_type, device=device) + output = dpt.empty(width, dtype=output_type, device=device) - if func(input, output): - print(output_type) assert func(input, output) is None diff --git a/tests/test_mathematical.py b/tests/test_mathematical.py index fed1928e076..e0a16869567 100644 --- a/tests/test_mathematical.py +++ b/tests/test_mathematical.py @@ -1,3 +1,5 @@ +from itertools import permutations + import numpy import pytest from numpy.testing import ( @@ -1056,23 +1058,42 @@ def test_sum_empty_out(dtype): @pytest.mark.parametrize( - "shape", [(), (1, 2, 3), (1, 0, 2), (10), (3, 3, 3), (5, 5), (0, 6)] -) -@pytest.mark.parametrize( - "dtype_in", get_all_dtypes(no_complex=True, no_bool=True) -) -@pytest.mark.parametrize( - "dtype_out", get_all_dtypes(no_complex=True, no_bool=True) + "shape", + [ + (), + (1, 2, 3), + (1, 0, 2), + (10,), + (3, 3, 3), + (5, 5), + (0, 6), + (10, 1), + (1, 10), + ], ) -def test_sum(shape, dtype_in, dtype_out): - a_np = numpy.ones(shape, dtype=dtype_in) - a = dpnp.ones(shape, dtype=dtype_in) - axes = [None, 0, 1, 2] +@pytest.mark.parametrize("dtype_in", get_all_dtypes()) +@pytest.mark.parametrize("dtype_out", get_all_dtypes()) +@pytest.mark.parametrize("transpose", [True, False]) +@pytest.mark.parametrize("keepdims", [True, False]) +def test_sum(shape, dtype_in, dtype_out, transpose, keepdims): + size = numpy.prod(shape) + a_np = numpy.arange(size).astype(dtype_in).reshape(shape) + a = dpnp.asarray(a_np) + + if transpose: + a_np = a_np.T + a = a.T + + axes_range = list(numpy.arange(len(shape))) + axes = [None] + axes += axes_range + axes += permutations(axes_range, 2) + axes.append(tuple(axes_range)) + for axis in axes: - if axis is None or axis < a.ndim: - numpy_res = a_np.sum(axis=axis, dtype=dtype_out) - dpnp_res = a.sum(axis=axis, dtype=dtype_out) - assert_array_equal(numpy_res, dpnp_res.asnumpy()) + numpy_res = a_np.sum(axis=axis, dtype=dtype_out, keepdims=keepdims) + dpnp_res = a.sum(axis=axis, dtype=dtype_out, keepdims=keepdims) + assert_array_equal(numpy_res, dpnp_res.asnumpy()) class TestMean: From 16507742825df44da7e63699348edd912db4b638 Mon Sep 17 00:00:00 2001 From: vlad-perevezentsev Date: Wed, 16 Aug 2023 13:52:33 +0200 Subject: [PATCH 28/58] Leverage `dpctl.tensor.all()` and `dpctl.tensor.any()` implementations (#1512) * Leverage dpctl.tensor.all implementation * Leverage dpctl.tensor.any implementation * Add new test_truth and test_logic files and expand scope of tests --- .github/workflows/conda-package.yml | 2 + dpnp/backend/include/dpnp_iface_fptr.hpp | 14 +- dpnp/backend/kernels/dpnp_krnl_logic.cpp | 30 ----- dpnp/dpnp_algo/dpnp_algo.pxd | 4 - dpnp/dpnp_algo/dpnp_algo_logic.pxi | 63 --------- dpnp/dpnp_iface_logic.py | 124 ++++++++++-------- .../cupy/logic_tests/test_truth.py | 91 +++++++++++++ 7 files changed, 169 insertions(+), 159 deletions(-) create mode 100644 tests/third_party/cupy/logic_tests/test_truth.py diff --git a/.github/workflows/conda-package.yml b/.github/workflows/conda-package.yml index 9ad801cf5fd..bc9f64aae4a 100644 --- a/.github/workflows/conda-package.yml +++ b/.github/workflows/conda-package.yml @@ -17,6 +17,7 @@ env: test_dparray.py test_fft.py test_linalg.py + test_logic.py test_mathematical.py test_random_state.py test_sort.py @@ -24,6 +25,7 @@ env: test_umath.py test_usm_type.py third_party/cupy/linalg_tests/test_product.py + third_party/cupy/logic_tests/test_truth.py third_party/cupy/manipulation_tests/test_join.py third_party/cupy/math_tests/test_explog.py third_party/cupy/math_tests/test_misc.py diff --git a/dpnp/backend/include/dpnp_iface_fptr.hpp b/dpnp/backend/include/dpnp_iface_fptr.hpp index 96b85885473..c7a9f9f701a 100644 --- a/dpnp/backend/include/dpnp_iface_fptr.hpp +++ b/dpnp/backend/include/dpnp_iface_fptr.hpp @@ -65,14 +65,12 @@ enum class DPNPFuncName : size_t DPNP_FN_ADD, /**< Used in numpy.add() impl */ DPNP_FN_ADD_EXT, /**< Used in numpy.add() impl, requires extra parameters */ DPNP_FN_ALL, /**< Used in numpy.all() impl */ - DPNP_FN_ALL_EXT, /**< Used in numpy.all() impl, requires extra parameters */ - DPNP_FN_ALLCLOSE, /**< Used in numpy.allclose() impl */ - DPNP_FN_ALLCLOSE_EXT, /**< Used in numpy.allclose() impl, requires extra - parameters */ - DPNP_FN_ANY, /**< Used in numpy.any() impl */ - DPNP_FN_ANY_EXT, /**< Used in numpy.any() impl, requires extra parameters */ - DPNP_FN_ARANGE, /**< Used in numpy.arange() impl */ - DPNP_FN_ARCCOS, /**< Used in numpy.arccos() impl */ + DPNP_FN_ALLCLOSE, /**< Used in numpy.allclose() impl */ + DPNP_FN_ALLCLOSE_EXT, /**< Used in numpy.allclose() impl, requires extra + parameters */ + DPNP_FN_ANY, /**< Used in numpy.any() impl */ + DPNP_FN_ARANGE, /**< Used in numpy.arange() impl */ + DPNP_FN_ARCCOS, /**< Used in numpy.arccos() impl */ DPNP_FN_ARCCOS_EXT, /**< Used in numpy.arccos() impl, requires extra parameters */ DPNP_FN_ARCCOSH, /**< Used in numpy.arccosh() impl */ diff --git a/dpnp/backend/kernels/dpnp_krnl_logic.cpp b/dpnp/backend/kernels/dpnp_krnl_logic.cpp index e2fe0d460cf..1757d053416 100644 --- a/dpnp/backend/kernels/dpnp_krnl_logic.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_logic.cpp @@ -640,21 +640,6 @@ void func_map_init_logic(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_ALL][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_all_default_c}; - fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_BLN][eft_BLN] = { - eft_BLN, (void *)dpnp_all_ext_c}; - fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_all_ext_c}; - fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_all_ext_c}; - fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_all_ext_c}; - fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_all_ext_c}; - fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_C64][eft_C64] = { - eft_C64, (void *)dpnp_all_ext_c, bool>}; - fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_C128][eft_C128] = { - eft_C128, (void *)dpnp_all_ext_c, bool>}; - fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_INT][eft_INT] = { eft_BLN, (void *)dpnp_allclose_default_c}; fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_LNG][eft_INT] = { @@ -732,21 +717,6 @@ void func_map_init_logic(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_ANY][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_any_default_c}; - fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_BLN][eft_BLN] = { - eft_BLN, (void *)dpnp_any_ext_c}; - fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_any_ext_c}; - fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_any_ext_c}; - fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_any_ext_c}; - fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_any_ext_c}; - fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_C64][eft_C64] = { - eft_C64, (void *)dpnp_any_ext_c, bool>}; - fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_C128][eft_C128] = { - eft_C128, (void *)dpnp_any_ext_c, bool>}; - func_map_logic_2arg_2type_helper(fmap); diff --git a/dpnp/dpnp_algo/dpnp_algo.pxd b/dpnp/dpnp_algo/dpnp_algo.pxd index 37d4a7d3694..f560545f502 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pxd +++ b/dpnp/dpnp_algo/dpnp_algo.pxd @@ -35,12 +35,8 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na cdef enum DPNPFuncName "DPNPFuncName": DPNP_FN_ABSOLUTE DPNP_FN_ABSOLUTE_EXT - DPNP_FN_ALL - DPNP_FN_ALL_EXT DPNP_FN_ALLCLOSE DPNP_FN_ALLCLOSE_EXT - DPNP_FN_ANY - DPNP_FN_ANY_EXT DPNP_FN_ARANGE DPNP_FN_ARCCOS DPNP_FN_ARCCOS_EXT diff --git a/dpnp/dpnp_algo/dpnp_algo_logic.pxi b/dpnp/dpnp_algo/dpnp_algo_logic.pxi index d5cf2aa740d..cb97670f477 100644 --- a/dpnp/dpnp_algo/dpnp_algo_logic.pxi +++ b/dpnp/dpnp_algo/dpnp_algo_logic.pxi @@ -36,16 +36,11 @@ and the rest of the library # NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file __all__ += [ - "dpnp_all", "dpnp_allclose", - "dpnp_any", "dpnp_isclose", ] -ctypedef c_dpctl.DPCTLSyclEventRef(*custom_logic_1in_1out_func_ptr_t)(c_dpctl.DPCTLSyclQueueRef, - void *, void * , const size_t, - const c_dpctl.DPCTLEventVectorRef) ctypedef c_dpctl.DPCTLSyclEventRef(*custom_allclose_1in_1out_func_ptr_t)(c_dpctl.DPCTLSyclQueueRef, void * , void * , @@ -56,35 +51,6 @@ ctypedef c_dpctl.DPCTLSyclEventRef(*custom_allclose_1in_1out_func_ptr_t)(c_dpctl const c_dpctl.DPCTLEventVectorRef) -cpdef utils.dpnp_descriptor dpnp_all(utils.dpnp_descriptor array1): - array1_obj = array1.get_array() - - cdef utils.dpnp_descriptor result = utils_py.create_output_descriptor_py((1,), - dpnp.bool, - None, - device=array1_obj.sycl_device, - usm_type=array1_obj.usm_type, - sycl_queue=array1_obj.sycl_queue) - - result_sycl_queue = result.get_array().sycl_queue - - cdef c_dpctl.SyclQueue q = result_sycl_queue - cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref() - - cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(array1.dtype) - - cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_ALL_EXT, param1_type, param1_type) - - cdef custom_logic_1in_1out_func_ptr_t func = kernel_data.ptr - - cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref, array1.get_data(), result.get_data(), array1.size, NULL) - - with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref) - c_dpctl.DPCTLEvent_Delete(event_ref) - - return result - - cpdef utils.dpnp_descriptor dpnp_allclose(utils.dpnp_descriptor array1, utils.dpnp_descriptor array2, double rtol_val, @@ -125,35 +91,6 @@ cpdef utils.dpnp_descriptor dpnp_allclose(utils.dpnp_descriptor array1, return result -cpdef utils.dpnp_descriptor dpnp_any(utils.dpnp_descriptor array1): - array1_obj = array1.get_array() - - cdef utils.dpnp_descriptor result = utils_py.create_output_descriptor_py((1,), - dpnp.bool, - None, - device=array1_obj.sycl_device, - usm_type=array1_obj.usm_type, - sycl_queue=array1_obj.sycl_queue) - - cdef DPNPFuncType param1_type = dpnp_dtype_to_DPNPFuncType(array1.dtype) - - cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_ANY_EXT, param1_type, param1_type) - - result_sycl_queue = result.get_array().sycl_queue - - cdef c_dpctl.SyclQueue q = result_sycl_queue - cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref() - - cdef custom_logic_1in_1out_func_ptr_t func = kernel_data.ptr - - cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref, array1.get_data(), result.get_data(), array1.size, NULL) - - with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref) - c_dpctl.DPCTLEvent_Delete(event_ref) - - return result - - cpdef utils.dpnp_descriptor dpnp_isclose(utils.dpnp_descriptor input1, utils.dpnp_descriptor input2, double rtol=1e-05, diff --git a/dpnp/dpnp_iface_logic.py b/dpnp/dpnp_iface_logic.py index f00b61f8ed2..a560b71a1a6 100644 --- a/dpnp/dpnp_iface_logic.py +++ b/dpnp/dpnp_iface_logic.py @@ -40,10 +40,12 @@ """ +import dpctl.tensor as dpt import numpy import dpnp from dpnp.dpnp_algo import * +from dpnp.dpnp_array import dpnp_array from dpnp.dpnp_utils import * from .dpnp_algo.dpnp_elementwise_common import ( @@ -84,24 +86,29 @@ ] -def all(x1, /, axis=None, out=None, keepdims=False, *, where=True): +def all(x, /, axis=None, out=None, keepdims=False, *, where=True): """ Test whether all array elements along a given axis evaluate to True. For full documentation refer to :obj:`numpy.all`. + Returns + ------- + dpnp.ndarray + An array with a data type of `bool` + containing the results of the logical AND reduction. + Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. - Otherwise the function will be executed sequentially on CPU. + Parameters `x` is supported either as :class:`dpnp.ndarray` + or :class:`dpctl.tensor.usm_ndarray`. + Parameters `out` and `where` are supported with default value. Input array data types are limited by supported DPNP :ref:`Data types`. - Parameter `axis` is supported only with default value `None`. - Parameter `out` is supported only with default value `None`. - Parameter `keepdims` is supported only with default value `False`. - Parameter `where` is supported only with default value `True`. + Otherwise the function will be executed sequentially on CPU. See Also -------- + :obj:`dpnp.ndarray.all` : equivalent method :obj:`dpnp.any` : Test whether any element along a given axis evaluates to True. Notes @@ -111,35 +118,37 @@ def all(x1, /, axis=None, out=None, keepdims=False, *, where=True): Examples -------- - >>> import dpnp as dp - >>> x = dp.array([[True, False], [True, True]]) - >>> dp.all(x) - False - >>> x2 = dp.array([-1, 4, 5]) - >>> dp.all(x2) - True - >>> x3 = dp.array([1.0, dp.nan]) - >>> dp.all(x3) - True + >>> import dpnp as np + >>> x = np.array([[True, False], [True, True]]) + >>> np.all(x) + array(False) + + >>> np.all(x, axis=0) + array([ True, False]) + + >>> x2 = np.array([-1, 4, 5]) + >>> np.all(x2) + array(True) + + >>> x3 = np.array([1.0, np.nan]) + >>> np.all(x3) + array(True) """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc: - if axis is not None: - pass - elif out is not None: - pass - elif keepdims is not False: + if dpnp.is_supported_array_type(x): + if out is not None: pass elif where is not True: pass else: - result_obj = dpnp_all(x1_desc).get_pyobj() - return dpnp.convert_single_elem_array_to_scalar(result_obj) + dpt_array = dpnp.get_usm_ndarray(x) + return dpnp_array._create_from_usm_ndarray( + dpt.all(dpt_array, axis=axis, keepdims=keepdims) + ) return call_origin( - numpy.all, x1, axis=axis, out=out, keepdims=keepdims, where=where + numpy.all, x, axis=axis, out=out, keepdims=keepdims, where=where ) @@ -181,24 +190,29 @@ def allclose(x1, x2, rtol=1.0e-5, atol=1.0e-8, **kwargs): return call_origin(numpy.allclose, x1, x2, rtol=rtol, atol=atol, **kwargs) -def any(x1, /, axis=None, out=None, keepdims=False, *, where=True): +def any(x, /, axis=None, out=None, keepdims=False, *, where=True): """ Test whether any array element along a given axis evaluates to True. For full documentation refer to :obj:`numpy.any`. + Returns + ------- + dpnp.ndarray + An array with a data type of `bool` + containing the results of the logical OR reduction. + Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. - Otherwise the function will be executed sequentially on CPU. + Parameters `x` is supported either as :class:`dpnp.ndarray` + or :class:`dpctl.tensor.usm_ndarray`. + Parameters `out` and `where` are supported with default value. Input array data types are limited by supported DPNP :ref:`Data types`. - Parameter `axis` is supported only with default value `None`. - Parameter `out` is supported only with default value `None`. - Parameter `keepdims` is supported only with default value `False`. - Parameter `where` is supported only with default value `True`. + Otherwise the function will be executed sequentially on CPU. See Also -------- + :obj:`dpnp.ndarray.any` : equivalent method :obj:`dpnp.all` : Test whether all elements along a given axis evaluate to True. Notes @@ -208,35 +222,37 @@ def any(x1, /, axis=None, out=None, keepdims=False, *, where=True): Examples -------- - >>> import dpnp as dp - >>> x = dp.array([[True, False], [True, True]]) - >>> dp.any(x) - True - >>> x2 = dp.array([0, 0, 0]) - >>> dp.any(x2) - False - >>> x3 = dp.array([1.0, dp.nan]) - >>> dp.any(x3) - True + >>> import dpnp as np + >>> x = np.array([[True, False], [True, True]]) + >>> np.any(x) + array(True) + + >>> np.any(x, axis=0) + array([ True, True]) + + >>> x2 = np.array([0, 0, 0]) + >>> np.any(x2) + array(False) + + >>> x3 = np.array([1.0, np.nan]) + >>> np.any(x3) + array(True) """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc: - if axis is not None: - pass - elif out is not None: - pass - elif keepdims is not False: + if dpnp.is_supported_array_type(x): + if out is not None: pass elif where is not True: pass else: - result_obj = dpnp_any(x1_desc).get_pyobj() - return dpnp.convert_single_elem_array_to_scalar(result_obj) + dpt_array = dpnp.get_usm_ndarray(x) + return dpnp_array._create_from_usm_ndarray( + dpt.any(dpt_array, axis=axis, keepdims=keepdims) + ) return call_origin( - numpy.any, x1, axis=axis, out=out, keepdims=keepdims, where=where + numpy.any, x, axis=axis, out=out, keepdims=keepdims, where=where ) diff --git a/tests/third_party/cupy/logic_tests/test_truth.py b/tests/third_party/cupy/logic_tests/test_truth.py new file mode 100644 index 00000000000..aa0e563ec42 --- /dev/null +++ b/tests/third_party/cupy/logic_tests/test_truth.py @@ -0,0 +1,91 @@ +import unittest + +import numpy +import pytest + +from tests.third_party.cupy import testing + + +def _calc_out_shape(shape, axis, keepdims): + if axis is None: + axis = list(range(len(shape))) + elif isinstance(axis, int): + axis = [axis] + else: + axis = list(axis) + + shape = numpy.array(shape) + + if keepdims: + shape[axis] = 1 + else: + shape[axis] = -1 + shape = filter(lambda x: x != -1, shape) + return tuple(shape) + + +@testing.parameterize( + *testing.product( + { + "f": ["all", "any"], + "x": [ + numpy.arange(24).reshape(2, 3, 4) - 10, + numpy.zeros((2, 3, 4)), + numpy.ones((2, 3, 4)), + numpy.zeros((0, 3, 4)), + numpy.ones((0, 3, 4)), + ], + "axis": [None, (0, 1, 2), 0, 1, 2, (0, 1)], + "keepdims": [False, True], + } + ) +) +class TestAllAny(unittest.TestCase): + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_without_out(self, xp, dtype): + x = xp.asarray(self.x).astype(dtype) + return getattr(xp, self.f)(x, self.axis, None, self.keepdims) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_with_out(self, xp, dtype): + x = xp.asarray(self.x).astype(dtype) + out_shape = _calc_out_shape(x.shape, self.axis, self.keepdims) + out = xp.empty(out_shape, dtype=x.dtype) + getattr(xp, self.f)(x, self.axis, out, self.keepdims) + return out + + +@testing.parameterize( + *testing.product( + { + "f": ["all", "any"], + "x": [ + numpy.array([[[numpy.nan]]]), + numpy.array([[[numpy.nan, 0]]]), + numpy.array([[[numpy.nan, 1]]]), + numpy.array([[[numpy.nan, 0, 1]]]), + ], + "axis": [None, (0, 1, 2), 0, 1, 2, (0, 1)], + "keepdims": [False, True], + } + ) +) +class TestAllAnyWithNaN(unittest.TestCase): + @testing.for_dtypes((*testing.helper._float_dtypes, numpy.bool_)) + @testing.numpy_cupy_array_equal() + def test_without_out(self, xp, dtype): + x = xp.asarray(self.x).astype(dtype) + return getattr(xp, self.f)(x, self.axis, None, self.keepdims) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.for_dtypes((*testing.helper._float_dtypes, numpy.bool_)) + @testing.numpy_cupy_array_equal() + def test_with_out(self, xp, dtype): + x = xp.asarray(self.x).astype(dtype) + out_shape = _calc_out_shape(x.shape, self.axis, self.keepdims) + out = xp.empty(out_shape, dtype=x.dtype) + getattr(xp, self.f)(x, self.axis, out, self.keepdims) + return out From 15e2611cd9742491a5363a11fb264cb7d87fca9a Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Fri, 4 Aug 2023 11:32:13 -0500 Subject: [PATCH 29/58] impl_usage_dpctl_bitwise_op --- dpnp/backend/include/dpnp_iface_fptr.hpp | 142 ++++---- dpnp/backend/kernels/dpnp_krnl_bitwise.cpp | 57 +--- dpnp/dpnp_algo/CMakeLists.txt | 1 - dpnp/dpnp_algo/dpnp_algo.pxd | 43 --- dpnp/dpnp_algo/dpnp_algo.pyx | 1 - dpnp/dpnp_algo/dpnp_algo_bitwise.pxi | 88 ----- dpnp/dpnp_algo/dpnp_elementwise_common.py | 272 ++++++++++++++- dpnp/dpnp_iface_bitwise.py | 322 +++++++++--------- tests/test_bitwise.py | 12 + .../cupy/binary_tests/test_elementwise.py | 6 +- 10 files changed, 517 insertions(+), 427 deletions(-) delete mode 100644 dpnp/dpnp_algo/dpnp_algo_bitwise.pxi diff --git a/dpnp/backend/include/dpnp_iface_fptr.hpp b/dpnp/backend/include/dpnp_iface_fptr.hpp index c7a9f9f701a..43450778019 100644 --- a/dpnp/backend/include/dpnp_iface_fptr.hpp +++ b/dpnp/backend/include/dpnp_iface_fptr.hpp @@ -65,57 +65,51 @@ enum class DPNPFuncName : size_t DPNP_FN_ADD, /**< Used in numpy.add() impl */ DPNP_FN_ADD_EXT, /**< Used in numpy.add() impl, requires extra parameters */ DPNP_FN_ALL, /**< Used in numpy.all() impl */ - DPNP_FN_ALLCLOSE, /**< Used in numpy.allclose() impl */ - DPNP_FN_ALLCLOSE_EXT, /**< Used in numpy.allclose() impl, requires extra - parameters */ - DPNP_FN_ANY, /**< Used in numpy.any() impl */ - DPNP_FN_ARANGE, /**< Used in numpy.arange() impl */ - DPNP_FN_ARCCOS, /**< Used in numpy.arccos() impl */ - DPNP_FN_ARCCOS_EXT, /**< Used in numpy.arccos() impl, requires extra - parameters */ - DPNP_FN_ARCCOSH, /**< Used in numpy.arccosh() impl */ - DPNP_FN_ARCCOSH_EXT, /**< Used in numpy.arccosh() impl, requires extra - parameters */ - DPNP_FN_ARCSIN, /**< Used in numpy.arcsin() impl */ - DPNP_FN_ARCSIN_EXT, /**< Used in numpy.arcsin() impl, requires extra - parameters */ - DPNP_FN_ARCSINH, /**< Used in numpy.arcsinh() impl */ - DPNP_FN_ARCSINH_EXT, /**< Used in numpy.arcsinh() impl, requires extra - parameters */ - DPNP_FN_ARCTAN, /**< Used in numpy.arctan() impl */ - DPNP_FN_ARCTAN_EXT, /**< Used in numpy.arctan() impl, requires extra - parameters */ - DPNP_FN_ARCTAN2, /**< Used in numpy.arctan2() impl */ - DPNP_FN_ARCTAN2_EXT, /**< Used in numpy.arctan2() impl, requires extra - parameters */ - DPNP_FN_ARCTANH, /**< Used in numpy.arctanh() impl */ - DPNP_FN_ARCTANH_EXT, /**< Used in numpy.arctanh() impl, requires extra - parameters */ - DPNP_FN_ARGMAX, /**< Used in numpy.argmax() impl */ - DPNP_FN_ARGMAX_EXT, /**< Used in numpy.argmax() impl, requires extra - parameters */ - DPNP_FN_ARGMIN, /**< Used in numpy.argmin() impl */ - DPNP_FN_ARGMIN_EXT, /**< Used in numpy.argmin() impl, requires extra - parameters */ - DPNP_FN_ARGSORT, /**< Used in numpy.argsort() impl */ - DPNP_FN_ARGSORT_EXT, /**< Used in numpy.argsort() impl, requires extra - parameters */ - DPNP_FN_AROUND, /**< Used in numpy.around() impl */ - DPNP_FN_AROUND_EXT, /**< Used in numpy.around() impl, requires extra - parameters */ - DPNP_FN_ASTYPE, /**< Used in numpy.astype() impl */ - DPNP_FN_ASTYPE_EXT, /**< Used in numpy.astype() impl, requires extra - parameters */ - DPNP_FN_BITWISE_AND, /**< Used in numpy.bitwise_and() impl */ - DPNP_FN_BITWISE_AND_EXT, /**< Used in numpy.bitwise_and() impl, requires - extra parameters */ - DPNP_FN_BITWISE_OR, /**< Used in numpy.bitwise_or() impl */ - DPNP_FN_BITWISE_OR_EXT, /**< Used in numpy.bitwise_or() impl, requires extra - parameters */ - DPNP_FN_BITWISE_XOR, /**< Used in numpy.bitwise_xor() impl */ - DPNP_FN_BITWISE_XOR_EXT, /**< Used in numpy.bitwise_xor() impl, requires - extra parameters */ - DPNP_FN_CBRT, /**< Used in numpy.cbrt() impl */ + DPNP_FN_ALLCLOSE, /**< Used in numpy.allclose() impl */ + DPNP_FN_ALLCLOSE_EXT, /**< Used in numpy.allclose() impl, requires extra + parameters */ + DPNP_FN_ANY, /**< Used in numpy.any() impl */ + DPNP_FN_ARANGE, /**< Used in numpy.arange() impl */ + DPNP_FN_ARCCOS, /**< Used in numpy.arccos() impl */ + DPNP_FN_ARCCOS_EXT, /**< Used in numpy.arccos() impl, requires extra + parameters */ + DPNP_FN_ARCCOSH, /**< Used in numpy.arccosh() impl */ + DPNP_FN_ARCCOSH_EXT, /**< Used in numpy.arccosh() impl, requires extra + parameters */ + DPNP_FN_ARCSIN, /**< Used in numpy.arcsin() impl */ + DPNP_FN_ARCSIN_EXT, /**< Used in numpy.arcsin() impl, requires extra + parameters */ + DPNP_FN_ARCSINH, /**< Used in numpy.arcsinh() impl */ + DPNP_FN_ARCSINH_EXT, /**< Used in numpy.arcsinh() impl, requires extra + parameters */ + DPNP_FN_ARCTAN, /**< Used in numpy.arctan() impl */ + DPNP_FN_ARCTAN_EXT, /**< Used in numpy.arctan() impl, requires extra + parameters */ + DPNP_FN_ARCTAN2, /**< Used in numpy.arctan2() impl */ + DPNP_FN_ARCTAN2_EXT, /**< Used in numpy.arctan2() impl, requires extra + parameters */ + DPNP_FN_ARCTANH, /**< Used in numpy.arctanh() impl */ + DPNP_FN_ARCTANH_EXT, /**< Used in numpy.arctanh() impl, requires extra + parameters */ + DPNP_FN_ARGMAX, /**< Used in numpy.argmax() impl */ + DPNP_FN_ARGMAX_EXT, /**< Used in numpy.argmax() impl, requires extra + parameters */ + DPNP_FN_ARGMIN, /**< Used in numpy.argmin() impl */ + DPNP_FN_ARGMIN_EXT, /**< Used in numpy.argmin() impl, requires extra + parameters */ + DPNP_FN_ARGSORT, /**< Used in numpy.argsort() impl */ + DPNP_FN_ARGSORT_EXT, /**< Used in numpy.argsort() impl, requires extra + parameters */ + DPNP_FN_AROUND, /**< Used in numpy.around() impl */ + DPNP_FN_AROUND_EXT, /**< Used in numpy.around() impl, requires extra + parameters */ + DPNP_FN_ASTYPE, /**< Used in numpy.astype() impl */ + DPNP_FN_ASTYPE_EXT, /**< Used in numpy.astype() impl, requires extra + parameters */ + DPNP_FN_BITWISE_AND, /**< Used in numpy.bitwise_and() impl */ + DPNP_FN_BITWISE_OR, /**< Used in numpy.bitwise_or() impl */ + DPNP_FN_BITWISE_XOR, /**< Used in numpy.bitwise_xor() impl */ + DPNP_FN_CBRT, /**< Used in numpy.cbrt() impl */ DPNP_FN_CBRT_EXT, /**< Used in numpy.cbrt() impl, requires extra parameters */ DPNP_FN_CEIL, /**< Used in numpy.ceil() impl */ @@ -246,14 +240,10 @@ enum class DPNPFuncName : size_t DPNP_FN_INV_EXT, /**< Used in numpy.linalg.inv() impl, requires extra parameters */ DPNP_FN_INVERT, /**< Used in numpy.invert() impl */ - DPNP_FN_INVERT_EXT, /**< Used in numpy.invert() impl, requires extra - parameters */ DPNP_FN_KRON, /**< Used in numpy.kron() impl */ DPNP_FN_KRON_EXT, /**< Used in numpy.kron() impl, requires extra parameters */ - DPNP_FN_LEFT_SHIFT, /**< Used in numpy.left_shift() impl */ - DPNP_FN_LEFT_SHIFT_EXT, /**< Used in numpy.left_shift() impl, requires extra - parameters */ + DPNP_FN_LEFT_SHIFT, /**< Used in numpy.left_shift() impl */ DPNP_FN_LESS_EXT, /**< Used in numpy.less() impl, requires extra parameters */ DPNP_FN_LESS_EQUAL_EXT, /**< Used in numpy.less_equal() impl, requires extra @@ -323,27 +313,25 @@ enum class DPNPFuncName : size_t DPNP_FN_PUT_ALONG_AXIS_EXT, /**< Used in numpy.put_along_axis() impl, requires extra parameters */ DPNP_FN_QR, /**< Used in numpy.linalg.qr() impl */ - DPNP_FN_QR_EXT, /**< Used in numpy.linalg.qr() impl, requires extra - parameters */ - DPNP_FN_RADIANS, /**< Used in numpy.radians() impl */ - DPNP_FN_RADIANS_EXT, /**< Used in numpy.radians() impl, requires extra - parameters */ - DPNP_FN_REMAINDER, /**< Used in numpy.remainder() impl */ - DPNP_FN_REMAINDER_EXT, /**< Used in numpy.remainder() impl, requires extra - parameters */ - DPNP_FN_RECIP, /**< Used in numpy.recip() impl */ - DPNP_FN_RECIP_EXT, /**< Used in numpy.recip() impl, requires extra - parameters */ - DPNP_FN_REPEAT, /**< Used in numpy.repeat() impl */ - DPNP_FN_REPEAT_EXT, /**< Used in numpy.repeat() impl, requires extra - parameters */ - DPNP_FN_RIGHT_SHIFT, /**< Used in numpy.right_shift() impl */ - DPNP_FN_RIGHT_SHIFT_EXT, /**< Used in numpy.right_shift() impl, requires - extra parameters */ - DPNP_FN_RNG_BETA, /**< Used in numpy.random.beta() impl */ - DPNP_FN_RNG_BETA_EXT, /**< Used in numpy.random.beta() impl, requires extra - parameters */ - DPNP_FN_RNG_BINOMIAL, /**< Used in numpy.random.binomial() impl */ + DPNP_FN_QR_EXT, /**< Used in numpy.linalg.qr() impl, requires extra + parameters */ + DPNP_FN_RADIANS, /**< Used in numpy.radians() impl */ + DPNP_FN_RADIANS_EXT, /**< Used in numpy.radians() impl, requires extra + parameters */ + DPNP_FN_REMAINDER, /**< Used in numpy.remainder() impl */ + DPNP_FN_REMAINDER_EXT, /**< Used in numpy.remainder() impl, requires extra + parameters */ + DPNP_FN_RECIP, /**< Used in numpy.recip() impl */ + DPNP_FN_RECIP_EXT, /**< Used in numpy.recip() impl, requires extra + parameters */ + DPNP_FN_REPEAT, /**< Used in numpy.repeat() impl */ + DPNP_FN_REPEAT_EXT, /**< Used in numpy.repeat() impl, requires extra + parameters */ + DPNP_FN_RIGHT_SHIFT, /**< Used in numpy.right_shift() impl */ + DPNP_FN_RNG_BETA, /**< Used in numpy.random.beta() impl */ + DPNP_FN_RNG_BETA_EXT, /**< Used in numpy.random.beta() impl, requires extra + parameters */ + DPNP_FN_RNG_BINOMIAL, /**< Used in numpy.random.binomial() impl */ DPNP_FN_RNG_BINOMIAL_EXT, /**< Used in numpy.random.binomial() impl, requires extra parameters */ DPNP_FN_RNG_CHISQUARE, /**< Used in numpy.random.chisquare() impl */ diff --git a/dpnp/backend/kernels/dpnp_krnl_bitwise.cpp b/dpnp/backend/kernels/dpnp_krnl_bitwise.cpp index 658d0297e23..d1e3d41235a 100644 --- a/dpnp/backend/kernels/dpnp_krnl_bitwise.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_bitwise.cpp @@ -127,14 +127,6 @@ void (*dpnp_invert_default_c)(void *, void *, size_t) = dpnp_invert_c<_DataType>; -template -DPCTLSyclEventRef (*dpnp_invert_ext_c)(DPCTLSyclQueueRef, - void *, - void *, - size_t, - const DPCTLEventVectorRef) = - dpnp_invert_c<_DataType>; - static void func_map_init_bitwise_1arg_1type(func_map_t &fmap) { fmap[DPNPFuncName::DPNP_FN_INVERT][eft_BLN][eft_BLN] = { @@ -144,13 +136,6 @@ static void func_map_init_bitwise_1arg_1type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_INVERT][eft_LNG][eft_LNG] = { eft_LNG, (void *)dpnp_invert_default_c}; - fmap[DPNPFuncName::DPNP_FN_INVERT_EXT][eft_BLN][eft_BLN] = { - eft_BLN, (void *)dpnp_invert_ext_c}; - fmap[DPNPFuncName::DPNP_FN_INVERT_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_invert_ext_c}; - fmap[DPNPFuncName::DPNP_FN_INVERT_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_invert_ext_c}; - return; } @@ -399,16 +384,7 @@ static void func_map_init_bitwise_1arg_1type(func_map_t &fmap) const shape_elem_type *, const void *, const size_t, const size_t, \ const shape_elem_type *, const shape_elem_type *, const void *, \ const size_t, const size_t, const shape_elem_type *, \ - const shape_elem_type *, const size_t *) = __name__<_DataType>; \ - \ - template \ - DPCTLSyclEventRef (*__name__##_ext)( \ - DPCTLSyclQueueRef, void *, const size_t, const size_t, \ - const shape_elem_type *, const shape_elem_type *, const void *, \ - const size_t, const size_t, const shape_elem_type *, \ - const shape_elem_type *, const void *, const size_t, const size_t, \ - const shape_elem_type *, const shape_elem_type *, const size_t *, \ - const DPCTLEventVectorRef) = __name__<_DataType>; + const shape_elem_type *, const size_t *) = __name__<_DataType>; #include @@ -419,57 +395,26 @@ static void func_map_init_bitwise_2arg_1type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_BITWISE_AND][eft_LNG][eft_LNG] = { eft_LNG, (void *)dpnp_bitwise_and_c_default}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_AND_EXT][eft_BLN][eft_BLN] = { - eft_BLN, (void *)dpnp_bitwise_and_c_ext}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_AND_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_bitwise_and_c_ext}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_AND_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_bitwise_and_c_ext}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_OR][eft_INT][eft_INT] = { eft_INT, (void *)dpnp_bitwise_or_c_default}; fmap[DPNPFuncName::DPNP_FN_BITWISE_OR][eft_LNG][eft_LNG] = { eft_LNG, (void *)dpnp_bitwise_or_c_default}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_OR_EXT][eft_BLN][eft_BLN] = { - eft_BLN, (void *)dpnp_bitwise_or_c_ext}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_OR_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_bitwise_or_c_ext}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_OR_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_bitwise_or_c_ext}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_XOR][eft_INT][eft_INT] = { eft_INT, (void *)dpnp_bitwise_xor_c_default}; fmap[DPNPFuncName::DPNP_FN_BITWISE_XOR][eft_LNG][eft_LNG] = { eft_LNG, (void *)dpnp_bitwise_xor_c_default}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_XOR_EXT][eft_BLN][eft_BLN] = { - eft_BLN, (void *)dpnp_bitwise_xor_c_ext}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_XOR_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_bitwise_xor_c_ext}; - fmap[DPNPFuncName::DPNP_FN_BITWISE_XOR_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_bitwise_xor_c_ext}; - fmap[DPNPFuncName::DPNP_FN_LEFT_SHIFT][eft_INT][eft_INT] = { eft_INT, (void *)dpnp_left_shift_c_default}; fmap[DPNPFuncName::DPNP_FN_LEFT_SHIFT][eft_LNG][eft_LNG] = { eft_LNG, (void *)dpnp_left_shift_c_default}; - fmap[DPNPFuncName::DPNP_FN_LEFT_SHIFT_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_left_shift_c_ext}; - fmap[DPNPFuncName::DPNP_FN_LEFT_SHIFT_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_left_shift_c_ext}; - fmap[DPNPFuncName::DPNP_FN_RIGHT_SHIFT][eft_INT][eft_INT] = { eft_INT, (void *)dpnp_right_shift_c_default}; fmap[DPNPFuncName::DPNP_FN_RIGHT_SHIFT][eft_LNG][eft_LNG] = { eft_LNG, (void *)dpnp_right_shift_c_default}; - fmap[DPNPFuncName::DPNP_FN_RIGHT_SHIFT_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_right_shift_c_ext}; - fmap[DPNPFuncName::DPNP_FN_RIGHT_SHIFT_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_right_shift_c_ext}; - return; } diff --git a/dpnp/dpnp_algo/CMakeLists.txt b/dpnp/dpnp_algo/CMakeLists.txt index abdf9ae0cb7..8aa419220de 100644 --- a/dpnp/dpnp_algo/CMakeLists.txt +++ b/dpnp/dpnp_algo/CMakeLists.txt @@ -11,7 +11,6 @@ set(dpnp_algo_pyx_deps ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_searching.pxi ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_indexing.pxi ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_logic.pxi - ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_bitwise.pxi ${CMAKE_CURRENT_SOURCE_DIR}/dpnp_algo_special.pxi ) diff --git a/dpnp/dpnp_algo/dpnp_algo.pxd b/dpnp/dpnp_algo/dpnp_algo.pxd index f560545f502..66cfc96a8c8 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pxd +++ b/dpnp/dpnp_algo/dpnp_algo.pxd @@ -62,12 +62,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_AROUND_EXT DPNP_FN_ASTYPE DPNP_FN_ASTYPE_EXT - DPNP_FN_BITWISE_AND - DPNP_FN_BITWISE_AND_EXT - DPNP_FN_BITWISE_OR - DPNP_FN_BITWISE_OR_EXT - DPNP_FN_BITWISE_XOR - DPNP_FN_BITWISE_XOR_EXT DPNP_FN_CBRT DPNP_FN_CBRT_EXT DPNP_FN_CEIL @@ -148,12 +142,8 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_INITVAL_EXT DPNP_FN_INV DPNP_FN_INV_EXT - DPNP_FN_INVERT - DPNP_FN_INVERT_EXT DPNP_FN_KRON DPNP_FN_KRON_EXT - DPNP_FN_LEFT_SHIFT - DPNP_FN_LEFT_SHIFT_EXT DPNP_FN_LOG10 DPNP_FN_LOG10_EXT DPNP_FN_LOG1P @@ -204,8 +194,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_RECIP_EXT DPNP_FN_REPEAT DPNP_FN_REPEAT_EXT - DPNP_FN_RIGHT_SHIFT - DPNP_FN_RIGHT_SHIFT_EXT DPNP_FN_RNG_BETA DPNP_FN_RNG_BETA_EXT DPNP_FN_RNG_BINOMIAL @@ -421,37 +409,6 @@ cdef DPNPFuncType dpnp_dtype_to_DPNPFuncType(dtype) cdef dpnp_DPNPFuncType_to_dtype(size_t type) -""" -Bitwise functions -""" -cpdef dpnp_descriptor dpnp_bitwise_and(dpnp_descriptor x1_obj, - dpnp_descriptor x2_obj, - object dtype=*, - dpnp_descriptor out=*, - object where=*) -cpdef dpnp_descriptor dpnp_bitwise_or(dpnp_descriptor x1_obj, - dpnp_descriptor x2_obj, - object dtype=*, - dpnp_descriptor out=*, - object where=*) -cpdef dpnp_descriptor dpnp_bitwise_xor(dpnp_descriptor x1_obj, - dpnp_descriptor x2_obj, - object dtype=*, - dpnp_descriptor out=*, - object where=*) -cpdef dpnp_descriptor dpnp_invert(dpnp_descriptor x1) -cpdef dpnp_descriptor dpnp_left_shift(dpnp_descriptor x1_obj, - dpnp_descriptor x2_obj, - object dtype=*, - dpnp_descriptor out=*, - object where=*) -cpdef dpnp_descriptor dpnp_right_shift(dpnp_descriptor x1_obj, - dpnp_descriptor x2_obj, - object dtype=*, - dpnp_descriptor out=*, - object where=*) - - """ Logic functions """ diff --git a/dpnp/dpnp_algo/dpnp_algo.pyx b/dpnp/dpnp_algo/dpnp_algo.pyx index 89ce50169c8..32cb9ff02ab 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pyx +++ b/dpnp/dpnp_algo/dpnp_algo.pyx @@ -63,7 +63,6 @@ __all__ = [ include "dpnp_algo_arraycreation.pxi" -include "dpnp_algo_bitwise.pxi" include "dpnp_algo_counting.pxi" include "dpnp_algo_indexing.pxi" include "dpnp_algo_linearalgebra.pxi" diff --git a/dpnp/dpnp_algo/dpnp_algo_bitwise.pxi b/dpnp/dpnp_algo/dpnp_algo_bitwise.pxi deleted file mode 100644 index 6c630f791ad..00000000000 --- a/dpnp/dpnp_algo/dpnp_algo_bitwise.pxi +++ /dev/null @@ -1,88 +0,0 @@ -# cython: language_level=3 -# cython: linetrace=True -# -*- coding: utf-8 -*- -# ***************************************************************************** -# Copyright (c) 2016-2023, Intel Corporation -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# - Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# - Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF -# THE POSSIBILITY OF SUCH DAMAGE. -# ***************************************************************************** - -"""Module Backend (Binary operations) - -This module contains interface functions between C backend layer -and the rest of the library - -""" - -# NO IMPORTs here. All imports must be placed into main "dpnp_algo.pyx" file - -__all__ += [ - "dpnp_bitwise_and", - "dpnp_bitwise_or", - "dpnp_bitwise_xor", - "dpnp_invert", - "dpnp_left_shift", - "dpnp_right_shift", -] - - -cpdef utils.dpnp_descriptor dpnp_bitwise_and(utils.dpnp_descriptor x1_obj, - utils.dpnp_descriptor x2_obj, - object dtype=None, - utils.dpnp_descriptor out=None, - object where=True): - return call_fptr_2in_1out_strides(DPNP_FN_BITWISE_AND_EXT, x1_obj, x2_obj, dtype=dtype, out=out, where=where) - - -cpdef utils.dpnp_descriptor dpnp_bitwise_or(utils.dpnp_descriptor x1_obj, - utils.dpnp_descriptor x2_obj, - object dtype=None, - utils.dpnp_descriptor out=None, - object where=True): - return call_fptr_2in_1out_strides(DPNP_FN_BITWISE_OR_EXT, x1_obj, x2_obj, dtype=dtype, out=out, where=where) - - -cpdef utils.dpnp_descriptor dpnp_bitwise_xor(utils.dpnp_descriptor x1_obj, - utils.dpnp_descriptor x2_obj, - object dtype=None, - utils.dpnp_descriptor out=None, - object where=True): - return call_fptr_2in_1out_strides(DPNP_FN_BITWISE_XOR_EXT, x1_obj, x2_obj, dtype=dtype, out=out, where=where) - - -cpdef utils.dpnp_descriptor dpnp_invert(utils.dpnp_descriptor arr, utils.dpnp_descriptor out=None): - return call_fptr_1in_1out(DPNP_FN_INVERT_EXT, arr, arr.shape, out=out, func_name="invert") - - -cpdef utils.dpnp_descriptor dpnp_left_shift(utils.dpnp_descriptor x1_obj, - utils.dpnp_descriptor x2_obj, - object dtype=None, - utils.dpnp_descriptor out=None, - object where=True): - return call_fptr_2in_1out_strides(DPNP_FN_LEFT_SHIFT_EXT, x1_obj, x2_obj, dtype=dtype, out=out, where=where) - -cpdef utils.dpnp_descriptor dpnp_right_shift(utils.dpnp_descriptor x1_obj, - utils.dpnp_descriptor x2_obj, - object dtype=None, - utils.dpnp_descriptor out=None, - object where=True): - return call_fptr_2in_1out_strides(DPNP_FN_RIGHT_SHIFT_EXT, x1_obj, x2_obj, dtype=dtype, out=out, where=where) diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index c97c79ec0ad..50ba73bed92 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -43,15 +43,20 @@ __all__ = [ "check_nd_call_func", "dpnp_add", + "dpnp_bitwise_and", + "dpnp_bitwise_or", + "dpnp_bitwise_xor", "dpnp_cos", "dpnp_divide", "dpnp_equal", "dpnp_floor_divide", "dpnp_greater", "dpnp_greater_equal", + "dpnp_invert", "dpnp_isfinite", "dpnp_isinf", "dpnp_isnan", + "dpnp_left_shift", "dpnp_less", "dpnp_less_equal", "dpnp_log", @@ -61,6 +66,7 @@ "dpnp_logical_xor", "dpnp_multiply", "dpnp_not_equal", + "dpnp_right_shift", "dpnp_sin", "dpnp_sqrt", "dpnp_square", @@ -229,6 +235,138 @@ def _call_cos(src, dst, sycl_queue, depends=None): return dpnp_array._create_from_usm_ndarray(res_usm) +_bitwise_and_docstring_ = """ +bitwise_and(x1, x2, out=None, order='K') + +Computes the bitwise AND of the underlying binary representation of each +element `x1_i` of the input array `x1` with the respective element `x2_i` +of the input array `x2`. + +Args: + x1 (dpnp.ndarray): + First input array, expected to have integer or boolean data type. + x2 (dpnp.ndarray): + Second input array, also expected to have integer or boolean data + type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + An array containing the element-wise results. The data type + of the returned array is determined by the Type Promotion Rules. +""" + + +def dpnp_bitwise_and(x1, x2, out=None, order="K"): + """Invokes bitwise_and() from dpctl.tensor implementation for bitwise_and() function.""" + + # dpctl.tensor only works with usm_ndarray or scalar + x1_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x1) + x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = BinaryElementwiseFunc( + "bitwise_and", + ti._bitwise_and_result_type, + ti._bitwise_and, + _bitwise_and_docstring_, + ) + res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + +_bitwise_or_docstring_ = """ +bitwise_or(x1, x2, out=None, order='K') + +Computes the bitwise OR of the underlying binary representation of each +element `x1_i` of the input array `x1` with the respective element `x2_i` +of the input array `x2`. + +Args: + x1 (dpnp.ndarray): + First input array, expected to have integer or boolean data type. + x2 (dpnp.ndarray): + Second input array, also expected to have integer or boolean data + type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + An array containing the element-wise results. The data type + of the returned array is determined by the Type Promotion Rules. +""" + + +def dpnp_bitwise_or(x1, x2, out=None, order="K"): + """Invokes bitwise_or() from dpctl.tensor implementation for bitwise_or() function.""" + + # dpctl.tensor only works with usm_ndarray or scalar + x1_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x1) + x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = BinaryElementwiseFunc( + "bitwise_or", + ti._bitwise_or_result_type, + ti._bitwise_or, + _bitwise_or_docstring_, + ) + res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + +_bitwise_xor_docstring_ = """ +bitwise_xor(x1, x2, out=None, order='K') + +Computes the bitwise XOR of the underlying binary representation of each +element `x1_i` of the input array `x1` with the respective element `x2_i` +of the input array `x2`. + +Args: + x1 (dpnp.ndarray): + First input array, expected to have integer or boolean data type. + x2 (dpnp.ndarray): + Second input array, also expected to have integer or boolean data + type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + An array containing the element-wise results. The data type + of the returned array is determined by the Type Promotion Rules. +""" + + +def dpnp_bitwise_xor(x1, x2, out=None, order="K"): + """Invokes bitwise_xor() from dpctl.tensor implementation for bitwise_xor() function.""" + + # dpctl.tensor only works with usm_ndarray or scalar + x1_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x1) + x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = BinaryElementwiseFunc( + "bitwise_xor", + ti._bitwise_xor_result_type, + ti._bitwise_xor, + _bitwise_xor_docstring_, + ) + res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _divide_docstring_ = """ divide(x1, x2, out=None, order="K") @@ -469,6 +607,45 @@ def dpnp_greater_equal(x1, x2, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_invert_docstring = """ +invert(x, out=None, order='K') + +Inverts (flips) each bit for each element `x_i` of the input array `x`. + +Args: + x (dpnp.ndarray): + Input array, expected to have integer or boolean data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array must have the correct shape and the expected data type. + order ("C","F","A","K", optional): memory layout of the new + output array, if parameter `out` is `None`. + Default: "K". +Return: + dpnp.ndarray: + An array containing the element-wise results. + The data type of the returned array is same as the data type of the + input array. +""" + + +def dpnp_invert(x, out=None, order="K"): + """Invokes bitwise_invert() from dpctl.tensor implementation for invert() function.""" + + # dpctl.tensor only works with usm_ndarray or scalar + x_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = UnaryElementwiseFunc( + "invert", + ti._bitwise_invert_result_type, + ti._bitwise_invert, + _invert_docstring, + ) + res_usm = func(x_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _isfinite_docstring = """ isfinite(x, out=None, order="K") @@ -575,6 +752,50 @@ def dpnp_isnan(x, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_left_shift_docstring_ = """ +left_shift(x1, x2, out=None, order='K') + +Shifts the bits of each element `x1_i` of the input array x1 to the left by +appending `x2_i` (i.e., the respective element in the input array `x2`) zeros to +the right of `x1_i`. + +Args: + x1 (dpnp.ndarray): + First input array, expected to have integer data type. + x2 (dpnp.ndarray): + Second input array, also expected to have integer data type. + Each element must be greater than or equal to 0. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + An array containing the element-wise results. The data type + of the returned array is determined by the Type Promotion Rules. +""" + + +def dpnp_left_shift(x1, x2, out=None, order="K"): + """Invokes bitwise_left_shift() from dpctl.tensor implementation for left_shift() function.""" + + # dpctl.tensor only works with usm_ndarray or scalar + x1_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x1) + x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = BinaryElementwiseFunc( + "bitwise_leftt_shift", + ti._bitwise_left_shift_result_type, + ti._bitwise_left_shift, + _left_shift_docstring_, + ) + res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _less_docstring_ = """ less(x1, x2, out=None, order="K") @@ -722,7 +943,7 @@ def _call_log(src, dst, sycl_queue, depends=None): Memory layout of the newly output array, if parameter `out` is `None`. Default: "K". Returns: - usm_narray: + dpnp.ndarray: An array containing the element-wise logical AND results. """ @@ -758,7 +979,7 @@ def dpnp_logical_and(x1, x2, out=None, order="K"): output array, if parameter `out` is `None`. Default: "K". Return: - usm_ndarray: + dpnp.ndarray: An array containing the element-wise logical NOT results. """ @@ -798,7 +1019,7 @@ def dpnp_logical_not(x, out=None, order="K"): Memory layout of the newly output array, if parameter `out` is `None`. Default: "K". Returns: - usm_narray: + dpnp.ndarray: An array containing the element-wise logical OR results. """ @@ -839,7 +1060,7 @@ def dpnp_logical_or(x1, x2, out=None, order="K"): Memory layout of the newly output array, if parameter `out` is `None`. Default: "K". Returns: - usm_narray: + dpnp.ndarray: An array containing the element-wise logical XOR results. """ @@ -953,6 +1174,49 @@ def dpnp_not_equal(x1, x2, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_right_shift_docstring_ = """ +right_shift(x1, x2, out=None, order='K') + +Shifts the bits of each element `x1_i` of the input array `x1` to the right +according to the respective element `x2_i` of the input array `x2`. + +Args: + x1 (dpnp.ndarray): + First input array, expected to have integer data type. + x2 (dpnp.ndarray): + Second input array, also expected to have integer data type. + Each element must be greater than or equal to 0. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + An array containing the element-wise results. The data type + of the returned array is determined by the Type Promotion Rules. +""" + + +def dpnp_right_shift(x1, x2, out=None, order="K"): + """Invokes bitwise_right_shift() from dpctl.tensor implementation for right_shift() function.""" + + # dpctl.tensor only works with usm_ndarray or scalar + x1_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x1) + x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = BinaryElementwiseFunc( + "bitwise_right_shift", + ti._bitwise_right_shift_result_type, + ti._bitwise_right_shift, + _right_shift_docstring_, + ) + res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _sin_docstring = """ sin(x, out=None, order='K') Computes sine for each element `x_i` of input array `x`. diff --git a/dpnp/dpnp_iface_bitwise.py b/dpnp/dpnp_iface_bitwise.py index 4bffb71417f..f3f0e2cfec8 100644 --- a/dpnp/dpnp_iface_bitwise.py +++ b/dpnp/dpnp_iface_bitwise.py @@ -40,12 +40,17 @@ """ -import dpctl.tensor as dpt import numpy -import dpnp -from dpnp.dpnp_algo import * -from dpnp.dpnp_utils import * +from .dpnp_algo.dpnp_elementwise_common import ( + check_nd_call_func, + dpnp_bitwise_and, + dpnp_bitwise_or, + dpnp_bitwise_xor, + dpnp_invert, + dpnp_left_shift, + dpnp_right_shift, +) __all__ = [ "bitwise_and", @@ -58,73 +63,18 @@ ] -def _check_nd_call( - origin_func, dpnp_func, x1, x2, dtype=None, out=None, where=True, **kwargs +def bitwise_and( + x1, + x2, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, ): - """Choose function to call based on input and call chosen fucntion.""" - - if kwargs: - pass - elif where is not True: - pass - elif dtype is not None: - pass - elif dpnp.isscalar(x1) and dpnp.isscalar(x2): - # at least either x1 or x2 has to be an array - pass - else: - # get USM type and queue to copy scalar from the host memory into a USM allocation - if dpnp.isscalar(x1) or dpnp.isscalar(x2): - usm_type, queue = ( - get_usm_allocations([x1, x2]) - if dpnp.isscalar(x1) or dpnp.isscalar(x2) - else (None, None) - ) - dtype = x1.dtype if not dpnp.isscalar(x1) else x2.dtype - else: - dtype, usm_type, queue = (None, None, None) - - x1_desc = dpnp.get_dpnp_descriptor( - x1, - copy_when_strides=False, - copy_when_nondefault_queue=False, - alloc_dtype=dtype, - alloc_usm_type=usm_type, - alloc_queue=queue, - ) - x2_desc = dpnp.get_dpnp_descriptor( - x2, - copy_when_strides=False, - copy_when_nondefault_queue=False, - alloc_dtype=dtype, - alloc_usm_type=usm_type, - alloc_queue=queue, - ) - if x1_desc and x2_desc: - if out is not None: - if not isinstance(out, (dpnp.ndarray, dpt.usm_ndarray)): - raise TypeError( - "return array must be of supported array type" - ) - out_desc = ( - dpnp.get_dpnp_descriptor( - out, copy_when_nondefault_queue=False - ) - or None - ) - else: - out_desc = None - - return dpnp_func( - x1_desc, x2_desc, dtype=dtype, out=out_desc, where=where - ).get_pyobj() - - return call_origin( - origin_func, x1, x2, dtype=dtype, out=out, where=where, **kwargs - ) - - -def bitwise_and(x1, x2, dtype=None, out=None, where=True, **kwargs): """ Compute the bit-wise AND of two arrays element-wise. @@ -132,18 +82,17 @@ def bitwise_and(x1, x2, dtype=None, out=None, where=True, **kwargs): Returns ------- - y : dpnp.ndarray - An array containing the element-wise results of positive square root. + out : dpnp.ndarray + An array containing the element-wise results. Limitations ----------- Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. - Parameters `dtype` and `where` are supported with their default values. + Parameters `where`, `dtype` and `subok` are supported with their default values. Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. - Data type of input arrays `x` and `y` are limited by :obj:`dpnp.bool`, :obj:`dpnp.int32` - and :obj:`dpnp.int64`. + Data type of input arrays `x1` and `x2` has to be an integer or boolean data type. See Also -------- @@ -156,24 +105,46 @@ def bitwise_and(x1, x2, dtype=None, out=None, where=True, **kwargs): >>> import dpnp as np >>> x1 = np.array([2, 5, 255]) >>> x2 = np.array([3,14,16]) - >>> out = np.bitwise_and(x1, x2) - >>> [i for i in out] + >>> np.bitwise_and(x1, x2) [2, 4, 16] + >>> a = np.array([True, True]) + >>> b = np.array([False, True]) + >>> np.bitwise_and(a, b) + array([False, True]) + + The ``&`` operator can be used as a shorthand for ``bitwise_and`` on + :class:`dpnp.ndarray`. + + >>> x1 & x2 + array([ 2, 4, 16]) """ - return _check_nd_call( + return check_nd_call_func( numpy.bitwise_and, dpnp_bitwise_and, x1, x2, - dtype=dtype, out=out, + order=order, where=where, + dtype=dtype, + subok=subok, **kwargs, ) -def bitwise_or(x1, x2, dtype=None, out=None, where=True, **kwargs): +def bitwise_or( + x1, + x2, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, +): """ Compute the bit-wise OR of two arrays element-wise. @@ -181,18 +152,17 @@ def bitwise_or(x1, x2, dtype=None, out=None, where=True, **kwargs): Returns ------- - y : dpnp.ndarray + out : dpnp.ndarray An array containing the element-wise results. Limitations ----------- Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. - Parameters `dtype` and `where` are supported with their default values. + Parameters `where`, `dtype` and `subok` are supported with their default values. Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. - Data type of input arrays `x` and `y` are limited by :obj:`dpnp.bool`, :obj:`dpnp.int32` - and :obj:`dpnp.int64`. + Data type of input arrays `x1` and `x2` has to be an integer or boolean data type. See Also -------- @@ -204,25 +174,42 @@ def bitwise_or(x1, x2, dtype=None, out=None, where=True, **kwargs): -------- >>> import dpnp as np >>> x1 = np.array([2, 5, 255]) - >>> x2 = np.array([4, 4, 4]) - >>> out = np.bitwise_or(x1, x2) - >>> [i for i in out] - [6, 5, 255] + >>> x2 = np.array([4]) + >>> np.bitwise_or(x1, x2) + array([ 6, 5, 255]) + + The ``|`` operator can be used as a shorthand for ``bitwise_or`` on + :class:`dpnp.ndarray`. + >>> x1 | x2 + array([ 6, 5, 255]) """ - return _check_nd_call( + return check_nd_call_func( numpy.bitwise_or, dpnp_bitwise_or, x1, x2, - dtype=dtype, out=out, + order=order, where=where, + dtype=dtype, + subok=subok, **kwargs, ) -def bitwise_xor(x1, x2, dtype=None, out=None, where=True, **kwargs): +def bitwise_xor( + x1, + x2, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, +): """ Compute the bit-wise XOR of two arrays element-wise. @@ -230,18 +217,17 @@ def bitwise_xor(x1, x2, dtype=None, out=None, where=True, **kwargs): Returns ------- - y : dpnp.ndarray + out : dpnp.ndarray An array containing the element-wise results. Limitations ----------- Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. - Parameters `dtype` and `where` are supported with their default values. + Parameters `where`, `dtype` and `subok` are supported with their default values. Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. - Data type of input arrays `x` and `y` are limited by :obj:`dpnp.bool`, :obj:`dpnp.int32` - and :obj:`dpnp.int64`. + Data type of input arrays `x1` and `x2` has to be an integer or boolean data type. See Also -------- @@ -254,24 +240,37 @@ def bitwise_xor(x1, x2, dtype=None, out=None, where=True, **kwargs): >>> import dpnp as np >>> x1 = np.array([31, 3]) >>> x2 = np.array([5, 6]) - >>> out = np.bitwise_xor(x1, x2) - >>> [i for i in out] - [26, 5] + >>> np.bitwise_xor(x1, x2) + array([26, 5]) + + >>> a = np.array([True, True]) + >>> b = np.array([False, True]) + >>> np.bitwise_xor(a, b) + array([ True, False]) + + The ``^`` operator can be used as a shorthand for ``bitwise_xor`` on + :class:`dpnp.ndarray`. + >>> a ^ b + array([ True, False]) """ - return _check_nd_call( + return check_nd_call_func( numpy.bitwise_xor, dpnp_bitwise_xor, x1, x2, - dtype=dtype, out=out, + order=order, where=where, + dtype=dtype, + subok=subok, **kwargs, ) -def invert(x, /, out=None, *, where=True, dtype=None, subok=True, **kwargs): +def invert( + x, /, out=None, *, order="K", dtype=None, where=True, subok=True, **kwargs +): """ Compute bit-wise inversion, or bit-wise NOT, element-wise. @@ -279,7 +278,7 @@ def invert(x, /, out=None, *, where=True, dtype=None, subok=True, **kwargs): Returns ------- - y : dpnp.ndarray + out : dpnp.ndarray An array containing the element-wise results. Limitations @@ -289,8 +288,7 @@ def invert(x, /, out=None, *, where=True, dtype=None, subok=True, **kwargs): Parameters `where`, `dtype` and `subok` are supported with their default values. Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. - Data type of input array `x` is limited by :obj:`dpnp.bool`, :obj:`dpnp.int32` - and :obj:`dpnp.int64`. + Data type of input array `x` has to be an integer data type. See Also -------- @@ -301,44 +299,28 @@ def invert(x, /, out=None, *, where=True, dtype=None, subok=True, **kwargs): Examples -------- - >>> import dpnp as dp - >>> x = dp.array([13]) - >>> out = dp.invert(x) - >>> out[0] + >>> import dpnp as np + >>> x = np.array([13]) + >>> np.invert(x) -14 + >>> a = np.array([True, False]) + >>> np.invert(a) + array([False, True]) + + The ``~`` operator can be used as a shorthand for ``invert`` on + :class:`dpnp.ndarray`. + + >>> ~a + array([False, True]) """ - if kwargs: - pass - elif where is not True: - pass - elif dtype is not None: - pass - elif subok is not True: - pass - else: - x1_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) - if x1_desc: - if out is not None: - if not isinstance(out, (dpnp.ndarray, dpt.usm_ndarray)): - raise TypeError( - "return array must be of supported array type" - ) - out_desc = ( - dpnp.get_dpnp_descriptor( - out, copy_when_nondefault_queue=False - ) - or None - ) - else: - out_desc = None - return dpnp_invert(x1_desc, out_desc).get_pyobj() - - return call_origin( + return check_nd_call_func( numpy.invert, + dpnp_invert, x, out=out, + order=order, where=where, dtype=dtype, subok=subok, @@ -349,7 +331,18 @@ def invert(x, /, out=None, *, where=True, dtype=None, subok=True, **kwargs): bitwise_not = invert # bitwise_not is an alias for invert -def left_shift(x1, x2, dtype=None, out=None, where=True, **kwargs): +def left_shift( + x1, + x2, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, +): """ Shift the bits of an integer to the left. @@ -357,14 +350,14 @@ def left_shift(x1, x2, dtype=None, out=None, where=True, **kwargs): Returns ------- - y : dpnp.ndarray + out : dpnp.ndarray An array containing the element-wise results. Limitations ----------- Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. - Parameters `dtype` and `where` are supported with their default values. + Parameters `where`, `dtype` and `subok` are supported with their default values. Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input data is supported as integer only. @@ -376,26 +369,43 @@ def left_shift(x1, x2, dtype=None, out=None, where=True, **kwargs): Examples -------- >>> import dpnp as np - >>> x1 = np.array([5, 5, 5]) + >>> x1 = np.array([5]) >>> x2 = np.array([1, 2, 3]) - >>> out = np.left_shift(x1, x2) - >>> [i for i in out] - [10, 20, 40] + >>> np.left_shift(x1, x2) + array([10, 20, 40]) + + The ``<<`` operator can be used as a shorthand for ``right_shift`` on + :class:`dpnp.ndarray`. + >>> x1 << x2 + array([10, 20, 40]) """ - return _check_nd_call( + return check_nd_call_func( numpy.left_shift, dpnp_left_shift, x1, x2, - dtype=dtype, out=out, + order=order, where=where, + dtype=dtype, + subok=subok, **kwargs, ) -def right_shift(x1, x2, dtype=None, out=None, where=True, **kwargs): +def right_shift( + x1, + x2, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, +): """ Shift the bits of an integer to the right. @@ -403,14 +413,14 @@ def right_shift(x1, x2, dtype=None, out=None, where=True, **kwargs): Returns ------- - y : dpnp.ndarray + out : dpnp.ndarray An array containing the element-wise results. Limitations ----------- Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. - Parameters `dtype` and `where` are supported with their default values. + Parameters `where`, `dtype` and `subok` are supported with their default values. Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input data is supported as integer only. @@ -422,20 +432,26 @@ def right_shift(x1, x2, dtype=None, out=None, where=True, **kwargs): Examples -------- >>> import dpnp as np - >>> x1 = np.array([10, 10, 10]) + >>> x1 = np.array([10]) >>> x2 = np.array([1, 2, 3]) - >>> out = np.right_shift(x1, x2) - >>> [i for i in out] - [5, 2, 1] + >>> np.right_shift(x1, x2) + array([5, 2, 1]) + The ``>>`` operator can be used as a shorthand for ``left_shift`` on + :class:`dpnp.ndarray`. + + >>> x1 >> x2 + array([5, 2, 1]) """ - return _check_nd_call( + return check_nd_call_func( numpy.right_shift, dpnp_right_shift, x1, x2, - dtype=dtype, out=out, + order=order, where=where, + dtype=dtype, + subok=subok, **kwargs, ) diff --git a/tests/test_bitwise.py b/tests/test_bitwise.py index cdcc1701ae0..21515d723a6 100644 --- a/tests/test_bitwise.py +++ b/tests/test_bitwise.py @@ -33,6 +33,8 @@ def array_or_scalar(xp, data, dtype=None): return xp.array(data, dtype=dtype) def _test_unary_int(self, name, data, dtype): + if numpy.isscalar(data): + pytest.skip("Input can't be scalar") dp_a = self.array_or_scalar(inp, data, dtype=dtype) result = getattr(inp, name)(dp_a) @@ -65,6 +67,7 @@ def test_bitwise_and(self, lhs, rhs, dtype): ) assert_array_equal(dp_a & dp_b, np_a & np_b) + """ if ( not (inp.isscalar(dp_a) or inp.isscalar(dp_b)) and dp_a.shape == dp_b.shape @@ -72,6 +75,7 @@ def test_bitwise_and(self, lhs, rhs, dtype): dp_a &= dp_b np_a &= np_b assert_array_equal(dp_a, np_a) + """ def test_bitwise_or(self, lhs, rhs, dtype): dp_a, dp_b, np_a, np_b = self._test_binary_int( @@ -79,6 +83,7 @@ def test_bitwise_or(self, lhs, rhs, dtype): ) assert_array_equal(dp_a | dp_b, np_a | np_b) + """ if ( not (inp.isscalar(dp_a) or inp.isscalar(dp_b)) and dp_a.shape == dp_b.shape @@ -86,6 +91,7 @@ def test_bitwise_or(self, lhs, rhs, dtype): dp_a |= dp_b np_a |= np_b assert_array_equal(dp_a, np_a) + """ def test_bitwise_xor(self, lhs, rhs, dtype): dp_a, dp_b, np_a, np_b = self._test_binary_int( @@ -93,6 +99,7 @@ def test_bitwise_xor(self, lhs, rhs, dtype): ) assert_array_equal(dp_a ^ dp_b, np_a ^ np_b) + """ if ( not (inp.isscalar(dp_a) or inp.isscalar(dp_b)) and dp_a.shape == dp_b.shape @@ -100,6 +107,7 @@ def test_bitwise_xor(self, lhs, rhs, dtype): dp_a ^= dp_b np_a ^= np_b assert_array_equal(dp_a, np_a) + """ def test_invert(self, lhs, rhs, dtype): dp_a, np_a = self._test_unary_int("invert", lhs, dtype) @@ -111,6 +119,7 @@ def test_left_shift(self, lhs, rhs, dtype): ) assert_array_equal(dp_a << dp_b, np_a << np_b) + """ if ( not (inp.isscalar(dp_a) or inp.isscalar(dp_b)) and dp_a.shape == dp_b.shape @@ -118,6 +127,7 @@ def test_left_shift(self, lhs, rhs, dtype): dp_a <<= dp_b np_a <<= np_b assert_array_equal(dp_a, np_a) + """ def test_right_shift(self, lhs, rhs, dtype): dp_a, dp_b, np_a, np_b = self._test_binary_int( @@ -125,6 +135,7 @@ def test_right_shift(self, lhs, rhs, dtype): ) assert_array_equal(dp_a >> dp_b, np_a >> np_b) + """ if ( not (inp.isscalar(dp_a) or inp.isscalar(dp_b)) and dp_a.shape == dp_b.shape @@ -132,3 +143,4 @@ def test_right_shift(self, lhs, rhs, dtype): dp_a >>= dp_b np_a >>= np_b assert_array_equal(dp_a, np_a) + """ diff --git a/tests/third_party/cupy/binary_tests/test_elementwise.py b/tests/third_party/cupy/binary_tests/test_elementwise.py index e23a07f708f..3d69999b0b9 100644 --- a/tests/third_party/cupy/binary_tests/test_elementwise.py +++ b/tests/third_party/cupy/binary_tests/test_elementwise.py @@ -1,19 +1,17 @@ import unittest -import numpy - from tests.third_party.cupy import testing @testing.gpu class TestElementwise(unittest.TestCase): - @testing.for_dtypes((numpy.bool_, numpy.int32, numpy.int64)) + @testing.for_int_dtypes() @testing.numpy_cupy_array_equal() def check_unary_int(self, name, xp, dtype): a = xp.array([-3, -2, -1, 0, 1, 2, 3], dtype=dtype) return getattr(xp, name)(a) - @testing.for_dtypes((numpy.int32, numpy.int64)) + @testing.for_int_dtypes() @testing.numpy_cupy_array_equal() def check_binary_int(self, name, xp, dtype): a = xp.array([-3, -2, -1, 0, 1, 2, 3], dtype=dtype) From b60eb4179f2d9dea7048fee79e689a498b85ea63 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Tue, 8 Aug 2023 16:33:28 -0500 Subject: [PATCH 30/58] address reviewer's comments --- dpnp/backend/include/dpnp_iface_fptr.hpp | 82 +++++++++---------- dpnp/dpnp_algo/dpnp_elementwise_common.py | 98 +++++++++++------------ tests/test_bitwise.py | 5 ++ 3 files changed, 95 insertions(+), 90 deletions(-) diff --git a/dpnp/backend/include/dpnp_iface_fptr.hpp b/dpnp/backend/include/dpnp_iface_fptr.hpp index 43450778019..005f9c8e6d5 100644 --- a/dpnp/backend/include/dpnp_iface_fptr.hpp +++ b/dpnp/backend/include/dpnp_iface_fptr.hpp @@ -69,47 +69,47 @@ enum class DPNPFuncName : size_t DPNP_FN_ALLCLOSE_EXT, /**< Used in numpy.allclose() impl, requires extra parameters */ DPNP_FN_ANY, /**< Used in numpy.any() impl */ - DPNP_FN_ARANGE, /**< Used in numpy.arange() impl */ - DPNP_FN_ARCCOS, /**< Used in numpy.arccos() impl */ - DPNP_FN_ARCCOS_EXT, /**< Used in numpy.arccos() impl, requires extra - parameters */ - DPNP_FN_ARCCOSH, /**< Used in numpy.arccosh() impl */ - DPNP_FN_ARCCOSH_EXT, /**< Used in numpy.arccosh() impl, requires extra - parameters */ - DPNP_FN_ARCSIN, /**< Used in numpy.arcsin() impl */ - DPNP_FN_ARCSIN_EXT, /**< Used in numpy.arcsin() impl, requires extra - parameters */ - DPNP_FN_ARCSINH, /**< Used in numpy.arcsinh() impl */ - DPNP_FN_ARCSINH_EXT, /**< Used in numpy.arcsinh() impl, requires extra - parameters */ - DPNP_FN_ARCTAN, /**< Used in numpy.arctan() impl */ - DPNP_FN_ARCTAN_EXT, /**< Used in numpy.arctan() impl, requires extra - parameters */ - DPNP_FN_ARCTAN2, /**< Used in numpy.arctan2() impl */ - DPNP_FN_ARCTAN2_EXT, /**< Used in numpy.arctan2() impl, requires extra - parameters */ - DPNP_FN_ARCTANH, /**< Used in numpy.arctanh() impl */ - DPNP_FN_ARCTANH_EXT, /**< Used in numpy.arctanh() impl, requires extra - parameters */ - DPNP_FN_ARGMAX, /**< Used in numpy.argmax() impl */ - DPNP_FN_ARGMAX_EXT, /**< Used in numpy.argmax() impl, requires extra - parameters */ - DPNP_FN_ARGMIN, /**< Used in numpy.argmin() impl */ - DPNP_FN_ARGMIN_EXT, /**< Used in numpy.argmin() impl, requires extra - parameters */ - DPNP_FN_ARGSORT, /**< Used in numpy.argsort() impl */ - DPNP_FN_ARGSORT_EXT, /**< Used in numpy.argsort() impl, requires extra - parameters */ - DPNP_FN_AROUND, /**< Used in numpy.around() impl */ - DPNP_FN_AROUND_EXT, /**< Used in numpy.around() impl, requires extra - parameters */ - DPNP_FN_ASTYPE, /**< Used in numpy.astype() impl */ - DPNP_FN_ASTYPE_EXT, /**< Used in numpy.astype() impl, requires extra - parameters */ - DPNP_FN_BITWISE_AND, /**< Used in numpy.bitwise_and() impl */ - DPNP_FN_BITWISE_OR, /**< Used in numpy.bitwise_or() impl */ - DPNP_FN_BITWISE_XOR, /**< Used in numpy.bitwise_xor() impl */ - DPNP_FN_CBRT, /**< Used in numpy.cbrt() impl */ + DPNP_FN_ARANGE, /**< Used in numpy.arange() impl */ + DPNP_FN_ARCCOS, /**< Used in numpy.arccos() impl */ + DPNP_FN_ARCCOS_EXT, /**< Used in numpy.arccos() impl, requires extra + parameters */ + DPNP_FN_ARCCOSH, /**< Used in numpy.arccosh() impl */ + DPNP_FN_ARCCOSH_EXT, /**< Used in numpy.arccosh() impl, requires extra + parameters */ + DPNP_FN_ARCSIN, /**< Used in numpy.arcsin() impl */ + DPNP_FN_ARCSIN_EXT, /**< Used in numpy.arcsin() impl, requires extra + parameters */ + DPNP_FN_ARCSINH, /**< Used in numpy.arcsinh() impl */ + DPNP_FN_ARCSINH_EXT, /**< Used in numpy.arcsinh() impl, requires extra + parameters */ + DPNP_FN_ARCTAN, /**< Used in numpy.arctan() impl */ + DPNP_FN_ARCTAN_EXT, /**< Used in numpy.arctan() impl, requires extra + parameters */ + DPNP_FN_ARCTAN2, /**< Used in numpy.arctan2() impl */ + DPNP_FN_ARCTAN2_EXT, /**< Used in numpy.arctan2() impl, requires extra + parameters */ + DPNP_FN_ARCTANH, /**< Used in numpy.arctanh() impl */ + DPNP_FN_ARCTANH_EXT, /**< Used in numpy.arctanh() impl, requires extra + parameters */ + DPNP_FN_ARGMAX, /**< Used in numpy.argmax() impl */ + DPNP_FN_ARGMAX_EXT, /**< Used in numpy.argmax() impl, requires extra + parameters */ + DPNP_FN_ARGMIN, /**< Used in numpy.argmin() impl */ + DPNP_FN_ARGMIN_EXT, /**< Used in numpy.argmin() impl, requires extra + parameters */ + DPNP_FN_ARGSORT, /**< Used in numpy.argsort() impl */ + DPNP_FN_ARGSORT_EXT, /**< Used in numpy.argsort() impl, requires extra + parameters */ + DPNP_FN_AROUND, /**< Used in numpy.around() impl */ + DPNP_FN_AROUND_EXT, /**< Used in numpy.around() impl, requires extra + parameters */ + DPNP_FN_ASTYPE, /**< Used in numpy.astype() impl */ + DPNP_FN_ASTYPE_EXT, /**< Used in numpy.astype() impl, requires extra + parameters */ + DPNP_FN_BITWISE_AND, /**< Used in numpy.bitwise_and() impl */ + DPNP_FN_BITWISE_OR, /**< Used in numpy.bitwise_or() impl */ + DPNP_FN_BITWISE_XOR, /**< Used in numpy.bitwise_xor() impl */ + DPNP_FN_CBRT, /**< Used in numpy.cbrt() impl */ DPNP_FN_CBRT_EXT, /**< Used in numpy.cbrt() impl, requires extra parameters */ DPNP_FN_CEIL, /**< Used in numpy.ceil() impl */ diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index 50ba73bed92..410f5542f7b 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -186,55 +186,6 @@ def dpnp_add(x1, x2, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) -_cos_docstring = """ -cos(x, out=None, order='K') -Computes cosine for each element `x_i` for input array `x`. -Args: - x (dpnp.ndarray): - Input array, expected to have numeric data type. - out ({None, dpnp.ndarray}, optional): - Output array to populate. Array must have the correct - shape and the expected data type. - order ("C","F","A","K", optional): memory layout of the new - output array, if parameter `out` is `None`. - Default: "K". -Return: - dpnp.ndarray: - An array containing the element-wise cosine. The data type - of the returned array is determined by the Type Promotion Rules. -""" - - -def dpnp_cos(x, out=None, order="K"): - """ - Invokes cos() function from pybind11 extension of OneMKL VM if possible. - - Otherwise fully relies on dpctl.tensor implementation for cos() function. - - """ - - def _call_cos(src, dst, sycl_queue, depends=None): - """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" - - if depends is None: - depends = [] - - if vmi._mkl_cos_to_call(sycl_queue, src, dst): - # call pybind11 extension for cos() function from OneMKL VM - return vmi._cos(sycl_queue, src, dst, depends) - return ti._cos(src, dst, sycl_queue, depends) - - # dpctl.tensor only works with usm_ndarray - x1_usm = dpnp.get_usm_ndarray(x) - out_usm = None if out is None else dpnp.get_usm_ndarray(out) - - func = UnaryElementwiseFunc( - "cos", ti._cos_result_type, _call_cos, _cos_docstring - ) - res_usm = func(x1_usm, out=out_usm, order=order) - return dpnp_array._create_from_usm_ndarray(res_usm) - - _bitwise_and_docstring_ = """ bitwise_and(x1, x2, out=None, order='K') @@ -367,6 +318,55 @@ def dpnp_bitwise_xor(x1, x2, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_cos_docstring = """ +cos(x, out=None, order='K') +Computes cosine for each element `x_i` for input array `x`. +Args: + x (dpnp.ndarray): + Input array, expected to have numeric data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. Array must have the correct + shape and the expected data type. + order ("C","F","A","K", optional): memory layout of the new + output array, if parameter `out` is `None`. + Default: "K". +Return: + dpnp.ndarray: + An array containing the element-wise cosine. The data type + of the returned array is determined by the Type Promotion Rules. +""" + + +def dpnp_cos(x, out=None, order="K"): + """ + Invokes cos() function from pybind11 extension of OneMKL VM if possible. + + Otherwise fully relies on dpctl.tensor implementation for cos() function. + + """ + + def _call_cos(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_cos_to_call(sycl_queue, src, dst): + # call pybind11 extension for cos() function from OneMKL VM + return vmi._cos(sycl_queue, src, dst, depends) + return ti._cos(src, dst, sycl_queue, depends) + + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = UnaryElementwiseFunc( + "cos", ti._cos_result_type, _call_cos, _cos_docstring + ) + res_usm = func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _divide_docstring_ = """ divide(x1, x2, out=None, order="K") diff --git a/tests/test_bitwise.py b/tests/test_bitwise.py index 21515d723a6..9529ac35011 100644 --- a/tests/test_bitwise.py +++ b/tests/test_bitwise.py @@ -68,6 +68,7 @@ def test_bitwise_and(self, lhs, rhs, dtype): assert_array_equal(dp_a & dp_b, np_a & np_b) """ + TODO: unmute once dpctl support that if ( not (inp.isscalar(dp_a) or inp.isscalar(dp_b)) and dp_a.shape == dp_b.shape @@ -84,6 +85,7 @@ def test_bitwise_or(self, lhs, rhs, dtype): assert_array_equal(dp_a | dp_b, np_a | np_b) """ + TODO: unmute once dpctl support that if ( not (inp.isscalar(dp_a) or inp.isscalar(dp_b)) and dp_a.shape == dp_b.shape @@ -100,6 +102,7 @@ def test_bitwise_xor(self, lhs, rhs, dtype): assert_array_equal(dp_a ^ dp_b, np_a ^ np_b) """ + TODO: unmute once dpctl support that if ( not (inp.isscalar(dp_a) or inp.isscalar(dp_b)) and dp_a.shape == dp_b.shape @@ -120,6 +123,7 @@ def test_left_shift(self, lhs, rhs, dtype): assert_array_equal(dp_a << dp_b, np_a << np_b) """ + TODO: unmute once dpctl support that if ( not (inp.isscalar(dp_a) or inp.isscalar(dp_b)) and dp_a.shape == dp_b.shape @@ -136,6 +140,7 @@ def test_right_shift(self, lhs, rhs, dtype): assert_array_equal(dp_a >> dp_b, np_a >> np_b) """ + TODO: unmute once dpctl support that if ( not (inp.isscalar(dp_a) or inp.isscalar(dp_b)) and dp_a.shape == dp_b.shape From 8ace62fc088b0c3670da2084aed5ad3476d50084 Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Wed, 16 Aug 2023 21:29:57 +0200 Subject: [PATCH 31/58] Skipped tests which are currently not working on GPU without fp64 (#1521) --- dpnp/dpnp_algo/dpnp_algo.pxd | 1 - dpnp/dpnp_algo/dpnp_algo.pyx | 8 - dpnp/dpnp_iface.py | 1 - tests/conftest.py | 20 +- tests/skipped_tests_gpu_no_fp64.tbl | 2470 +++++++++++++++++++++++++++ 5 files changed, 2487 insertions(+), 13 deletions(-) create mode 100644 tests/skipped_tests_gpu_no_fp64.tbl diff --git a/dpnp/dpnp_algo/dpnp_algo.pxd b/dpnp/dpnp_algo/dpnp_algo.pxd index 66cfc96a8c8..5bbb0dc4a01 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pxd +++ b/dpnp/dpnp_algo/dpnp_algo.pxd @@ -335,7 +335,6 @@ cdef extern from "constants.hpp": cdef extern from "dpnp_iface.hpp": void dpnp_queue_initialize_c(QueueOptions selector) - size_t dpnp_queue_is_cpu_c() except + char * dpnp_memory_alloc_c(size_t size_in_bytes) except + void dpnp_memory_free_c(void * ptr) diff --git a/dpnp/dpnp_algo/dpnp_algo.pyx b/dpnp/dpnp_algo/dpnp_algo.pyx index 32cb9ff02ab..c697e76c682 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pyx +++ b/dpnp/dpnp_algo/dpnp_algo.pyx @@ -58,7 +58,6 @@ __all__ = [ "dpnp_flatten", "dpnp_init_val", "dpnp_queue_initialize", - "dpnp_queue_is_cpu" ] @@ -223,13 +222,6 @@ cpdef dpnp_queue_initialize(): dpnp_rng_srand_c(seed_from_time) -cpdef dpnp_queue_is_cpu(): - """Return 1 if current queue is CPU. Return 0 otherwise. - - """ - return dpnp_queue_is_cpu_c() - - """ Internal functions """ diff --git a/dpnp/dpnp_iface.py b/dpnp/dpnp_iface.py index 515d3051323..76046691622 100644 --- a/dpnp/dpnp_iface.py +++ b/dpnp/dpnp_iface.py @@ -60,7 +60,6 @@ "convert_single_elem_array_to_scalar", "default_float_type", "dpnp_queue_initialize", - "dpnp_queue_is_cpu", "from_dlpack", "get_dpnp_descriptor", "get_include", diff --git a/tests/conftest.py b/tests/conftest.py index ee37c6a68aa..0213f52e09e 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -27,6 +27,7 @@ import os import sys +import dpctl import numpy import pytest @@ -52,15 +53,28 @@ def pytest_collection_modifyitems(config, items): # global skip file, where gpu device is not supported test_exclude_file_gpu = os.path.join(test_path, "skipped_tests_gpu.tbl") - current_queue_is_cpu = dpnp.dpnp_queue_is_cpu() + # global skip file, where gpu device with no fp64 support + test_exclude_file_gpu_no_fp64 = os.path.join( + test_path, "skipped_tests_gpu_no_fp64.tbl" + ) + + dev = dpctl.select_default_device() + is_cpu = dev.is_cpu + is_gpu_no_fp64 = not dev.has_aspect_fp64 + print("") - print(f"DPNP current queue is CPU: {current_queue_is_cpu}") + print(f"DPNP current device is CPU: {is_cpu}") + print(f"DPNP current device is GPU without fp64 support: {is_gpu_no_fp64}") print(f"DPNP version: {dpnp.__version__}, location: {dpnp}") print(f"NumPy version: {numpy.__version__}, location: {numpy}") print(f"Python version: {sys.version}") print("") - if not current_queue_is_cpu or os.getenv("DPNP_QUEUE_GPU") == "1": + if not is_cpu or os.getenv("DPNP_QUEUE_GPU") == "1": excluded_tests.extend(get_excluded_tests(test_exclude_file_gpu)) + if is_gpu_no_fp64: + excluded_tests.extend( + get_excluded_tests(test_exclude_file_gpu_no_fp64) + ) else: excluded_tests.extend(get_excluded_tests(test_exclude_file)) diff --git a/tests/skipped_tests_gpu_no_fp64.tbl b/tests/skipped_tests_gpu_no_fp64.tbl new file mode 100644 index 00000000000..b57fe1eb399 --- /dev/null +++ b/tests/skipped_tests_gpu_no_fp64.tbl @@ -0,0 +1,2470 @@ +tests/test_linalg.py::test_norm1[0-3-[7]] +tests/test_linalg.py::test_norm1[0-3-[1, 2]] +tests/test_linalg.py::test_norm1[0-3-[1, 0]] +tests/test_linalg.py::test_norm1[None-3-[7]] +tests/test_linalg.py::test_norm1[None-3-[1, 2]] +tests/test_linalg.py::test_norm1[None-3-[1, 0]] + +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-bool_-bool_] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-bool_-int32] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-bool_-int64] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-bool_-None] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-int32-bool_] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-int32-int32] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-int32-int64] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-int32-None] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-int64-bool_] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-int64-int32] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-int64-int64] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-int64-None] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-None-bool_] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-None-int32] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-None-int64] +tests/test_mathematical.py::test_op_multiple_dtypes[[[1, 2], [3, 4]]-power-None-None] +tests/test_mathematical.py::TestMathematical::test_power[bool_-lhs0-rhs0] +tests/test_mathematical.py::TestMathematical::test_power[bool_-lhs0-rhs1] +tests/test_mathematical.py::TestMathematical::test_power[bool_-lhs0-3] +tests/test_mathematical.py::TestMathematical::test_power[bool_-lhs1-rhs0] +tests/test_mathematical.py::TestMathematical::test_power[bool_-lhs1-rhs1] +tests/test_mathematical.py::TestMathematical::test_power[bool_-lhs1-3] +tests/test_mathematical.py::TestMathematical::test_power[int32-lhs0-rhs0] +tests/test_mathematical.py::TestMathematical::test_power[int32-lhs0-rhs1] +tests/test_mathematical.py::TestMathematical::test_power[int32-lhs0-3] +tests/test_mathematical.py::TestMathematical::test_power[int32-lhs1-rhs0] +tests/test_mathematical.py::TestMathematical::test_power[int32-lhs1-rhs1] +tests/test_mathematical.py::TestMathematical::test_power[int32-lhs1-3] +tests/test_mathematical.py::TestMathematical::test_power[int64-lhs0-rhs0] +tests/test_mathematical.py::TestMathematical::test_power[int64-lhs0-rhs1] +tests/test_mathematical.py::TestMathematical::test_power[int64-lhs0-3] +tests/test_mathematical.py::TestMathematical::test_power[int64-lhs1-rhs0] +tests/test_mathematical.py::TestMathematical::test_power[int64-lhs1-rhs1] +tests/test_mathematical.py::TestMathematical::test_power[int64-lhs1-3] +tests/test_mathematical.py::TestMathematical::test_power[None-lhs0-rhs0] +tests/test_mathematical.py::TestMathematical::test_power[None-lhs0-3] +tests/test_mathematical.py::test_op_with_scalar[[[0, 0], [0, 0]]-0-power-bool_-bool] +tests/test_mathematical.py::test_op_with_scalar[[[0, 0], [0, 0]]-0-power-bool_-int] +tests/test_mathematical.py::test_op_with_scalar[[[0, 0], [0, 0]]-0-power-int32-bool] +tests/test_mathematical.py::test_op_with_scalar[[[0, 0], [0, 0]]-0-power-int32-int] +tests/test_mathematical.py::test_op_with_scalar[[[0, 0], [0, 0]]-0-power-int64-bool] +tests/test_mathematical.py::test_op_with_scalar[[[0, 0], [0, 0]]-0-power-int64-int] +tests/test_mathematical.py::test_op_with_scalar[[[0, 0], [0, 0]]-0-power-None-bool] 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length=20000}::test_partition_invalid_axis1 +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_invalid_axis2 +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_invalid_kth +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_invalid_negative_axis1 +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_invalid_negative_axis2 +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_invalid_negative_kth +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_negative_axis +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_negative_kth +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_non_contiguous +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_none_axis +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_sequence_kth +tests/third_party/cupy/sorting_tests/test_sort.py::TestPartition_param_3_{external=True, length=20000}::test_partition_zero_dim + +tests/third_party/cupy/statistics_tests/test_correlation.py::TestCorrelate_param_0_{mode='valid'}::test_correlate_diff_types +tests/third_party/cupy/statistics_tests/test_correlation.py::TestCorrelate_param_1_{mode='full'}::test_correlate_diff_types +tests/third_party/cupy/statistics_tests/test_correlation.py::TestCorrelate_param_2_{mode='same'}::test_correlate_diff_types +tests/third_party/cupy/statistics_tests/test_correlation.py::TestCorrelate_param_0_{mode='valid'}::test_correlate_diff_types +tests/third_party/cupy/statistics_tests/test_correlation.py::TestCorrelate_param_1_{mode='full'}::test_correlate_diff_types +tests/third_party/cupy/statistics_tests/test_correlation.py::TestCorrelate_param_2_{mode='same'}::test_correlate_diff_types + +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedian::test_median_axis1 +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedian::test_median_axis2 +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedian::test_median_invalid_axis +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedian::test_median_keepdims_axis1 +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedian::test_median_keepdims_noaxis +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedian::test_median_noaxis +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedianAxis_param_0_{axis=(0, 1), keepdims=True, shape=(3, 4, 5)}::test_median_axis_sequence +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedianAxis_param_1_{axis=(0, 1), keepdims=False, shape=(3, 4, 5)}::test_median_axis_sequence +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedianAxis_param_2_{axis=(0, -1), keepdims=True, shape=(3, 4, 5)}::test_median_axis_sequence +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedianAxis_param_3_{axis=(0, -1), keepdims=False, shape=(3, 4, 5)}::test_median_axis_sequence +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedianAxis_param_4_{axis=(1, 2), keepdims=True, shape=(3, 4, 5)}::test_median_axis_sequence +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedianAxis_param_5_{axis=(1, 2), keepdims=False, shape=(3, 4, 5)}::test_median_axis_sequence +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedianAxis_param_6_{axis=(1,), keepdims=True, shape=(3, 4, 5)}::test_median_axis_sequence +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMedianAxis_param_7_{axis=(1,), keepdims=False, shape=(3, 4, 5)}::test_median_axis_sequence +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestAverage::test_average_axis +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestAverage::test_average_axis_weights +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMeanVar::test_external_mean_all +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMeanVar::test_external_mean_axis +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMeanVar::test_external_std_all +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMeanVar::test_external_std_all_ddof +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMeanVar::test_external_std_axis 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+tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMeanVar::test_std_axis_ddof +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMeanVar::test_var_all +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMeanVar::test_var_all_ddof +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMeanVar::test_var_axis +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestMeanVar::test_var_axis_ddof +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_0_{func='mean', params=((), None)}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_1_{func='mean', params=((0,), None)}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_2_{func='mean', params=((0, 0), None)}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_3_{func='mean', params=((0, 0), 1)}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_4_{func='mean', params=((0, 0, 0), None)}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_5_{func='mean', params=((0, 0, 0), (0, 2))}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_6_{func='std', params=((), None)}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_9_{func='std', params=((0, 0), 1)}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_11_{func='std', params=((0, 0, 0), (0, 2))}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_12_{func='var', params=((), None)}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_15_{func='var', params=((0, 0), 1)}::test_external_mean_zero_len +tests/third_party/cupy/statistics_tests/test_meanvar.py::TestProductZeroLength_param_17_{func='var', params=((0, 0, 0), (0, 2))}::test_external_mean_zero_len From 6ab618d0aad61ece28dae925300582547dd0a512 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Mon, 7 Aug 2023 19:00:53 -0500 Subject: [PATCH 32/58] use_dpctl_remainder_func --- dpnp/backend/extensions/vm/remainder.hpp | 83 ++++++ dpnp/backend/extensions/vm/types_matrix.hpp | 15 ++ dpnp/backend/extensions/vm/vm_py.cpp | 33 +++ dpnp/backend/include/dpnp_iface_fptr.hpp | 36 ++- .../kernels/dpnp_krnl_mathematical.cpp | 50 ---- dpnp/dpnp_algo/dpnp_algo.pxd | 5 - dpnp/dpnp_algo/dpnp_algo_mathematical.pxi | 9 - dpnp/dpnp_algo/dpnp_elementwise_common.py | 46 +++- dpnp/dpnp_iface_mathematical.py | 244 +++++++++--------- tests/skipped_tests.tbl | 7 - tests/skipped_tests_gpu.tbl | 5 - tests/test_mathematical.py | 4 - 12 files changed, 308 insertions(+), 229 deletions(-) create mode 100644 dpnp/backend/extensions/vm/remainder.hpp diff --git a/dpnp/backend/extensions/vm/remainder.hpp b/dpnp/backend/extensions/vm/remainder.hpp new file mode 100644 index 00000000000..ac77dbe7f62 --- /dev/null +++ b/dpnp/backend/extensions/vm/remainder.hpp @@ -0,0 +1,83 @@ +//***************************************************************************** +// Copyright (c) 2023, Intel Corporation +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// - Redistributions of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// - Redistributions in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +// THE POSSIBILITY OF SUCH DAMAGE. +//***************************************************************************** + +#pragma once + +#include + +#include "common.hpp" +#include "types_matrix.hpp" + +namespace dpnp +{ +namespace backend +{ +namespace ext +{ +namespace vm +{ +template +sycl::event remainder_contig_impl(sycl::queue exec_q, + const std::int64_t n, + const char *in_a, + const char *in_b, + char *out_y, + const std::vector &depends) +{ + type_utils::validate_type_for_device(exec_q); + + const T *a = reinterpret_cast(in_a); + const T *b = reinterpret_cast(in_b); + T *y = reinterpret_cast(out_y); + + return mkl_vm::remainder( + exec_q, + n, // number of elements to be calculated + a, // pointer `a` containing 1st input vector of size n + b, // pointer `b` containing 2nd input vector of size n + y, // pointer `y` to the output vector of size n + depends); +} + +template +struct RemainderContigFactory +{ + fnT get() + { + if constexpr (std::is_same_v< + typename types::RemainderOutputType::value_type, + void>) + { + return nullptr; + } + else { + return remainder_contig_impl; + } + } +}; +} // namespace vm +} // namespace ext +} // namespace backend +} // namespace dpnp diff --git a/dpnp/backend/extensions/vm/types_matrix.hpp b/dpnp/backend/extensions/vm/types_matrix.hpp index 667e27f0c9a..1ca03e3b3a7 100644 --- a/dpnp/backend/extensions/vm/types_matrix.hpp +++ b/dpnp/backend/extensions/vm/types_matrix.hpp @@ -68,6 +68,21 @@ struct DivOutputType dpctl_td_ns::DefaultResultEntry>::result_type; }; +/** + * @brief A factory to define pairs of supported types for which + * MKL VM library provides support in oneapi::mkl::vm::remainder function. + * + * @tparam T Type of input vectors `a` and `b` and of result vector `y`. + */ +template +struct RemainderOutputType +{ + using value_type = typename std::disjunction< + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::DefaultResultEntry>::result_type; +}; + /** * @brief A factory to define pairs of supported types for which * MKL VM library provides support in oneapi::mkl::vm::cos function. diff --git a/dpnp/backend/extensions/vm/vm_py.cpp b/dpnp/backend/extensions/vm/vm_py.cpp index 924db66025b..2c2578105b6 100644 --- a/dpnp/backend/extensions/vm/vm_py.cpp +++ b/dpnp/backend/extensions/vm/vm_py.cpp @@ -34,6 +34,7 @@ #include "cos.hpp" #include "div.hpp" #include "ln.hpp" +#include "remainder.hpp" #include "sin.hpp" #include "sqr.hpp" #include "sqrt.hpp" @@ -46,6 +47,7 @@ using vm_ext::binary_impl_fn_ptr_t; using vm_ext::unary_impl_fn_ptr_t; static binary_impl_fn_ptr_t div_dispatch_vector[dpctl_td_ns::num_types]; +static binary_impl_fn_ptr_t remainder_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t cos_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t ln_dispatch_vector[dpctl_td_ns::num_types]; @@ -88,6 +90,37 @@ PYBIND11_MODULE(_vm_impl, m) py::arg("dst")); } + // BinaryUfunc: ==== REMAINDER(x1, x2) ==== + { + vm_ext::init_ufunc_dispatch_vector( + remainder_dispatch_vector); + + auto remainder_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, + arrayT dst, const event_vecT &depends = {}) { + return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, + remainder_dispatch_vector); + }; + m.def("_remainder", remainder_pyapi, + "Call `remainder` function from OneMKL VM library to performs " + "element " + "by element remainder of vector `src1` by vector `src2` " + "to resulting vector `dst`", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst"), py::arg("depends") = py::list()); + + auto remainder_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, + arrayT src2, arrayT dst) { + return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, + remainder_dispatch_vector); + }; + m.def("_mkl_remainder_to_call", remainder_need_to_call_pyapi, + "Check input arguments to answer if `remainder` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst")); + } + // UnaryUfunc: ==== Cos(x) ==== { vm_ext::init_ufunc_dispatch_vector; -template -DPCTLSyclEventRef (*dpnp_remainder_ext_c)(DPCTLSyclQueueRef, - void *, - const void *, - const size_t, - const shape_elem_type *, - const size_t, - const void *, - const size_t, - const shape_elem_type *, - const size_t, - const size_t *, - const DPCTLEventVectorRef) = - dpnp_remainder_c<_DataType_output, _DataType_input1, _DataType_input2>; - template @@ -1385,39 +1368,6 @@ void func_map_init_mathematical(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_REMAINDER][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_remainder_default_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_INT][eft_LNG] = { - eft_LNG, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_INT][eft_FLT] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_INT][eft_DBL] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_LNG][eft_INT] = { - eft_LNG, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_LNG][eft_FLT] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_LNG][eft_DBL] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_FLT][eft_INT] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_FLT][eft_LNG] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_FLT][eft_DBL] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_DBL][eft_INT] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_DBL][eft_LNG] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_DBL][eft_FLT] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_REMAINDER_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_remainder_ext_c}; - fmap[DPNPFuncName::DPNP_FN_TRAPZ][eft_INT][eft_INT] = { eft_DBL, (void *)dpnp_trapz_default_c}; fmap[DPNPFuncName::DPNP_FN_TRAPZ][eft_INT][eft_LNG] = { diff --git a/dpnp/dpnp_algo/dpnp_algo.pxd b/dpnp/dpnp_algo/dpnp_algo.pxd index 5bbb0dc4a01..7427c6c7ce4 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pxd +++ b/dpnp/dpnp_algo/dpnp_algo.pxd @@ -188,8 +188,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_QR_EXT DPNP_FN_RADIANS DPNP_FN_RADIANS_EXT - DPNP_FN_REMAINDER - DPNP_FN_REMAINDER_EXT DPNP_FN_RECIP DPNP_FN_RECIP_EXT DPNP_FN_REPEAT @@ -442,9 +440,6 @@ cpdef dpnp_descriptor dpnp_minimum(dpnp_descriptor x1_obj, dpnp_descriptor x2_ob cpdef dpnp_descriptor dpnp_negative(dpnp_descriptor array1) cpdef dpnp_descriptor dpnp_power(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*, dpnp_descriptor out=*, object where=*) -cpdef dpnp_descriptor dpnp_remainder(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*, - dpnp_descriptor out=*, object where=*) - """ Array manipulation routines diff --git a/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi b/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi index ae03fa54051..1685c770d4e 100644 --- a/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi +++ b/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi @@ -62,7 +62,6 @@ __all__ += [ "dpnp_negative", "dpnp_power", "dpnp_prod", - "dpnp_remainder", "dpnp_sign", "dpnp_sum", "dpnp_trapz", @@ -546,14 +545,6 @@ cpdef utils.dpnp_descriptor dpnp_prod(utils.dpnp_descriptor x1, return result -cpdef utils.dpnp_descriptor dpnp_remainder(utils.dpnp_descriptor x1_obj, - utils.dpnp_descriptor x2_obj, - object dtype=None, - utils.dpnp_descriptor out=None, - object where=True): - return call_fptr_2in_1out(DPNP_FN_REMAINDER_EXT, x1_obj, x2_obj, dtype, out, where) - - cpdef utils.dpnp_descriptor dpnp_sign(utils.dpnp_descriptor x1): return call_fptr_1in_1out_strides(DPNP_FN_SIGN_EXT, x1) diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index 410f5542f7b..c9a0796c865 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -66,6 +66,7 @@ "dpnp_logical_xor", "dpnp_multiply", "dpnp_not_equal", + "dpnp_remainder", "dpnp_right_shift", "dpnp_sin", "dpnp_sqrt", @@ -86,7 +87,7 @@ def check_nd_call_func( **kwargs, ): """ - Checks arguments and calls function with a single input array. + Checks arguments and calls a function. Chooses a common internal elementwise function to call in DPNP based on input arguments or to fallback on NumPy call if any passed argument is not currently supported. @@ -127,7 +128,6 @@ def check_nd_call_func( order ) ) - return dpnp_func(*x_args, out=out, order=order) return call_origin( origin_func, @@ -1174,6 +1174,48 @@ def dpnp_not_equal(x1, x2, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_remainder_docstring_ = """ +remainder(x1, x2, out=None, order='K') +Calculates the remainder of division for each element `x1_i` of the input array +`x1` with the respective element `x2_i` of the input array `x2`. +This function is equivalent to the Python modulus operator. +Args: + x1 (dpnp.ndarray): + First input array, expected to have a real-valued data type. + x2 (dpnp.ndarray): + Second input array, also expected to have a real-valued data type. + out ({None, usm_ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + an array containing the element-wise remainders. The data type of + the returned array is determined by the Type Promotion Rules. +""" + + +remainder_func = BinaryElementwiseFunc( + "remainder", + ti._remainder_result_type, + ti._remainder, + _remainder_docstring_, +) + + +def dpnp_remainder(x1, x2, out=None, order="K"): + # dpctl.tensor only works with usm_ndarray or scalar + x1_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x1) + x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + res_usm = remainder_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order + ) + return dpnp_array._create_from_usm_ndarray(res_usm) + _right_shift_docstring_ = """ right_shift(x1, x2, out=None, order='K') diff --git a/dpnp/dpnp_iface_mathematical.py b/dpnp/dpnp_iface_mathematical.py index 36e9804618f..114b2097aee 100644 --- a/dpnp/dpnp_iface_mathematical.py +++ b/dpnp/dpnp_iface_mathematical.py @@ -49,10 +49,12 @@ from .dpnp_algo import * from .dpnp_algo.dpnp_elementwise_common import ( + check_nd_call_func, dpnp_add, dpnp_divide, dpnp_floor_divide, dpnp_multiply, + dpnp_remainder, dpnp_subtract, ) from .dpnp_utils import * @@ -103,63 +105,6 @@ ] -def _check_nd_call( - origin_func, - dpnp_func, - x1, - x2, - out=None, - where=True, - order="K", - dtype=None, - subok=True, - **kwargs, -): - """ - Checks arguments and calls a function. - - Chooses a common internal elementwise function to call in DPNP based on input arguments - or to fallback on NumPy call if any passed argument is not currently supported. - - """ - - if kwargs: - pass - elif where is not True: - pass - elif dtype is not None: - pass - elif subok is not True: - pass - elif dpnp.isscalar(x1) and dpnp.isscalar(x2): - # at least either x1 or x2 has to be an array - pass - else: - if order in "afkcAFKC": - order = order.upper() - elif order is None: - order = "K" - else: - raise ValueError( - "order must be one of 'C', 'F', 'A', or 'K' (got '{}')".format( - order - ) - ) - - return dpnp_func(x1, x2, out=out, order=order) - return call_origin( - origin_func, - x1, - x2, - out=out, - where=where, - order=order, - dtype=dtype, - subok=subok, - **kwargs, - ) - - def abs(*args, **kwargs): """ Calculate the absolute value element-wise. @@ -281,16 +226,29 @@ def add( Examples -------- - >>> import dpnp as dp - >>> a = dp.array([1, 2, 3]) - >>> b = dp.array([1, 2, 3]) - >>> result = dp.add(a, b) - >>> print(result) - [2, 4, 6] + >>> import dpnp as np + >>> a = np.array([1, 2, 3]) + >>> b = np.array([1, 2, 3]) + >>> np.add(a, b) + array([2, 4, 6]) + >>> x1 = np.arange(9.0).reshape((3, 3)) + >>> x2 = np.arange(3.0) + >>> np.add(x1, x2) + array([[ 0., 2., 4.], + [ 3., 5., 7.], + [ 6., 8., 10.]]) + + The ``+`` operator can be used as a shorthand for ``add`` on + :class:`dpnp.ndarray`. + + >>> x1 + x2 + array([[ 0., 2., 4.], + [ 3., 5., 7.], + [ 6., 8., 10.]]) """ - return _check_nd_call( + return check_nd_call_func( numpy.add, dpnp_add, x1, @@ -681,14 +639,29 @@ def divide( Examples -------- - >>> import dpnp as dp - >>> result = dp.divide(dp.array([1, -2, 6, -9]), dp.array([-2, -2, -2, -2])) - >>> print(result) - [-0.5, 1.0, -3.0, 4.5] + >>> import dpnp as np + >>> np.divide(dp.array([1, -2, 6, -9]), np.array([-2, -2, -2, -2])) + array([-0.5, 1. , -3. , 4.5]) + >>> x1 = np.arange(9.0).reshape((3, 3)) + >>> x2 = np.arange(3.0) + >>> np.divide(x1, x2) + array([[nan, 1. , 1. ], + [inf, 4. , 2.5], + [inf, 7. , 4. ]]) + + The ``/`` operator can be used as a shorthand for ``divide`` on + :class:`dpnp.ndarray`. + + >>> x1 = np.arange(9.0).reshape((3, 3)) + >>> x2 = 2 * np.ones(3) + >>> x1/x2 + array([[0. , 0.5, 1. ], + [1.5, 2. , 2.5], + [3. , 3.5, 4. ]]) """ - return _check_nd_call( + return check_nd_call_func( numpy.divide, dpnp_divide, x1, @@ -858,12 +831,14 @@ def floor_divide( Examples -------- >>> import dpnp as np - >>> np.floor_divide(np.array([1, -1, -2, -9]), np.array([-2, -2, -2, -2])) + >>> np.floor_divide(np.array([1, -1, -2, -9]), -2) array([-1, 0, 1, 4]) + >>> np.floor_divide(np.array([1., 2., 3., 4.]), 2.5) + array([ 0., 0., 1., 1.]) """ - return _check_nd_call( + return check_nd_call_func( numpy.floor_divide, dpnp_floor_divide, x1, @@ -1247,15 +1222,28 @@ def multiply( Examples -------- - >>> import dpnp as dp - >>> a = dp.array([1, 2, 3, 4, 5]) - >>> result = dp.multiply(a, a) - >>> print(result) - [1, 4, 9, 16, 25] + >>> import dpnp as np + >>> a = np.array([1, 2, 3, 4, 5]) + >>> np.multiply(a, a) + array([ 1, 4, 9, 16, 25])] + + >>> x1 = np.arange(9.0).reshape((3, 3)) + >>> x2 = np.arange(3.0) + >>> np.multiply(x1, x2) + array([[ 0., 1., 4.], + [ 0., 4., 10.], + [ 0., 7., 16.]]) + The ``*`` operator can be used as a shorthand for ``multiply`` on + :class:`dpnp.ndarray`. + + >>> x1 * x2 + array([[ 0., 1., 4.], + [ 0., 4., 10.], + [ 0., 7., 16.]]) """ - return _check_nd_call( + return check_nd_call_func( numpy.multiply, dpnp_multiply, x1, @@ -1595,7 +1583,18 @@ def prod( ) -def remainder(x1, x2, out=None, where=True, dtype=None, **kwargs): +def remainder( + x1, + x2, + /, + out=None, + *, + where=True, + order="K", + dtype=None, + subok=True, + **kwargs, +): """ Return element-wise remainder of division. @@ -1619,55 +1618,31 @@ def remainder(x1, x2, out=None, where=True, dtype=None, **kwargs): Example ------- >>> import dpnp as np - >>> result = np.remainder(np.array([4, 7]), np.array([2, 3])) - >>> [x for x in result] - [0, 1] + >>> np.remainder(np.array([4, 7]), np.array([2, 3])) + array([0, 1]) - """ + >>> np.remainder(np.arange(7), 5) + array([0, 1, 2, 3, 4, 0, 1]) - x1_is_scalar = dpnp.isscalar(x1) - x2_is_scalar = dpnp.isscalar(x2) - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - x2_desc = dpnp.get_dpnp_descriptor(x2, copy_when_nondefault_queue=False) + The ``%`` operator can be used as a shorthand for ``remainder`` on + :class:`dpnp.ndarray`. - if x1_desc and x2_desc and not kwargs: - if not x1_desc and not x1_is_scalar: - pass - elif not x2_desc and not x2_is_scalar: - pass - elif x1_is_scalar and x2_is_scalar: - pass - elif x1_desc and x1_desc.ndim == 0: - pass - elif x2_desc and x2_desc.ndim == 0: - pass - elif x2_is_scalar and not x2_desc: - pass - elif x1_desc and x2_desc and x1_desc.size != x2_desc.size: - # TODO: enable broadcasting - pass - elif x1_desc and x2_desc and x1_desc.shape != x2_desc.shape: - pass - elif dtype is not None: - pass - elif out is not None: - pass - elif not where: - pass - elif x1_is_scalar and x2_desc.ndim > 1: - pass - else: - out_desc = ( - dpnp.get_dpnp_descriptor(out, copy_when_nondefault_queue=False) - if out is not None - else None - ) - return dpnp_remainder( - x1_desc, x2_desc, dtype, out_desc, where - ).get_pyobj() + >>> x1 = np.arange(7) + >>> x1 % 5 + array([0, 1, 2, 3, 4, 0, 1]) + """ - return call_origin( - numpy.remainder, x1, x2, out=out, where=where, dtype=dtype, **kwargs + return check_nd_call_func( + numpy.remainder, + dpnp_remainder, + x1, + x2, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, ) @@ -1750,14 +1725,27 @@ def subtract( Example ------- - >>> import dpnp as dp - >>> result = dp.subtract(dp.array([4, 3]), dp.array([2, 7])) - >>> print(result) - [2, -4] + >>> import dpnp as np + >>> np.subtract(dp.array([4, 3]), np.array([2, 7])) + array([ 2, -4]) + + >>> x1 = np.arange(9.0).reshape((3, 3)) + >>> x2 = np.arange(3.0) + >>> np.subtract(x1, x2) + array([[ 0., 0., 0.], + [ 3., 3., 3.], + [ 6., 6., 6.]]) + + The ``-`` operator can be used as a shorthand for ``subtract`` on + :class:`dpnp.ndarray`. + >>> x1 - x2 + array([[ 0., 0., 0.], + [ 3., 3., 3.], + [ 6., 6., 6.]]) """ - return _check_nd_call( + return check_nd_call_func( numpy.subtract, dpnp_subtract, x1, diff --git a/tests/skipped_tests.tbl b/tests/skipped_tests.tbl index cd00837d037..07df45e2915 100644 --- a/tests/skipped_tests.tbl +++ b/tests/skipped_tests.tbl @@ -7,8 +7,6 @@ tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp. tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-trapz-data19] tests/test_sycl_queue.py::test_1in_1out[opencl:cpu:0-trapz-data19] -tests/test_sycl_queue.py::test_broadcasting[opencl:gpu:0-remainder-data15-data25] -tests/test_sycl_queue.py::test_broadcasting[opencl:cpu:0-remainder-data15-data25] tests/third_party/cupy/fft_tests/test_fft.py::TestFft2_param_1_{axes=None, norm=None, s=(1, None), shape=(3, 4)}::test_fft2 tests/third_party/cupy/fft_tests/test_fft.py::TestFft2_param_7_{axes=(), norm=None, s=None, shape=(3, 4)}::test_fft2 @@ -577,20 +575,15 @@ tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_3_{reps tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_4_{reps=(2, 3)}::test_array_tile tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_5_{reps=(2, 3, 4, 5)}::test_array_tile tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_457_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_459_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), dtype=float64, name='remainder', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_461_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), dtype=float64, name='mod', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_465_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_467_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='remainder', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_469_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='mod', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_537_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_539_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), dtype=float64, name='remainder', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_541_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), dtype=float64, name='mod', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_545_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_547_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='remainder', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_549_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='mod', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticModf::test_modf -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_10_{name='remainder', nargs=2}::test_raises_with_numpy_input tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_11_{name='mod', nargs=2}::test_raises_with_numpy_input tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_1_{name='angle', nargs=1}::test_raises_with_numpy_input diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index d0a477666ef..22b409df03d 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -40,10 +40,6 @@ tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-sum-data18] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-trapz-data19] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-trunc-data20] -tests/test_sycl_queue.py::test_broadcasting[level_zero:gpu:0-remainder-data15-data25] -tests/test_sycl_queue.py::test_broadcasting[opencl:gpu:0-remainder-data15-data25] -tests/test_sycl_queue.py::test_broadcasting[opencl:cpu:0-remainder-data15-data25] - tests/test_sycl_queue.py::test_modf[level_zero:gpu:0] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-trapz-data19] @@ -770,7 +766,6 @@ tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_3_{reps tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_4_{reps=(2, 3)}::test_array_tile tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_5_{reps=(2, 3, 4, 5)}::test_array_tile -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_10_{name='remainder', nargs=2}::test_raises_with_numpy_input tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_11_{name='mod', nargs=2}::test_raises_with_numpy_input tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_1_{name='angle', nargs=1}::test_raises_with_numpy_input diff --git a/tests/test_mathematical.py b/tests/test_mathematical.py index e0a16869567..49fb6101b62 100644 --- a/tests/test_mathematical.py +++ b/tests/test_mathematical.py @@ -210,10 +210,6 @@ def test_minimum(self, dtype, lhs, rhs): def test_multiply(self, dtype, lhs, rhs): self._test_mathematical("multiply", dtype, lhs, rhs) - @pytest.mark.skipif( - not has_support_aspect64(), reason="Aborted on Iris Xe: SAT-6039" - ) - @pytest.mark.usefixtures("allow_fall_back_on_numpy") @pytest.mark.parametrize( "dtype", get_all_dtypes(no_bool=True, no_complex=True) ) From 2550ab6a64921449a58477ec2798fc4c5b01bfa0 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Thu, 10 Aug 2023 11:24:02 -0500 Subject: [PATCH 33/58] remove mkl::remainder implementation --- dpnp/backend/extensions/vm/remainder.hpp | 83 --------------------- dpnp/backend/extensions/vm/types_matrix.hpp | 15 ---- dpnp/backend/extensions/vm/vm_py.cpp | 33 -------- dpnp/dpnp_iface_mathematical.py | 8 +- 4 files changed, 4 insertions(+), 135 deletions(-) delete mode 100644 dpnp/backend/extensions/vm/remainder.hpp diff --git a/dpnp/backend/extensions/vm/remainder.hpp b/dpnp/backend/extensions/vm/remainder.hpp deleted file mode 100644 index ac77dbe7f62..00000000000 --- a/dpnp/backend/extensions/vm/remainder.hpp +++ /dev/null @@ -1,83 +0,0 @@ -//***************************************************************************** -// Copyright (c) 2023, Intel Corporation -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// - Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// - Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE -// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF -// THE POSSIBILITY OF SUCH DAMAGE. -//***************************************************************************** - -#pragma once - -#include - -#include "common.hpp" -#include "types_matrix.hpp" - -namespace dpnp -{ -namespace backend -{ -namespace ext -{ -namespace vm -{ -template -sycl::event remainder_contig_impl(sycl::queue exec_q, - const std::int64_t n, - const char *in_a, - const char *in_b, - char *out_y, - const std::vector &depends) -{ - type_utils::validate_type_for_device(exec_q); - - const T *a = reinterpret_cast(in_a); - const T *b = reinterpret_cast(in_b); - T *y = reinterpret_cast(out_y); - - return mkl_vm::remainder( - exec_q, - n, // number of elements to be calculated - a, // pointer `a` containing 1st input vector of size n - b, // pointer `b` containing 2nd input vector of size n - y, // pointer `y` to the output vector of size n - depends); -} - -template -struct RemainderContigFactory -{ - fnT get() - { - if constexpr (std::is_same_v< - typename types::RemainderOutputType::value_type, - void>) - { - return nullptr; - } - else { - return remainder_contig_impl; - } - } -}; -} // namespace vm -} // namespace ext -} // namespace backend -} // namespace dpnp diff --git a/dpnp/backend/extensions/vm/types_matrix.hpp b/dpnp/backend/extensions/vm/types_matrix.hpp index 1ca03e3b3a7..667e27f0c9a 100644 --- a/dpnp/backend/extensions/vm/types_matrix.hpp +++ b/dpnp/backend/extensions/vm/types_matrix.hpp @@ -68,21 +68,6 @@ struct DivOutputType dpctl_td_ns::DefaultResultEntry>::result_type; }; -/** - * @brief A factory to define pairs of supported types for which - * MKL VM library provides support in oneapi::mkl::vm::remainder function. - * - * @tparam T Type of input vectors `a` and `b` and of result vector `y`. - */ -template -struct RemainderOutputType -{ - using value_type = typename std::disjunction< - dpctl_td_ns::BinaryTypeMapResultEntry, - dpctl_td_ns::BinaryTypeMapResultEntry, - dpctl_td_ns::DefaultResultEntry>::result_type; -}; - /** * @brief A factory to define pairs of supported types for which * MKL VM library provides support in oneapi::mkl::vm::cos function. diff --git a/dpnp/backend/extensions/vm/vm_py.cpp b/dpnp/backend/extensions/vm/vm_py.cpp index 2c2578105b6..924db66025b 100644 --- a/dpnp/backend/extensions/vm/vm_py.cpp +++ b/dpnp/backend/extensions/vm/vm_py.cpp @@ -34,7 +34,6 @@ #include "cos.hpp" #include "div.hpp" #include "ln.hpp" -#include "remainder.hpp" #include "sin.hpp" #include "sqr.hpp" #include "sqrt.hpp" @@ -47,7 +46,6 @@ using vm_ext::binary_impl_fn_ptr_t; using vm_ext::unary_impl_fn_ptr_t; static binary_impl_fn_ptr_t div_dispatch_vector[dpctl_td_ns::num_types]; -static binary_impl_fn_ptr_t remainder_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t cos_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t ln_dispatch_vector[dpctl_td_ns::num_types]; @@ -90,37 +88,6 @@ PYBIND11_MODULE(_vm_impl, m) py::arg("dst")); } - // BinaryUfunc: ==== REMAINDER(x1, x2) ==== - { - vm_ext::init_ufunc_dispatch_vector( - remainder_dispatch_vector); - - auto remainder_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, - arrayT dst, const event_vecT &depends = {}) { - return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, - remainder_dispatch_vector); - }; - m.def("_remainder", remainder_pyapi, - "Call `remainder` function from OneMKL VM library to performs " - "element " - "by element remainder of vector `src1` by vector `src2` " - "to resulting vector `dst`", - py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), - py::arg("dst"), py::arg("depends") = py::list()); - - auto remainder_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, - arrayT src2, arrayT dst) { - return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, - remainder_dispatch_vector); - }; - m.def("_mkl_remainder_to_call", remainder_need_to_call_pyapi, - "Check input arguments to answer if `remainder` function from " - "OneMKL VM library can be used", - py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), - py::arg("dst")); - } - // UnaryUfunc: ==== Cos(x) ==== { vm_ext::init_ufunc_dispatch_vector Date: Fri, 11 Aug 2023 10:53:46 -0500 Subject: [PATCH 34/58] address reviewer's comments --- dpnp/dpnp_algo/dpnp_elementwise_common.py | 4 +- dpnp/dpnp_iface_mathematical.py | 71 +++++++++++++------ tests/test_mathematical.py | 10 ++- tests/test_strides.py | 21 +++++- tests/test_usm_type.py | 14 ++++ .../cupy/math_tests/test_arithmetic.py | 10 --- 6 files changed, 93 insertions(+), 37 deletions(-) diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index c9a0796c865..d1ffc6694c9 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -1213,6 +1213,7 @@ def dpnp_remainder(x1, x2, out=None, order="K"): res_usm = remainder_func( x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order +<<<<<<< HEAD ) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1254,8 +1255,9 @@ def dpnp_right_shift(x1, x2, out=None, order="K"): ti._bitwise_right_shift_result_type, ti._bitwise_right_shift, _right_shift_docstring_, +======= +>>>>>>> address reviewer's comments ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) diff --git a/dpnp/dpnp_iface_mathematical.py b/dpnp/dpnp_iface_mathematical.py index 800ffb251db..f5c9530046f 100644 --- a/dpnp/dpnp_iface_mathematical.py +++ b/dpnp/dpnp_iface_mathematical.py @@ -220,7 +220,7 @@ def add( Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. Parameters `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. + Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -626,14 +626,14 @@ def divide( Returns ------- y : dpnp.ndarray - The quotient ``x1/x2``, element-wise. + The quotient `x1/x2`, element-wise. Limitations ----------- Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. - Parameters `out`, `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. + Parameters `where`, `dtype` and `subok` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -823,7 +823,7 @@ def floor_divide( See Also -------- - :obj:`dpnp.reminder` : Remainder complementary to floor_divide. + :obj:`dpnp.remainder` : Remainder complementary to floor_divide. :obj:`dpnp.divide` : Standard division. :obj:`dpnp.floor` : Round a number to the nearest integer toward minus infinity. :obj:`dpnp.ceil` : Round a number to the nearest integer toward infinity. @@ -910,7 +910,7 @@ def fmod(x1, x2, dtype=None, out=None, where=True, **kwargs): See Also -------- - :obj:`dpnp.reminder` : Remainder complementary to floor_divide. + :obj:`dpnp.remainder` : Remainder complementary to floor_divide. :obj:`dpnp.divide` : Standard division. Examples @@ -1137,16 +1137,36 @@ def minimum(x1, x2, dtype=None, out=None, where=True, **kwargs): ) -def mod(*args, **kwargs): +def mod( + x1, + x2, + /, + out=None, + *, + where=True, + order="K", + dtype=None, + subok=True, + **kwargs, +): """ Compute element-wise remainder of division. For full documentation refer to :obj:`numpy.mod`. + Limitations + ----------- + Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` + or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. + Parameters `where`, `dtype` and `subok` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. + See Also -------- :obj:`dpnp.fmod` : Calculate the element-wise remainder of division - :obj:`dpnp.reminder` : Remainder complementary to floor_divide. + :obj:`dpnp.remainder` : Remainder complementary to floor_divide. :obj:`dpnp.divide` : Standard division. Notes @@ -1155,7 +1175,16 @@ def mod(*args, **kwargs): """ - return dpnp.remainder(*args, **kwargs) + return dpnp.remainder( + x1, + x2, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, + ) def modf(x1, **kwargs): @@ -1602,18 +1631,20 @@ def remainder( Limitations ----------- - Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray`, - :class:`dpctl.tensor.usm_ndarray` or scalar. - Parameters `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments `kwargs` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` + or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. + Parameters `where`, `dtype` and `subok` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. See Also -------- - :obj:`dpnp.fmod` : Calculate the element-wise remainder of division. - :obj:`dpnp.divide` : Standard division. - :obj:`dpnp.floor` : Round a number to the nearest integer toward minus infinity. + :obj:`dpnp.fmod` : Calculate the element-wise remainder of division. + :obj:`dpnp.divide` : Standard division. + :obj:`dpnp.floor` : Round a number to the nearest integer toward minus infinity. + :obj:`dpnp.floor_divide` : Compute the largest integer smaller or equal to the division of the inputs. + :obj:`dpnp.mod` : Calculate the element-wise remainder of division. Example ------- @@ -1718,8 +1749,8 @@ def subtract( ----------- Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. - Parameters `out`, `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. + Parameters `where`, `dtype` and `subok` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. diff --git a/tests/test_mathematical.py b/tests/test_mathematical.py index 49fb6101b62..c5b5ea336ba 100644 --- a/tests/test_mathematical.py +++ b/tests/test_mathematical.py @@ -136,6 +136,12 @@ def _test_mathematical(self, name, dtype, lhs, rhs): else: result = getattr(dpnp, name)(a_dpnp, b_dpnp) expected = getattr(numpy, name)(a_np, b_np) + if ( + name == "remainder" + and result.dtype != expected.dtype + and not has_support_aspect64() + ): + pytest.skip("skipping since output is promoted differently") assert_allclose(result, expected, rtol=1e-6) @pytest.mark.parametrize("dtype", get_all_dtypes()) @@ -210,9 +216,7 @@ def test_minimum(self, dtype, lhs, rhs): def test_multiply(self, dtype, lhs, rhs): self._test_mathematical("multiply", dtype, lhs, rhs) - @pytest.mark.parametrize( - "dtype", get_all_dtypes(no_bool=True, no_complex=True) - ) + @pytest.mark.parametrize("dtype", get_all_dtypes(no_complex=True)) def test_remainder(self, dtype, lhs, rhs): self._test_mathematical("remainder", dtype, lhs, rhs) diff --git a/tests/test_strides.py b/tests/test_strides.py index 68e9dc4ea37..e39414a7bd7 100644 --- a/tests/test_strides.py +++ b/tests/test_strides.py @@ -233,15 +233,30 @@ def test_strides_fmod(dtype, shape): @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) @pytest.mark.parametrize("shape", [(3, 3)], ids=["(3, 3)"]) -def test_strides_true_devide(dtype, shape): +def test_strides_true_divide(dtype, shape): a = numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape) b = a.T + 1 dpa = dpnp.reshape(dpnp.arange(numpy.prod(shape), dtype=dtype), shape) dpb = dpa.T + 1 - result = dpnp.fmod(dpa, dpb) - expected = numpy.fmod(a, b) + result = dpnp.true_divide(dpa, dpb) + expected = numpy.true_divide(a, b) + + assert_allclose(result, expected, atol=1e-08) + + +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) +@pytest.mark.parametrize("shape", [(3, 3)], ids=["(3, 3)"]) +def test_strides_remainder(dtype, shape): + a = numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape) + b = a.T + 1 + + dpa = dpnp.reshape(dpnp.arange(numpy.prod(shape), dtype=dtype), shape) + dpb = dpa.T + 1 + + result = dpnp.remainder(dpa, dpb) + expected = numpy.remainder(a, b) assert_allclose(result, expected) diff --git a/tests/test_usm_type.py b/tests/test_usm_type.py index f6e0f38a4e7..bda39505a1c 100644 --- a/tests/test_usm_type.py +++ b/tests/test_usm_type.py @@ -74,6 +74,20 @@ def test_coerced_usm_types_divide(usm_type_x, usm_type_y): assert z.usm_type == du.get_coerced_usm_type([usm_type_x, usm_type_y]) +@pytest.mark.parametrize("usm_type_x", list_of_usm_types, ids=list_of_usm_types) +@pytest.mark.parametrize("usm_type_y", list_of_usm_types, ids=list_of_usm_types) +def test_coerced_usm_types_remainder(usm_type_x, usm_type_y): + x = dp.arange(100, usm_type=usm_type_x).reshape(10, 10) + y = dp.arange(100, usm_type=usm_type_y).reshape(10, 10) + y = y.T + 1 + + z = x % y + + assert x.usm_type == usm_type_x + assert y.usm_type == usm_type_y + assert z.usm_type == du.get_coerced_usm_type([usm_type_x, usm_type_y]) + + @pytest.mark.parametrize("usm_type_x", list_of_usm_types, ids=list_of_usm_types) @pytest.mark.parametrize("usm_type_y", list_of_usm_types, ids=list_of_usm_types) def test_coerced_usm_types_power(usm_type_x, usm_type_y): diff --git a/tests/third_party/cupy/math_tests/test_arithmetic.py b/tests/third_party/cupy/math_tests/test_arithmetic.py index 955bd9e2247..b61c3df9a3a 100644 --- a/tests/third_party/cupy/math_tests/test_arithmetic.py +++ b/tests/third_party/cupy/math_tests/test_arithmetic.py @@ -199,16 +199,6 @@ def check_binary(self, xp): elif is_array_arg2 and not is_array_arg1: y = y.astype(dtype2) - # NumPy returns different values (nan/inf) on division by zero - # depending on the architecture. - # As it is not possible for CuPy to replicate this behavior, we ignore - # the difference here. - # if self.name in ('floor_divide', 'remainder', 'mod'): - # if y.dtype in (float_types + complex_types) and (np2 == 0).any(): - # y = xp.asarray(y) - # y[y == numpy.inf] = numpy.nan - # y[y == -numpy.inf] = numpy.nan - return y From 307db4d014c8d52e9df761b082fefd2230e9e00b Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Mon, 14 Aug 2023 15:18:06 -0500 Subject: [PATCH 35/58] remove skipping tests for mod function --- tests/skipped_tests.tbl | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tests/skipped_tests.tbl b/tests/skipped_tests.tbl index 07df45e2915..fbaa9eae002 100644 --- a/tests/skipped_tests.tbl +++ b/tests/skipped_tests.tbl @@ -575,16 +575,11 @@ tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_3_{reps tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_4_{reps=(2, 3)}::test_array_tile tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_5_{reps=(2, 3, 4, 5)}::test_array_tile tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_457_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_461_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), dtype=float64, name='mod', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_465_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_469_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='mod', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_537_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_541_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), dtype=float64, name='mod', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_545_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_549_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='mod', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticModf::test_modf -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_11_{name='mod', nargs=2}::test_raises_with_numpy_input tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_1_{name='angle', nargs=1}::test_raises_with_numpy_input tests/third_party/cupy/math_tests/test_explog.py::TestExplog::test_logaddexp From 988ec2091de4c9f4d0e3e7a1653d4117d5d860fd Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Wed, 16 Aug 2023 13:23:22 -0500 Subject: [PATCH 36/58] rebase and resolve conflicts --- dpnp/backend/include/dpnp_iface_fptr.hpp | 28 +++++++++++------------ dpnp/dpnp_algo/dpnp_elementwise_common.py | 5 ++-- 2 files changed, 16 insertions(+), 17 deletions(-) diff --git a/dpnp/backend/include/dpnp_iface_fptr.hpp b/dpnp/backend/include/dpnp_iface_fptr.hpp index d56f3440c6a..e2c29a0f4a8 100644 --- a/dpnp/backend/include/dpnp_iface_fptr.hpp +++ b/dpnp/backend/include/dpnp_iface_fptr.hpp @@ -313,20 +313,20 @@ enum class DPNPFuncName : size_t DPNP_FN_PUT_ALONG_AXIS_EXT, /**< Used in numpy.put_along_axis() impl, requires extra parameters */ DPNP_FN_QR, /**< Used in numpy.linalg.qr() impl */ - DPNP_FN_QR_EXT, /**< Used in numpy.linalg.qr() impl, requires extra - parameters */ - DPNP_FN_RADIANS, /**< Used in numpy.radians() impl */ - DPNP_FN_RADIANS_EXT, /**< Used in numpy.radians() impl, requires extra - parameters */ - DPNP_FN_REMAINDER, /**< Used in numpy.remainder() impl */ - DPNP_FN_RECIP, /**< Used in numpy.recip() impl */ - DPNP_FN_RECIP_EXT, /**< Used in numpy.recip() impl, requires extra - parameters */ - DPNP_FN_REPEAT, /**< Used in numpy.repeat() impl */ - DPNP_FN_REPEAT_EXT, /**< Used in numpy.repeat() impl, requires extra - parameters */ - DPNP_FN_RIGHT_SHIFT, /**< Used in numpy.right_shift() impl */ - DPNP_FN_RNG_BETA, /**< Used in numpy.random.beta() impl */ + DPNP_FN_QR_EXT, /**< Used in numpy.linalg.qr() impl, requires extra + parameters */ + DPNP_FN_RADIANS, /**< Used in numpy.radians() impl */ + DPNP_FN_RADIANS_EXT, /**< Used in numpy.radians() impl, requires extra + parameters */ + DPNP_FN_REMAINDER, /**< Used in numpy.remainder() impl */ + DPNP_FN_RECIP, /**< Used in numpy.recip() impl */ + DPNP_FN_RECIP_EXT, /**< Used in numpy.recip() impl, requires extra + parameters */ + DPNP_FN_REPEAT, /**< Used in numpy.repeat() impl */ + DPNP_FN_REPEAT_EXT, /**< Used in numpy.repeat() impl, requires extra + parameters */ + DPNP_FN_RIGHT_SHIFT, /**< Used in numpy.right_shift() impl */ + DPNP_FN_RNG_BETA, /**< Used in numpy.random.beta() impl */ DPNP_FN_RNG_BETA_EXT, /**< Used in numpy.random.beta() impl, requires extra parameters */ DPNP_FN_RNG_BINOMIAL, /**< Used in numpy.random.binomial() impl */ diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index d1ffc6694c9..33776e26a01 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -1213,10 +1213,10 @@ def dpnp_remainder(x1, x2, out=None, order="K"): res_usm = remainder_func( x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order -<<<<<<< HEAD ) return dpnp_array._create_from_usm_ndarray(res_usm) + _right_shift_docstring_ = """ right_shift(x1, x2, out=None, order='K') @@ -1255,9 +1255,8 @@ def dpnp_right_shift(x1, x2, out=None, order="K"): ti._bitwise_right_shift_result_type, ti._bitwise_right_shift, _right_shift_docstring_, -======= ->>>>>>> address reviewer's comments ) + res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) From 2a43630b438ce172a681edf2dd7b129f1d312593 Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Fri, 18 Aug 2023 11:49:04 +0200 Subject: [PATCH 37/58] Reduce scope of skipping tests on device without fp64 aspect (#1525) * Reduced scope of tests which are not failing on device without fp64 anymore * Addressed review comments * Updated test_umaths to apply the review comment * Resolved pre-commit formatting issue * Suppressed numpy warnings * Muted missing test_umaths for GPU with fp64 support --- tests/skipped_tests.tbl | 34 + tests/skipped_tests_gpu.tbl | 35 +- tests/skipped_tests_gpu_no_fp64.tbl | 934 +----------------- tests/test_umath.py | 50 +- .../cupy/math_tests/test_arithmetic.py | 3 +- 5 files changed, 102 insertions(+), 954 deletions(-) diff --git a/tests/skipped_tests.tbl b/tests/skipped_tests.tbl index fbaa9eae002..2424ade4cee 100644 --- a/tests/skipped_tests.tbl +++ b/tests/skipped_tests.tbl @@ -84,6 +84,40 @@ tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: (dpnp tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray([(i, i) for i in x], [("a", object), ("b", dpnp.int32)])]] tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.asarray(x).astype(dpnp.int8)] +tests/test_umath.py::test_umaths[('divmod', 'ii')] +tests/test_umath.py::test_umaths[('divmod', 'll')] +tests/test_umath.py::test_umaths[('divmod', 'ff')] +tests/test_umath.py::test_umaths[('divmod', 'dd')] +tests/test_umath.py::test_umaths[('float_power', 'dd')] +tests/test_umath.py::test_umaths[('frexp', 'f')] +tests/test_umath.py::test_umaths[('frexp', 'd')] +tests/test_umath.py::test_umaths[('gcd', 'ii')] +tests/test_umath.py::test_umaths[('gcd', 'll')] +tests/test_umath.py::test_umaths[('heaviside', 'ff')] +tests/test_umath.py::test_umaths[('heaviside', 'dd')] +tests/test_umath.py::test_umaths[('lcm', 'ii')] +tests/test_umath.py::test_umaths[('lcm', 'll')] +tests/test_umath.py::test_umaths[('ldexp', 'fi')] +tests/test_umath.py::test_umaths[('ldexp', 'fl')] +tests/test_umath.py::test_umaths[('ldexp', 'di')] +tests/test_umath.py::test_umaths[('ldexp', 'dl')] +tests/test_umath.py::test_umaths[('logaddexp', 'ff')] +tests/test_umath.py::test_umaths[('logaddexp', 'dd')] +tests/test_umath.py::test_umaths[('logaddexp2', 'ff')] +tests/test_umath.py::test_umaths[('logaddexp2', 'dd')] +tests/test_umath.py::test_umaths[('nextafter', 'ff')] +tests/test_umath.py::test_umaths[('nextafter', 'dd')] +tests/test_umath.py::test_umaths[('positive', 'i')] +tests/test_umath.py::test_umaths[('positive', 'l')] +tests/test_umath.py::test_umaths[('positive', 'f')] +tests/test_umath.py::test_umaths[('positive', 'd')] +tests/test_umath.py::test_umaths[('rint', 'f')] +tests/test_umath.py::test_umaths[('rint', 'd')] +tests/test_umath.py::test_umaths[('signbit', 'f')] +tests/test_umath.py::test_umaths[('signbit', 'd')] +tests/test_umath.py::test_umaths[('spacing', 'f')] +tests/test_umath.py::test_umaths[('spacing', 'd')] + tests/third_party/cupy/core_tests/test_ndarray_complex_ops.py::TestAngle::test_angle tests/third_party/cupy/core_tests/test_ndarray_complex_ops.py::TestRealImag::test_imag tests/third_party/cupy/core_tests/test_ndarray_complex_ops.py::TestRealImag::test_imag_inplace diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index 22b409df03d..85f25b90028 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -45,6 +45,40 @@ tests/test_sycl_queue.py::test_modf[level_zero:gpu:0] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-trapz-data19] tests/test_sycl_queue.py::test_1in_1out[opencl:cpu:0-trapz-data19] +tests/test_umath.py::test_umaths[('divmod', 'ii')] +tests/test_umath.py::test_umaths[('divmod', 'll')] +tests/test_umath.py::test_umaths[('divmod', 'ff')] +tests/test_umath.py::test_umaths[('divmod', 'dd')] +tests/test_umath.py::test_umaths[('float_power', 'dd')] +tests/test_umath.py::test_umaths[('frexp', 'f')] +tests/test_umath.py::test_umaths[('frexp', 'd')] +tests/test_umath.py::test_umaths[('gcd', 'ii')] +tests/test_umath.py::test_umaths[('gcd', 'll')] +tests/test_umath.py::test_umaths[('heaviside', 'ff')] +tests/test_umath.py::test_umaths[('heaviside', 'dd')] +tests/test_umath.py::test_umaths[('lcm', 'ii')] +tests/test_umath.py::test_umaths[('lcm', 'll')] +tests/test_umath.py::test_umaths[('ldexp', 'fi')] +tests/test_umath.py::test_umaths[('ldexp', 'fl')] +tests/test_umath.py::test_umaths[('ldexp', 'di')] +tests/test_umath.py::test_umaths[('ldexp', 'dl')] +tests/test_umath.py::test_umaths[('logaddexp', 'ff')] +tests/test_umath.py::test_umaths[('logaddexp', 'dd')] +tests/test_umath.py::test_umaths[('logaddexp2', 'ff')] +tests/test_umath.py::test_umaths[('logaddexp2', 'dd')] +tests/test_umath.py::test_umaths[('nextafter', 'ff')] +tests/test_umath.py::test_umaths[('nextafter', 'dd')] +tests/test_umath.py::test_umaths[('positive', 'i')] +tests/test_umath.py::test_umaths[('positive', 'l')] +tests/test_umath.py::test_umaths[('positive', 'f')] +tests/test_umath.py::test_umaths[('positive', 'd')] +tests/test_umath.py::test_umaths[('rint', 'f')] +tests/test_umath.py::test_umaths[('rint', 'd')] +tests/test_umath.py::test_umaths[('signbit', 'f')] +tests/test_umath.py::test_umaths[('signbit', 'd')] +tests/test_umath.py::test_umaths[('spacing', 'f')] +tests/test_umath.py::test_umaths[('spacing', 'd')] + tests/third_party/cupy/indexing_tests/test_insert.py::TestDiagIndices_param_0_{n=2, ndim=2}::test_diag_indices tests/third_party/cupy/indexing_tests/test_insert.py::TestDiagIndices_param_1_{n=2, ndim=3}::test_diag_indices tests/third_party/cupy/indexing_tests/test_insert.py::TestDiagIndices_param_2_{n=2, ndim=1}::test_diag_indices @@ -766,7 +800,6 @@ tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_3_{reps tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_4_{reps=(2, 3)}::test_array_tile tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_5_{reps=(2, 3, 4, 5)}::test_array_tile -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_11_{name='mod', nargs=2}::test_raises_with_numpy_input tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_1_{name='angle', nargs=1}::test_raises_with_numpy_input tests/third_party/cupy/math_tests/test_explog.py::TestExplog::test_logaddexp diff --git a/tests/skipped_tests_gpu_no_fp64.tbl b/tests/skipped_tests_gpu_no_fp64.tbl index b57fe1eb399..2658f178bf8 100644 --- a/tests/skipped_tests_gpu_no_fp64.tbl +++ b/tests/skipped_tests_gpu_no_fp64.tbl @@ -488,23 +488,10 @@ tests/test_sycl_queue.py::test_svd[level_zero:gpu:0] tests/test_sycl_queue.py::test_to_device[opencl:gpu:0-opencl:cpu:0] tests/test_sycl_queue.py::test_to_device[level_zero:gpu:0-opencl:cpu:0] -tests/test_umath.py::test_umaths[('equal', 'dd')] -tests/test_umath.py::test_umaths[('arccos', 'd')0] -tests/test_umath.py::test_umaths[('arccos', 'd')1] -tests/test_umath.py::test_umaths[('arcsin', 'd')0] -tests/test_umath.py::test_umaths[('arcsin', 'd')1] -tests/test_umath.py::test_umaths[('arctan', 'd')0] -tests/test_umath.py::test_umaths[('arctan', 'd')1] -tests/test_umath.py::test_umaths[('cos', 'd')] -tests/test_umath.py::test_umaths[('deg2rad', 'd')] -tests/test_umath.py::test_umaths[('degrees', 'd')] -tests/test_umath.py::test_umaths[('rad2deg', 'd')] -tests/test_umath.py::test_umaths[('radians', 'd')] -tests/test_umath.py::test_umaths[('sin', 'd')] -tests/test_umath.py::test_umaths[('tan', 'd')0] -tests/test_umath.py::test_umaths[('tan', 'd')1] -tests/test_umath.py::test_umaths[('arctan2', 'dd')] -tests/test_umath.py::test_umaths[('hypot', 'dd')] +tests/test_umath.py::test_umaths[('floor_divide', 'ff')] +tests/test_umath.py::test_umaths[('power', 'ii')] +tests/test_umath.py::test_umaths[('power', 'll')] + tests/test_umath.py::TestExp::test_exp tests/test_umath.py::TestExp::test_invalid_dtype[numpy.float32] tests/test_umath.py::TestExp::test_invalid_dtype[numpy.int64] @@ -930,1155 +917,256 @@ tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_pa tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticUnary_param_5_{arg1=2.0, name='reciprocal'}::test_unary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_4_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='add'}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_5_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='multiply'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_6_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_7_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='subtract'}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_8_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='add'}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_9_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='multiply'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_10_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_11_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='subtract'}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_12_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='add'}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_13_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='multiply'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_14_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_15_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='subtract'}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_36_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='add'}::test_binary 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-tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1251_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1252_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1253_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1254_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1256_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='fmod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1258_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1259_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1260_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='mod', use_dtype=True}::test_binary 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tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1272_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0, dtype=float64, name='fmod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1274_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0, dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1276_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0, dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1278_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0.0, dtype=float64, name='floor_divide', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1279_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0.0, dtype=float64, name='floor_divide', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1280_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0.0, dtype=float64, name='fmod', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1281_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0.0, dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1282_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0.0, dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1283_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0.0, dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1284_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0.0, dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1285_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=0.0, dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1286_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=2, dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1288_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=2, dtype=float64, name='fmod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1290_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=2, dtype=float64, name='remainder', use_dtype=True}::test_binary 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name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1298_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=2.0, dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1299_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=2.0, dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1300_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=2.0, dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1301_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=2.0, dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1302_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2, dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1304_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2, dtype=float64, name='fmod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1306_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2, dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1308_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2, dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1310_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2.0, dtype=float64, name='floor_divide', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1311_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2.0, dtype=float64, name='floor_divide', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1312_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2.0, dtype=float64, name='fmod', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1313_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2.0, dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1314_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2.0, dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1315_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2.0, dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1316_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2.0, dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1317_{arg1=array([-3, -2, -1, 1, 2, 3], dtype=int32), arg2=-2.0, dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1318_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='floor_divide', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1319_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='floor_divide', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1320_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='fmod', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1321_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1322_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1323_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1324_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1325_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1326_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='floor_divide', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1327_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='floor_divide', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1328_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='fmod', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1329_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1330_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1331_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1332_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1333_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1334_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1336_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='fmod', use_dtype=True}::test_binary 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3]), arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1349_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1350_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=0, dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1352_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=0, dtype=float64, name='fmod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1354_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=0, dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1356_{arg1=array([-3, -2, 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tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1377_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=2.0, dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1378_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=2.0, dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1379_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=2.0, dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1380_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=2.0, dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1381_{arg1=array([-3, -2, -1, 1, 2, 3]), arg2=2.0, dtype=float64, name='mod', use_dtype=False}::test_binary 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use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1404_{arg1=0, arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1406_{arg1=0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='floor_divide', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1407_{arg1=0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='floor_divide', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1408_{arg1=0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='fmod', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1409_{arg1=0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1410_{arg1=0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1411_{arg1=0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1412_{arg1=0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1413_{arg1=0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1414_{arg1=0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1416_{arg1=0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='fmod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1418_{arg1=0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1420_{arg1=0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1422_{arg1=0, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1424_{arg1=0, arg2=array([-3, -2, -1, 1, 2, 3]), 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tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1489_{arg1=0.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1490_{arg1=0.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1491_{arg1=0.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1492_{arg1=0.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1493_{arg1=0.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=False}::test_binary 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name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1718_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1720_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='fmod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1722_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1724_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1726_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='floor_divide', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1727_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='floor_divide', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1728_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='fmod', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1729_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1730_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1731_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1732_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1733_{arg1=-2, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1734_{arg1=-2, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1736_{arg1=-2, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='fmod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1738_{arg1=-2, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1740_{arg1=-2, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1742_{arg1=-2, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1744_{arg1=-2, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='fmod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1746_{arg1=-2, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1748_{arg1=-2, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1798_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='floor_divide', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1800_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='fmod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1802_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1804_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.], dtype=float32), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1806_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='floor_divide', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1807_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='floor_divide', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1808_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='fmod', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1809_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1810_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1811_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1812_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1813_{arg1=-2.0, arg2=array([-3., -2., -1., 1., 2., 3.]), dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1814_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='floor_divide', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1815_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='floor_divide', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1816_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='fmod', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1817_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1818_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1819_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1820_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1821_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3], dtype=int32), dtype=float64, name='mod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1822_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='floor_divide', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1823_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='floor_divide', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1824_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='fmod', use_dtype=True}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1825_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='fmod', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1826_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='remainder', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1827_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='remainder', use_dtype=False}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1828_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='mod', use_dtype=True}::test_binary -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_1829_{arg1=-2.0, arg2=array([-3, -2, -1, 1, 2, 3]), dtype=float64, name='mod', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_rounding.py::TestRound_param_0_{decimals=-2}::test_round_out tests/third_party/cupy/math_tests/test_rounding.py::TestRound_param_1_{decimals=-1}::test_round_out diff --git a/tests/test_umath.py b/tests/test_umath.py index 96ff2a849c8..044510330af 100644 --- a/tests/test_umath.py +++ b/tests/test_umath.py @@ -17,26 +17,6 @@ # full list of umaths umaths = [i for i in dir(numpy) if isinstance(getattr(numpy, i), numpy.ufunc)] -# print(umaths) -umaths = ["equal"] -# trigonometric -umaths.extend( - [ - "arccos", - "arcsin", - "arctan", - "cos", - "deg2rad", - "degrees", - "rad2deg", - "radians", - "sin", - "tan", - "arctan2", - "hypot", - ] -) -# 'unwrap' types = { "d": numpy.float64, @@ -45,7 +25,9 @@ "i": numpy.int32, } -supported_types = "dfli" +supported_types = "fli" +if has_support_aspect64(): + supported_types += "d" def check_types(args_str): @@ -63,21 +45,23 @@ def shaped_arange(shape, xp=numpy, dtype=numpy.float32): return xp.reshape(xp.array(array_data, dtype=dtype), shape) -def get_args(args_str, xp=numpy): +def get_args(args_str, sh, xp=numpy): args = [] for s in args_str: - args.append(shaped_arange(shape=(3, 4), xp=xp, dtype=types[s])) + args.append(shaped_arange(shape=sh, xp=xp, dtype=types[s])) return tuple(args) test_cases = [] for umath in umaths: np_umath = getattr(numpy, umath) - _types = np_umath.types - for type in _types: - args_str = type[: type.find("->")] + + for type_ in np_umath.types: + args_str = type_[: type_.find("->")] if check_types(args_str): - test_cases.append((umath, args_str)) + val_ = (umath, args_str) + if val_ not in test_cases: + test_cases.append(val_) def get_id(val): @@ -85,11 +69,19 @@ def get_id(val): @pytest.mark.usefixtures("allow_fall_back_on_numpy") +@pytest.mark.usefixtures("suppress_divide_invalid_numpy_warnings") @pytest.mark.parametrize("test_cases", test_cases, ids=get_id) def test_umaths(test_cases): umath, args_str = test_cases - args = get_args(args_str, xp=numpy) - iargs = get_args(args_str, xp=dpnp) + if umath == "matmul": + sh = (4, 4) + elif umath == "power": + sh = (2, 3) + else: + sh = (3, 4) + + args = get_args(args_str, sh, xp=numpy) + iargs = get_args(args_str, sh, xp=dpnp) # original expected = getattr(numpy, umath)(*args) diff --git a/tests/third_party/cupy/math_tests/test_arithmetic.py b/tests/third_party/cupy/math_tests/test_arithmetic.py index b61c3df9a3a..de37826cd3b 100644 --- a/tests/third_party/cupy/math_tests/test_arithmetic.py +++ b/tests/third_party/cupy/math_tests/test_arithmetic.py @@ -6,6 +6,7 @@ import pytest import dpnp as cupy +from tests.helper import has_support_aspect64 from tests.third_party.cupy import testing float_types = [numpy.float32, numpy.float64] @@ -108,7 +109,7 @@ def test_unary(self, xp): class ArithmeticBinaryBase: - @testing.numpy_cupy_allclose(atol=1e-4) + @testing.numpy_cupy_allclose(atol=1e-4, type_check=has_support_aspect64()) def check_binary(self, xp): arg1 = self.arg1 arg2 = self.arg2 From e3fd4778747094d2ffff5b5299168689e8460789 Mon Sep 17 00:00:00 2001 From: vlad-perevezentsev Date: Fri, 18 Aug 2023 13:48:02 +0200 Subject: [PATCH 38/58] Update API References and ndarray documentation (#1490) * Update API Reference * Refresh Multi-Dimensional Array section * Pin to numpy<1.24 in build_sphinx (#1491) * Make subchapters for ndarray chapter * Rename Difference with NumPy chapter * Update Available array data types chapter * Delete structured data types line * Add missing docstrings * Hide empty subchapter of routines --- doc/reference/comparison.rst | 6 +- doc/reference/creation.rst | 20 +- doc/reference/difference.rst | 10 - doc/reference/dtype.rst | 58 ++++++ doc/reference/dtypes_table.rst | 50 +++++ doc/reference/index.rst | 2 +- doc/reference/indexing.rst | 74 +++++++ doc/reference/linalg.rst | 17 +- doc/reference/manipulation.rst | 17 +- doc/reference/math.rst | 39 +++- doc/reference/misc.rst | 5 + doc/reference/ndarray.rst | 348 +++++++++++++++++++++++++++++++++ doc/reference/random.rst | 1 - doc/reference/routines.rst | 6 +- doc/reference/sorting.rst | 1 + doc/reference/statistics.rst | 18 +- doc/reference/ufunc.rst | 5 + dpnp/dpnp_array.py | 30 ++- dpnp/dpnp_flatiter.py | 2 + 19 files changed, 659 insertions(+), 50 deletions(-) delete mode 100644 doc/reference/difference.rst create mode 100644 doc/reference/dtype.rst create mode 100644 doc/reference/dtypes_table.rst create mode 100644 doc/reference/indexing.rst diff --git a/doc/reference/comparison.rst b/doc/reference/comparison.rst index b42eecc1518..d7157d8ff0b 100644 --- a/doc/reference/comparison.rst +++ b/doc/reference/comparison.rst @@ -1,7 +1,7 @@ -Comparison Table -================ +Comparison Table NumPy/ DPNP/ CuPy +================================== -Here is a list of NumPy APIs and its corresponding DPNP implementations. +Here is a list of NumPy and CuPy APIs and its corresponding DPNP implementations. ``-`` in DPNP column means that DPNP implementation is not provided yet. diff --git a/doc/reference/creation.rst b/doc/reference/creation.rst index 57d1e5d41d2..c898b628fe6 100644 --- a/doc/reference/creation.rst +++ b/doc/reference/creation.rst @@ -1,3 +1,5 @@ +.. _routines.creation: + Array Creation Routines ======================= @@ -20,7 +22,6 @@ Basic creation routines dpnp.zeros_like dpnp.full dpnp.full_like - dpnp.vander Creation from other data @@ -35,7 +36,13 @@ Creation from other data dpnp.asanyarray dpnp.ascontiguousarray dpnp.copy + dpnp.frombuffer + dpnp.from_dlpack dpnp.fromfile + dpnp.fromfunction + dpnp.fromiter + dpnp.fromstring + dpnp.loadtxt Numerical ranges @@ -66,3 +73,14 @@ Matrix creation dpnp.tri dpnp.tril dpnp.triu + dpnp.vander + + +The Matrix class +---------------- +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.mat + dpnp.bmat diff --git a/doc/reference/difference.rst b/doc/reference/difference.rst deleted file mode 100644 index 9f2d28f3b97..00000000000 --- a/doc/reference/difference.rst +++ /dev/null @@ -1,10 +0,0 @@ -Difference with NumPy* -====================== - -.. _Data types: - -Data types ----------- - -Data types (dtypes) of DPNP arrays are limited by the following types: -``bool``, ``complex128``, ``float32``, ``float64``, ``int32``, ``int64``, ``nan`` diff --git a/doc/reference/dtype.rst b/doc/reference/dtype.rst new file mode 100644 index 00000000000..29607c52fc8 --- /dev/null +++ b/doc/reference/dtype.rst @@ -0,0 +1,58 @@ +.. _dtype: + +Data type routines +================== + +.. https://docs.scipy.org/doc/numpy/reference/routines.dtype.html + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.can_cast + dpnp.promote_types + dpnp.min_scalar_type + dpnp.result_type + dpnp.common_type + dpnp.obj2sctype + +Creating data types +------------------- +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.dtype + dpnp.format_parser + +Data type information +--------------------- +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.finfo + dpnp.iinfo + +Data type testing +----------------- +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.issctype + dpnp.issubdtype + dpnp.issubsctype + dpnp.issubclass_ + dpnp.find_common_type + +Miscellaneous +------------- +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.typename + dpnp.sctype2char + dpnp.mintypecode + dpnp.maximum_sctype diff --git a/doc/reference/dtypes_table.rst b/doc/reference/dtypes_table.rst new file mode 100644 index 00000000000..879cf8b59cd --- /dev/null +++ b/doc/reference/dtypes_table.rst @@ -0,0 +1,50 @@ +Available array data types +========================== + +.. _Data types: + + +Table below shows a list of all supported data types (dtypes) and constants of the Data Parallel Extension for NumPy*. + +.. list-table:: + :header-rows: 1 + + * - Data Types + - Type aliases + - Constants + * - + - :obj:`bool ` + - :obj:`int32 ` + - :obj:`int64 ` + - :obj:`complex64 ` + - :obj:`complex128 ` + - + - :obj:`bool_ ` + - :obj:`cdouble ` + - :obj:`complex ` + - :obj:`cfloat ` + - :obj:`csingle ` + - :obj:`double ` + - :obj:`float ` + - :obj:`float_ ` + - :obj:`float16 ` + - :obj:`int ` + - :obj:`int_ ` + - :obj:`intc ` + - :obj:`single ` + - :obj:`singlecomplex ` + - + - :obj:`e ` + - :obj:`euler_gamma ` + - :obj:`Inf ` + - :obj:`inf ` + - :obj:`Infinity ` + - :obj:`infty ` + - :obj:`NAN ` + - :obj:`NaN ` + - :obj:`nan ` + - :obj:`NINF ` + - :obj:`NZERO ` + - :obj:`pi ` + - :obj:`PINF ` + - :obj:`PZERO ` diff --git a/doc/reference/index.rst b/doc/reference/index.rst index 2885ac86df3..44a29008871 100644 --- a/doc/reference/index.rst +++ b/doc/reference/index.rst @@ -30,6 +30,6 @@ API reference of the Data Parallel Extension for NumPy* testing prof environment - difference + dtypes_table comparison misc diff --git a/doc/reference/indexing.rst b/doc/reference/indexing.rst new file mode 100644 index 00000000000..6b77133dbe6 --- /dev/null +++ b/doc/reference/indexing.rst @@ -0,0 +1,74 @@ +.. _routines.indexing: + +Array Indexing Routines +======================= + +.. https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html + +Generating index arrays +----------------------- + + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.c_ + dpnp.r_ + dpnp.s_ + dpnp.nonzero + dpnp.where + dpnp.indices + dpnp.ix_ + dpnp.ogrid + dpnp.ravel_multi_index + dpnp.unravel_index + dpnp.diag_indices + dpnp.diag_indices_from + dpnp.mask_indices + dpnp.tril_indices + dpnp.tril_indices_from + dpnp.triu_indices + dpnp.triu_indices_from + + +Indexing-like operations +------------------------ +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.take + dpnp.take_along_axis + dpnp.choose + dpnp.compress + dpnp.diag + dpnp.diagonal + dpnp.select + + +Inserting data into arrays +-------------------------- +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.place + dpnp.put + dpnp.put_along_axis + dpnp.putmask + dpnp.fill_diagonal + + +Iterating over arrays +--------------------- +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.nditer + dpnp.ndenumerate + dpnp.ndindex + dpnp.nested_iters + dpnp.flatiter + dpnp.iterable diff --git a/doc/reference/linalg.rst b/doc/reference/linalg.rst index 00c1332c9ac..e85f6a1ee2c 100644 --- a/doc/reference/linalg.rst +++ b/doc/reference/linalg.rst @@ -10,7 +10,6 @@ Matrix and vector products :toctree: generated/ :nosignatures: - dpnp.cross dpnp.dot dpnp.linalg.multi_dot dpnp.vdot @@ -46,15 +45,16 @@ Matrix eigenvalues dpnp.linalg.eigvals dpnp.linalg.eigvalsh -Norms etc. +Norms and other numbers ---------- .. autosummary:: :toctree: generated/ :nosignatures: - dpnp.linalg.det dpnp.linalg.norm + dpnp.linalg.cond + dpnp.linalg.det dpnp.linalg.matrix_rank dpnp.linalg.slogdet dpnp.trace @@ -73,14 +73,3 @@ Solving linear equations dpnp.linalg.inv dpnp.linalg.pinv dpnp.linalg.tensorinv - -Special Matrices ----------------- - -.. autosummary:: - :toctree: generated/ - :nosignatures: - - dpnp.tri - dpnp.tril - dpnp.triu diff --git a/doc/reference/manipulation.rst b/doc/reference/manipulation.rst index b36e8760185..9d8dbd12842 100644 --- a/doc/reference/manipulation.rst +++ b/doc/reference/manipulation.rst @@ -11,6 +11,7 @@ Basic operations :nosignatures: dpnp.copyto + dpnp.shape Changing array shape @@ -22,6 +23,8 @@ Changing array shape dpnp.reshape dpnp.ravel + dpnp.ndarray.flat + dpnp.ndarray.flatten Transpose-like operations @@ -34,10 +37,9 @@ Transpose-like operations dpnp.moveaxis dpnp.rollaxis dpnp.swapaxes + dpnp.ndarray.T dpnp.transpose -.. seealso:: - :attr:`dpnp.dparray.T` Changing number of dimensions ----------------------------- @@ -66,6 +68,7 @@ Changing kind of array dpnp.asarray dpnp.asnumpy dpnp.asanyarray + dpnp.asmatrix dpnp.asfarray dpnp.asfortranarray dpnp.ascontiguousarray @@ -81,10 +84,12 @@ Joining arrays dpnp.concatenate dpnp.stack + dpnp.block + dpnp.vstack + dpnp.hstack dpnp.column_stack + dpnp.row_stack dpnp.dstack - dpnp.hstack - dpnp.vstack Splitting arrays @@ -119,6 +124,10 @@ Adding and removing elements :toctree: generated/ :nosignatures: + dpnp.delete + dpnp.insert + dpnp.append + dpnp.resize dpnp.unique dpnp.trim_zeros diff --git a/doc/reference/math.rst b/doc/reference/math.rst index dee52ba710f..0d75e08b7d8 100644 --- a/doc/reference/math.rst +++ b/doc/reference/math.rst @@ -48,7 +48,7 @@ Rounding :nosignatures: dpnp.around - dpnp.round_ + dpnp.round dpnp.rint dpnp.fix dpnp.floor @@ -119,6 +119,17 @@ Floating point routines dpnp.frexp dpnp.ldexp dpnp.nextafter + dpnp.spacing + + +Rational routines +----------------- +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.lcm + dpnp.gcd Arithmetic operations @@ -131,12 +142,14 @@ Arithmetic operations dpnp.add dpnp.reciprocal dpnp.negative + dpnp.positive dpnp.multiply dpnp.divide dpnp.power dpnp.subtract dpnp.true_divide dpnp.floor_divide + dpnp.floor_power dpnp.fmod dpnp.mod dpnp.modf @@ -155,6 +168,26 @@ Handling complex numbers dpnp.real dpnp.imag dpnp.conj + dpnp.conjugate + + +Extrema Finding +--------------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.maximum + dpnp.max + dpnp.amax + dpnp.fmax + dpnp.nanmax + dpnp.minimum + dpnp.min + dpnp.amin + dpnp.fmin + dpnp.nanmin Miscellaneous @@ -173,10 +206,6 @@ Miscellaneous dpnp.absolute dpnp.fabs dpnp.sign - dpnp.maximum - dpnp.minimum - dpnp.fmax - dpnp.fmin dpnp.nan_to_num dpnp.bartlett dpnp.blackman diff --git a/doc/reference/misc.rst b/doc/reference/misc.rst index da3d17b8c02..413aace06cc 100644 --- a/doc/reference/misc.rst +++ b/doc/reference/misc.rst @@ -11,3 +11,8 @@ Utility :nosignatures: dpnp.get_include + dpnp.show_config + dpnp.show_runtime + dpnp.deprecate + dpnp.deprecate_with_doc + dpnp.broadcast_shapes diff --git a/doc/reference/ndarray.rst b/doc/reference/ndarray.rst index 5fca216ec94..3ebfeb96028 100644 --- a/doc/reference/ndarray.rst +++ b/doc/reference/ndarray.rst @@ -12,3 +12,351 @@ For the basic concept of ``ndarray``\s, please refer to the `NumPy documentation dpnp.ndarray dpnp.dpnp_array.dpnp_array + + +Constructing arrays +------------------- + +New arrays can be constructed using the routines detailed in +:ref:`Array Creation Routines `, and also by using the low-level +:class:`dpnp.ndarray` constructor: + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray + + +Indexing arrays +--------------- + +Arrays can be indexed using an extended Python slicing syntax, +``array[selection]``. + +.. seealso:: :ref:`Array Indexing Routines `. + + +Array attributes +---------------- + +Array attributes reflect information that is intrinsic to the array +itself. Generally, accessing an array through its attributes allows +you to get and sometimes set intrinsic properties of the array without +creating a new array. The exposed attributes are the core parts of an +array and only some of them can be reset meaningfully without creating +a new array. Information on each attribute is given below. + + +Memory layout +------------- + +The following attributes contain information about the memory layout +of the array: + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.flags + dpnp.ndarray.shape + dpnp.ndarray.strides + dpnp.ndarray.ndim + dpnp.ndarray.data + dpnp.ndarray.size + dpnp.ndarray.itemsize + dpnp.ndarray.nbytes + dpnp.ndarray.base + + +Data type +--------- + +.. seealso:: :ref:`Available array data types ` + +The data type object associated with the array can be found in the +:attr:`dtype ` attribute: + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.dtype + + +Other attributes +---------------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.T + dpnp.ndarray.real + dpnp.ndarray.imag + dpnp.ndarray.flat + + +Array methods +------------- + +An :class:`dpnp.ndarray` object has many methods which operate on or with +the array in some fashion, typically returning an array result. These +methods are briefly explained below. (Each method's docstring has a +more complete description.) + +For the following methods there are also corresponding functions in +:mod:`dpnp`: :func:`all `, :func:`any `, +:func:`argmax `, :func:`argmin `, +:func:`argpartition `, :func:`argsort `, +:func:`choose `, :func:`clip `, +:func:`compress `, :func:`copy `, +:func:`cumprod `, :func:`cumsum `, +:func:`diagonal `, :func:`imag `, +:func:`max `, :func:`mean `, :func:`min `, +:func:`nonzero `, :func:`partition `, +:func:`prod `, :func:`ptp `, :func:`put `, +:func:`ravel `, :func:`real `, :func:`repeat `, +:func:`reshape `, :func:`round `, +:func:`searchsorted `, :func:`sort `, +:func:`squeeze `, :func:`std `, :func:`sum `, +:func:`swapaxes `, :func:`take `, :func:`trace `, +:func:`transpose `, :func:`var `. + + +Array conversion +---------------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.item + dpnp.ndarray.tolist + dpnp.ndarray.itemset + dpnp.ndarray.tostring + dpnp.ndarray.tobytes + dpnp.ndarray.tofile + dpnp.ndarray.dump + dpnp.ndarray.dumps + dpnp.ndarray.astype + dpnp.ndarray.byteswap + dpnp.ndarray.copy + dpnp.ndarray.view + dpnp.ndarray.getfield + dpnp.ndarray.setflags + dpnp.ndarray.fill + + +Shape manipulation +------------------ + +For reshape, resize, and transpose, the single tuple argument may be +replaced with ``n`` integers which will be interpreted as an n-tuple. + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.reshape + dpnp.ndarray.resize + dpnp.ndarray.transpose + dpnp.ndarray.swapaxes + dpnp.ndarray.flatten + dpnp.ndarray.ravel + dpnp.ndarray.squeeze + + +Item selection and manipulation +------------------------------- + +For array methods that take an *axis* keyword, it defaults to +*None*. If axis is *None*, then the array is treated as a 1-D +array. Any other value for *axis* represents the dimension along which +the operation should proceed. + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.take + dpnp.ndarray.put + dpnp.ndarray.repeat + dpnp.ndarray.choose + dpnp.ndarray.sort + dpnp.ndarray.argsort + dpnp.ndarray.partition + dpnp.ndarray.argpartition + dpnp.ndarray.searchsorted + dpnp.ndarray.nonzero + dpnp.ndarray.compress + dpnp.ndarray.diagonal + + +Calculation +----------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.max + dpnp.ndarray.argmax + dpnp.ndarray.min + dpnp.ndarray.argmin + dpnp.ndarray.ptp + dpnp.ndarray.clip + dpnp.ndarray.conj + dpnp.ndarray.conjugate + dpnp.ndarray.round + dpnp.ndarray.trace + dpnp.ndarray.sum + dpnp.ndarray.cumsum + dpnp.ndarray.mean + dpnp.ndarray.var + dpnp.ndarray.std + dpnp.ndarray.prod + dpnp.ndarray.cumprod + dpnp.ndarray.all + dpnp.ndarray.any + + +Arithmetic, matrix multiplication, and comparison operations +------------------------------------------------------------ + +Arithmetic and comparison operations on :class:`dpnp.ndarrays ` +are defined as element-wise operations, and generally yield +:class:`dpnp.ndarray` objects as results. + +Each of the arithmetic operations (``+``, ``-``, ``*``, ``/``, ``//``, +``%``, ``divmod()``, ``**`` or ``pow()``, ``<<``, ``>>``, ``&``, +``^``, ``|``, ``~``) and the comparisons (``==``, ``<``, ``>``, +``<=``, ``>=``, ``!=``) is equivalent to the corresponding +universal function (or **ufunc** for short) in DPNP. For +more information, see the section on :ref:`Universal Functions +`. + + +Comparison operators: + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.__lt__ + dpnp.ndarray.__le__ + dpnp.ndarray.__gt__ + dpnp.ndarray.__ge__ + dpnp.ndarray.__eq__ + dpnp.ndarray.__ne__ + + +Unary operations: + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.__neg__ + dpnp.ndarray.__pos__ + dpnp.ndarray.__abs__ + dpnp.ndarray.__invert__ + + +Arithmetic: + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.__add__ + dpnp.ndarray.__sub__ + dpnp.ndarray.__mul__ + dpnp.ndarray.__truediv__ + dpnp.ndarray.__floordiv__ + dpnp.ndarray.__mod__ + dpnp.ndarray.__divmod__ + dpnp.ndarray.__pow__ + dpnp.ndarray.__lshift__ + dpnp.ndarray.__rshift__ + dpnp.ndarray.__and__ + dpnp.ndarray.__or__ + dpnp.ndarray.__xor__ + + +Arithmetic, in-place: + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.__iadd__ + dpnp.ndarray.__isub__ + dpnp.ndarray.__imul__ + dpnp.ndarray.__itruediv__ + dpnp.ndarray.__ifloordiv__ + dpnp.ndarray.__imod__ + dpnp.ndarray.__ipow__ + dpnp.ndarray.__ilshift__ + dpnp.ndarray.__irshift__ + dpnp.ndarray.__iand__ + dpnp.ndarray.__ior__ + dpnp.ndarray.__ixor__ + + +Special methods +--------------- + +For standard library functions: + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.__copy__ + dpnp.ndarray.__deepcopy__ + .. dpnp.ndarray.__reduce__ + dpnp.ndarray.__setstate__ + +Basic customization: + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.__new__ + dpnp.ndarray.__array__ + dpnp.ndarray.__array_wrap__ + +Container customization: (see :ref:`Indexing `) + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.__len__ + dpnp.ndarray.__getitem__ + dpnp.ndarray.__setitem__ + dpnp.ndarray.__contains__ + +Conversion; the operations :class:`int() `, +:class:`float() ` and :class:`complex() `. +They work only on arrays that have one element in them +and return the appropriate scalar. + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.__int__ + dpnp.ndarray.__float__ + dpnp.ndarray.__complex__ + +String representations: + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + dpnp.ndarray.__str__ + dpnp.ndarray.__repr__ diff --git a/doc/reference/random.rst b/doc/reference/random.rst index 602483d14ba..1a20e76bf7d 100644 --- a/doc/reference/random.rst +++ b/doc/reference/random.rst @@ -3,7 +3,6 @@ Random Sampling (``dpnp.random``) ================================= -.. https://docs.scipy.org/doc/numpy-1.16.1/reference/routines.random.html .. https://docs.scipy.org/doc/numpy/reference/random/legacy.html diff --git a/doc/reference/routines.rst b/doc/reference/routines.rst index e920a611534..645d1a6be5d 100644 --- a/doc/reference/routines.rst +++ b/doc/reference/routines.rst @@ -13,13 +13,15 @@ These functions cover a subset of creation manipulation + indexing binary + dtype fft linalg logic math - pad - polynomials + .. pad + .. polynomials random sorting statistics diff --git a/doc/reference/sorting.rst b/doc/reference/sorting.rst index cd0ef99b9a7..170e0d33662 100644 --- a/doc/reference/sorting.rst +++ b/doc/reference/sorting.rst @@ -37,6 +37,7 @@ Searching dpnp.where dpnp.argwhere dpnp.searchsorted + dpnp.extract Counting -------- diff --git a/doc/reference/statistics.rst b/doc/reference/statistics.rst index 2e191675978..cdacffda29e 100644 --- a/doc/reference/statistics.rst +++ b/doc/reference/statistics.rst @@ -1,7 +1,7 @@ Statistical Functions ===================== -.. https://docs.scipy.org/doc/scipy/reference/stats.html +.. https://docs.scipy.org/doc/numpy/reference/routines.statistics.html Order statistics ---------------- @@ -10,16 +10,14 @@ Order statistics :toctree: generated/ :nosignatures: - dpnp.amin - dpnp.amax - dpnp.min - dpnp.max - dpnp.nanmin - dpnp.nanmax + dpnp.ptp dpnp.percentile + dpnp.nanpercentile + dpnp.quantile + dpnp.nanquantile -Means and variances +Averages and variances ------------------- .. autosummary:: @@ -44,7 +42,11 @@ Histograms :nosignatures: dpnp.histogram + dpnp.histogram2d + dpnp.histogramdd dpnp.bincount + dpnp.histogram_bin_edges + dpnp.digitize Correlations diff --git a/doc/reference/ufunc.rst b/doc/reference/ufunc.rst index e2953bc45aa..4e74ac78ca3 100644 --- a/doc/reference/ufunc.rst +++ b/doc/reference/ufunc.rst @@ -1,3 +1,5 @@ +.. _ufunc: + Universal Functions (ufunc) =========================== @@ -120,9 +122,12 @@ Floating functions dpnp.isfinite dpnp.isinf dpnp.isnan + dpnp.isnat + dpnp.fabs dpnp.signbit dpnp.copysign dpnp.nextafter + dpnp.spacing dpnp.modf dpnp.ldexp dpnp.frexp diff --git a/dpnp/dpnp_array.py b/dpnp/dpnp_array.py index 17957e15404..8b2a8c90716 100644 --- a/dpnp/dpnp_array.py +++ b/dpnp/dpnp_array.py @@ -138,12 +138,15 @@ def usm_type(self): return self._array_obj.usm_type def __abs__(self): + r"""Return \|self\|.""" return dpnp.abs(self) def __add__(self, other): + """Return self+value.""" return dpnp.add(self, other) def __and__(self, other): + """Return self&value.""" return dpnp.bitwise_and(self, other) # '__array__', @@ -194,6 +197,7 @@ def __ge__(self, other): # '__getattribute__', def __getitem__(self, key): + """Return self[key].""" key = _get_unwrapped_index_key(key) item = self._array_obj.__getitem__(key) @@ -214,16 +218,19 @@ def __gt__(self, other): # '__hash__', def __iadd__(self, other): + """Return self+=value.""" dpnp.add(self, other, out=self) return self def __iand__(self, other): + """Return self&=value.""" dpnp.bitwise_and(self, other, out=self) return self # '__ifloordiv__', def __ilshift__(self, other): + """Return self<<=value.""" dpnp.left_shift(self, other, out=self) return self @@ -231,6 +238,7 @@ def __ilshift__(self, other): # '__imod__', def __imul__(self, other): + """Return self*=value.""" dpnp.multiply(self, other, out=self) return self @@ -244,31 +252,38 @@ def __int__(self): return self._array_obj.__int__() def __invert__(self): + """Return ~self.""" return dpnp.invert(self) def __ior__(self, other): + """Return self|=value.""" dpnp.bitwise_or(self, other, out=self) return self def __ipow__(self, other): + """Return self**=value.""" dpnp.power(self, other, out=self) return self def __irshift__(self, other): + """Return self>>=value.""" dpnp.right_shift(self, other, out=self) return self def __isub__(self, other): + """Return self-=value.""" dpnp.subtract(self, other, out=self) return self # '__iter__', def __itruediv__(self, other): + """Return self/=value.""" dpnp.true_divide(self, other, out=self) return self def __ixor__(self, other): + """Return self^=value.""" dpnp.bitwise_xor(self, other, out=self) return self @@ -276,11 +291,12 @@ def __le__(self, other): return dpnp.less_equal(self, other) def __len__(self): - """Performs the operation __len__.""" + """Return len(self).""" return self._array_obj.__len__() def __lshift__(self, other): + """Return self<>value.""" return dpnp.right_shift(self, other) def __rsub__(self, other): @@ -362,6 +384,7 @@ def __rxor__(self, other): # '__setattr__', def __setitem__(self, key, val): + """Set self[key] to value.""" key = _get_unwrapped_index_key(key) if isinstance(val, dpnp_array): @@ -388,14 +411,17 @@ def __str__(self): return self._array_obj.__str__() def __sub__(self, other): + """Return self-value.""" return dpnp.subtract(self, other) # '__subclasshook__', def __truediv__(self, other): + """Return self/value.""" return dpnp.true_divide(self, other) def __xor__(self, other): + """Return self^value.""" return dpnp.bitwise_xor(self, other) @staticmethod @@ -823,6 +849,8 @@ def ndim(self): # 'newbyteorder', def nonzero(self): + """Return the indices of the elements that are non-zero.""" + return dpnp.nonzero(self) def partition(self, kth, axis=-1, kind="introselect", order=None): diff --git a/dpnp/dpnp_flatiter.py b/dpnp/dpnp_flatiter.py index 4370d1e09eb..00e6ec44770 100644 --- a/dpnp/dpnp_flatiter.py +++ b/dpnp/dpnp_flatiter.py @@ -30,6 +30,8 @@ class flatiter: + """Flat iterator object to iterate over arrays.""" + def __init__(self, X): if type(X) is not dpnp.ndarray: raise TypeError( From 77840e6e13e91efb3c8266e7686c39708c563125 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Mon, 14 Aug 2023 13:15:30 -0500 Subject: [PATCH 39/58] use_dpctl_ceil_floor_trunc_in_dpnp --- dpnp/backend/extensions/vm/ceil.hpp | 78 ++++ dpnp/backend/extensions/vm/floor.hpp | 78 ++++ dpnp/backend/extensions/vm/trunc.hpp | 78 ++++ dpnp/backend/extensions/vm/types_matrix.hpp | 46 ++ dpnp/backend/extensions/vm/vm_py.cpp | 90 ++++ dpnp/backend/include/dpnp_iface_fptr.hpp | 8 +- dpnp/backend/kernels/dpnp_krnl_elemwise.cpp | 27 -- dpnp/dpnp_algo/dpnp_algo.pxd | 6 - dpnp/dpnp_algo/dpnp_algo_mathematical.pxi | 15 - dpnp/dpnp_algo/dpnp_elementwise_common.py | 164 ++++++++ dpnp/dpnp_iface_mathematical.py | 392 ++++++++++-------- dpnp/dpnp_iface_trigonometric.py | 6 +- tests/skipped_tests.tbl | 3 - tests/skipped_tests_gpu.tbl | 8 - .../cupy/math_tests/test_rounding.py | 2 +- 15 files changed, 765 insertions(+), 236 deletions(-) create mode 100644 dpnp/backend/extensions/vm/ceil.hpp create mode 100644 dpnp/backend/extensions/vm/floor.hpp create mode 100644 dpnp/backend/extensions/vm/trunc.hpp diff --git a/dpnp/backend/extensions/vm/ceil.hpp b/dpnp/backend/extensions/vm/ceil.hpp new file mode 100644 index 00000000000..b1354b884c3 --- /dev/null +++ b/dpnp/backend/extensions/vm/ceil.hpp @@ -0,0 +1,78 @@ +//***************************************************************************** +// Copyright (c) 2023, Intel Corporation +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// - Redistributions of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// - Redistributions in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +// THE POSSIBILITY OF SUCH DAMAGE. +//***************************************************************************** + +#pragma once + +#include + +#include "common.hpp" +#include "types_matrix.hpp" + +namespace dpnp +{ +namespace backend +{ +namespace ext +{ +namespace vm +{ +template +sycl::event ceil_contig_impl(sycl::queue exec_q, + const std::int64_t n, + const char *in_a, + char *out_y, + const std::vector &depends) +{ + type_utils::validate_type_for_device(exec_q); + + const T *a = reinterpret_cast(in_a); + T *y = reinterpret_cast(out_y); + + return mkl_vm::ceil(exec_q, + n, // number of elements to be calculated + a, // pointer `a` containing input vector of size n + y, // pointer `y` to the output vector of size n + depends); +} + +template +struct CeilContigFactory +{ + fnT get() + { + if constexpr (std::is_same_v< + typename types::CeilOutputType::value_type, void>) + { + return nullptr; + } + else { + return ceil_contig_impl; + } + } +}; +} // namespace vm +} // namespace ext +} // namespace backend +} // namespace dpnp diff --git a/dpnp/backend/extensions/vm/floor.hpp b/dpnp/backend/extensions/vm/floor.hpp new file mode 100644 index 00000000000..66707ba2979 --- /dev/null +++ b/dpnp/backend/extensions/vm/floor.hpp @@ -0,0 +1,78 @@ +//***************************************************************************** +// Copyright (c) 2023, Intel Corporation +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// - Redistributions of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// - Redistributions in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +// THE POSSIBILITY OF SUCH DAMAGE. +//***************************************************************************** + +#pragma once + +#include + +#include "common.hpp" +#include "types_matrix.hpp" + +namespace dpnp +{ +namespace backend +{ +namespace ext +{ +namespace vm +{ +template +sycl::event floor_contig_impl(sycl::queue exec_q, + const std::int64_t n, + const char *in_a, + char *out_y, + const std::vector &depends) +{ + type_utils::validate_type_for_device(exec_q); + + const T *a = reinterpret_cast(in_a); + T *y = reinterpret_cast(out_y); + + return mkl_vm::floor(exec_q, + n, // number of elements to be calculated + a, // pointer `a` containing input vector of size n + y, // pointer `y` to the output vector of size n + depends); +} + +template +struct FloorContigFactory +{ + fnT get() + { + if constexpr (std::is_same_v< + typename types::FloorOutputType::value_type, void>) + { + return nullptr; + } + else { + return floor_contig_impl; + } + } +}; +} // namespace vm +} // namespace ext +} // namespace backend +} // namespace dpnp diff --git a/dpnp/backend/extensions/vm/trunc.hpp b/dpnp/backend/extensions/vm/trunc.hpp new file mode 100644 index 00000000000..d8ecadff363 --- /dev/null +++ b/dpnp/backend/extensions/vm/trunc.hpp @@ -0,0 +1,78 @@ +//***************************************************************************** +// Copyright (c) 2023, Intel Corporation +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// - Redistributions of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// - Redistributions in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +// THE POSSIBILITY OF SUCH DAMAGE. +//***************************************************************************** + +#pragma once + +#include + +#include "common.hpp" +#include "types_matrix.hpp" + +namespace dpnp +{ +namespace backend +{ +namespace ext +{ +namespace vm +{ +template +sycl::event trunc_contig_impl(sycl::queue exec_q, + const std::int64_t n, + const char *in_a, + char *out_y, + const std::vector &depends) +{ + type_utils::validate_type_for_device(exec_q); + + const T *a = reinterpret_cast(in_a); + T *y = reinterpret_cast(out_y); + + return mkl_vm::trunc(exec_q, + n, // number of elements to be calculated + a, // pointer `a` containing input vector of size n + y, // pointer `y` to the output vector of size n + depends); +} + +template +struct TruncContigFactory +{ + fnT get() + { + if constexpr (std::is_same_v< + typename types::TruncOutputType::value_type, void>) + { + return nullptr; + } + else { + return trunc_contig_impl; + } + } +}; +} // namespace vm +} // namespace ext +} // namespace backend +} // namespace dpnp diff --git a/dpnp/backend/extensions/vm/types_matrix.hpp b/dpnp/backend/extensions/vm/types_matrix.hpp index 667e27f0c9a..cd4fd76d4be 100644 --- a/dpnp/backend/extensions/vm/types_matrix.hpp +++ b/dpnp/backend/extensions/vm/types_matrix.hpp @@ -68,6 +68,21 @@ struct DivOutputType dpctl_td_ns::DefaultResultEntry>::result_type; }; +/** + * @brief A factory to define pairs of supported types for which + * MKL VM library provides support in oneapi::mkl::vm::ceil function. + * + * @tparam T Type of input vector `a` and of result vector `y`. + */ +template +struct CeilOutputType +{ + using value_type = typename std::disjunction< + dpctl_td_ns::TypeMapResultEntry, + dpctl_td_ns::TypeMapResultEntry, + dpctl_td_ns::DefaultResultEntry>::result_type; +}; + /** * @brief A factory to define pairs of supported types for which * MKL VM library provides support in oneapi::mkl::vm::cos function. @@ -87,6 +102,21 @@ struct CosOutputType dpctl_td_ns::DefaultResultEntry>::result_type; }; +/** + * @brief A factory to define pairs of supported types for which + * MKL VM library provides support in oneapi::mkl::vm::floor function. + * + * @tparam T Type of input vector `a` and of result vector `y`. + */ +template +struct FloorOutputType +{ + using value_type = typename std::disjunction< + dpctl_td_ns::TypeMapResultEntry, + dpctl_td_ns::TypeMapResultEntry, + dpctl_td_ns::DefaultResultEntry>::result_type; +}; + /** * @brief A factory to define pairs of supported types for which * MKL VM library provides support in oneapi::mkl::vm::ln function. @@ -158,6 +188,22 @@ struct SqrtOutputType dpctl_td_ns::TypeMapResultEntry, dpctl_td_ns::DefaultResultEntry>::result_type; }; + +/** + * @brief A factory to define pairs of supported types for which + * MKL VM library provides support in oneapi::mkl::vm::trunc function. + * + * @tparam T Type of input vector `a` and of result vector `y`. + */ +template +struct TruncOutputType +{ + using value_type = typename std::disjunction< + dpctl_td_ns::TypeMapResultEntry, + dpctl_td_ns::TypeMapResultEntry, + dpctl_td_ns::DefaultResultEntry>::result_type; +}; + } // namespace types } // namespace vm } // namespace ext diff --git a/dpnp/backend/extensions/vm/vm_py.cpp b/dpnp/backend/extensions/vm/vm_py.cpp index 924db66025b..c7435ae9e2e 100644 --- a/dpnp/backend/extensions/vm/vm_py.cpp +++ b/dpnp/backend/extensions/vm/vm_py.cpp @@ -30,13 +30,16 @@ #include #include +#include "ceil.hpp" #include "common.hpp" #include "cos.hpp" #include "div.hpp" +#include "floor.hpp" #include "ln.hpp" #include "sin.hpp" #include "sqr.hpp" #include "sqrt.hpp" +#include "trunc.hpp" #include "types_matrix.hpp" namespace py = pybind11; @@ -47,11 +50,14 @@ using vm_ext::unary_impl_fn_ptr_t; static binary_impl_fn_ptr_t div_dispatch_vector[dpctl_td_ns::num_types]; +static unary_impl_fn_ptr_t ceil_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t cos_dispatch_vector[dpctl_td_ns::num_types]; +static unary_impl_fn_ptr_t floor_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t ln_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t sin_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t sqr_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t sqrt_dispatch_vector[dpctl_td_ns::num_types]; +static unary_impl_fn_ptr_t trunc_dispatch_vector[dpctl_td_ns::num_types]; PYBIND11_MODULE(_vm_impl, m) { @@ -88,6 +94,34 @@ PYBIND11_MODULE(_vm_impl, m) py::arg("dst")); } + // UnaryUfunc: ==== Ceil(x) ==== + { + vm_ext::init_ufunc_dispatch_vector( + ceil_dispatch_vector); + + auto ceil_pyapi = [&](sycl::queue exec_q, arrayT src, arrayT dst, + const event_vecT &depends = {}) { + return vm_ext::unary_ufunc(exec_q, src, dst, depends, + ceil_dispatch_vector); + }; + m.def("_ceil", ceil_pyapi, + "Call `ceil` function from OneMKL VM library to compute " + "ceiling of vector elements", + py::arg("sycl_queue"), py::arg("src"), py::arg("dst"), + py::arg("depends") = py::list()); + + auto ceil_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src, + arrayT dst) { + return vm_ext::need_to_call_unary_ufunc(exec_q, src, dst, + ceil_dispatch_vector); + }; + m.def("_mkl_ceil_to_call", ceil_need_to_call_pyapi, + "Check input arguments to answer if `ceil` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src"), py::arg("dst")); + } + // UnaryUfunc: ==== Cos(x) ==== { vm_ext::init_ufunc_dispatch_vector( + floor_dispatch_vector); + + auto floor_pyapi = [&](sycl::queue exec_q, arrayT src, arrayT dst, + const event_vecT &depends = {}) { + return vm_ext::unary_ufunc(exec_q, src, dst, depends, + floor_dispatch_vector); + }; + m.def("_floor", floor_pyapi, + "Call `floor` function from OneMKL VM library to compute " + "floor of vector elements", + py::arg("sycl_queue"), py::arg("src"), py::arg("dst"), + py::arg("depends") = py::list()); + + auto floor_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src, + arrayT dst) { + return vm_ext::need_to_call_unary_ufunc(exec_q, src, dst, + floor_dispatch_vector); + }; + m.def("_mkl_floor_to_call", floor_need_to_call_pyapi, + "Check input arguments to answer if `floor` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src"), py::arg("dst")); + } + // UnaryUfunc: ==== Ln(x) ==== { vm_ext::init_ufunc_dispatch_vector( + trunc_dispatch_vector); + + auto trunc_pyapi = [&](sycl::queue exec_q, arrayT src, arrayT dst, + const event_vecT &depends = {}) { + return vm_ext::unary_ufunc(exec_q, src, dst, depends, + trunc_dispatch_vector); + }; + m.def("_trunc", trunc_pyapi, + "Call `trunc` function from OneMKL VM library to compute " + "the truncated value of vector elements", + py::arg("sycl_queue"), py::arg("src"), py::arg("dst"), + py::arg("depends") = py::list()); + + auto trunc_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src, + arrayT dst) { + return vm_ext::need_to_call_unary_ufunc(exec_q, src, dst, + trunc_dispatch_vector); + }; + m.def("_mkl_trunc_to_call", trunc_need_to_call_pyapi, + "Check input arguments to answer if `trunc` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src"), py::arg("dst")); + } } diff --git a/dpnp/backend/include/dpnp_iface_fptr.hpp b/dpnp/backend/include/dpnp_iface_fptr.hpp index e2c29a0f4a8..9917eedd678 100644 --- a/dpnp/backend/include/dpnp_iface_fptr.hpp +++ b/dpnp/backend/include/dpnp_iface_fptr.hpp @@ -113,8 +113,6 @@ enum class DPNPFuncName : size_t DPNP_FN_CBRT_EXT, /**< Used in numpy.cbrt() impl, requires extra parameters */ DPNP_FN_CEIL, /**< Used in numpy.ceil() impl */ - DPNP_FN_CEIL_EXT, /**< Used in numpy.ceil() impl, requires extra parameters - */ DPNP_FN_CHOLESKY, /**< Used in numpy.linalg.cholesky() impl */ DPNP_FN_CHOLESKY_EXT, /**< Used in numpy.linalg.cholesky() impl, requires extra parameters */ @@ -212,8 +210,6 @@ enum class DPNPFuncName : size_t DPNP_FN_FLATTEN_EXT, /**< Used in numpy.flatten() impl, requires extra parameters */ DPNP_FN_FLOOR, /**< Used in numpy.floor() impl */ - DPNP_FN_FLOOR_EXT, /**< Used in numpy.floor() impl, requires extra - parameters */ DPNP_FN_FLOOR_DIVIDE, /**< Used in numpy.floor_divide() impl */ DPNP_FN_FLOOR_DIVIDE_EXT, /**< Used in numpy.floor_divide() impl, requires extra parameters */ @@ -488,9 +484,7 @@ enum class DPNPFuncName : size_t DPNP_FN_TRIL, /**< Used in numpy.tril() impl */ DPNP_FN_TRIU, /**< Used in numpy.triu() impl */ DPNP_FN_TRUNC, /**< Used in numpy.trunc() impl */ - DPNP_FN_TRUNC_EXT, /**< Used in numpy.trunc() impl, requires extra - parameters */ - DPNP_FN_VANDER, /**< Used in numpy.vander() impl */ + DPNP_FN_VANDER, /**< Used in numpy.vander() impl */ DPNP_FN_VANDER_EXT, /**< Used in numpy.vander() impl, requires extra parameters */ DPNP_FN_VAR, /**< Used in numpy.var() impl */ diff --git a/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp b/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp index 1bd0d1922e0..a382d85b573 100644 --- a/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp @@ -359,15 +359,6 @@ static void func_map_init_elemwise_1arg_2type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_CEIL][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_ceil_c_default}; - fmap[DPNPFuncName::DPNP_FN_CEIL_EXT][eft_INT][eft_INT] = { - eft_DBL, (void *)dpnp_ceil_c_ext}; - fmap[DPNPFuncName::DPNP_FN_CEIL_EXT][eft_LNG][eft_LNG] = { - eft_DBL, (void *)dpnp_ceil_c_ext}; - fmap[DPNPFuncName::DPNP_FN_CEIL_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_ceil_c_ext}; - fmap[DPNPFuncName::DPNP_FN_CEIL_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_ceil_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO][eft_BLN][eft_BLN] = { eft_BLN, (void *)dpnp_copyto_c_default}; fmap[DPNPFuncName::DPNP_FN_COPYTO][eft_BLN][eft_INT] = { @@ -603,15 +594,6 @@ static void func_map_init_elemwise_1arg_2type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_FLOOR][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_floor_c_default}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_EXT][eft_INT][eft_INT] = { - eft_DBL, (void *)dpnp_floor_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_EXT][eft_LNG][eft_LNG] = { - eft_DBL, (void *)dpnp_floor_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_floor_c_ext}; - fmap[DPNPFuncName::DPNP_FN_FLOOR_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_floor_c_ext}; - fmap[DPNPFuncName::DPNP_FN_LOG10][eft_INT][eft_INT] = { eft_DBL, (void *)dpnp_log10_c_default}; fmap[DPNPFuncName::DPNP_FN_LOG10][eft_LNG][eft_LNG] = { @@ -780,15 +762,6 @@ static void func_map_init_elemwise_1arg_2type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_TRUNC][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_trunc_c_default}; - fmap[DPNPFuncName::DPNP_FN_TRUNC_EXT][eft_INT][eft_INT] = { - eft_DBL, (void *)dpnp_trunc_c_ext}; - fmap[DPNPFuncName::DPNP_FN_TRUNC_EXT][eft_LNG][eft_LNG] = { - eft_DBL, (void *)dpnp_trunc_c_ext}; - fmap[DPNPFuncName::DPNP_FN_TRUNC_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_trunc_c_ext}; - fmap[DPNPFuncName::DPNP_FN_TRUNC_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_trunc_c_ext}; - return; } diff --git a/dpnp/dpnp_algo/dpnp_algo.pxd b/dpnp/dpnp_algo/dpnp_algo.pxd index 7427c6c7ce4..80f5471831d 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pxd +++ b/dpnp/dpnp_algo/dpnp_algo.pxd @@ -64,8 +64,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_ASTYPE_EXT DPNP_FN_CBRT DPNP_FN_CBRT_EXT - DPNP_FN_CEIL - DPNP_FN_CEIL_EXT DPNP_FN_CHOLESKY DPNP_FN_CHOLESKY_EXT DPNP_FN_CHOOSE @@ -128,8 +126,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_FILL_DIAGONAL_EXT DPNP_FN_FLATTEN DPNP_FN_FLATTEN_EXT - DPNP_FN_FLOOR - DPNP_FN_FLOOR_EXT DPNP_FN_FMOD DPNP_FN_FMOD_EXT DPNP_FN_FULL @@ -292,8 +288,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_TRIL_EXT DPNP_FN_TRIU DPNP_FN_TRIU_EXT - DPNP_FN_TRUNC - DPNP_FN_TRUNC_EXT DPNP_FN_VANDER DPNP_FN_VANDER_EXT DPNP_FN_VAR diff --git a/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi b/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi index 1685c770d4e..6f5aa2a614c 100644 --- a/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi +++ b/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi @@ -39,7 +39,6 @@ __all__ += [ "dpnp_absolute", "dpnp_arctan2", "dpnp_around", - "dpnp_ceil", "dpnp_conjugate", "dpnp_copysign", "dpnp_cross", @@ -48,7 +47,6 @@ __all__ += [ "dpnp_diff", "dpnp_ediff1d", "dpnp_fabs", - "dpnp_floor", "dpnp_fmod", "dpnp_gradient", 'dpnp_hypot', @@ -65,7 +63,6 @@ __all__ += [ "dpnp_sign", "dpnp_sum", "dpnp_trapz", - "dpnp_trunc" ] @@ -158,10 +155,6 @@ cpdef utils.dpnp_descriptor dpnp_around(utils.dpnp_descriptor x1, int decimals): return result -cpdef utils.dpnp_descriptor dpnp_ceil(utils.dpnp_descriptor x1, utils.dpnp_descriptor out): - return call_fptr_1in_1out_strides(DPNP_FN_CEIL_EXT, x1, dtype=None, out=out, where=True, func_name='ceil') - - cpdef utils.dpnp_descriptor dpnp_conjugate(utils.dpnp_descriptor x1): return call_fptr_1in_1out_strides(DPNP_FN_CONJIGUATE_EXT, x1) @@ -295,10 +288,6 @@ cpdef utils.dpnp_descriptor dpnp_fabs(utils.dpnp_descriptor x1): return call_fptr_1in_1out_strides(DPNP_FN_FABS_EXT, x1) -cpdef utils.dpnp_descriptor dpnp_floor(utils.dpnp_descriptor x1, utils.dpnp_descriptor out): - return call_fptr_1in_1out_strides(DPNP_FN_FLOOR_EXT, x1, dtype=None, out=out, where=True, func_name='floor') - - cpdef utils.dpnp_descriptor dpnp_fmod(utils.dpnp_descriptor x1_obj, utils.dpnp_descriptor x2_obj, object dtype=None, @@ -641,7 +630,3 @@ cpdef utils.dpnp_descriptor dpnp_trapz(utils.dpnp_descriptor y1, utils.dpnp_desc c_dpctl.DPCTLEvent_Delete(event_ref) return result - - -cpdef utils.dpnp_descriptor dpnp_trunc(utils.dpnp_descriptor x1, utils.dpnp_descriptor out): - return call_fptr_1in_1out_strides(DPNP_FN_TRUNC_EXT, x1, dtype=None, out=out, where=True, func_name='trunc') diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index 33776e26a01..14c5f6058d8 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -46,9 +46,11 @@ "dpnp_bitwise_and", "dpnp_bitwise_or", "dpnp_bitwise_xor", + "dpnp_ceil", "dpnp_cos", "dpnp_divide", "dpnp_equal", + "dpnp_floor", "dpnp_floor_divide", "dpnp_greater", "dpnp_greater_equal", @@ -72,6 +74,7 @@ "dpnp_sqrt", "dpnp_square", "dpnp_subtract", + "dpnp_trunc", ] @@ -318,6 +321,59 @@ def dpnp_bitwise_xor(x1, x2, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_ceil_docstring = """ +ceil(x, out=None, order='K') + +Returns the ceiling for each element `x_i` for input array `x`. +The ceil of the scalar `x` is the smallest integer `i`, such that `i >= x`. + +Args: + x (dpnp.ndarray): + Input array, expected to have a real-valued data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. Array must have the correct + shape and the expected data type. + order ("C","F","A","K", optional): memory layout of the new + output array, if parameter `out` is `None`. + Default: "K". +Return: + dpnp.ndarray: + An array containing the element-wise ceiling of input array. + The returned array has the same data type as `x`. +""" + + +def _call_ceil(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_ceil_to_call(sycl_queue, src, dst): + # call pybind11 extension for ceil() function from OneMKL VM + return vmi._ceil(sycl_queue, src, dst, depends) + return ti._ceil(src, dst, sycl_queue, depends) + + +ceil_func = UnaryElementwiseFunc( + "ceil", ti._ceil_result_type, _call_ceil, _ceil_docstring +) + + +def dpnp_ceil(x, out=None, order="K"): + """ + Invokes ceil() function from pybind11 extension of OneMKL VM if possible. + + Otherwise fully relies on dpctl.tensor implementation for ceil() function. + """ + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + res_usm = ceil_func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _cos_docstring = """ cos(x, out=None, order='K') Computes cosine for each element `x_i` for input array `x`. @@ -484,6 +540,59 @@ def dpnp_equal(x1, x2, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_floor_docstring = """ +floor(x, out=None, order='K') + +Returns the floor for each element `x_i` for input array `x`. +The floor of the scalar `x` is the largest integer `i`, such that `i <= x`. + +Args: + x (dpnp.ndarray): + Input array, expected to have a real-valued data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. Array must have the correct + shape and the expected data type. + order ("C","F","A","K", optional): memory layout of the new + output array, if parameter `out` is `None`. + Default: "K". +Return: + dpnp.ndarray: + An array containing the element-wise floor of input array. + The returned array has the same data type as `x`. +""" + + +def _call_floor(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_floor_to_call(sycl_queue, src, dst): + # call pybind11 extension for floor() function from OneMKL VM + return vmi._floor(sycl_queue, src, dst, depends) + return ti._floor(src, dst, sycl_queue, depends) + + +floor_func = UnaryElementwiseFunc( + "floor", ti._floor_result_type, _call_floor, _floor_docstring +) + + +def dpnp_floor(x, out=None, order="K"): + """ + Invokes floor() function from pybind11 extension of OneMKL VM if possible. + + Otherwise fully relies on dpctl.tensor implementation for floor() function. + """ + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + res_usm = floor_func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _floor_divide_docstring_ = """ floor_divide(x1, x2, out=None, order="K") Calculates the ratio for each element `x1_i` of the input array `x1` with @@ -1469,3 +1578,58 @@ def dpnp_subtract(x1, x2, out=None, order="K"): ) res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) + + +_trunc_docstring = """ +trunc(x, out=None, order='K') + +Returns the truncated value for each element `x_i` for input array `x`. +The truncated value of the scalar `x` is the nearest integer `i` which is +closer to zero than `x` is. In short, the fractional part of the +signed number `x` is discarded. + +Args: + x (dpnp.ndarray): + Input array, expected to have a real-valued data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. Array must have the correct + shape and the expected data type. + order ("C","F","A","K", optional): memory layout of the new + output array, if parameter `out` is `None`. + Default: "K". +Return: + dpnp.ndarray: + An array containing the truncated value of each element in `x`. The data type + of the returned array is determined by the Type Promotion Rules. +""" + + +def _call_trunc(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_trunc_to_call(sycl_queue, src, dst): + # call pybind11 extension for trunc() function from OneMKL VM + return vmi._trunc(sycl_queue, src, dst, depends) + return ti._trunc(src, dst, sycl_queue, depends) + + +trunc_func = UnaryElementwiseFunc( + "trunc", ti._trunc_result_type, _call_trunc, _trunc_docstring +) + + +def dpnp_trunc(x, out=None, order="K"): + """ + Invokes trunc() function from pybind11 extension of OneMKL VM if possible. + + Otherwise fully relies on dpctl.tensor implementation for trunc() function. + """ + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + res_usm = trunc_func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) diff --git a/dpnp/dpnp_iface_mathematical.py b/dpnp/dpnp_iface_mathematical.py index f5c9530046f..8d56d53ca73 100644 --- a/dpnp/dpnp_iface_mathematical.py +++ b/dpnp/dpnp_iface_mathematical.py @@ -51,11 +51,14 @@ from .dpnp_algo.dpnp_elementwise_common import ( check_nd_call_func, dpnp_add, + dpnp_ceil, dpnp_divide, + dpnp_floor, dpnp_floor_divide, dpnp_multiply, dpnp_remainder, dpnp_subtract, + dpnp_trunc, ) from .dpnp_utils import * @@ -145,7 +148,7 @@ def absolute(x, /, out=None, *, where=True, dtype=None, subok=True, **kwargs): ----------- Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. Parameters `out`, `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. + Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -270,9 +273,9 @@ def around(x1, decimals=0, out=None): Limitations ----------- - Parameters ``x1`` is supported as :obj:`dpnp.ndarray`. - Parameters ``decimals`` and ``out`` are supported with their default values. - Otherwise the functions will be executed sequentially on CPU. + Parameters `x1` is supported as :class:`dpnp.ndarray`. + Parameters `decimals` and `out` are supported with their default values. + Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. Examples @@ -303,18 +306,34 @@ def around(x1, decimals=0, out=None): return call_origin(numpy.around, x1, decimals=decimals, out=out) -def ceil(x1, out=None, **kwargs): +def ceil( + x, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, +): """ - Compute the ceiling of the input, element-wise. + Compute the ceiling of the input, element-wise. For full documentation refer to :obj:`numpy.ceil`. + Returns + ------- + out : dpnp.ndarray + The ceiling of each element of `x`. + Limitations ----------- - Parameter ``x1`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameters `where`, `dtype`, and `subok` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by real-value data types. See Also -------- @@ -325,24 +344,22 @@ def ceil(x1, out=None, **kwargs): -------- >>> import dpnp as np >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) - >>> result = np.ceil(a) - >>> [x for x in result] - [-1.0, -1.0, -0.0, 1.0, 2.0, 2.0, 2.0] + >>> np.ceil(a) + array([-1.0, -1.0, -0.0, 1.0, 2.0, 2.0, 2.0]) """ - x1_desc = dpnp.get_dpnp_descriptor( - x1, copy_when_strides=False, copy_when_nondefault_queue=False + return check_nd_call_func( + numpy.ceil, + dpnp_ceil, + x, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, ) - if x1_desc and not kwargs: - out_desc = ( - dpnp.get_dpnp_descriptor(out, copy_when_nondefault_queue=False) - if out is not None - else None - ) - return dpnp_ceil(x1_desc, out_desc).get_pyobj() - - return call_origin(numpy.ceil, x1, out=out, **kwargs) def conjugate(x1, **kwargs): @@ -404,10 +421,10 @@ def copysign(x1, x2, dtype=None, out=None, where=True, **kwargs): Limitations ----------- - Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar. - Parameters ``dtype``, ``out`` and ``where`` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. + Parameters `x1` and `x2` are supported as either :class:`dpnp.ndarray` or scalar. + Parameters `dtype`, `out` and `where` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. Examples @@ -463,12 +480,12 @@ def cross(x1, x2, axisa=-1, axisb=-1, axisc=-1, axis=None): Limitations ----------- - Parameters ``x1`` and ``x2`` are supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Sizes of input arrays are limited by ``x1.size == 3 and x2.size == 3``. - Shapes of input arrays are limited by ``x1.shape == (3,) and x2.shape == (3,)``. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameters `x1` and `x2` are supported as :class:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Sizes of input arrays are limited by `x1.size == 3 and x2.size == 3`. + Shapes of input arrays are limited by `x1.shape == (3,) and x2.shape == (3,)`. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. Examples -------- @@ -511,10 +528,10 @@ def cumprod(x1, **kwargs): Limitations ----------- - Parameter ``x`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x` is supported as :class:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. Examples -------- @@ -545,10 +562,10 @@ def cumsum(x1, **kwargs): Limitations ----------- - Parameter ``x`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x` is supported as :obj:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. Examples -------- @@ -580,7 +597,7 @@ def diff(x1, n=1, axis=-1, prepend=numpy._NoValue, append=numpy._NoValue): Limitations ----------- Input array is supported as :obj:`dpnp.ndarray`. - Parameters ``axis``, ``prepend`` and ``append`` are supported only with default values. + Parameters `axis`, `prepend` and `append` are supported only with default values. Otherwise the function will be executed sequentially on CPU. """ @@ -633,7 +650,7 @@ def divide( Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. Parameters `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments `kwargs` are currently unsupported. + Keyword argument `kwargs` is currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -683,12 +700,14 @@ def ediff1d(x1, to_end=None, to_begin=None): Limitations ----------- - Parameter ``x1``is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``to_end`` and ``to_begin`` are currently supported only with default values `None`. - Otherwise the function will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x1`is supported as :class:`dpnp.ndarray`. + Keyword arguments `to_end` and `to_begin` are currently supported only with default values `None`. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. - ..seealso:: :obj:`dpnp.diff` : Calculate the n-th discrete difference along the given axis. + See Also + -------- + :obj:`dpnp.diff` : Calculate the n-th discrete difference along the given axis. Examples -------- @@ -724,12 +743,14 @@ def fabs(x1, **kwargs): Limitations ----------- - Parameter ``x1`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x1` is supported as :class:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. - .. seealso:: :obj:`dpnp.abs` : Calculate the absolute value element-wise. + See Also + -------- + :obj:`dpnp.abs` : Calculate the absolute value element-wise. Examples -------- @@ -749,51 +770,65 @@ def fabs(x1, **kwargs): return call_origin(numpy.fabs, x1, **kwargs) -def floor(x1, out=None, **kwargs): +def floor( + x, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, +): """ Round a number to the nearest integer toward minus infinity. For full documentation refer to :obj:`numpy.floor`. + Returns + ------- + out : dpnp.ndarray + The floor of each element of `x`. + Limitations ----------- - Parameter ``x1`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameters `where`, `dtype`, and `subok` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by real-value data types. See Also -------- - :obj:`dpnp.ceil` : Compute the ceiling of the input, element-wise. - :obj:`dpnp.trunc` : Return the truncated value of the input, element-wise. + :obj:`dpnp.ceil` : Compute the ceiling of the input, element-wise. + :obj:`dpnp.trunc` : Return the truncated value of the input, element-wise. Notes ----- - Some spreadsheet programs calculate the "floor-towards-zero", in other words floor(-2.5) == -2. - dpNP instead uses the definition of floor where floor(-2.5) == -3. + Some spreadsheet programs calculate the "floor-towards-zero", in other words floor(-2.5) == -2. + DPNP instead uses the definition of floor where floor(-2.5) == -3. Examples -------- >>> import dpnp as np >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) - >>> result = np.floor(a) - >>> [x for x in result] - [-2.0, -2.0, -1.0, 0.0, 1.0, 1.0, 2.0] + >>> np.floor(a) + array([-2.0, -2.0, -1.0, 0.0, 1.0, 1.0, 2.0]) """ - x1_desc = dpnp.get_dpnp_descriptor( - x1, copy_when_strides=False, copy_when_nondefault_queue=False + return check_nd_call_func( + numpy.floor, + dpnp_floor, + x, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, ) - if x1_desc and not kwargs: - out_desc = ( - dpnp.get_dpnp_descriptor(out, copy_when_nondefault_queue=False) - if out is not None - else None - ) - return dpnp_floor(x1_desc, out_desc).get_pyobj() - - return call_origin(numpy.floor, x1, out=out, **kwargs) def floor_divide( @@ -815,11 +850,12 @@ def floor_divide( Limitations ----------- - Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar. - Parameters ``where``, ``dtype`` and ``subok`` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` + or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. + Parameters `where`, `dtype` and `subok` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. See Also -------- @@ -902,10 +938,10 @@ def fmod(x1, x2, dtype=None, out=None, where=True, **kwargs): Limitations ----------- - Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar. - Parameters ``dtype``, ``out`` and ``where`` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. + Parameters `x1` and `x2` are supported as either :class:`dpnp.ndarray` or scalar. + Parameters `dtype`, `out` and `where` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. See Also @@ -973,11 +1009,11 @@ def gradient(x1, *varargs, **kwargs): Limitations ----------- - Parameter ``y1`` is supported as :obj:`dpnp.ndarray`. - Argument ``varargs[0]`` is supported as `int`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `y1` is supported as :class:`dpnp.ndarray`. + Argument `varargs[0]` is supported as `int`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. Example ------- @@ -1015,10 +1051,10 @@ def maximum(x1, x2, dtype=None, out=None, where=True, **kwargs): Limitations ----------- - Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar. - Parameters ``dtype``, ``out`` and ``where`` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. + Parameters `x1` and `x2` are supported as either :class:`dpnp.ndarray` or scalar. + Parameters `dtype`, `out` and `where` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. See Also @@ -1080,10 +1116,10 @@ def minimum(x1, x2, dtype=None, out=None, where=True, **kwargs): Limitations ----------- - Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar. - Parameters ``dtype``, ``out`` and ``where`` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. + Parameters `x1` and `x2` are supported as either :class:`dpnp.ndarray` or scalar. + Parameters `dtype`, `out` and `where` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. See Also @@ -1195,10 +1231,10 @@ def modf(x1, **kwargs): Limitations ----------- - Parameter ``x`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x` is supported as :obj:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. Examples -------- @@ -1245,8 +1281,8 @@ def multiply( Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. Parameters `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. Examples @@ -1294,10 +1330,10 @@ def nancumprod(x1, **kwargs): Limitations ----------- - Parameter ``x`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x` is supported as :class:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. .. seealso:: :obj:`dpnp.cumprod` : Return the cumulative product of elements along a given axis. @@ -1331,12 +1367,14 @@ def nancumsum(x1, **kwargs): Limitations ----------- - Parameter ``x`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x` is supported as :class:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. - .. seealso:: :obj:`dpnp.cumsum` : Return the cumulative sum of the elements along a given axis. + See Also + -------- + :obj:`dpnp.cumsum` : Return the cumulative sum of the elements along a given axis. Examples -------- @@ -1367,10 +1405,10 @@ def nanprod(x1, **kwargs): Limitations ----------- - Parameter ``x1`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x1` is supported as :obj:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. Examples -------- @@ -1397,10 +1435,10 @@ def nansum(x1, **kwargs): Limitations ----------- - Parameter ``x1`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x1` is supported as :class:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. Examples -------- @@ -1429,12 +1467,14 @@ def negative(x1, **kwargs): Limitations ----------- - Parameter ``x1`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x1` is supported as :class:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. - .. see also: :obj:`dpnp.copysign` : Change the sign of x1 to that of x2, element-wise. + See Also + -------- + :obj:`dpnp.copysign` : Change the sign of x1 to that of x2, element-wise. Examples -------- @@ -1473,7 +1513,7 @@ def power(x1, x2, /, out=None, *, where=True, dtype=None, subok=True, **kwargs): Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. Parameters `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. + Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -1568,8 +1608,8 @@ def prod( Limitations ----------- - Parameter ``where`` is unsupported. - Input array data types are limited by DPNP :ref:`Data types`. + Parameter `where` is unsupported. + Input array data types are limited by DPNP :ref:`Data types`. Examples -------- @@ -1634,7 +1674,7 @@ def remainder( Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. Parameters `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments `kwargs` are currently unsupported. + Keyword argument `kwargs` is currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -1685,7 +1725,7 @@ def round_(a, decimals=0, out=None): See Also -------- - :obj:`dpnp.around` : equivalent function; see for details. + :obj:`dpnp.around` : equivalent function; see for details. """ @@ -1700,10 +1740,10 @@ def sign(x1, **kwargs): Limitations ----------- - Parameter ``x1`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x1` is supported as :class:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. Examples -------- @@ -1749,7 +1789,11 @@ def subtract( ----------- Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. +<<<<<<< HEAD Parameters `where`, `dtype` and `subok` are supported with their default values. +======= + Parameters `out`, `where`, `dtype` and `subok` are supported with their default values. +>>>>>>> use_dpctl_ceil_floor_trunc_in_dpnp Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -1816,11 +1860,11 @@ def sum( Limitations ----------- - Parameters `x` is supported as either :class:`dpnp.ndarray` - or :class:`dpctl.tensor.usm_ndarray`. - Parameters `out`, `initial` and `where` are supported with their default values. - Otherwise the function will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameters `x` is supported as either :class:`dpnp.ndarray` + or :class:`dpctl.tensor.usm_ndarray`. + Parameters `out`, `initial` and `where` are supported with their default values. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. Examples -------- @@ -1911,10 +1955,10 @@ def trapz(y1, x1=None, dx=1.0, axis=-1): Limitations ----------- - Parameters ``y`` and ``x`` are supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameters `y` and `x` are supported as :class:`dpnp.ndarray`. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. Examples -------- @@ -1971,7 +2015,7 @@ def true_divide(*args, **kwargs): See Also -------- - .. seealso:: :obj:`dpnp.divide` : Standard division. + :obj:`dpnp.divide` : Standard division. Notes ----- @@ -1983,43 +2027,57 @@ def true_divide(*args, **kwargs): return dpnp.divide(*args, **kwargs) -def trunc(x1, out=None, **kwargs): +def trunc( + x, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, +): """ Compute the truncated value of the input, element-wise. For full documentation refer to :obj:`numpy.trunc`. + Returns + ------- + out : dpnp.ndarray + The truncated value of each element of `x`. + Limitations ----------- - Parameter ``x1`` is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. - Input array data types are limited by supported DPNP :ref:`Data types`. + Parameter `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameters `where`, `dtype`, and `subok` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by real-value data types. See Also -------- - :obj:`dpnp.floor` : Round a number to the nearest integer toward minus infinity. - :obj:`dpnp.ceil` : Round a number to the nearest integer toward infinity. + :obj:`dpnp.floor` : Round a number to the nearest integer toward minus infinity. + :obj:`dpnp.ceil` : Round a number to the nearest integer toward infinity. Examples -------- >>> import dpnp as np >>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) - >>> result = np.trunc(a) - >>> [x for x in result] - [-1.0, -1.0, -0.0, 0.0, 1.0, 1.0, 2.0] + >>> np.trunc(a) + array([-1.0, -1.0, -0.0, 0.0, 1.0, 1.0, 2.0]) """ - x1_desc = dpnp.get_dpnp_descriptor( - x1, copy_when_strides=False, copy_when_nondefault_queue=False + return check_nd_call_func( + numpy.trunc, + dpnp_trunc, + x, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, ) - if x1_desc and not kwargs: - out_desc = ( - dpnp.get_dpnp_descriptor(out, copy_when_nondefault_queue=False) - if out is not None - else None - ) - return dpnp_trunc(x1_desc, out_desc).get_pyobj() - - return call_origin(numpy.trunc, x1, out=out, **kwargs) diff --git a/dpnp/dpnp_iface_trigonometric.py b/dpnp/dpnp_iface_trigonometric.py index 904e1e3a1a1..d64359e1db8 100644 --- a/dpnp/dpnp_iface_trigonometric.py +++ b/dpnp/dpnp_iface_trigonometric.py @@ -434,8 +434,9 @@ def cos( Limitations ----------- - Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameter `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. Parameters `where`, `dtype` and `subok` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -986,8 +987,9 @@ def sin( Limitations ----------- - Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameter `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. Parameters `where`, `dtype` and `subok` are supported with their default values. + Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. diff --git a/tests/skipped_tests.tbl b/tests/skipped_tests.tbl index 2424ade4cee..c0b051f2d21 100644 --- a/tests/skipped_tests.tbl +++ b/tests/skipped_tests.tbl @@ -806,13 +806,10 @@ tests/third_party/cupy/math_tests/test_rounding.py::TestRoundExtreme_param_5_{de tests/third_party/cupy/math_tests/test_rounding.py::TestRoundExtreme_param_6_{decimals=100}::test_round_large tests/third_party/cupy/math_tests/test_rounding.py::TestRoundExtreme_param_6_{decimals=100}::test_round_small tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_around -tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_ceil tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_fix -tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_floor tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_rint tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_rint_negative tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_round_ -tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_trunc tests/third_party/cupy/math_tests/test_sumprod.py::TestSumprod::test_sum_out tests/third_party/cupy/math_tests/test_sumprod.py::TestSumprod::test_sum_out_wrong_shape tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_ndarray_cumprod_2dim_with_axis diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index 85f25b90028..8b05060b707 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -12,23 +12,19 @@ tests/test_random.py::TestPermutationsTestShuffle::test_no_miss_numbers[int64] tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.array([])] tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.astype(dpnp.asarray(x), dpnp.float32)] -tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-ceil-data1] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-conjugate-data2] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-copy-data3] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-cumprod-data4] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-cumsum-data5] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-ediff1d-data7] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-fabs-data8] -tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-floor-data9] -tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-ceil-data1] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-conjugate-data2] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-copy-data3] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-cumprod-data4] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-cumsum-data5] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-ediff1d-data7] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-fabs-data8] -tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-floor-data9] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-nancumprod-data11] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-nancumsum-data12] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-nanprod-data13] @@ -38,7 +34,6 @@ tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-prod-data16] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-sign-data17] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-sum-data18] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-trapz-data19] -tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-trunc-data20] tests/test_sycl_queue.py::test_modf[level_zero:gpu:0] @@ -992,13 +987,10 @@ tests/third_party/cupy/math_tests/test_rounding.py::TestRoundExtreme_param_5_{de tests/third_party/cupy/math_tests/test_rounding.py::TestRoundExtreme_param_6_{decimals=100}::test_round_large tests/third_party/cupy/math_tests/test_rounding.py::TestRoundExtreme_param_6_{decimals=100}::test_round_small tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_around -tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_ceil tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_fix -tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_floor tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_rint tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_rint_negative tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_round_ -tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_trunc tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_ndarray_cumprod_2dim_with_axis tests/third_party/cupy/math_tests/test_sumprod.py::TestDiff::test_diff_1dim tests/third_party/cupy/math_tests/test_sumprod.py::TestDiff::test_diff_1dim_with_n diff --git a/tests/third_party/cupy/math_tests/test_rounding.py b/tests/third_party/cupy/math_tests/test_rounding.py index e4927495cc5..e17a48aa9e3 100644 --- a/tests/third_party/cupy/math_tests/test_rounding.py +++ b/tests/third_party/cupy/math_tests/test_rounding.py @@ -10,7 +10,7 @@ @testing.gpu class TestRounding(unittest.TestCase): @testing.for_all_dtypes(no_complex=True) - @testing.numpy_cupy_allclose(atol=1e-5) + @testing.numpy_cupy_allclose(type_check=False, atol=1e-5) def check_unary(self, name, xp, dtype): a = testing.shaped_arange((2, 3), xp, dtype) return getattr(xp, name)(a) From 1b069da3a0b23686a3f7c03b473f9555c6ecd67c Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Mon, 14 Aug 2023 13:16:09 -0500 Subject: [PATCH 40/58] address reviewers' comments --- tests/test_mathematical.py | 91 ++++++++++++++++++++------------------ tests/test_usm_type.py | 8 ++-- 2 files changed, 53 insertions(+), 46 deletions(-) diff --git a/tests/test_mathematical.py b/tests/test_mathematical.py index c5b5ea336ba..5fd00ab726c 100644 --- a/tests/test_mathematical.py +++ b/tests/test_mathematical.py @@ -15,6 +15,7 @@ from .helper import ( get_all_dtypes, get_float_complex_dtypes, + get_float_dtypes, has_support_aspect64, is_cpu_device, is_win_platform, @@ -597,119 +598,125 @@ def test_gradient_y1_dx(self, array, dx): class TestCeil: - def test_ceil(self): + @pytest.mark.parametrize("dtype", get_float_dtypes()) + def test_ceil(self, dtype): array_data = numpy.arange(10) - out = numpy.empty(10, dtype=numpy.float64) + out = numpy.empty(10, dtype) # DPNP - dp_array = dpnp.array(array_data, dtype=dpnp.float64) - dp_out = dpnp.array(out, dtype=dpnp.float64) + dp_array = dpnp.array(array_data, dtype=dtype) + dp_out = dpnp.array(out, dtype=dtype) result = dpnp.ceil(dp_array, out=dp_out) # original - np_array = numpy.array(array_data, dtype=numpy.float64) + np_array = numpy.array(array_data, dtype=dtype) expected = numpy.ceil(np_array, out=out) assert_array_equal(expected, result) @pytest.mark.parametrize( - "dtype", - [numpy.float32, numpy.int64, numpy.int32], - ids=["numpy.float32", "numpy.int64", "numpy.int32"], + "dtype", get_all_dtypes(no_bool=True, no_complex=True, no_none=True) ) def test_invalid_dtype(self, dtype): - dp_array = dpnp.arange(10, dtype=dpnp.float64) + dpnp_dtype = dpnp.float64 if has_support_aspect64() else dpnp.float32 + pytest.skip("similar data types") if dpnp_dtype == dtype else None + dp_array = dpnp.arange(10, dtype=dpnp_dtype) dp_out = dpnp.empty(10, dtype=dtype) - with pytest.raises(ValueError): + with pytest.raises(TypeError): dpnp.ceil(dp_array, out=dp_out) + @pytest.mark.parametrize("dtype", get_float_dtypes()) @pytest.mark.parametrize( "shape", [(0,), (15,), (2, 2)], ids=["(0,)", "(15, )", "(2,2)"] ) - def test_invalid_shape(self, shape): - dp_array = dpnp.arange(10, dtype=dpnp.float64) - dp_out = dpnp.empty(shape, dtype=dpnp.float64) + def test_invalid_shape(self, shape, dtype): + dp_array = dpnp.arange(10, dtype=dtype) + dp_out = dpnp.empty(shape, dtype=dtype) - with pytest.raises(ValueError): + with pytest.raises(TypeError): dpnp.ceil(dp_array, out=dp_out) class TestFloor: - def test_floor(self): + @pytest.mark.parametrize("dtype", get_float_dtypes()) + def test_floor(self, dtype): array_data = numpy.arange(10) - out = numpy.empty(10, dtype=numpy.float64) + out = numpy.empty(10, dtype=dtype) # DPNP - dp_array = dpnp.array(array_data, dtype=dpnp.float64) - dp_out = dpnp.array(out, dtype=dpnp.float64) + dp_array = dpnp.array(array_data, dtype=dtype) + dp_out = dpnp.array(out, dtype=dtype) result = dpnp.floor(dp_array, out=dp_out) # original - np_array = numpy.array(array_data, dtype=numpy.float64) + np_array = numpy.array(array_data, dtype=dtype) expected = numpy.floor(np_array, out=out) assert_array_equal(expected, result) @pytest.mark.parametrize( - "dtype", - [numpy.float32, numpy.int64, numpy.int32], - ids=["numpy.float32", "numpy.int64", "numpy.int32"], + "dtype", get_all_dtypes(no_bool=True, no_complex=True, no_none=True) ) def test_invalid_dtype(self, dtype): - dp_array = dpnp.arange(10, dtype=dpnp.float64) + dpnp_dtype = dpnp.float64 if has_support_aspect64() else dpnp.float32 + pytest.skip("similar data types") if dpnp_dtype == dtype else None + dp_array = dpnp.arange(10, dtype=dpnp_dtype) dp_out = dpnp.empty(10, dtype=dtype) - with pytest.raises(ValueError): + with pytest.raises(TypeError): dpnp.floor(dp_array, out=dp_out) + @pytest.mark.parametrize("dtype", get_float_dtypes()) @pytest.mark.parametrize( "shape", [(0,), (15,), (2, 2)], ids=["(0,)", "(15, )", "(2,2)"] ) - def test_invalid_shape(self, shape): - dp_array = dpnp.arange(10, dtype=dpnp.float64) - dp_out = dpnp.empty(shape, dtype=dpnp.float64) + def test_invalid_shape(self, shape, dtype): + dp_array = dpnp.arange(10, dtype=dtype) + dp_out = dpnp.empty(shape, dtype=dtype) - with pytest.raises(ValueError): + with pytest.raises(TypeError): dpnp.floor(dp_array, out=dp_out) class TestTrunc: - def test_trunc(self): + @pytest.mark.parametrize("dtype", get_float_dtypes()) + def test_trunc(self, dtype): array_data = numpy.arange(10) - out = numpy.empty(10, dtype=numpy.float64) + out = numpy.empty(10, dtype=dtype) # DPNP - dp_array = dpnp.array(array_data, dtype=dpnp.float64) - dp_out = dpnp.array(out, dtype=dpnp.float64) + dp_array = dpnp.array(array_data, dtype=dtype) + dp_out = dpnp.array(out, dtype=dtype) result = dpnp.trunc(dp_array, out=dp_out) # original - np_array = numpy.array(array_data, dtype=numpy.float64) + np_array = numpy.array(array_data, dtype=dtype) expected = numpy.trunc(np_array, out=out) assert_array_equal(expected, result) @pytest.mark.parametrize( - "dtype", - [numpy.float32, numpy.int64, numpy.int32], - ids=["numpy.float32", "numpy.int64", "numpy.int32"], + "dtype", get_all_dtypes(no_bool=True, no_complex=True, no_none=True) ) def test_invalid_dtype(self, dtype): - dp_array = dpnp.arange(10, dtype=dpnp.float64) + dpnp_dtype = dpnp.float64 if has_support_aspect64() else dpnp.float32 + pytest.skip("similar data types") if dpnp_dtype == dtype else None + dp_array = dpnp.arange(10, dtype=dpnp_dtype) dp_out = dpnp.empty(10, dtype=dtype) - with pytest.raises(ValueError): + with pytest.raises(TypeError): dpnp.trunc(dp_array, out=dp_out) + @pytest.mark.parametrize("dtype", get_float_dtypes()) @pytest.mark.parametrize( "shape", [(0,), (15,), (2, 2)], ids=["(0,)", "(15, )", "(2,2)"] ) - def test_invalid_shape(self, shape): - dp_array = dpnp.arange(10, dtype=dpnp.float64) - dp_out = dpnp.empty(shape, dtype=dpnp.float64) + def test_invalid_shape(self, shape, dtype): + dp_array = dpnp.arange(10, dtype=dtype) + dp_out = dpnp.empty(shape, dtype=dtype) - with pytest.raises(ValueError): + with pytest.raises(TypeError): dpnp.trunc(dp_array, out=dp_out) diff --git a/tests/test_usm_type.py b/tests/test_usm_type.py index bda39505a1c..b197897f373 100644 --- a/tests/test_usm_type.py +++ b/tests/test_usm_type.py @@ -230,10 +230,10 @@ def test_meshgrid(usm_type_x, usm_type_y): @pytest.mark.parametrize( "func,data", [ - pytest.param( - "sqrt", - [1.0, 3.0, 9.0], - ), + pytest.param("ceil", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), + pytest.param("floor", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), + pytest.param("trunc", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), + pytest.param("sqrt", [1.0, 3.0, 9.0]), ], ) @pytest.mark.parametrize("usm_type", list_of_usm_types, ids=list_of_usm_types) From 6454b3e8fd1949d8d070f2b6070c96ce591593a2 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Wed, 16 Aug 2023 22:16:05 -0500 Subject: [PATCH 41/58] remove skipping tests in gpu_no_f64 --- dpnp/dpnp_iface_mathematical.py | 6 +----- tests/skipped_tests_gpu_no_fp64.tbl | 30 ----------------------------- 2 files changed, 1 insertion(+), 35 deletions(-) diff --git a/dpnp/dpnp_iface_mathematical.py b/dpnp/dpnp_iface_mathematical.py index 8d56d53ca73..724cc6bb643 100644 --- a/dpnp/dpnp_iface_mathematical.py +++ b/dpnp/dpnp_iface_mathematical.py @@ -1789,12 +1789,8 @@ def subtract( ----------- Parameters `x1` and `x2` are supported as either scalar, :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`, but both `x1` and `x2` can not be scalars at the same time. -<<<<<<< HEAD Parameters `where`, `dtype` and `subok` are supported with their default values. -======= - Parameters `out`, `where`, `dtype` and `subok` are supported with their default values. ->>>>>>> use_dpctl_ceil_floor_trunc_in_dpnp - Keyword arguments `kwargs` are currently unsupported. + Keyword argument `kwargs` is currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. diff --git a/tests/skipped_tests_gpu_no_fp64.tbl b/tests/skipped_tests_gpu_no_fp64.tbl index 2658f178bf8..5d23dad7782 100644 --- a/tests/skipped_tests_gpu_no_fp64.tbl +++ b/tests/skipped_tests_gpu_no_fp64.tbl @@ -350,27 +350,6 @@ tests/test_mathematical.py::TestGradient::test_gradient_y1_dx[2-array2] tests/test_mathematical.py::TestGradient::test_gradient_y1_dx[3.5-array0] tests/test_mathematical.py::TestGradient::test_gradient_y1_dx[3.5-array1] tests/test_mathematical.py::TestGradient::test_gradient_y1_dx[3.5-array2] -tests/test_mathematical.py::TestCeil::test_ceil -tests/test_mathematical.py::TestCeil::test_invalid_dtype[numpy.float32] -tests/test_mathematical.py::TestCeil::test_invalid_dtype[numpy.int64] -tests/test_mathematical.py::TestCeil::test_invalid_dtype[numpy.int32] -tests/test_mathematical.py::TestCeil::test_invalid_shape[(0,)] -tests/test_mathematical.py::TestCeil::test_invalid_shape[(15, )] -tests/test_mathematical.py::TestCeil::test_invalid_shape[(2,2)] -tests/test_mathematical.py::TestFloor::test_floor -tests/test_mathematical.py::TestFloor::test_invalid_dtype[numpy.float32] -tests/test_mathematical.py::TestFloor::test_invalid_dtype[numpy.int64] -tests/test_mathematical.py::TestFloor::test_invalid_dtype[numpy.int32] -tests/test_mathematical.py::TestFloor::test_invalid_shape[(0,)] -tests/test_mathematical.py::TestFloor::test_invalid_shape[(15, )] -tests/test_mathematical.py::TestFloor::test_invalid_shape[(2,2)] -tests/test_mathematical.py::TestTrunc::test_trunc -tests/test_mathematical.py::TestTrunc::test_invalid_dtype[numpy.float32] -tests/test_mathematical.py::TestTrunc::test_invalid_dtype[numpy.int64] -tests/test_mathematical.py::TestTrunc::test_invalid_dtype[numpy.int32] -tests/test_mathematical.py::TestTrunc::test_invalid_shape[(0,)] -tests/test_mathematical.py::TestTrunc::test_invalid_shape[(15, )] -tests/test_mathematical.py::TestTrunc::test_invalid_shape[(2,2)] tests/test_mathematical.py::TestPower::test_power[complex64] tests/test_mathematical.py::TestPower::test_out_dtypes[bool_] tests/test_mathematical.py::TestPower::test_out_dtypes[int32] @@ -397,21 +376,18 @@ tests/test_strides.py::test_strides_1arg[(10,)-int32-arcsinh] tests/test_strides.py::test_strides_1arg[(10,)-int32-arctan] tests/test_strides.py::test_strides_1arg[(10,)-int32-arctanh] tests/test_strides.py::test_strides_1arg[(10,)-int32-cbrt] -tests/test_strides.py::test_strides_1arg[(10,)-int32-ceil] tests/test_strides.py::test_strides_1arg[(10,)-int32-cosh] tests/test_strides.py::test_strides_1arg[(10,)-int32-degrees] tests/test_strides.py::test_strides_1arg[(10,)-int32-exp] tests/test_strides.py::test_strides_1arg[(10,)-int32-exp2] tests/test_strides.py::test_strides_1arg[(10,)-int32-expm1] tests/test_strides.py::test_strides_1arg[(10,)-int32-fabs] -tests/test_strides.py::test_strides_1arg[(10,)-int32-floor] tests/test_strides.py::test_strides_1arg[(10,)-int32-log10] tests/test_strides.py::test_strides_1arg[(10,)-int32-log1p] tests/test_strides.py::test_strides_1arg[(10,)-int32-log2] tests/test_strides.py::test_strides_1arg[(10,)-int32-radians] tests/test_strides.py::test_strides_1arg[(10,)-int32-sinh] tests/test_strides.py::test_strides_1arg[(10,)-int32-tanh] -tests/test_strides.py::test_strides_1arg[(10,)-int32-trunc] tests/test_strides.py::test_strides_1arg[(10,)-int64-arccos] tests/test_strides.py::test_strides_1arg[(10,)-int64-arccosh] tests/test_strides.py::test_strides_1arg[(10,)-int64-arcsin] @@ -419,21 +395,18 @@ tests/test_strides.py::test_strides_1arg[(10,)-int64-arcsinh] tests/test_strides.py::test_strides_1arg[(10,)-int64-arctan] tests/test_strides.py::test_strides_1arg[(10,)-int64-arctanh] tests/test_strides.py::test_strides_1arg[(10,)-int64-cbrt] -tests/test_strides.py::test_strides_1arg[(10,)-int64-ceil] tests/test_strides.py::test_strides_1arg[(10,)-int64-cosh] tests/test_strides.py::test_strides_1arg[(10,)-int64-degrees] tests/test_strides.py::test_strides_1arg[(10,)-int64-exp] tests/test_strides.py::test_strides_1arg[(10,)-int64-exp2] tests/test_strides.py::test_strides_1arg[(10,)-int64-expm1] tests/test_strides.py::test_strides_1arg[(10,)-int64-fabs] -tests/test_strides.py::test_strides_1arg[(10,)-int64-floor] tests/test_strides.py::test_strides_1arg[(10,)-int64-log10] tests/test_strides.py::test_strides_1arg[(10,)-int64-log1p] tests/test_strides.py::test_strides_1arg[(10,)-int64-log2] tests/test_strides.py::test_strides_1arg[(10,)-int64-radians] tests/test_strides.py::test_strides_1arg[(10,)-int64-sinh] tests/test_strides.py::test_strides_1arg[(10,)-int64-tanh] -tests/test_strides.py::test_strides_1arg[(10,)-int64-trunc] tests/test_strides.py::test_strides_1arg[(10,)-None-arccos] tests/test_strides.py::test_strides_1arg[(10,)-None-arccosh] tests/test_strides.py::test_strides_1arg[(10,)-None-arcsin] @@ -441,21 +414,18 @@ tests/test_strides.py::test_strides_1arg[(10,)-None-arcsinh] tests/test_strides.py::test_strides_1arg[(10,)-None-arctan] tests/test_strides.py::test_strides_1arg[(10,)-None-arctanh] tests/test_strides.py::test_strides_1arg[(10,)-None-cbrt] -tests/test_strides.py::test_strides_1arg[(10,)-None-ceil] tests/test_strides.py::test_strides_1arg[(10,)-None-cosh] tests/test_strides.py::test_strides_1arg[(10,)-None-degrees] tests/test_strides.py::test_strides_1arg[(10,)-None-exp] tests/test_strides.py::test_strides_1arg[(10,)-None-exp2] tests/test_strides.py::test_strides_1arg[(10,)-None-expm1] tests/test_strides.py::test_strides_1arg[(10,)-None-fabs] -tests/test_strides.py::test_strides_1arg[(10,)-None-floor] tests/test_strides.py::test_strides_1arg[(10,)-None-log10] tests/test_strides.py::test_strides_1arg[(10,)-None-log1p] tests/test_strides.py::test_strides_1arg[(10,)-None-log2] tests/test_strides.py::test_strides_1arg[(10,)-None-radians] tests/test_strides.py::test_strides_1arg[(10,)-None-sinh] tests/test_strides.py::test_strides_1arg[(10,)-None-tanh] -tests/test_strides.py::test_strides_1arg[(10,)-None-trunc] tests/test_strides.py::test_strides_tan[(10,)-int32] tests/test_strides.py::test_strides_tan[(10,)-int64] tests/test_strides.py::test_strides_tan[(10,)-None] From e0bf7318ae28c9552bbfbea3dae82598139e2ea5 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Fri, 18 Aug 2023 07:52:07 -0500 Subject: [PATCH 42/58] modifying related tests suggested by reviewers --- tests/test_mathematical.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/tests/test_mathematical.py b/tests/test_mathematical.py index 5fd00ab726c..9d944e3c597 100644 --- a/tests/test_mathematical.py +++ b/tests/test_mathematical.py @@ -615,11 +615,10 @@ def test_ceil(self, dtype): assert_array_equal(expected, result) @pytest.mark.parametrize( - "dtype", get_all_dtypes(no_bool=True, no_complex=True, no_none=True) + "dtype", get_all_dtypes(no_complex=True, no_none=True)[:-1] ) def test_invalid_dtype(self, dtype): - dpnp_dtype = dpnp.float64 if has_support_aspect64() else dpnp.float32 - pytest.skip("similar data types") if dpnp_dtype == dtype else None + dpnp_dtype = get_all_dtypes(no_complex=True, no_none=True)[-1] dp_array = dpnp.arange(10, dtype=dpnp_dtype) dp_out = dpnp.empty(10, dtype=dtype) @@ -656,11 +655,10 @@ def test_floor(self, dtype): assert_array_equal(expected, result) @pytest.mark.parametrize( - "dtype", get_all_dtypes(no_bool=True, no_complex=True, no_none=True) + "dtype", get_all_dtypes(no_complex=True, no_none=True)[:-1] ) def test_invalid_dtype(self, dtype): - dpnp_dtype = dpnp.float64 if has_support_aspect64() else dpnp.float32 - pytest.skip("similar data types") if dpnp_dtype == dtype else None + dpnp_dtype = get_all_dtypes(no_complex=True, no_none=True)[-1] dp_array = dpnp.arange(10, dtype=dpnp_dtype) dp_out = dpnp.empty(10, dtype=dtype) @@ -697,11 +695,10 @@ def test_trunc(self, dtype): assert_array_equal(expected, result) @pytest.mark.parametrize( - "dtype", get_all_dtypes(no_bool=True, no_complex=True, no_none=True) + "dtype", get_all_dtypes(no_complex=True, no_none=True)[:-1] ) def test_invalid_dtype(self, dtype): - dpnp_dtype = dpnp.float64 if has_support_aspect64() else dpnp.float32 - pytest.skip("similar data types") if dpnp_dtype == dtype else None + dpnp_dtype = get_all_dtypes(no_complex=True, no_none=True)[-1] dp_array = dpnp.arange(10, dtype=dpnp_dtype) dp_out = dpnp.empty(10, dtype=dtype) From 306aa74d083344c7e8f3adedd929e473e312145d Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Fri, 18 Aug 2023 16:21:38 +0200 Subject: [PATCH 43/58] Align default order value with numpy in asarray-like functions (#1526) * Aligned default order value with numpy * Added test for exceptions use case * Muted missing not working tests & python 3.10 --- dpnp/dpnp_container.py | 11 +- dpnp/dpnp_iface.py | 2 +- dpnp/dpnp_iface_arraycreation.py | 265 +++++--- tests/skipped_tests.tbl | 55 -- tests/skipped_tests_gpu.tbl | 62 +- tests/skipped_tests_gpu_no_fp64.tbl | 22 + tests/test_arraycreation.py | 25 + tests/test_sycl_queue.py | 5 +- tests/test_usm_type.py | 18 + .../cupy/creation_tests/test_from_data.py | 585 ++++++++++++------ 10 files changed, 652 insertions(+), 398 deletions(-) diff --git a/dpnp/dpnp_container.py b/dpnp/dpnp_container.py index bd77c0998c8..08522ceaafa 100644 --- a/dpnp/dpnp_container.py +++ b/dpnp/dpnp_container.py @@ -86,8 +86,8 @@ def arange( def asarray( x1, dtype=None, - copy=False, - order="C", + copy=None, + order=None, device=None, usm_type=None, sycl_queue=None, @@ -96,7 +96,7 @@ def asarray( dpu.validate_usm_type(usm_type, allow_none=True) if order is None: - order = "C" + order = "K" """Converts incoming 'x1' object to 'dpnp_array'.""" if isinstance(x1, (list, tuple, range)): @@ -127,6 +127,11 @@ def asarray( usm_type=usm_type, sycl_queue=sycl_queue_normalized, ) + + # return x1 if dpctl returns a zero copy of x1_obj + if array_obj is x1_obj: + return x1 + return dpnp_array(array_obj.shape, buffer=array_obj, order=order) diff --git a/dpnp/dpnp_iface.py b/dpnp/dpnp_iface.py index 76046691622..066b28e8e2c 100644 --- a/dpnp/dpnp_iface.py +++ b/dpnp/dpnp_iface.py @@ -310,7 +310,7 @@ def get_dpnp_descriptor( if ext_obj.strides != shape_offsets or ext_obj_offset != 0: orig_desc = dpnp_descriptor(ext_obj) - ext_obj = array(ext_obj) + ext_obj = array(ext_obj, order="C") # while dpnp functions are based on DPNP_QUEUE # we need to create a copy on device associated with DPNP_QUEUE diff --git a/dpnp/dpnp_iface_arraycreation.py b/dpnp/dpnp_iface_arraycreation.py index 3be59df4eb3..549920066ac 100644 --- a/dpnp/dpnp_iface_arraycreation.py +++ b/dpnp/dpnp_iface_arraycreation.py @@ -56,6 +56,7 @@ "asanyarray", "asarray", "ascontiguousarray", + "asfortranarray", "copy", "diag", "diagflat", @@ -150,10 +151,11 @@ def arange( def array( - x1, + x, dtype=None, + *, copy=True, - order="C", + order="K", subok=False, ndmin=0, like=None, @@ -162,15 +164,16 @@ def array( sycl_queue=None, ): """ - Creates an array. + Create an array. For full documentation refer to :obj:`numpy.array`. Limitations ----------- - Parameter ``subok`` is supported only with default value ``False``. - Parameter ``ndmin`` is supported only with default value ``0``. - Parameter ``like`` is supported only with default value ``None``. + Parameter `subok` is supported only with default value ``False``. + Parameter `ndmin` is supported only with default value ``0``. + Parameter `like` is supported only with default value ``None``. + Otherwise, the function raises `ValueError` exception. See Also -------- @@ -204,35 +207,47 @@ def array( """ if subok is not False: - pass + raise ValueError( + "Keyword argument `subok` is supported only with " + f"default value ``False``, but got {subok}" + ) elif ndmin != 0: - pass + raise ValueError( + "Keyword argument `ndmin` is supported only with " + f"default value ``0``, but got {ndmin}" + ) elif like is not None: - pass - else: - return dpnp_container.asarray( - x1, - dtype=dtype, - copy=copy, - order=order, - device=device, - usm_type=usm_type, - sycl_queue=sycl_queue, + raise ValueError( + "Keyword argument `like` is supported only with " + f"default value ``None``, but got {like}" ) - return call_origin( - numpy.array, - x1, + # `False`` in numpy means exactly the same like `None` in python array API: + # that is to reuse existing memory buffer if possible or to copy otherwise. + if copy is False: + copy = None + + return dpnp_container.asarray( + x, dtype=dtype, copy=copy, order=order, - subok=subok, - ndmin=ndmin, - like=like, + device=device, + usm_type=usm_type, + sycl_queue=sycl_queue, ) -def asanyarray(a, dtype=None, order="C"): +def asanyarray( + x, + dtype=None, + order=None, + *, + like=None, + device=None, + usm_type=None, + sycl_queue=None, +): """ Convert the input to an ndarray, but pass ndarray subclasses through. @@ -240,7 +255,8 @@ def asanyarray(a, dtype=None, order="C"): Limitations ----------- - Parameter ``order`` is supported only with default value ``"C"``. + Parameter `like` is supported only with default value ``None``. + Otherwise, the function raises `ValueError` exception. See Also -------- @@ -258,29 +274,31 @@ def asanyarray(a, dtype=None, order="C"): Examples -------- >>> import dpnp as np - >>> x = np.asanyarray([1, 2, 3]) - >>> [i for i in x] - [1, 2, 3] + >>> np.asanyarray([1, 2, 3]) + array([1, 2, 3]) """ - if not use_origin_backend(a): - # if it is already dpnp.ndarray then same object should be returned - if isinstance(a, dpnp.ndarray): - return a - - if order != "C": - pass - else: - return array(a, dtype=dtype, order=order) + if like is not None: + raise ValueError( + "Keyword argument `like` is supported only with " + f"default value ``None``, but got {like}" + ) - return call_origin(numpy.asanyarray, a, dtype, order) + return asarray( + x, + dtype=dtype, + order=order, + device=device, + usm_type=usm_type, + sycl_queue=sycl_queue, + ) def asarray( - x1, + x, dtype=None, - order="C", + order=None, like=None, device=None, usm_type=None, @@ -293,7 +311,8 @@ def asarray( Limitations ----------- - Parameter ``like`` is supported only with default value ``None``. + Parameter `like` is supported only with default value ``None``. + Otherwise, the function raises `ValueError` exception. See Also -------- @@ -311,64 +330,168 @@ def asarray( Examples -------- >>> import dpnp as np - >>> x = np.asarray([1, 2, 3]) - >>> print(x) - [1 2 3] + >>> np.asarray([1, 2, 3]) + array([1, 2, 3]) """ if like is not None: - pass - else: - return dpnp_container.asarray( - x1, - dtype=dtype, - copy=True, # Converting Python sequence to usm_ndarray requires a copy - order=order, - device=device, - usm_type=usm_type, - sycl_queue=sycl_queue, + raise ValueError( + "Keyword argument `like` is supported only with " + f"default value ``None``, but got {like}" ) - return call_origin(numpy.asarray, x1, dtype=dtype, order=order, like=like) + return dpnp_container.asarray( + x, + dtype=dtype, + order=order, + device=device, + usm_type=usm_type, + sycl_queue=sycl_queue, + ) -def ascontiguousarray(a, dtype=None): +def ascontiguousarray( + x, dtype=None, *, like=None, device=None, usm_type=None, sycl_queue=None +): """ Return a contiguous array (ndim >= 1) in memory (C order). For full documentation refer to :obj:`numpy.ascontiguousarray`. + Limitations + ----------- + Parameter `like` is supported only with default value ``None``. + Otherwise, the function raises `ValueError` exception. + See Also -------- :obj:`dpnp.asfortranarray` : Convert input to an ndarray with column-major memory order. :obj:`dpnp.require` : Return an ndarray that satisfies requirements. + :obj:`dpnp.ndarray.flags` : Information about the memory layout of the array. Examples -------- >>> import dpnp as np - >>> x = np.arange(6).reshape((2, 3)) - >>> out = np.ascontiguousarray(x) - >>> out.shape - (2, 3) - >>> [i for i in out] - [0, 1, 2, 3, 4, 5] + >>> x = np.ones((2, 3), order='F') + >>> x.flags['F_CONTIGUOUS'] + True + + Calling ``ascontiguousarray`` makes a C-contiguous copy: + + >>> y = np.ascontiguousarray(x) + >>> y.flags['F_CONTIGUOUS'] + True + >>> x is y + False + + Now, starting with a C-contiguous array: + + >>> x = np.ones((2, 3), order='C') + >>> x.flags['C_CONTIGUOUS'] + True + + Then, calling ``ascontiguousarray`` returns the same object: + + >>> y = np.ascontiguousarray(x) + >>> x is y + True """ - if not use_origin_backend(a): - # we support only c-contiguous arrays for now - # if type is the same then same object should be returned - if isinstance(a, dpnp.ndarray) and a.dtype == dtype: - return a + if like is not None: + raise ValueError( + "Keyword argument `like` is supported only with " + f"default value ``None``, but got {like}" + ) - return array(a, dtype=dtype) + # at least 1-d array has to be returned + if x.ndim == 0: + x = [x] - return call_origin(numpy.ascontiguousarray, a, dtype) + return asarray( + x, + dtype=dtype, + order="C", + device=device, + usm_type=usm_type, + sycl_queue=sycl_queue, + ) + + +def asfortranarray( + x, dtype=None, *, like=None, device=None, usm_type=None, sycl_queue=None +): + """ + Return an array (ndim >= 1) laid out in Fortran order in memory. + + For full documentation refer to :obj:`numpy.asfortranarray`. + + Limitations + ----------- + Parameter `like` is supported only with default value ``None``. + Otherwise, the function raises `ValueError` exception. + + See Also + -------- + :obj:`dpnp.ascontiguousarray` : Convert input to a contiguous (C order) array. + :obj:`dpnp.asanyarray` : Convert input to an ndarray with either row or column-major memory order. + :obj:`dpnp.require` : Return an ndarray that satisfies requirements. + :obj:`dpnp.ndarray.flags` : Information about the memory layout of the array. + + Examples + -------- + >>> import dpnp as np + + Starting with a C-contiguous array: + + >>> x = np.ones((2, 3), order='C') + >>> x.flags['C_CONTIGUOUS'] + True + + Calling ``asfortranarray`` makes a Fortran-contiguous copy: + + >>> y = np.asfortranarray(x) + >>> y.flags['F_CONTIGUOUS'] + True + >>> x is y + False + + Now, starting with a Fortran-contiguous array: + + >>> x = np.ones((2, 3), order='F') + >>> x.flags['F_CONTIGUOUS'] + True + + Then, calling ``asfortranarray`` returns the same object: + + >>> y = np.asfortranarray(x) + >>> x is y + True + + """ + + if like is not None: + raise ValueError( + "Keyword argument `like` is supported only with " + f"default value ``None``, but got {like}" + ) + + # at least 1-d array has to be returned + if x.ndim == 0: + x = [x] + + return asarray( + x, + dtype=dtype, + order="F", + device=device, + usm_type=usm_type, + sycl_queue=sycl_queue, + ) -# numpy.copy(a, order='K', subok=False) def copy(x1, order="K", subok=False): """ Return an array copy of the given object. diff --git a/tests/skipped_tests.tbl b/tests/skipped_tests.tbl index 2424ade4cee..de05f8267a1 100644 --- a/tests/skipped_tests.tbl +++ b/tests/skipped_tests.tbl @@ -264,61 +264,6 @@ tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_full_like_s tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_ones_like_subok tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_zeros_like_subok tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_zeros_strides -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_0_{copy=True, ndmin=0, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_10_{copy=False, ndmin=1, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_11_{copy=False, ndmin=1, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_12_{copy=False, ndmin=2, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_13_{copy=False, ndmin=2, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_14_{copy=False, ndmin=3, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_15_{copy=False, ndmin=3, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_1_{copy=True, ndmin=0, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_2_{copy=True, ndmin=1, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_3_{copy=True, ndmin=1, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_4_{copy=True, ndmin=2, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_5_{copy=True, ndmin=2, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_6_{copy=True, ndmin=3, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_7_{copy=True, ndmin=3, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_8_{copy=False, ndmin=0, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_9_{copy=False, ndmin=0, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_10_{copy=False, ndmin=1, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_11_{copy=False, ndmin=1, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_12_{copy=False, ndmin=2, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_13_{copy=False, ndmin=2, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_14_{copy=False, ndmin=3, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_15_{copy=False, ndmin=3, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_2_{copy=True, ndmin=1, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_3_{copy=True, ndmin=1, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_4_{copy=True, ndmin=2, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_5_{copy=True, ndmin=2, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_6_{copy=True, ndmin=3, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_7_{copy=True, ndmin=3, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_8_{copy=False, ndmin=0, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_9_{copy=False, ndmin=0, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_copy_is_copied -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_copy_list_of_cupy_with_dtype -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_copy_list_of_cupy_with_dtype_char -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_copy_list_of_numpy_with_dtype -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_copy_list_of_numpy_with_dtype_char -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_f_contiguous_input -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_f_contiguous_output -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_cupy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_cupy_scalar -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_cupy_view -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_numpy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_numpy_scalar -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_numpy_view -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_nested_list_of_cupy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_nested_list_of_numpy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_numpy_scalar -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_no_copy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_no_copy_ndmin -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_cuda_array_zero_dim -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_cuda_array_zero_dim_dtype -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_is_not_copied -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_with_order_copy_behavior -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_ascontiguousarray_on_noncontiguous_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asfortranarray_cuda_array_zero_dim -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asfortranarray_cuda_array_zero_dim_dtype tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction_from_list tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction_from_tuple diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index 85f25b90028..8723eff71e6 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -169,12 +169,7 @@ tests/third_party/cupy/core_tests/test_ndarray_math.py::TestRoundHalfway_param_0 tests/third_party/cupy/core_tests/test_ndarray_math.py::TestRoundHalfway_param_1_{decimals=-2}::test_round_halfway_float tests/third_party/cupy/core_tests/test_ndarray_math.py::TestRoundHalfway_param_2_{decimals=-1}::test_round_halfway_float tests/third_party/cupy/core_tests/test_ndarray_math.py::TestRoundHalfway_param_3_{decimals=0}::test_round_halfway_float -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asanyarray_with_order -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_from_numpy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_preserves_numpy_array_order -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_with_order -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_ascontiguousarray_on_contiguous_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_copy_order + tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction_from_list tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction_from_tuple @@ -434,61 +429,6 @@ tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_full_like_s tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_ones_like_subok tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_zeros_like_subok tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_zeros_strides -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_0_{copy=True, ndmin=0, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_10_{copy=False, ndmin=1, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_11_{copy=False, ndmin=1, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_12_{copy=False, ndmin=2, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_13_{copy=False, ndmin=2, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_14_{copy=False, ndmin=3, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_15_{copy=False, ndmin=3, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_1_{copy=True, ndmin=0, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_2_{copy=True, ndmin=1, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_3_{copy=True, ndmin=1, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_4_{copy=True, ndmin=2, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_5_{copy=True, ndmin=2, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_6_{copy=True, ndmin=3, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_7_{copy=True, ndmin=3, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_8_{copy=False, ndmin=0, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayCopy_param_9_{copy=False, ndmin=0, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_10_{copy=False, ndmin=1, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_11_{copy=False, ndmin=1, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_12_{copy=False, ndmin=2, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_13_{copy=False, ndmin=2, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_14_{copy=False, ndmin=3, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_15_{copy=False, ndmin=3, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_2_{copy=True, ndmin=1, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_3_{copy=True, ndmin=1, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_4_{copy=True, ndmin=2, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_5_{copy=True, ndmin=2, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_6_{copy=True, ndmin=3, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_7_{copy=True, ndmin=3, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_8_{copy=False, ndmin=0, xp=numpy}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestArrayPreservationOfShape_param_9_{copy=False, ndmin=0, xp=dpnp}::test_cupy_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_copy_is_copied -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_copy_list_of_cupy_with_dtype -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_copy_list_of_cupy_with_dtype_char -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_copy_list_of_numpy_with_dtype -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_copy_list_of_numpy_with_dtype_char -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_f_contiguous_input -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_f_contiguous_output -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_cupy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_cupy_scalar -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_cupy_view -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_numpy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_numpy_scalar -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_list_of_numpy_view -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_nested_list_of_cupy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_nested_list_of_numpy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_from_numpy_scalar -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_no_copy -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_array_no_copy_ndmin -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_cuda_array_zero_dim -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_cuda_array_zero_dim_dtype -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_is_not_copied -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asarray_with_order_copy_behavior -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_ascontiguousarray_on_noncontiguous_array -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asfortranarray_cuda_array_zero_dim -tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_asfortranarray_cuda_array_zero_dim_dtype tests/third_party/cupy/creation_tests/test_ranges.py::TestMeshgrid_param_0_{copy=False, indexing='xy', sparse=False}::test_meshgrid0 tests/third_party/cupy/creation_tests/test_ranges.py::TestMeshgrid_param_0_{copy=False, indexing='xy', sparse=False}::test_meshgrid1 diff --git a/tests/skipped_tests_gpu_no_fp64.tbl b/tests/skipped_tests_gpu_no_fp64.tbl index 2658f178bf8..46ae7fb4c8f 100644 --- a/tests/skipped_tests_gpu_no_fp64.tbl +++ b/tests/skipped_tests_gpu_no_fp64.tbl @@ -525,6 +525,8 @@ tests/test_umath.py::TestArctan2::test_invalid_shape[(0,)] tests/test_umath.py::TestArctan2::test_invalid_shape[(15, )] tests/test_umath.py::TestArctan2::test_invalid_shape[(2,2)] +tests/test_umath.py::TestSqrt::test_sqrt_complex[complex64] + tests/third_party/cupy/core_tests/test_ndarray_math.py::TestRound_param_2_{decimals=0}::test_round tests/third_party/cupy/core_tests/test_ndarray_math.py::TestRound_param_0_{decimals=-2}::test_round_out tests/third_party/cupy/core_tests/test_ndarray_math.py::TestRound_param_1_{decimals=-1}::test_round_out @@ -919,35 +921,55 @@ tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticUnary_param_ tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_6_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_10_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_10_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_14_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_14_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_38_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_42_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_42_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_46_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_46_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_66_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0., 1., 2.], [3., 4., 5.]], dtype=float32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_66_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0., 1., 2.], [3., 4., 5.]], dtype=float32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_70_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_70_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_74_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_74_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_78_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_78_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_82_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=0, name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_82_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_86_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=0.0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_90_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=2, name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_90_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=2, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_94_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=2.0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_98_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0., 1., 2.], [3., 4., 5.]], dtype=float32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_98_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[0., 1., 2.], [3., 4., 5.]], dtype=float32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_102_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_102_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_106_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_106_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_110_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_110_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_114_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=0, name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_114_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_118_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=0.0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_122_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=2, name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_122_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=2, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_126_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=2.0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_134_{arg1=0, arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_138_{arg1=0, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_138_{arg1=0, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_142_{arg1=0, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_142_{arg1=0, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_166_{arg1=0.0, arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_170_{arg1=0.0, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_174_{arg1=0.0, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_198_{arg1=2, arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_202_{arg1=2, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_202_{arg1=2, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_206_{arg1=2, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_206_{arg1=2, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_230_{arg1=2.0, arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_234_{arg1=2.0, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_238_{arg1=2.0, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary diff --git a/tests/test_arraycreation.py b/tests/test_arraycreation.py index f846a74ef38..f8f6007c2f8 100644 --- a/tests/test_arraycreation.py +++ b/tests/test_arraycreation.py @@ -1,5 +1,6 @@ import operator import tempfile +from math import prod import dpctl import dpctl.tensor as dpt @@ -20,6 +21,30 @@ ) +@pytest.mark.parametrize( + "func, kwargs", + [ + pytest.param("array", {"subok": True}), + pytest.param("array", {"ndmin": 1}), + pytest.param("array", {"like": dpnp.ones(10)}), + pytest.param("asanyarray", {"like": dpnp.array(7)}), + pytest.param("asarray", {"like": dpnp.array([1, 5])}), + pytest.param("ascontiguousarray", {"like": dpnp.zeros(4)}), + pytest.param("asfortranarray", {"like": dpnp.empty((2, 4))}), + ], +) +def test_array_copy_exception(func, kwargs): + sh = (3, 5) + x = dpnp.arange(1, prod(sh) + 1, 1).reshape(sh) + + with pytest.raises( + ValueError, + match=f"Keyword argument `{list(kwargs.keys())[0]}` is supported " + "only with default value", + ): + getattr(dpnp, func)(x, **kwargs) + + @pytest.mark.parametrize( "start", [0, -5, 10, -2.5, 9.7], ids=["0", "-5", "10", "-2.5", "9.7"] ) diff --git a/tests/test_sycl_queue.py b/tests/test_sycl_queue.py index 6cc3cce50d1..78f98c61581 100644 --- a/tests/test_sycl_queue.py +++ b/tests/test_sycl_queue.py @@ -983,7 +983,10 @@ def test_to_device(device_from, device_to): valid_devices, ids=[device.filter_string for device in valid_devices], ) -@pytest.mark.parametrize("func", ["array", "asarray"]) +@pytest.mark.parametrize( + "func", + ["array", "asarray", "asanyarray", "ascontiguousarray", "asfortranarray"], +) @pytest.mark.parametrize( "device_param", ["", "None", "sycl_device"], ids=["Empty", "None", "device"] ) diff --git a/tests/test_usm_type.py b/tests/test_usm_type.py index bda39505a1c..522348e0205 100644 --- a/tests/test_usm_type.py +++ b/tests/test_usm_type.py @@ -1,3 +1,5 @@ +from math import prod + import dpctl.utils as du import pytest @@ -128,6 +130,22 @@ def test_array_creation(func, args, usm_type_x, usm_type_y): assert y.usm_type == usm_type_y +@pytest.mark.parametrize( + "func", + ["array", "asarray", "asanyarray", "ascontiguousarray", "asfortranarray"], +) +@pytest.mark.parametrize("usm_type_x", list_of_usm_types, ids=list_of_usm_types) +@pytest.mark.parametrize("usm_type_y", list_of_usm_types, ids=list_of_usm_types) +def test_array_copy(func, usm_type_x, usm_type_y): + sh = (3, 7, 5) + x = dp.arange(1, prod(sh) + 1, 1, usm_type=usm_type_x).reshape(sh) + + y = getattr(dp, func)(x, usm_type=usm_type_y) + + assert x.usm_type == usm_type_x + assert y.usm_type == usm_type_y + + @pytest.mark.parametrize( "usm_type_start", list_of_usm_types, ids=list_of_usm_types ) diff --git a/tests/third_party/cupy/creation_tests/test_from_data.py b/tests/third_party/cupy/creation_tests/test_from_data.py index 235b5041171..be606d1840f 100644 --- a/tests/third_party/cupy/creation_tests/test_from_data.py +++ b/tests/third_party/cupy/creation_tests/test_from_data.py @@ -1,54 +1,56 @@ import tempfile import unittest +import dpctl import numpy import pytest import dpnp as cupy +from tests.helper import has_support_aspect64 from tests.third_party.cupy import testing -@testing.gpu class TestFromData(unittest.TestCase): - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_array(self, xp, dtype, order): return xp.array([[1, 2, 3], [2, 3, 4]], dtype=dtype, order=order) - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_array_from_empty_list(self, xp, dtype, order): return xp.array([], dtype=dtype, order=order) - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_array_from_nested_empty_list(self, xp, dtype, order): return xp.array([[], []], dtype=dtype, order=order) - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_array_from_numpy(self, xp, dtype, order): a = testing.shaped_arange((2, 3, 4), numpy, dtype) return xp.array(a, order=order) - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_array_from_numpy_scalar(self, xp, dtype, order): a = numpy.array(2, dtype=dtype) return xp.array(a, order=order) - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_array_from_numpy_c_and_f(self, xp, dtype, order): + a = numpy.ones((1, 3, 1), dtype=dtype) + return xp.array(a, order=order) + + @testing.for_orders("CFAK") @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_array_from_numpy_broad_cast(self, xp, dtype, order): @@ -59,7 +61,7 @@ def test_array_from_numpy_broad_cast(self, xp, dtype, order): @testing.for_orders("CFAK", name="src_order") @testing.for_orders("CFAK", name="dst_order") @testing.for_all_dtypes() - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_from_list_of_numpy(self, xp, dtype, src_order, dst_order): # compares numpy.array() with # cupy.array() @@ -72,7 +74,7 @@ def test_array_from_list_of_numpy(self, xp, dtype, src_order, dst_order): @testing.for_orders("CFAK", name="src_order") @testing.for_orders("CFAK", name="dst_order") @testing.for_all_dtypes() - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_from_list_of_numpy_view( self, xp, dtype, src_order, dst_order ): @@ -90,7 +92,7 @@ def test_array_from_list_of_numpy_view( @testing.for_orders("CFAK") @testing.for_all_dtypes() - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_from_list_of_numpy_scalar(self, xp, dtype, order): # compares numpy.array() with # cupy.array() @@ -100,7 +102,7 @@ def test_array_from_list_of_numpy_scalar(self, xp, dtype, order): @testing.for_orders("CFAK", name="src_order") @testing.for_orders("CFAK", name="dst_order") @testing.for_all_dtypes() - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_from_nested_list_of_numpy( self, xp, dtype, src_order, dst_order ): @@ -115,7 +117,7 @@ def test_array_from_nested_list_of_numpy( @testing.for_orders("CFAK", name="src_order") @testing.for_orders("CFAK", name="dst_order") @testing.for_all_dtypes_combination(names=("dtype1", "dtype2")) - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_from_list_of_cupy( self, xp, dtype1, dtype2, src_order, dst_order ): @@ -125,12 +127,21 @@ def test_array_from_list_of_cupy( testing.shaped_arange((3, 4), xp, dtype1, src_order), testing.shaped_arange((3, 4), xp, dtype2, src_order), ] + + # need to align with the use case when device doesn't has aspect fp64 + if xp is numpy and not has_support_aspect64(): + dt = numpy.promote_types(dtype1, dtype2) + if dt == numpy.float64: + return numpy.array(a, order=dst_order, dtype=numpy.float32) + elif dt == numpy.complex128: + return numpy.array(a, order=dst_order, dtype=numpy.complex64) + return xp.array(a, order=dst_order) @testing.for_orders("CFAK", name="src_order") @testing.for_orders("CFAK", name="dst_order") @testing.for_all_dtypes() - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_from_list_of_cupy_view( self, xp, dtype, src_order, dst_order ): @@ -149,7 +160,7 @@ def test_array_from_list_of_cupy_view( @testing.for_orders("CFAK", name="src_order") @testing.for_orders("CFAK", name="dst_order") @testing.for_all_dtypes() - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_from_nested_list_of_cupy( self, xp, dtype, src_order, dst_order ): @@ -163,23 +174,44 @@ def test_array_from_nested_list_of_cupy( @testing.for_orders("CFAK") @testing.for_all_dtypes() - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_from_list_of_cupy_scalar(self, xp, dtype, order): # compares numpy.array() with # cupy.array() a = [xp.array(i, dtype=dtype) for i in range(2)] return xp.array(a, order=order) - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_array_from_nested_list_of_cupy_scalar(self, xp, dtype, order): + # compares numpy.array() with + # cupy.array() + a = [[xp.array(i, dtype=dtype)] for i in range(2)] + return xp.array(a, order=order) + + @testing.for_orders("CFAK") @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_array_copy(self, xp, dtype, order): a = testing.shaped_arange((2, 3, 4), xp, dtype) return xp.array(a, order=order) - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_array_both_c_and_f_contig_copy(self, xp, dtype, order): + a = testing.shaped_arange((1, 4, 1), xp, dtype, order="C") + return xp.array(a, order=order) + + @testing.for_orders("CFAK") + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_array_both_c_and_f_contig_f_strides_copy(self, xp, dtype, order): + a = testing.shaped_arange((1, 4, 1), xp, dtype, order="F") + return xp.array(a, order=order) + + @testing.for_orders("CFAK") @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_array_copy_is_copied(self, xp, dtype, order): @@ -188,8 +220,7 @@ def test_array_copy_is_copied(self, xp, dtype, order): a.fill(0) return b - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") @testing.for_all_dtypes(name="dtype1", no_complex=True) @testing.for_all_dtypes(name="dtype2") @testing.numpy_cupy_array_equal() @@ -198,8 +229,7 @@ def test_array_copy_with_dtype(self, xp, dtype1, dtype2, order): a = testing.shaped_arange((2, 3, 4), xp, dtype1) return xp.array(a, dtype=dtype2, order=order) - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") @testing.for_all_dtypes(name="dtype1", no_complex=True) @testing.for_all_dtypes(name="dtype2") @testing.numpy_cupy_array_equal() @@ -208,8 +238,7 @@ def test_array_copy_with_dtype_char(self, xp, dtype1, dtype2, order): a = testing.shaped_arange((2, 3, 4), xp, dtype1) return xp.array(a, dtype=numpy.dtype(dtype2).char, order=order) - # @testing.for_orders('CFAK') - @testing.for_orders("C") + @testing.for_orders("CFAK") @testing.numpy_cupy_array_equal() def test_array_copy_with_dtype_being_none(self, xp, order): a = testing.shaped_arange((2, 3, 4), xp) @@ -219,7 +248,7 @@ def test_array_copy_with_dtype_being_none(self, xp, order): @testing.for_orders("CFAK", name="dst_order") @testing.for_all_dtypes(name="dtype1", no_complex=True) @testing.for_all_dtypes(name="dtype2") - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_copy_list_of_numpy_with_dtype( self, xp, dtype1, dtype2, src_order, dst_order ): @@ -235,7 +264,7 @@ def test_array_copy_list_of_numpy_with_dtype( @testing.for_orders("CFAK", name="dst_order") @testing.for_all_dtypes(name="dtype1", no_complex=True) @testing.for_all_dtypes(name="dtype2") - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_copy_list_of_numpy_with_dtype_char( self, xp, dtype1, dtype2, src_order, dst_order ): @@ -251,7 +280,7 @@ def test_array_copy_list_of_numpy_with_dtype_char( @testing.for_orders("CFAK", name="dst_order") @testing.for_all_dtypes(name="dtype1", no_complex=True) @testing.for_all_dtypes(name="dtype2") - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_copy_list_of_cupy_with_dtype( self, xp, dtype1, dtype2, src_order, dst_order ): @@ -267,7 +296,7 @@ def test_array_copy_list_of_cupy_with_dtype( @testing.for_orders("CFAK", name="dst_order") @testing.for_all_dtypes(name="dtype1", no_complex=True) @testing.for_all_dtypes(name="dtype2") - @testing.numpy_cupy_array_equal(strides_check=True) + @testing.numpy_cupy_array_equal() def test_array_copy_list_of_cupy_with_dtype_char( self, xp, dtype1, dtype2, src_order, dst_order ): @@ -304,30 +333,31 @@ def test_array_f_contiguous_output(self, xp, dtype): assert b.flags.f_contiguous return b - # @testing.multi_gpu(2) - # def test_array_multi_device(self): - # with cuda.Device(0): - # x = testing.shaped_arange((2, 3, 4), cupy, dtype='f') - # with cuda.Device(1): - # y = cupy.array(x) - # assert isinstance(y, cupy.ndarray) - # assert x is not y # Do copy - # assert int(x.device) == 0 - # assert int(y.device) == 1 - # testing.assert_array_equal(x, y) - - # @testing.multi_gpu(2) - # def test_array_multi_device_zero_size(self): - # with cuda.Device(0): - # x = testing.shaped_arange((0,), cupy, dtype='f') - # with cuda.Device(1): - # y = cupy.array(x) - # assert isinstance(y, cupy.ndarray) - # assert x is not y # Do copy - # assert x.device.id == 0 - # assert y.device.id == 1 - # testing.assert_array_equal(x, y) - + def test_array_multi_device(self): + q1 = dpctl.SyclQueue() + q2 = dpctl.SyclQueue() + + x = cupy.ones((2, 3, 4), dtype=cupy.float32, device=q1) + y = cupy.array(x, device=q2) + assert isinstance(y, cupy.ndarray) + assert x is not y # Do copy + assert x.sycl_queue == q1 + assert y.sycl_queue == q2 + testing.assert_array_equal(x, y) + + def test_array_multi_device_zero_size(self): + q1 = dpctl.SyclQueue() + q2 = dpctl.SyclQueue() + + x = cupy.ones((0,), dtype=cupy.float32, device=q1) + y = cupy.array(x, device=q2) + assert isinstance(y, cupy.ndarray) + assert x is not y # Do copy + assert x.sycl_queue == q1 + assert y.sycl_queue == q2 + testing.assert_array_equal(x, y) + + @pytest.mark.skip("`ndmin` argument isn't supported") @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_array_no_copy_ndmin(self, xp, dtype): @@ -337,6 +367,27 @@ def test_array_no_copy_ndmin(self, xp, dtype): a.fill(0) return b + @pytest.mark.skip("only native byteorder is supported") + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_array_from_big_endian(self, xp, dtype): + dtype = numpy.dtype(dtype).newbyteorder(">") + a = testing.shaped_arange((2, 3, 4), numpy, dtype) + b = xp.array(a) + # Make a computation here as just moving big-endian data back and forth + # happens to work before the change in #5828 + return b + b + + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_array_from_list_of_numpy_big_endian(self, xp, dtype): + dtype = numpy.dtype(dtype).newbyteorder(">") + a = [testing.shaped_arange((3, 4), numpy, dtype) for i in range(2)] + b = xp.array(a) + # Make a computation here as just moving big-endian data back and forth + # happens to work before the change in #5828 + return b + b + @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_asarray(self, xp, dtype): @@ -351,8 +402,7 @@ def test_asarray_is_not_copied(self, xp, dtype): a.fill(0) return b - # @testing.for_CF_orders() - @testing.for_orders("C") + @testing.for_CF_orders() @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_asarray_with_order(self, xp, dtype, order): @@ -386,8 +436,18 @@ def test_asanyarray_with_order(self, xp, dtype, order): assert b.flags.c_contiguous return b - # @testing.for_CF_orders() - @testing.for_orders("C") + @pytest.mark.skip("only native byteorder is supported") + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_asanyarray_from_big_endian(self, xp, dtype): + dtype = numpy.dtype(dtype).newbyteorder(">") + a = testing.shaped_arange((2, 3, 4), numpy, dtype) + b = xp.asanyarray(a) + # Make a computation here as just moving big-endian data back and forth + # happens to work before the change in #5828 + return b + b + + @testing.for_CF_orders() @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_asarray_from_numpy(self, xp, dtype, order): @@ -420,19 +480,29 @@ def test_ascontiguousarray_on_contiguous_array(self): b = cupy.ascontiguousarray(a) assert a is b - @testing.numpy_cupy_array_equal() + @testing.numpy_cupy_array_equal(type_check=has_support_aspect64()) def test_asarray_cuda_array_zero_dim(self, xp): a = xp.ones(()) return xp.ascontiguousarray(a) @testing.numpy_cupy_array_equal() def test_asarray_cuda_array_zero_dim_dtype(self, xp): - a = xp.ones((), dtype=numpy.float64) + a = xp.ones((), dtype=numpy.float32) return xp.ascontiguousarray(a, dtype=numpy.int64) - # @testing.for_CF_orders() + @pytest.mark.skip("only native byteorder is supported") + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_asarray_from_big_endian(self, xp, dtype): + dtype = numpy.dtype(dtype).newbyteorder(">") + a = testing.shaped_arange((2, 3, 4), numpy, dtype) + b = xp.asarray(a) + # Make a computation here as just moving big-endian data back and forth + # happens to work before the change in #5828 + return b + b + @pytest.mark.usefixtures("allow_fall_back_on_numpy") - @testing.for_orders("C") + @testing.for_CF_orders() @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_copy(self, xp, dtype, order): @@ -441,15 +511,15 @@ def test_copy(self, xp, dtype, order): a[1] = 1 return b - # @testing.multi_gpu(2) - # @testing.for_CF_orders() - # @testing.for_all_dtypes() - # def test_copy_multigpu(self, dtype, order): - # with cuda.Device(0): - # src = cupy.random.uniform(-1, 1, (2, 3)).astype(dtype) - # with cuda.Device(1): - # dst = cupy.copy(src, order) - # testing.assert_allclose(src, dst, rtol=0, atol=0) + @pytest.mark.skip("`device` argument isn't supported") + @testing.for_CF_orders() + @testing.for_all_dtypes() + def test_copy_multigpu(self, dtype, order): + with cuda.Device(0): + src = cupy.random.uniform(-1, 1, (2, 3)).astype(dtype) + with cuda.Device(1): + dst = cupy.copy(src, order) + testing.assert_allclose(src, dst, rtol=0, atol=0) @testing.for_CF_orders() @testing.numpy_cupy_equal() @@ -458,7 +528,7 @@ def test_copy_order(self, xp, order): b = xp.copy(a) return (b.flags.c_contiguous, b.flags.f_contiguous) - @testing.numpy_cupy_array_equal() + @testing.numpy_cupy_array_equal(type_check=has_support_aspect64()) def test_asfortranarray_cuda_array_zero_dim(self, xp): a = xp.ones(()) return xp.asfortranarray(a) @@ -480,135 +550,237 @@ def test_fromfile(self, xp): fh.seek(0) return xp.fromfile(fh, dtype="u1") + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.numpy_cupy_array_equal() + def test_fromfunction(self, xp): + def function(i, j): + return i == j + + return xp.fromfunction(function, shape=(3, 3), dtype=int) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.numpy_cupy_array_equal(type_check=has_support_aspect64()) + def test_fromiter(self, xp): + iterable = (x * x for x in range(5)) + return xp.fromiter(iterable, float) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.numpy_cupy_array_equal() + def test_fromstring(self, xp): + return xp.fromstring("1 2", dtype=int, sep=" ") + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.numpy_cupy_array_equal() + def test_frombuffer(self, xp): + return xp.frombuffer(b"\x01\x02", dtype=numpy.uint8) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @testing.numpy_cupy_array_equal() + def test_loadtxt(self, xp): + with tempfile.TemporaryFile() as fh: + fh.write(b"0 1\n2 3") + fh.flush() + fh.seek(0) + return xp.loadtxt(fh, dtype="u1") + + @pytest.mark.skip("`genfromtxt` isn't supported") + @testing.numpy_cupy_array_equal() + def test_genfromtxt(self, xp): + with tempfile.TemporaryFile() as fh: + fh.write(b"0 1\n2 3") + fh.flush() + fh.seek(0) + return xp.genfromtxt(fh, dtype="u1") + + @pytest.mark.skip("only native byteorder is supported") + @testing.numpy_cupy_array_equal() + def test_fromfile_big_endian(self, xp): + with tempfile.TemporaryFile() as fh: + fh.write(b"\x00\x00\x00\x01") + fh.flush() + fh.seek(0) + a = xp.fromfile(fh, dtype=">u4") + # Make a computation here as just moving big-endian data back and + # forth happens to work before the change in #5828 + return a + a + + +max_cuda_array_interface_version = 1 -############################################################################### -# __cuda_array_interface__ tests -############################################################################### - -# max_cuda_array_interface_version = 2 - - -# @testing.gpu -# @testing.parameterize(*testing.product({ -# 'ver': tuple(range(max_cuda_array_interface_version+1)), -# 'strides': (False, None, True), -# })) -# class TestCudaArrayInterface(unittest.TestCase): -# @testing.for_all_dtypes() -# def test_base(self, dtype): -# a = testing.shaped_arange((2, 3, 4), cupy, dtype) -# b = cupy.asarray( -# DummyObjectWithCudaArrayInterface(a, self.ver, self.strides)) -# testing.assert_array_equal(a, b) - -# @testing.for_all_dtypes() -# def test_not_copied(self, dtype): -# a = testing.shaped_arange((2, 3, 4), cupy, dtype) -# b = cupy.asarray( -# DummyObjectWithCudaArrayInterface(a, self.ver, self.strides)) -# a.fill(0) -# testing.assert_array_equal(a, b) - -# @testing.for_all_dtypes() -# def test_order(self, dtype): -# a = testing.shaped_arange((2, 3, 4), cupy, dtype) -# b = cupy.asarray( -# DummyObjectWithCudaArrayInterface(a, self.ver, self.strides), -# order='F') -# assert b.flags.f_contiguous -# testing.assert_array_equal(a, b) - -# @testing.for_all_dtypes() -# def test_with_strides(self, dtype): -# a = testing.shaped_arange((2, 3, 4), cupy, dtype).T -# b = cupy.asarray( -# DummyObjectWithCudaArrayInterface(a, self.ver, self.strides)) -# assert a.strides == b.strides -# assert a.nbytes == b.data.mem.size - -# @testing.for_all_dtypes() -# def test_with_zero_size_array(self, dtype): -# a = testing.shaped_arange((0,), cupy, dtype) -# b = cupy.asarray( -# DummyObjectWithCudaArrayInterface(a, self.ver, self.strides)) -# assert a.strides == b.strides -# assert a.nbytes == b.data.mem.size -# assert a.data.ptr == 0 -# assert a.size == 0 - -# @testing.for_all_dtypes() -# def test_asnumpy(self, dtype): -# a = testing.shaped_arange((2, 3, 4), cupy, dtype) -# b = DummyObjectWithCudaArrayInterface(a, self.ver, self.strides) -# a_cpu = cupy.asnumpy(a) -# b_cpu = cupy.asnumpy(b) -# testing.assert_array_equal(a_cpu, b_cpu) - - -# @testing.gpu -# @testing.parameterize(*testing.product({ -# 'ver': tuple(range(1, max_cuda_array_interface_version+1)), -# 'strides': (False, None, True), -# })) -# class TestCudaArrayInterfaceMaskedArray(unittest.TestCase): -# # TODO(leofang): update this test when masked array is supported -# @testing.for_all_dtypes() -# def test_masked_array(self, dtype): -# a = testing.shaped_arange((2, 3, 4), cupy, dtype) -# mask = testing.shaped_arange((2, 3, 4), cupy, dtype) -# a = DummyObjectWithCudaArrayInterface(a, self.ver, self.strides, mask) -# with pytest.raises(ValueError) as ex: -# b = cupy.asarray(a) -# assert 'does not support' in str(ex.value) - - -# @testing.slow -# @testing.gpu -# class TestCudaArrayInterfaceBigArray(unittest.TestCase): -# def test_with_over_size_array(self): -# # real example from #3009 -# size = 5 * 10**8 -# try: -# a = testing.shaped_random((size,), cupy, cupy.float64) -# b = cupy.asarray(DummyObjectWithCudaArrayInterface(a, 2, None)) -# testing.assert_array_equal(a, b) -# except cupy.cuda.memory.OutOfMemoryError: -# pass -# else: -# del b, a -# finally: -# cupy.get_default_memory_pool().free_all_blocks() - - -# class DummyObjectWithCudaArrayInterface(object): -# def __init__(self, a, ver, include_strides=False, mask=None): -# assert ver in tuple(range(max_cuda_array_interface_version+1)) -# self.a = a -# self.ver = ver -# self.include_strides = include_strides -# self.mask = mask - -# @property -# def __cuda_array_interface__(self): -# desc = { -# 'shape': self.a.shape, -# 'typestr': self.a.dtype.str, -# 'descr': self.a.dtype.descr, -# 'data': (self.a.data.ptr, False), -# 'version': self.ver, -# } -# if self.a.flags.c_contiguous: -# if self.include_strides is True: -# desc['strides'] = self.a.strides -# elif self.include_strides is None: -# desc['strides'] = None -# else: # self.include_strides is False -# pass -# else: # F contiguous or neither -# desc['strides'] = self.a.strides -# if self.mask is not None: -# desc['mask'] = self.mask -# return desc + +@testing.parameterize( + *testing.product( + { + "ver": (max_cuda_array_interface_version,), + "strides": (False, None, True), + } + ) +) +class TestCudaArrayInterface(unittest.TestCase): + @testing.for_all_dtypes() + def test_base(self, dtype): + a = testing.shaped_arange((2, 3, 4), cupy, dtype) + b = cupy.asarray( + DummyObjectWithCudaArrayInterface(a, self.ver, self.strides) + ) + testing.assert_array_equal(a, b) + + @pytest.mark.skip("that isn't supported yet") + @testing.for_all_dtypes() + def test_not_copied(self, dtype): + a = testing.shaped_arange((2, 3, 4), cupy, dtype) + b = cupy.asarray( + DummyObjectWithCudaArrayInterface(a, self.ver, self.strides) + ) + a.fill(0) + testing.assert_array_equal(a, b) + + @testing.for_all_dtypes() + def test_order(self, dtype): + a = testing.shaped_arange((2, 3, 4), cupy, dtype) + b = cupy.asarray( + DummyObjectWithCudaArrayInterface(a, self.ver, self.strides), + order="F", + ) + assert b.flags.f_contiguous + testing.assert_array_equal(a, b) + + @testing.for_all_dtypes() + def test_with_strides(self, dtype): + a = testing.shaped_arange((2, 3, 4), cupy, dtype).T + b = cupy.asarray( + DummyObjectWithCudaArrayInterface(a, self.ver, self.strides) + ) + assert a.strides == b.strides + assert a.nbytes == b.nbytes + + @testing.for_all_dtypes() + def test_with_zero_size_array(self, dtype): + a = testing.shaped_arange((0,), cupy, dtype) + b = cupy.asarray( + DummyObjectWithCudaArrayInterface(a, self.ver, self.strides) + ) + assert a.strides == b.strides + assert a.nbytes == b.nbytes + assert a.size == 0 + + @pytest.mark.skip("that isn't supported yet") + @testing.for_all_dtypes() + def test_asnumpy(self, dtype): + a = testing.shaped_arange((2, 3, 4), cupy, dtype) + b = DummyObjectWithCudaArrayInterface(a, self.ver, self.strides) + a_cpu = cupy.asnumpy(a) + b_cpu = cupy.asnumpy(b) + testing.assert_array_equal(a_cpu, b_cpu) + + @pytest.mark.skip("only native byteorder is supported") + def test_big_endian(self): + a = cupy.array([0x1, 0x0, 0x0, 0x0], dtype=numpy.int8) + dtype = numpy.dtype(">i4") + shape = (1,) + strides = (4,) + data = a.data.ptr + b = DummyObjectWithCudaArrayInterface( + (shape, strides, dtype.str, dtype.descr, data), + self.ver, + self.strides, + ) + with pytest.raises(ValueError): + cupy.asarray(b) + + +@testing.parameterize( + *testing.product( + { + "ver": tuple(range(1, max_cuda_array_interface_version + 1)), + "strides": (False, None, True), + } + ) +) +class TestCudaArrayInterfaceMaskedArray(unittest.TestCase): + # TODO(leofang): update this test when masked array is supported + @pytest.mark.skip("that isn't supported") + @testing.for_all_dtypes() + def test_masked_array(self, dtype): + a = testing.shaped_arange((2, 3, 4), cupy, dtype) + mask = testing.shaped_arange((2, 3, 4), cupy, dtype) + a = DummyObjectWithCudaArrayInterface(a, self.ver, self.strides, mask) + with pytest.raises(ValueError) as ex: + b = cupy.asarray(a) + assert "does not support" in str(ex.value) + + +@pytest.mark.skip() +class TestCudaArrayInterfaceBigArray(unittest.TestCase): + @pytest.mark.skip("that isn't supported") + def test_with_over_size_array(self): + # real example from #3009 + size = 5 * 10**8 + a = testing.shaped_random((size,), cupy, cupy.float64) + b = cupy.asarray(DummyObjectWithCudaArrayInterface(a, 2, None)) + testing.assert_array_equal(a, b) + + +class DummyObjectWithCudaArrayInterface(object): + def __init__(self, a, ver, include_strides=False, mask=None, stream=None): + assert ver in tuple(range(max_cuda_array_interface_version + 1)) + self.a = None + if isinstance(a, cupy.ndarray): + self.a = a + else: + self.shape, self.strides, self.typestr, self.descr, self.data = a + self.ver = ver + self.include_strides = include_strides + self.mask = mask + self.stream = stream + + @property + def __sycl_usm_array_interface__(self): + if self.a is not None: + desc = { + "shape": self.a.shape, + "typestr": self.a.dtype.str, + "data": (self.a.get_array()._pointer, False), + "version": self.a.__sycl_usm_array_interface__["version"], + "syclobj": self.a.sycl_queue, + "offset": self.a.get_array()._element_offset, + } + if self.a.flags.c_contiguous: + if self.include_strides is True: + desc["strides"] = self.a.strides + elif self.include_strides is None: + desc["strides"] = None + else: # self.include_strides is False + pass + else: # F contiguous or neither + desc["strides"] = self.a.strides + else: + desc = { + "shape": self.shape, + "typestr": self.typestr, + "descr": self.descr, + "data": (self.data, False), + "version": self.ver, + } + if self.include_strides is True: + desc["strides"] = self.strides + elif self.include_strides is None: + desc["strides"] = None + else: # self.include_strides is False + pass + if self.mask is not None: + desc["mask"] = self.mask + # The stream field is kept here for compliance. However, since the + # synchronization is done via calling a cpdef function, which cannot + # be mock-tested. + if self.stream is not None: + if self.stream is cuda.Stream.null: + desc["stream"] = cuda.runtime.streamLegacy + elif (not cuda.runtime.is_hip) and self.stream is cuda.Stream.ptds: + desc["stream"] = cuda.runtime.streamPerThread + else: + desc["stream"] = self.stream.ptr + return desc @testing.parameterize( @@ -617,6 +789,7 @@ def test_fromfile(self, xp): ) ) class TestArrayPreservationOfShape(unittest.TestCase): + @pytest.mark.skip("`ndmin` argument isn't supported") @testing.for_all_dtypes() def test_cupy_array(self, dtype): shape = 2, 3 @@ -634,6 +807,7 @@ def test_cupy_array(self, dtype): ) ) class TestArrayCopy(unittest.TestCase): + @pytest.mark.skip("`ndmin` argument isn't supported") @testing.for_all_dtypes() def test_cupy_array(self, dtype): a = testing.shaped_arange((2, 3), self.xp, dtype) @@ -652,6 +826,5 @@ def test_cupy_array(self, dtype): class TestArrayInvalidObject(unittest.TestCase): def test_invalid_type(self): a = numpy.array([1, 2, 3], dtype=object) - # with self.assertRaises(ValueError): with self.assertRaises(TypeError): cupy.array(a) From 47a1ec4221f869ba0c0cc9791648034d555f5e99 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Wed, 16 Aug 2023 11:22:38 -0500 Subject: [PATCH 44/58] create Unary/BinaryElementwisefunc during module import --- dpnp/dpnp_algo/dpnp_elementwise_common.py | 537 +++++++++++++--------- 1 file changed, 310 insertions(+), 227 deletions(-) diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index 14c5f6058d8..4cf68ff55f0 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -215,6 +215,14 @@ def dpnp_add(x1, x2, out=None, order="K"): """ +bitwise_and_func = BinaryElementwiseFunc( + "bitwise_and", + ti._bitwise_and_result_type, + ti._bitwise_and, + _bitwise_and_docstring_, +) + + def dpnp_bitwise_and(x1, x2, out=None, order="K"): """Invokes bitwise_and() from dpctl.tensor implementation for bitwise_and() function.""" @@ -223,13 +231,9 @@ def dpnp_bitwise_and(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "bitwise_and", - ti._bitwise_and_result_type, - ti._bitwise_and, - _bitwise_and_docstring_, + res_usm = bitwise_and_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -259,6 +263,14 @@ def dpnp_bitwise_and(x1, x2, out=None, order="K"): """ +bitwise_or_func = BinaryElementwiseFunc( + "bitwise_or", + ti._bitwise_or_result_type, + ti._bitwise_or, + _bitwise_or_docstring_, +) + + def dpnp_bitwise_or(x1, x2, out=None, order="K"): """Invokes bitwise_or() from dpctl.tensor implementation for bitwise_or() function.""" @@ -267,13 +279,9 @@ def dpnp_bitwise_or(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "bitwise_or", - ti._bitwise_or_result_type, - ti._bitwise_or, - _bitwise_or_docstring_, + res_usm = bitwise_or_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -303,6 +311,14 @@ def dpnp_bitwise_or(x1, x2, out=None, order="K"): """ +bitwise_xor_func = BinaryElementwiseFunc( + "bitwise_xor", + ti._bitwise_xor_result_type, + ti._bitwise_xor, + _bitwise_xor_docstring_, +) + + def dpnp_bitwise_xor(x1, x2, out=None, order="K"): """Invokes bitwise_xor() from dpctl.tensor implementation for bitwise_xor() function.""" @@ -311,13 +327,9 @@ def dpnp_bitwise_xor(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "bitwise_xor", - ti._bitwise_xor_result_type, - ti._bitwise_xor, - _bitwise_xor_docstring_, + res_usm = bitwise_xor_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -393,33 +405,35 @@ def dpnp_ceil(x, out=None, order="K"): """ +def _call_cos(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_cos_to_call(sycl_queue, src, dst): + # call pybind11 extension for cos() function from OneMKL VM + return vmi._cos(sycl_queue, src, dst, depends) + return ti._cos(src, dst, sycl_queue, depends) + + +cos_func = UnaryElementwiseFunc( + "cos", ti._cos_result_type, _call_cos, _cos_docstring +) + + def dpnp_cos(x, out=None, order="K"): """ Invokes cos() function from pybind11 extension of OneMKL VM if possible. Otherwise fully relies on dpctl.tensor implementation for cos() function. - """ - def _call_cos(src, dst, sycl_queue, depends=None): - """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" - - if depends is None: - depends = [] - - if vmi._mkl_cos_to_call(sycl_queue, src, dst): - # call pybind11 extension for cos() function from OneMKL VM - return vmi._cos(sycl_queue, src, dst, depends) - return ti._cos(src, dst, sycl_queue, depends) - # dpctl.tensor only works with usm_ndarray x1_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = UnaryElementwiseFunc( - "cos", ti._cos_result_type, _call_cos, _cos_docstring - ) - res_usm = func(x1_usm, out=out_usm, order=order) + res_usm = cos_func(x1_usm, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -447,57 +461,62 @@ def _call_cos(src, dst, sycl_queue, depends=None): """ -def dpnp_divide(x1, x2, out=None, order="K"): - """ - Invokes div() function from pybind11 extension of OneMKL VM if possible. +def _call_divide(src1, src2, dst, sycl_queue, depends=None): + """A callback to register in BinaryElementwiseFunc class of dpctl.tensor""" - Otherwise fully relies on dpctl.tensor implementation for divide() function. + if depends is None: + depends = [] - """ + if vmi._mkl_div_to_call(sycl_queue, src1, src2, dst): + # call pybind11 extension for div() function from OneMKL VM + return vmi._div(sycl_queue, src1, src2, dst, depends) + return ti._divide(src1, src2, dst, sycl_queue, depends) - def _call_divide(src1, src2, dst, sycl_queue, depends=None): - """A callback to register in BinaryElementwiseFunc class of dpctl.tensor""" - if depends is None: - depends = [] +def _call_divide_inplace(lhs, rhs, sycl_queue, depends=None): + """In place workaround until dpctl.tensor provides the functionality.""" - if vmi._mkl_div_to_call(sycl_queue, src1, src2, dst): - # call pybind11 extension for div() function from OneMKL VM - return vmi._div(sycl_queue, src1, src2, dst, depends) - return ti._divide(src1, src2, dst, sycl_queue, depends) + if depends is None: + depends = [] - def _call_divide_inplace(lhs, rhs, sycl_queue, depends=None): - """In place workaround until dpctl.tensor provides the functionality.""" + # allocate temporary memory for out array + out = dpt.empty_like(lhs, dtype=dpnp.result_type(lhs.dtype, rhs.dtype)) - if depends is None: - depends = [] + # call a general callback + div_ht_, div_ev_ = _call_divide(lhs, rhs, out, sycl_queue, depends) - # allocate temporary memory for out array - out = dpt.empty_like(lhs, dtype=dpnp.result_type(lhs.dtype, rhs.dtype)) + # store the result into left input array and return events + cp_ht_, cp_ev_ = ti._copy_usm_ndarray_into_usm_ndarray( + src=out, dst=lhs, sycl_queue=sycl_queue, depends=[div_ev_] + ) + dpctl.SyclEvent.wait_for([div_ht_]) + return (cp_ht_, cp_ev_) - # call a general callback - div_ht_, div_ev_ = _call_divide(lhs, rhs, out, sycl_queue, depends) - # store the result into left input array and return events - cp_ht_, cp_ev_ = ti._copy_usm_ndarray_into_usm_ndarray( - src=out, dst=lhs, sycl_queue=sycl_queue, depends=[div_ev_] - ) - dpctl.SyclEvent.wait_for([div_ht_]) - return (cp_ht_, cp_ev_) +divide_func = BinaryElementwiseFunc( + "divide", + ti._divide_result_type, + _call_divide, + _divide_docstring_, + _call_divide_inplace, +) + + +def dpnp_divide(x1, x2, out=None, order="K"): + """ + Invokes div() function from pybind11 extension of OneMKL VM if possible. + + Otherwise fully relies on dpctl.tensor implementation for divide() function. + """ # dpctl.tensor only works with usm_ndarray or scalar x1_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x1) x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "divide", - ti._divide_result_type, - _call_divide, - _divide_docstring_, - _call_divide_inplace, + res_usm = divide_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -525,6 +544,11 @@ def _call_divide_inplace(lhs, rhs, sycl_queue, depends=None): """ +equal_func = BinaryElementwiseFunc( + "equal", ti._equal_result_type, ti._equal, _equal_docstring_ +) + + def dpnp_equal(x1, x2, out=None, order="K"): """Invokes equal() from dpctl.tensor implementation for equal() function.""" @@ -533,10 +557,9 @@ def dpnp_equal(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "equal", ti._equal_result_type, ti._equal, _equal_docstring_ + res_usm = equal_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -617,6 +640,14 @@ def dpnp_floor(x, out=None, order="K"): """ +floor_divide_func = BinaryElementwiseFunc( + "floor_divide", + ti._floor_divide_result_type, + ti._floor_divide, + _floor_divide_docstring_, +) + + def dpnp_floor_divide(x1, x2, out=None, order="K"): """Invokes floor_divide() from dpctl.tensor implementation for floor_divide() function.""" @@ -625,13 +656,9 @@ def dpnp_floor_divide(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "floor_divide", - ti._floor_divide_result_type, - ti._floor_divide, - _floor_divide_docstring_, + res_usm = floor_divide_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -659,6 +686,11 @@ def dpnp_floor_divide(x1, x2, out=None, order="K"): """ +greater_func = BinaryElementwiseFunc( + "greater", ti._greater_result_type, ti._greater, _greater_docstring_ +) + + def dpnp_greater(x1, x2, out=None, order="K"): """Invokes greater() from dpctl.tensor implementation for greater() function.""" @@ -667,10 +699,9 @@ def dpnp_greater(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "greater", ti._greater_result_type, ti._greater, _greater_docstring_ + res_usm = greater_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -698,6 +729,14 @@ def dpnp_greater(x1, x2, out=None, order="K"): """ +greater_equal_func = BinaryElementwiseFunc( + "greater_equal", + ti._greater_equal_result_type, + ti._greater_equal, + _greater_equal_docstring_, +) + + def dpnp_greater_equal(x1, x2, out=None, order="K"): """Invokes greater_equal() from dpctl.tensor implementation for greater_equal() function.""" @@ -706,13 +745,9 @@ def dpnp_greater_equal(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "greater_equal", - ti._greater_equal_result_type, - ti._greater_equal, - _greater_equal_docstring_, + res_usm = greater_equal_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -738,6 +773,14 @@ def dpnp_greater_equal(x1, x2, out=None, order="K"): """ +invert_func = UnaryElementwiseFunc( + "invert", + ti._bitwise_invert_result_type, + ti._bitwise_invert, + _invert_docstring, +) + + def dpnp_invert(x, out=None, order="K"): """Invokes bitwise_invert() from dpctl.tensor implementation for invert() function.""" @@ -745,13 +788,7 @@ def dpnp_invert(x, out=None, order="K"): x_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = UnaryElementwiseFunc( - "invert", - ti._bitwise_invert_result_type, - ti._bitwise_invert, - _invert_docstring, - ) - res_usm = func(x_usm, out=out_usm, order=order) + res_usm = invert_func(x_usm, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -777,6 +814,11 @@ def dpnp_invert(x, out=None, order="K"): """ +isfinite_func = UnaryElementwiseFunc( + "isfinite", ti._isfinite_result_type, ti._isfinite, _isfinite_docstring +) + + def dpnp_isfinite(x, out=None, order="K"): """Invokes isfinite() from dpctl.tensor implementation for isfinite() function.""" @@ -784,10 +826,7 @@ def dpnp_isfinite(x, out=None, order="K"): x1_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = UnaryElementwiseFunc( - "isfinite", ti._isfinite_result_type, ti._isfinite, _isfinite_docstring - ) - res_usm = func(x1_usm, out=out_usm, order=order) + res_usm = isfinite_func(x1_usm, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -812,6 +851,11 @@ def dpnp_isfinite(x, out=None, order="K"): """ +isinf_func = UnaryElementwiseFunc( + "isinf", ti._isinf_result_type, ti._isinf, _isinf_docstring +) + + def dpnp_isinf(x, out=None, order="K"): """Invokes isinf() from dpctl.tensor implementation for isinf() function.""" @@ -819,10 +863,7 @@ def dpnp_isinf(x, out=None, order="K"): x1_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = UnaryElementwiseFunc( - "isinf", ti._isinf_result_type, ti._isinf, _isinf_docstring - ) - res_usm = func(x1_usm, out=out_usm, order=order) + res_usm = isinf_func(x1_usm, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -847,6 +888,11 @@ def dpnp_isinf(x, out=None, order="K"): """ +isnan_func = UnaryElementwiseFunc( + "isnan", ti._isnan_result_type, ti._isnan, _isnan_docstring +) + + def dpnp_isnan(x, out=None, order="K"): """Invokes isnan() from dpctl.tensor implementation for isnan() function.""" @@ -854,10 +900,7 @@ def dpnp_isnan(x, out=None, order="K"): x1_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = UnaryElementwiseFunc( - "isnan", ti._isnan_result_type, ti._isnan, _isnan_docstring - ) - res_usm = func(x1_usm, out=out_usm, order=order) + res_usm = isnan_func(x1_usm, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -887,6 +930,14 @@ def dpnp_isnan(x, out=None, order="K"): """ +leftt_shift_func = BinaryElementwiseFunc( + "bitwise_leftt_shift", + ti._bitwise_left_shift_result_type, + ti._bitwise_left_shift, + _left_shift_docstring_, +) + + def dpnp_left_shift(x1, x2, out=None, order="K"): """Invokes bitwise_left_shift() from dpctl.tensor implementation for left_shift() function.""" @@ -895,13 +946,9 @@ def dpnp_left_shift(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "bitwise_leftt_shift", - ti._bitwise_left_shift_result_type, - ti._bitwise_left_shift, - _left_shift_docstring_, + res_usm = leftt_shift_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -929,6 +976,11 @@ def dpnp_left_shift(x1, x2, out=None, order="K"): """ +less_func = BinaryElementwiseFunc( + "less", ti._less_result_type, ti._less, _less_docstring_ +) + + def dpnp_less(x1, x2, out=None, order="K"): """Invokes less() from dpctl.tensor implementation for less() function.""" @@ -937,10 +989,9 @@ def dpnp_less(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "less", ti._less_result_type, ti._less, _less_docstring_ + res_usm = less_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -968,6 +1019,14 @@ def dpnp_less(x1, x2, out=None, order="K"): """ +less_equal_func = BinaryElementwiseFunc( + "less_equal", + ti._less_equal_result_type, + ti._less_equal, + _less_equal_docstring_, +) + + def dpnp_less_equal(x1, x2, out=None, order="K"): """Invokes less_equal() from dpctl.tensor implementation for less_equal() function.""" @@ -976,13 +1035,9 @@ def dpnp_less_equal(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "less_equal", - ti._less_equal_result_type, - ti._less_equal, - _less_equal_docstring_, + res_usm = less_equal_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1004,33 +1059,35 @@ def dpnp_less_equal(x1, x2, out=None, order="K"): """ +def _call_log(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_ln_to_call(sycl_queue, src, dst): + # call pybind11 extension for ln() function from OneMKL VM + return vmi._ln(sycl_queue, src, dst, depends) + return ti._log(src, dst, sycl_queue, depends) + + +log_func = UnaryElementwiseFunc( + "log", ti._log_result_type, _call_log, _log_docstring +) + + def dpnp_log(x, out=None, order="K"): """ Invokes log() function from pybind11 extension of OneMKL VM if possible. Otherwise fully relies on dpctl.tensor implementation for log() function. - """ - def _call_log(src, dst, sycl_queue, depends=None): - """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" - - if depends is None: - depends = [] - - if vmi._mkl_ln_to_call(sycl_queue, src, dst): - # call pybind11 extension for ln() function from OneMKL VM - return vmi._ln(sycl_queue, src, dst, depends) - return ti._log(src, dst, sycl_queue, depends) - # dpctl.tensor only works with usm_ndarray x1_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = UnaryElementwiseFunc( - "log", ti._log_result_type, _call_log, _log_docstring - ) - res_usm = func(x1_usm, out=out_usm, order=order) + res_usm = log_func(x1_usm, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1057,6 +1114,14 @@ def _call_log(src, dst, sycl_queue, depends=None): """ +logical_and_func = BinaryElementwiseFunc( + "logical_and", + ti._logical_and_result_type, + ti._logical_and, + _logical_and_docstring_, +) + + def dpnp_logical_and(x1, x2, out=None, order="K"): """Invokes logical_and() from dpctl.tensor implementation for logical_and() function.""" @@ -1065,13 +1130,9 @@ def dpnp_logical_and(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "logical_and", - ti._logical_and_result_type, - ti._logical_and, - _logical_and_docstring_, + res_usm = logical_and_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1093,6 +1154,14 @@ def dpnp_logical_and(x1, x2, out=None, order="K"): """ +logical_not_func = UnaryElementwiseFunc( + "logical_not", + ti._logical_not_result_type, + ti._logical_not, + _logical_not_docstring_, +) + + def dpnp_logical_not(x, out=None, order="K"): """Invokes logical_not() from dpctl.tensor implementation for logical_not() function.""" @@ -1100,13 +1169,7 @@ def dpnp_logical_not(x, out=None, order="K"): x_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = UnaryElementwiseFunc( - "logical_not", - ti._logical_not_result_type, - ti._logical_not, - _logical_not_docstring_, - ) - res_usm = func(x_usm, out=out_usm, order=order) + res_usm = logical_not_func(x_usm, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1133,6 +1196,14 @@ def dpnp_logical_not(x, out=None, order="K"): """ +logical_or_func = BinaryElementwiseFunc( + "logical_or", + ti._logical_or_result_type, + ti._logical_or, + _logical_or_docstring_, +) + + def dpnp_logical_or(x1, x2, out=None, order="K"): """Invokes logical_or() from dpctl.tensor implementation for logical_or() function.""" @@ -1141,13 +1212,9 @@ def dpnp_logical_or(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "logical_or", - ti._logical_or_result_type, - ti._logical_or, - _logical_or_docstring_, + res_usm = logical_or_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1173,6 +1240,13 @@ def dpnp_logical_or(x1, x2, out=None, order="K"): An array containing the element-wise logical XOR results. """ +logical_xor_func = BinaryElementwiseFunc( + "logical_xor", + ti._logical_xor_result_type, + ti._logical_xor, + _logical_xor_docstring_, +) + def dpnp_logical_xor(x1, x2, out=None, order="K"): """Invokes logical_xor() from dpctl.tensor implementation for logical_xor() function.""" @@ -1182,13 +1256,9 @@ def dpnp_logical_xor(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "logical_xor", - ti._logical_xor_result_type, - ti._logical_xor, - _logical_xor_docstring_, + res_usm = logical_xor_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1264,6 +1334,13 @@ def dpnp_multiply(x1, x2, out=None, order="K"): The data type of the returned array is determined by the Type Promotion Rules. """ +not_equal_func = BinaryElementwiseFunc( + "not_equal", + ti._not_equal_result_type, + ti._not_equal, + _not_equal_docstring_, +) + def dpnp_not_equal(x1, x2, out=None, order="K"): """Invokes not_equal() from dpctl.tensor implementation for not_equal() function.""" @@ -1273,13 +1350,9 @@ def dpnp_not_equal(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "not_equal", - ti._not_equal_result_type, - ti._not_equal, - _not_equal_docstring_, + res_usm = not_equal_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1351,6 +1424,14 @@ def dpnp_remainder(x1, x2, out=None, order="K"): """ +right_shift_func = BinaryElementwiseFunc( + "bitwise_right_shift", + ti._bitwise_right_shift_result_type, + ti._bitwise_right_shift, + _right_shift_docstring_, +) + + def dpnp_right_shift(x1, x2, out=None, order="K"): """Invokes bitwise_right_shift() from dpctl.tensor implementation for right_shift() function.""" @@ -1359,13 +1440,9 @@ def dpnp_right_shift(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "bitwise_right_shift", - ti._bitwise_right_shift_result_type, - ti._bitwise_right_shift, - _right_shift_docstring_, + res_usm = right_shift_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1388,33 +1465,35 @@ def dpnp_right_shift(x1, x2, out=None, order="K"): """ +def _call_sin(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_sin_to_call(sycl_queue, src, dst): + # call pybind11 extension for sin() function from OneMKL VM + return vmi._sin(sycl_queue, src, dst, depends) + return ti._sin(src, dst, sycl_queue, depends) + + +sin_func = UnaryElementwiseFunc( + "sin", ti._sin_result_type, _call_sin, _sin_docstring +) + + def dpnp_sin(x, out=None, order="K"): """ Invokes sin() function from pybind11 extension of OneMKL VM if possible. Otherwise fully relies on dpctl.tensor implementation for sin() function. - """ - def _call_sin(src, dst, sycl_queue, depends=None): - """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" - - if depends is None: - depends = [] - - if vmi._mkl_sin_to_call(sycl_queue, src, dst): - # call pybind11 extension for sin() function from OneMKL VM - return vmi._sin(sycl_queue, src, dst, depends) - return ti._sin(src, dst, sycl_queue, depends) - # dpctl.tensor only works with usm_ndarray x1_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = UnaryElementwiseFunc( - "sin", ti._sin_result_type, _call_sin, _sin_docstring - ) - res_usm = func(x1_usm, out=out_usm, order=order) + res_usm = sin_func(x1_usm, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1436,36 +1515,38 @@ def _call_sin(src, dst, sycl_queue, depends=None): """ +def _call_sqrt(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_sqrt_to_call(sycl_queue, src, dst): + # call pybind11 extension for sqrt() function from OneMKL VM + return vmi._sqrt(sycl_queue, src, dst, depends) + return ti._sqrt(src, dst, sycl_queue, depends) + + +sqrt_func = UnaryElementwiseFunc( + "sqrt", + ti._sqrt_result_type, + _call_sqrt, + _sqrt_docstring_, +) + + def dpnp_sqrt(x, out=None, order="K"): """ Invokes sqrt() function from pybind11 extension of OneMKL VM if possible. Otherwise fully relies on dpctl.tensor implementation for sqrt() function. - """ - def _call_sqrt(src, dst, sycl_queue, depends=None): - """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" - - if depends is None: - depends = [] - - if vmi._mkl_sqrt_to_call(sycl_queue, src, dst): - # call pybind11 extension for sqrt() function from OneMKL VM - return vmi._sqrt(sycl_queue, src, dst, depends) - return ti._sqrt(src, dst, sycl_queue, depends) - # dpctl.tensor only works with usm_ndarray or scalar x_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = UnaryElementwiseFunc( - "sqrt", - ti._sqrt_result_type, - _call_sqrt, - _sqrt_docstring_, - ) - res_usm = func(x_usm, out=out_usm, order=order) + res_usm = sqrt_func(x_usm, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1487,36 +1568,38 @@ def _call_sqrt(src, dst, sycl_queue, depends=None): """ +def _call_square(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_sqr_to_call(sycl_queue, src, dst): + # call pybind11 extension for sqr() function from OneMKL VM + return vmi._sqr(sycl_queue, src, dst, depends) + return ti._square(src, dst, sycl_queue, depends) + + +square_func = UnaryElementwiseFunc( + "square", + ti._square_result_type, + _call_square, + _square_docstring_, +) + + def dpnp_square(x, out=None, order="K"): """ Invokes sqr() function from pybind11 extension of OneMKL VM if possible. Otherwise fully relies on dpctl.tensor implementation for square() function. - """ - def _call_square(src, dst, sycl_queue, depends=None): - """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" - - if depends is None: - depends = [] - - if vmi._mkl_sqr_to_call(sycl_queue, src, dst): - # call pybind11 extension for sqr() function from OneMKL VM - return vmi._sqr(sycl_queue, src, dst, depends) - return ti._square(src, dst, sycl_queue, depends) - # dpctl.tensor only works with usm_ndarray or scalar x_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = UnaryElementwiseFunc( - "square", - ti._square_result_type, - _call_square, - _square_docstring_, - ) - res_usm = func(x_usm, out=out_usm, order=order) + res_usm = square_func(x_usm, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) From afb84af724f65a99197c90591f652aac8179f173 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Wed, 16 Aug 2023 12:07:43 -0500 Subject: [PATCH 45/58] implement mkl version of add, subtract and multiply --- dpnp/backend/extensions/vm/add.hpp | 81 +++++++++++++++ dpnp/backend/extensions/vm/mul.hpp | 81 +++++++++++++++ dpnp/backend/extensions/vm/sub.hpp | 81 +++++++++++++++ dpnp/backend/extensions/vm/types_matrix.hpp | 75 ++++++++++++++ dpnp/backend/extensions/vm/vm_py.cpp | 105 ++++++++++++++++++- dpnp/dpnp_algo/dpnp_elementwise_common.py | 106 ++++++++++++++------ tests/test_mathematical.py | 2 - tests/test_usm_type.py | 2 +- 8 files changed, 497 insertions(+), 36 deletions(-) create mode 100644 dpnp/backend/extensions/vm/add.hpp create mode 100644 dpnp/backend/extensions/vm/mul.hpp create mode 100644 dpnp/backend/extensions/vm/sub.hpp diff --git a/dpnp/backend/extensions/vm/add.hpp b/dpnp/backend/extensions/vm/add.hpp new file mode 100644 index 00000000000..d80755f0ec0 --- /dev/null +++ b/dpnp/backend/extensions/vm/add.hpp @@ -0,0 +1,81 @@ +//***************************************************************************** +// Copyright (c) 2023, Intel Corporation +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// - Redistributions of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// - Redistributions in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +// THE POSSIBILITY OF SUCH DAMAGE. +//***************************************************************************** + +#pragma once + +#include + +#include "common.hpp" +#include "types_matrix.hpp" + +namespace dpnp +{ +namespace backend +{ +namespace ext +{ +namespace vm +{ +template +sycl::event add_contig_impl(sycl::queue exec_q, + const std::int64_t n, + const char *in_a, + const char *in_b, + char *out_y, + const std::vector &depends) +{ + type_utils::validate_type_for_device(exec_q); + + const T *a = reinterpret_cast(in_a); + const T *b = reinterpret_cast(in_b); + T *y = reinterpret_cast(out_y); + + return mkl_vm::add(exec_q, + n, // number of elements to be calculated + a, // pointer `a` containing 1st input vector of size n + b, // pointer `b` containing 2nd input vector of size n + y, // pointer `y` to the output vector of size n + depends); +} + +template +struct AddContigFactory +{ + fnT get() + { + if constexpr (std::is_same_v< + typename types::AddOutputType::value_type, void>) + { + return nullptr; + } + else { + return add_contig_impl; + } + } +}; +} // namespace vm +} // namespace ext +} // namespace backend +} // namespace dpnp diff --git a/dpnp/backend/extensions/vm/mul.hpp b/dpnp/backend/extensions/vm/mul.hpp new file mode 100644 index 00000000000..4f827a05619 --- /dev/null +++ b/dpnp/backend/extensions/vm/mul.hpp @@ -0,0 +1,81 @@ +//***************************************************************************** +// Copyright (c) 2023, Intel Corporation +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// - Redistributions of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// - Redistributions in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +// THE POSSIBILITY OF SUCH DAMAGE. +//***************************************************************************** + +#pragma once + +#include + +#include "common.hpp" +#include "types_matrix.hpp" + +namespace dpnp +{ +namespace backend +{ +namespace ext +{ +namespace vm +{ +template +sycl::event mul_contig_impl(sycl::queue exec_q, + const std::int64_t n, + const char *in_a, + const char *in_b, + char *out_y, + const std::vector &depends) +{ + type_utils::validate_type_for_device(exec_q); + + const T *a = reinterpret_cast(in_a); + const T *b = reinterpret_cast(in_b); + T *y = reinterpret_cast(out_y); + + return mkl_vm::mul(exec_q, + n, // number of elements to be calculated + a, // pointer `a` containing 1st input vector of size n + b, // pointer `b` containing 2nd input vector of size n + y, // pointer `y` to the output vector of size n + depends); +} + +template +struct MulContigFactory +{ + fnT get() + { + if constexpr (std::is_same_v< + typename types::MulOutputType::value_type, void>) + { + return nullptr; + } + else { + return mul_contig_impl; + } + } +}; +} // namespace vm +} // namespace ext +} // namespace backend +} // namespace dpnp diff --git a/dpnp/backend/extensions/vm/sub.hpp b/dpnp/backend/extensions/vm/sub.hpp new file mode 100644 index 00000000000..f7bec14d48b --- /dev/null +++ b/dpnp/backend/extensions/vm/sub.hpp @@ -0,0 +1,81 @@ +//***************************************************************************** +// Copyright (c) 2023, Intel Corporation +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// - Redistributions of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// - Redistributions in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +// THE POSSIBILITY OF SUCH DAMAGE. +//***************************************************************************** + +#pragma once + +#include + +#include "common.hpp" +#include "types_matrix.hpp" + +namespace dpnp +{ +namespace backend +{ +namespace ext +{ +namespace vm +{ +template +sycl::event sub_contig_impl(sycl::queue exec_q, + const std::int64_t n, + const char *in_a, + const char *in_b, + char *out_y, + const std::vector &depends) +{ + type_utils::validate_type_for_device(exec_q); + + const T *a = reinterpret_cast(in_a); + const T *b = reinterpret_cast(in_b); + T *y = reinterpret_cast(out_y); + + return mkl_vm::sub(exec_q, + n, // number of elements to be calculated + a, // pointer `a` containing 1st input vector of size n + b, // pointer `b` containing 2nd input vector of size n + y, // pointer `y` to the output vector of size n + depends); +} + +template +struct SubContigFactory +{ + fnT get() + { + if constexpr (std::is_same_v< + typename types::SubOutputType::value_type, void>) + { + return nullptr; + } + else { + return sub_contig_impl; + } + } +}; +} // namespace vm +} // namespace ext +} // namespace backend +} // namespace dpnp diff --git a/dpnp/backend/extensions/vm/types_matrix.hpp b/dpnp/backend/extensions/vm/types_matrix.hpp index cd4fd76d4be..584e39de50a 100644 --- a/dpnp/backend/extensions/vm/types_matrix.hpp +++ b/dpnp/backend/extensions/vm/types_matrix.hpp @@ -43,6 +43,31 @@ namespace vm { namespace types { +/** + * @brief A factory to define pairs of supported types for which + * MKL VM library provides support in oneapi::mkl::vm::add function. + * + * @tparam T Type of input vectors `a` and `b` and of result vector `y`. + */ +template +struct AddOutputType +{ + using value_type = typename std::disjunction< + dpctl_td_ns::BinaryTypeMapResultEntry, + T, + std::complex, + std::complex>, + dpctl_td_ns::BinaryTypeMapResultEntry, + T, + std::complex, + std::complex>, + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::DefaultResultEntry>::result_type; +}; + /** * @brief A factory to define pairs of supported types for which * MKL VM library provides support in oneapi::mkl::vm::div function. @@ -83,6 +108,56 @@ struct CeilOutputType dpctl_td_ns::DefaultResultEntry>::result_type; }; +/** + * @brief A factory to define pairs of supported types for which + * MKL VM library provides support in oneapi::mkl::vm::mul function. + * + * @tparam T Type of input vectors `a` and `b` and of result vector `y`. + */ +template +struct MulOutputType +{ + using value_type = typename std::disjunction< + dpctl_td_ns::BinaryTypeMapResultEntry, + T, + std::complex, + std::complex>, + dpctl_td_ns::BinaryTypeMapResultEntry, + T, + std::complex, + std::complex>, + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::DefaultResultEntry>::result_type; +}; + +/** + * @brief A factory to define pairs of supported types for which + * MKL VM library provides support in oneapi::mkl::vm::sub function. + * + * @tparam T Type of input vectors `a` and `b` and of result vector `y`. + */ +template +struct SubOutputType +{ + using value_type = typename std::disjunction< + dpctl_td_ns::BinaryTypeMapResultEntry, + T, + std::complex, + std::complex>, + dpctl_td_ns::BinaryTypeMapResultEntry, + T, + std::complex, + std::complex>, + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::DefaultResultEntry>::result_type; +}; + /** * @brief A factory to define pairs of supported types for which * MKL VM library provides support in oneapi::mkl::vm::cos function. diff --git a/dpnp/backend/extensions/vm/vm_py.cpp b/dpnp/backend/extensions/vm/vm_py.cpp index c7435ae9e2e..170776c01ce 100644 --- a/dpnp/backend/extensions/vm/vm_py.cpp +++ b/dpnp/backend/extensions/vm/vm_py.cpp @@ -30,15 +30,18 @@ #include #include -#include "ceil.hpp" #include "common.hpp" +#include "add.hpp" +#include "ceil.hpp" #include "cos.hpp" #include "div.hpp" #include "floor.hpp" #include "ln.hpp" +#include "mul.hpp" #include "sin.hpp" #include "sqr.hpp" #include "sqrt.hpp" +#include "sub.hpp" #include "trunc.hpp" #include "types_matrix.hpp" @@ -48,7 +51,10 @@ namespace vm_ext = dpnp::backend::ext::vm; using vm_ext::binary_impl_fn_ptr_t; using vm_ext::unary_impl_fn_ptr_t; +static binary_impl_fn_ptr_t add_dispatch_vector[dpctl_td_ns::num_types]; static binary_impl_fn_ptr_t div_dispatch_vector[dpctl_td_ns::num_types]; +static binary_impl_fn_ptr_t mul_dispatch_vector[dpctl_td_ns::num_types]; +static binary_impl_fn_ptr_t sub_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t ceil_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t cos_dispatch_vector[dpctl_td_ns::num_types]; @@ -64,6 +70,39 @@ PYBIND11_MODULE(_vm_impl, m) using arrayT = dpctl::tensor::usm_ndarray; using event_vecT = std::vector; + // BinaryUfunc: ==== Add(x1, x2) ==== + { + vm_ext::init_ufunc_dispatch_vector( + add_dispatch_vector); + + auto add_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, + arrayT dst, const event_vecT &depends = {}) { + return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, + add_dispatch_vector); + }; + m.def("_add", add_pyapi, + "Call `add` function from OneMKL VM library to performs element " + "by element addition of vector `src1` by vector `src2` " + "to resulting vector `dst`", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst"), py::arg("depends") = py::list()); + + auto add_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, + arrayT src2, arrayT dst) { + return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, + add_dispatch_vector); + }; + m.def("_mkl_add_to_call", add_need_to_call_pyapi, + "Check input arguments to answer if `add` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst")); + } + + using arrayT = dpctl::tensor::usm_ndarray; + using event_vecT = std::vector; + // BinaryUfunc: ==== Div(x1, x2) ==== { vm_ext::init_ufunc_dispatch_vector; + + // BinaryUfunc: ==== Mul(x1, x2) ==== + { + vm_ext::init_ufunc_dispatch_vector( + mul_dispatch_vector); + + auto mul_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, + arrayT dst, const event_vecT &depends = {}) { + return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, + mul_dispatch_vector); + }; + m.def("_mul", mul_pyapi, + "Call `mul` function from OneMKL VM library to performs element " + "by element multiplication of vector `src1` by vector `src2` " + "to resulting vector `dst`", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst"), py::arg("depends") = py::list()); + + auto mul_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, + arrayT src2, arrayT dst) { + return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, + mul_dispatch_vector); + }; + m.def("_mkl_mul_to_call", mul_need_to_call_pyapi, + "Check input arguments to answer if `mul` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst")); + } + + using arrayT = dpctl::tensor::usm_ndarray; + using event_vecT = std::vector; + + // BinaryUfunc: ==== Sub(x1, x2) ==== + { + vm_ext::init_ufunc_dispatch_vector( + sub_dispatch_vector); + + auto sub_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, + arrayT dst, const event_vecT &depends = {}) { + return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, + sub_dispatch_vector); + }; + m.def("_sub", sub_pyapi, + "Call `sub` function from OneMKL VM library to performs element " + "by element subtraction of vector `src1` by vector `src2` " + "to resulting vector `dst`", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst"), py::arg("depends") = py::list()); + + auto sub_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, + arrayT src2, arrayT dst) { + return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, + sub_dispatch_vector); + }; + m.def("_mkl_sub_to_call", sub_need_to_call_pyapi, + "Check input arguments to answer if `sub` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst")); } // UnaryUfunc: ==== Cos(x) ==== diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index 4cf68ff55f0..926aa22453d 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -163,18 +163,33 @@ def check_nd_call_func( Default: "K". Returns: dpnp.ndarray: - an array containing the result of element-wise division. The data type + an array containing the result of element-wise addition. The data type of the returned array is determined by the Type Promotion Rules. """ +def _call_add(src1, src2, dst, sycl_queue, depends=None): + """A callback to register in BinaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_add_to_call(sycl_queue, src1, src2, dst): + # call pybind11 extension for add() function from OneMKL VM + return vmi._add(sycl_queue, src1, src2, dst, depends) + return ti._add(src1, src2, dst, sycl_queue, depends) + + +add_func = BinaryElementwiseFunc( + "add", ti._add_result_type, _call_add, _add_docstring_, ti._add_inplace +) + + def dpnp_add(x1, x2, out=None, order="K"): """ - Invokes add() from dpctl.tensor implementation for add() function. - - TODO: add a pybind11 extension of add() from OneMKL VM where possible - and would be performance effective. + Invokes add() function from pybind11 extension of OneMKL VM if possible. + Otherwise fully relies on dpctl.tensor implementation for add() function. """ # dpctl.tensor only works with usm_ndarray or scalar @@ -182,10 +197,9 @@ def dpnp_add(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "add", ti._add_result_type, ti._add, _add_docstring_, ti._add_inplace + res_usm = add_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1281,18 +1295,37 @@ def dpnp_logical_xor(x1, x2, out=None, order="K"): Default: "K". Returns: dpnp.ndarray: - an array containing the result of element-wise division. The data type + an array containing the result of element-wise multiplication. The data type of the returned array is determined by the Type Promotion Rules. """ +def _call_multiply(src1, src2, dst, sycl_queue, depends=None): + """A callback to register in BinaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_mul_to_call(sycl_queue, src1, src2, dst): + # call pybind11 extension for mul() function from OneMKL VM + return vmi._mul(sycl_queue, src1, src2, dst, depends) + return ti._multiply(src1, src2, dst, sycl_queue, depends) + + +multiply_func = BinaryElementwiseFunc( + "multiply", + ti._multiply_result_type, + _call_multiply, + _multiply_docstring_, + ti._multiply_inplace, +) + + def dpnp_multiply(x1, x2, out=None, order="K"): """ - Invokes multiply() from dpctl.tensor implementation for multiply() function. - - TODO: add a pybind11 extension of mul() from OneMKL VM where possible - and would be performance effective. + Invokes mul() function from pybind11 extension of OneMKL VM if possible. + Otherwise fully relies on dpctl.tensor implementation for multiply() function. """ # dpctl.tensor only works with usm_ndarray or scalar @@ -1300,14 +1333,9 @@ def dpnp_multiply(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "multiply", - ti._multiply_result_type, - ti._multiply, - _multiply_docstring_, - ti._multiply_inplace, + res_usm = multiply_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) @@ -1622,18 +1650,37 @@ def dpnp_square(x, out=None, order="K"): Default: "K". Returns: dpnp.ndarray: - an array containing the result of element-wise division. The data type + an array containing the result of element-wise subtraction. The data type of the returned array is determined by the Type Promotion Rules. """ +def _call_subtract(src1, src2, dst, sycl_queue, depends=None): + """A callback to register in BinaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_sub_to_call(sycl_queue, src1, src2, dst): + # call pybind11 extension for sub() function from OneMKL VM + return vmi._sub(sycl_queue, src1, src2, dst, depends) + return ti._subtract(src1, src2, dst, sycl_queue, depends) + + +subtract_func = BinaryElementwiseFunc( + "subtract", + ti._subtract_result_type, + _call_subtract, + _subtract_docstring_, + ti._subtract_inplace, +) + + def dpnp_subtract(x1, x2, out=None, order="K"): """ - Invokes subtract() from dpctl.tensor implementation for subtract() function. - - TODO: add a pybind11 extension of sub() from OneMKL VM where possible - and would be performance effective. + Invokes sub() function from pybind11 extension of OneMKL VM if possible. + Otherwise fully relies on dpctl.tensor implementation for subtract() function. """ # TODO: discuss with dpctl if the check is needed to be moved there @@ -1652,14 +1699,9 @@ def dpnp_subtract(x1, x2, out=None, order="K"): x2_usm_or_scalar = dpnp.get_usm_ndarray_or_scalar(x2) out_usm = None if out is None else dpnp.get_usm_ndarray(out) - func = BinaryElementwiseFunc( - "subtract", - ti._subtract_result_type, - ti._subtract, - _subtract_docstring_, - ti._subtract_inplace, + res_usm = subtract_func( + x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order ) - res_usm = func(x1_usm_or_scalar, x2_usm_or_scalar, out=out_usm, order=order) return dpnp_array._create_from_usm_ndarray(res_usm) diff --git a/tests/test_mathematical.py b/tests/test_mathematical.py index 9d944e3c597..31fc664a8d5 100644 --- a/tests/test_mathematical.py +++ b/tests/test_mathematical.py @@ -772,7 +772,6 @@ def test_out_overlap(self, dtype): @pytest.mark.parametrize( "dtype", get_all_dtypes(no_bool=True, no_none=True) ) - @pytest.mark.skip("mute unttil in-place support in dpctl is done") def test_inplace_strided_out(self, dtype): size = 21 @@ -862,7 +861,6 @@ def test_out_overlap(self, dtype): @pytest.mark.parametrize( "dtype", get_all_dtypes(no_bool=True, no_none=True) ) - @pytest.mark.skip("mute unttil in-place support in dpctl is done") def test_inplace_strided_out(self, dtype): size = 21 diff --git a/tests/test_usm_type.py b/tests/test_usm_type.py index 345a2d55250..239cf887ab4 100644 --- a/tests/test_usm_type.py +++ b/tests/test_usm_type.py @@ -10,7 +10,7 @@ @pytest.mark.parametrize("usm_type_x", list_of_usm_types, ids=list_of_usm_types) @pytest.mark.parametrize("usm_type_y", list_of_usm_types, ids=list_of_usm_types) -def test_coerced_usm_types_sum(usm_type_x, usm_type_y): +def test_coerced_usm_types_add(usm_type_x, usm_type_y): x = dp.arange(1000, usm_type=usm_type_x) y = dp.arange(1000, usm_type=usm_type_y) From 24116e10f3fa7b5516ceb39a21c91fc5fbe24e7c Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Fri, 18 Aug 2023 12:02:50 -0500 Subject: [PATCH 46/58] address comments --- dpnp/backend/extensions/vm/types_matrix.hpp | 120 ++++++------ dpnp/backend/extensions/vm/vm_py.cpp | 196 ++++++++++---------- 2 files changed, 154 insertions(+), 162 deletions(-) diff --git a/dpnp/backend/extensions/vm/types_matrix.hpp b/dpnp/backend/extensions/vm/types_matrix.hpp index 584e39de50a..ba88f192908 100644 --- a/dpnp/backend/extensions/vm/types_matrix.hpp +++ b/dpnp/backend/extensions/vm/types_matrix.hpp @@ -68,31 +68,6 @@ struct AddOutputType dpctl_td_ns::DefaultResultEntry>::result_type; }; -/** - * @brief A factory to define pairs of supported types for which - * MKL VM library provides support in oneapi::mkl::vm::div function. - * - * @tparam T Type of input vectors `a` and `b` and of result vector `y`. - */ -template -struct DivOutputType -{ - using value_type = typename std::disjunction< - dpctl_td_ns::BinaryTypeMapResultEntry, - T, - std::complex, - std::complex>, - dpctl_td_ns::BinaryTypeMapResultEntry, - T, - std::complex, - std::complex>, - dpctl_td_ns::BinaryTypeMapResultEntry, - dpctl_td_ns::BinaryTypeMapResultEntry, - dpctl_td_ns::DefaultResultEntry>::result_type; -}; - /** * @brief A factory to define pairs of supported types for which * MKL VM library provides support in oneapi::mkl::vm::ceil function. @@ -109,38 +84,32 @@ struct CeilOutputType }; /** - * @brief A factory to define pairs of supported types for which - * MKL VM library provides support in oneapi::mkl::vm::mul function. + * @brief A factory to define pairs of supported types for which + * MKL VM library provides support in oneapi::mkl::vm::cos function. * - * @tparam T Type of input vectors `a` and `b` and of result vector `y`. + * @tparam T Type of input vector `a` and of result vector `y`. */ template -struct MulOutputType +struct CosOutputType { using value_type = typename std::disjunction< - dpctl_td_ns::BinaryTypeMapResultEntry, - T, - std::complex, - std::complex>, - dpctl_td_ns::BinaryTypeMapResultEntry, - T, - std::complex, - std::complex>, - dpctl_td_ns::BinaryTypeMapResultEntry, - dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns:: + TypeMapResultEntry, std::complex>, + dpctl_td_ns:: + TypeMapResultEntry, std::complex>, + dpctl_td_ns::TypeMapResultEntry, + dpctl_td_ns::TypeMapResultEntry, dpctl_td_ns::DefaultResultEntry>::result_type; }; /** * @brief A factory to define pairs of supported types for which - * MKL VM library provides support in oneapi::mkl::vm::sub function. + * MKL VM library provides support in oneapi::mkl::vm::div function. * * @tparam T Type of input vectors `a` and `b` and of result vector `y`. */ template -struct SubOutputType +struct DivOutputType { using value_type = typename std::disjunction< dpctl_td_ns::BinaryTypeMapResultEntry function. + * MKL VM library provides support in oneapi::mkl::vm::floor function. * * @tparam T Type of input vector `a` and of result vector `y`. */ template -struct CosOutputType +struct FloorOutputType { using value_type = typename std::disjunction< - dpctl_td_ns:: - TypeMapResultEntry, std::complex>, - dpctl_td_ns:: - TypeMapResultEntry, std::complex>, dpctl_td_ns::TypeMapResultEntry, dpctl_td_ns::TypeMapResultEntry, dpctl_td_ns::DefaultResultEntry>::result_type; @@ -179,14 +144,18 @@ struct CosOutputType /** * @brief A factory to define pairs of supported types for which - * MKL VM library provides support in oneapi::mkl::vm::floor function. + * MKL VM library provides support in oneapi::mkl::vm::ln function. * * @tparam T Type of input vector `a` and of result vector `y`. */ template -struct FloorOutputType +struct LnOutputType { using value_type = typename std::disjunction< + dpctl_td_ns:: + TypeMapResultEntry, std::complex>, + dpctl_td_ns:: + TypeMapResultEntry, std::complex>, dpctl_td_ns::TypeMapResultEntry, dpctl_td_ns::TypeMapResultEntry, dpctl_td_ns::DefaultResultEntry>::result_type; @@ -194,20 +163,26 @@ struct FloorOutputType /** * @brief A factory to define pairs of supported types for which - * MKL VM library provides support in oneapi::mkl::vm::ln function. + * MKL VM library provides support in oneapi::mkl::vm::mul function. * - * @tparam T Type of input vector `a` and of result vector `y`. + * @tparam T Type of input vectors `a` and `b` and of result vector `y`. */ template -struct LnOutputType +struct MulOutputType { using value_type = typename std::disjunction< - dpctl_td_ns:: - TypeMapResultEntry, std::complex>, - dpctl_td_ns:: - TypeMapResultEntry, std::complex>, - dpctl_td_ns::TypeMapResultEntry, - dpctl_td_ns::TypeMapResultEntry, + dpctl_td_ns::BinaryTypeMapResultEntry, + T, + std::complex, + std::complex>, + dpctl_td_ns::BinaryTypeMapResultEntry, + T, + std::complex, + std::complex>, + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::BinaryTypeMapResultEntry, dpctl_td_ns::DefaultResultEntry>::result_type; }; @@ -264,6 +239,31 @@ struct SqrtOutputType dpctl_td_ns::DefaultResultEntry>::result_type; }; +/** + * @brief A factory to define pairs of supported types for which + * MKL VM library provides support in oneapi::mkl::vm::sub function. + * + * @tparam T Type of input vectors `a` and `b` and of result vector `y`. + */ +template +struct SubOutputType +{ + using value_type = typename std::disjunction< + dpctl_td_ns::BinaryTypeMapResultEntry, + T, + std::complex, + std::complex>, + dpctl_td_ns::BinaryTypeMapResultEntry, + T, + std::complex, + std::complex>, + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::BinaryTypeMapResultEntry, + dpctl_td_ns::DefaultResultEntry>::result_type; +}; + /** * @brief A factory to define pairs of supported types for which * MKL VM library provides support in oneapi::mkl::vm::trunc function. diff --git a/dpnp/backend/extensions/vm/vm_py.cpp b/dpnp/backend/extensions/vm/vm_py.cpp index 170776c01ce..e4f470bee3c 100644 --- a/dpnp/backend/extensions/vm/vm_py.cpp +++ b/dpnp/backend/extensions/vm/vm_py.cpp @@ -30,9 +30,9 @@ #include #include -#include "common.hpp" #include "add.hpp" #include "ceil.hpp" +#include "common.hpp" #include "cos.hpp" #include "div.hpp" #include "floor.hpp" @@ -52,17 +52,16 @@ using vm_ext::binary_impl_fn_ptr_t; using vm_ext::unary_impl_fn_ptr_t; static binary_impl_fn_ptr_t add_dispatch_vector[dpctl_td_ns::num_types]; -static binary_impl_fn_ptr_t div_dispatch_vector[dpctl_td_ns::num_types]; -static binary_impl_fn_ptr_t mul_dispatch_vector[dpctl_td_ns::num_types]; -static binary_impl_fn_ptr_t sub_dispatch_vector[dpctl_td_ns::num_types]; - static unary_impl_fn_ptr_t ceil_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t cos_dispatch_vector[dpctl_td_ns::num_types]; +static binary_impl_fn_ptr_t div_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t floor_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t ln_dispatch_vector[dpctl_td_ns::num_types]; +static binary_impl_fn_ptr_t mul_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t sin_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t sqr_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t sqrt_dispatch_vector[dpctl_td_ns::num_types]; +static binary_impl_fn_ptr_t sub_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t trunc_dispatch_vector[dpctl_td_ns::num_types]; PYBIND11_MODULE(_vm_impl, m) @@ -100,39 +99,6 @@ PYBIND11_MODULE(_vm_impl, m) py::arg("dst")); } - using arrayT = dpctl::tensor::usm_ndarray; - using event_vecT = std::vector; - - // BinaryUfunc: ==== Div(x1, x2) ==== - { - vm_ext::init_ufunc_dispatch_vector( - div_dispatch_vector); - - auto div_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, - arrayT dst, const event_vecT &depends = {}) { - return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, - div_dispatch_vector); - }; - m.def("_div", div_pyapi, - "Call `div` function from OneMKL VM library to performs element " - "by element division of vector `src1` by vector `src2` " - "to resulting vector `dst`", - py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), - py::arg("dst"), py::arg("depends") = py::list()); - - auto div_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, - arrayT src2, arrayT dst) { - return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, - div_dispatch_vector); - }; - m.def("_mkl_div_to_call", div_need_to_call_pyapi, - "Check input arguments to answer if `div` function from " - "OneMKL VM library can be used", - py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), - py::arg("dst")); - } - // UnaryUfunc: ==== Ceil(x) ==== { vm_ext::init_ufunc_dispatch_vector; - - // BinaryUfunc: ==== Mul(x1, x2) ==== - { - vm_ext::init_ufunc_dispatch_vector( - mul_dispatch_vector); - - auto mul_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, - arrayT dst, const event_vecT &depends = {}) { - return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, - mul_dispatch_vector); - }; - m.def("_mul", mul_pyapi, - "Call `mul` function from OneMKL VM library to performs element " - "by element multiplication of vector `src1` by vector `src2` " - "to resulting vector `dst`", - py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), - py::arg("dst"), py::arg("depends") = py::list()); - - auto mul_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, - arrayT src2, arrayT dst) { - return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, - mul_dispatch_vector); - }; - m.def("_mkl_mul_to_call", mul_need_to_call_pyapi, - "Check input arguments to answer if `mul` function from " - "OneMKL VM library can be used", - py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), - py::arg("dst")); - } - - using arrayT = dpctl::tensor::usm_ndarray; - using event_vecT = std::vector; - - // BinaryUfunc: ==== Sub(x1, x2) ==== - { - vm_ext::init_ufunc_dispatch_vector( - sub_dispatch_vector); - - auto sub_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, - arrayT dst, const event_vecT &depends = {}) { - return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, - sub_dispatch_vector); - }; - m.def("_sub", sub_pyapi, - "Call `sub` function from OneMKL VM library to performs element " - "by element subtraction of vector `src1` by vector `src2` " - "to resulting vector `dst`", - py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), - py::arg("dst"), py::arg("depends") = py::list()); - - auto sub_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, - arrayT src2, arrayT dst) { - return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, - sub_dispatch_vector); - }; - m.def("_mkl_sub_to_call", sub_need_to_call_pyapi, - "Check input arguments to answer if `sub` function from " - "OneMKL VM library can be used", - py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), - py::arg("dst")); } // UnaryUfunc: ==== Cos(x) ==== @@ -253,6 +155,36 @@ PYBIND11_MODULE(_vm_impl, m) py::arg("sycl_queue"), py::arg("src"), py::arg("dst")); } + // BinaryUfunc: ==== Div(x1, x2) ==== + { + vm_ext::init_ufunc_dispatch_vector( + div_dispatch_vector); + + auto div_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, + arrayT dst, const event_vecT &depends = {}) { + return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, + div_dispatch_vector); + }; + m.def("_div", div_pyapi, + "Call `div` function from OneMKL VM library to performs element " + "by element division of vector `src1` by vector `src2` " + "to resulting vector `dst`", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst"), py::arg("depends") = py::list()); + + auto div_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, + arrayT src2, arrayT dst) { + return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, + div_dispatch_vector); + }; + m.def("_mkl_div_to_call", div_need_to_call_pyapi, + "Check input arguments to answer if `div` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst")); + } + // UnaryUfunc: ==== Floor(x) ==== { vm_ext::init_ufunc_dispatch_vector( + mul_dispatch_vector); + + auto mul_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, + arrayT dst, const event_vecT &depends = {}) { + return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, + mul_dispatch_vector); + }; + m.def("_mul", mul_pyapi, + "Call `mul` function from OneMKL VM library to performs element " + "by element multiplication of vector `src1` by vector `src2` " + "to resulting vector `dst`", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst"), py::arg("depends") = py::list()); + + auto mul_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, + arrayT src2, arrayT dst) { + return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, + mul_dispatch_vector); + }; + m.def("_mkl_mul_to_call", mul_need_to_call_pyapi, + "Check input arguments to answer if `mul` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst")); + } + // UnaryUfunc: ==== Sin(x) ==== { vm_ext::init_ufunc_dispatch_vector( + sub_dispatch_vector); + + auto sub_pyapi = [&](sycl::queue exec_q, arrayT src1, arrayT src2, + arrayT dst, const event_vecT &depends = {}) { + return vm_ext::binary_ufunc(exec_q, src1, src2, dst, depends, + sub_dispatch_vector); + }; + m.def("_sub", sub_pyapi, + "Call `sub` function from OneMKL VM library to performs element " + "by element subtraction of vector `src1` by vector `src2` " + "to resulting vector `dst`", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst"), py::arg("depends") = py::list()); + + auto sub_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src1, + arrayT src2, arrayT dst) { + return vm_ext::need_to_call_binary_ufunc(exec_q, src1, src2, dst, + sub_dispatch_vector); + }; + m.def("_mkl_sub_to_call", sub_need_to_call_pyapi, + "Check input arguments to answer if `sub` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src1"), py::arg("src2"), + py::arg("dst")); + } + // UnaryUfunc: ==== Trunc(x) ==== { vm_ext::init_ufunc_dispatch_vector Date: Fri, 11 Aug 2023 14:50:41 -0500 Subject: [PATCH 47/58] use_dpctl_conj_for_dpnp --- dpnp/backend/extensions/vm/conj.hpp | 78 +++++++++++++++++++++ dpnp/backend/extensions/vm/types_matrix.hpp | 16 +++++ dpnp/backend/extensions/vm/vm_py.cpp | 30 ++++++++ dpnp/backend/include/dpnp_iface_fptr.hpp | 16 ++--- dpnp/backend/kernels/dpnp_krnl_elemwise.cpp | 21 ++---- dpnp/dpnp_algo/dpnp_algo.pxd | 2 - dpnp/dpnp_algo/dpnp_algo_mathematical.pxi | 5 -- dpnp/dpnp_algo/dpnp_elementwise_common.py | 53 ++++++++++++++ dpnp/dpnp_iface_mathematical.py | 41 +++++++++-- tests/skipped_tests_gpu.tbl | 2 - tests/test_usm_type.py | 3 +- 11 files changed, 225 insertions(+), 42 deletions(-) create mode 100644 dpnp/backend/extensions/vm/conj.hpp diff --git a/dpnp/backend/extensions/vm/conj.hpp b/dpnp/backend/extensions/vm/conj.hpp new file mode 100644 index 00000000000..a18226509b4 --- /dev/null +++ b/dpnp/backend/extensions/vm/conj.hpp @@ -0,0 +1,78 @@ +//***************************************************************************** +// Copyright (c) 2023, Intel Corporation +// All rights reserved. +// +// Redistribution and use in source and binary forms, with or without +// modification, are permitted provided that the following conditions are met: +// - Redistributions of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// - Redistributions in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +// THE POSSIBILITY OF SUCH DAMAGE. +//***************************************************************************** + +#pragma once + +#include + +#include "common.hpp" +#include "types_matrix.hpp" + +namespace dpnp +{ +namespace backend +{ +namespace ext +{ +namespace vm +{ +template +sycl::event conj_contig_impl(sycl::queue exec_q, + const std::int64_t n, + const char *in_a, + char *out_y, + const std::vector &depends) +{ + type_utils::validate_type_for_device(exec_q); + + const T *a = reinterpret_cast(in_a); + T *y = reinterpret_cast(out_y); + + return mkl_vm::conj(exec_q, + n, // number of elements to be calculated + a, // pointer `a` containing input vector of size n + y, // pointer `y` to the output vector of size n + depends); +} + +template +struct ConjContigFactory +{ + fnT get() + { + if constexpr (std::is_same_v< + typename types::ConjOutputType::value_type, void>) + { + return nullptr; + } + else { + return conj_contig_impl; + } + } +}; +} // namespace vm +} // namespace ext +} // namespace backend +} // namespace dpnp diff --git a/dpnp/backend/extensions/vm/types_matrix.hpp b/dpnp/backend/extensions/vm/types_matrix.hpp index ba88f192908..4f93307f073 100644 --- a/dpnp/backend/extensions/vm/types_matrix.hpp +++ b/dpnp/backend/extensions/vm/types_matrix.hpp @@ -81,6 +81,22 @@ struct CeilOutputType dpctl_td_ns::TypeMapResultEntry, dpctl_td_ns::TypeMapResultEntry, dpctl_td_ns::DefaultResultEntry>::result_type; +} + +/** + * @brief A factory to define pairs of supported types for which + * MKL VM library provides support in oneapi::mkl::vm::conj function. + * + * @tparam T Type of input vector `a` and of result vector `y`. + */ +struct ConjOutputType +{ + using value_type = typename std::disjunction< + dpctl_td_ns:: + TypeMapResultEntry, std::complex>, + dpctl_td_ns:: + TypeMapResultEntry, std::complex>, + dpctl_td_ns::DefaultResultEntry>::result_type; }; /** diff --git a/dpnp/backend/extensions/vm/vm_py.cpp b/dpnp/backend/extensions/vm/vm_py.cpp index e4f470bee3c..da088582af1 100644 --- a/dpnp/backend/extensions/vm/vm_py.cpp +++ b/dpnp/backend/extensions/vm/vm_py.cpp @@ -33,6 +33,7 @@ #include "add.hpp" #include "ceil.hpp" #include "common.hpp" +#include "conj.hpp" #include "cos.hpp" #include "div.hpp" #include "floor.hpp" @@ -56,6 +57,7 @@ static unary_impl_fn_ptr_t ceil_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t cos_dispatch_vector[dpctl_td_ns::num_types]; static binary_impl_fn_ptr_t div_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t floor_dispatch_vector[dpctl_td_ns::num_types]; +static unary_impl_fn_ptr_t conj_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t ln_dispatch_vector[dpctl_td_ns::num_types]; static binary_impl_fn_ptr_t mul_dispatch_vector[dpctl_td_ns::num_types]; static unary_impl_fn_ptr_t sin_dispatch_vector[dpctl_td_ns::num_types]; @@ -127,6 +129,34 @@ PYBIND11_MODULE(_vm_impl, m) py::arg("sycl_queue"), py::arg("src"), py::arg("dst")); } + // UnaryUfunc: ==== Conj(x) ==== + { + vm_ext::init_ufunc_dispatch_vector( + conj_dispatch_vector); + + auto conj_pyapi = [&](sycl::queue exec_q, arrayT src, arrayT dst, + const event_vecT &depends = {}) { + return vm_ext::unary_ufunc(exec_q, src, dst, depends, + conj_dispatch_vector); + }; + m.def("_conj", conj_pyapi, + "Call `conj` function from OneMKL VM library to compute " + "conjugate of vector elements", + py::arg("sycl_queue"), py::arg("src"), py::arg("dst"), + py::arg("depends") = py::list()); + + auto conj_need_to_call_pyapi = [&](sycl::queue exec_q, arrayT src, + arrayT dst) { + return vm_ext::need_to_call_unary_ufunc(exec_q, src, dst, + conj_dispatch_vector); + }; + m.def("_mkl_conj_to_call", conj_need_to_call_pyapi, + "Check input arguments to answer if `conj` function from " + "OneMKL VM library can be used", + py::arg("sycl_queue"), py::arg("src"), py::arg("dst")); + } + // UnaryUfunc: ==== Cos(x) ==== { vm_ext::init_ufunc_dispatch_vector}; - fmap[DPNPFuncName::DPNP_FN_CONJIGUATE][eft_LNG][eft_LNG] = { + fmap[DPNPFuncName::DPNP_FN_CONJUGATE][eft_LNG][eft_LNG] = { eft_LNG, (void *)dpnp_copy_c_default}; - fmap[DPNPFuncName::DPNP_FN_CONJIGUATE][eft_FLT][eft_FLT] = { + fmap[DPNPFuncName::DPNP_FN_CONJUGATE][eft_FLT][eft_FLT] = { eft_FLT, (void *)dpnp_copy_c_default}; - fmap[DPNPFuncName::DPNP_FN_CONJIGUATE][eft_DBL][eft_DBL] = { + fmap[DPNPFuncName::DPNP_FN_CONJUGATE][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_copy_c_default}; - fmap[DPNPFuncName::DPNP_FN_CONJIGUATE][eft_C128][eft_C128] = { + fmap[DPNPFuncName::DPNP_FN_CONJUGATE][eft_C128][eft_C128] = { eft_C128, (void *)dpnp_conjugate_c_default>}; - fmap[DPNPFuncName::DPNP_FN_CONJIGUATE_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_copy_c_ext}; - fmap[DPNPFuncName::DPNP_FN_CONJIGUATE_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_copy_c_ext}; - fmap[DPNPFuncName::DPNP_FN_CONJIGUATE_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_copy_c_ext}; - fmap[DPNPFuncName::DPNP_FN_CONJIGUATE_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_copy_c_ext}; - fmap[DPNPFuncName::DPNP_FN_CONJIGUATE_EXT][eft_C128][eft_C128] = { - eft_C128, (void *)dpnp_conjugate_c_ext>}; - fmap[DPNPFuncName::DPNP_FN_COPY][eft_BLN][eft_BLN] = { eft_BLN, (void *)dpnp_copy_c_default}; fmap[DPNPFuncName::DPNP_FN_COPY][eft_INT][eft_INT] = { diff --git a/dpnp/dpnp_algo/dpnp_algo.pxd b/dpnp/dpnp_algo/dpnp_algo.pxd index 80f5471831d..c59c7ced636 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pxd +++ b/dpnp/dpnp_algo/dpnp_algo.pxd @@ -68,8 +68,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_CHOLESKY_EXT DPNP_FN_CHOOSE DPNP_FN_CHOOSE_EXT - DPNP_FN_CONJIGUATE - DPNP_FN_CONJIGUATE_EXT DPNP_FN_COPY DPNP_FN_COPY_EXT DPNP_FN_COPYSIGN diff --git a/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi b/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi index 6f5aa2a614c..64003efd7ac 100644 --- a/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi +++ b/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi @@ -39,7 +39,6 @@ __all__ += [ "dpnp_absolute", "dpnp_arctan2", "dpnp_around", - "dpnp_conjugate", "dpnp_copysign", "dpnp_cross", "dpnp_cumprod", @@ -155,10 +154,6 @@ cpdef utils.dpnp_descriptor dpnp_around(utils.dpnp_descriptor x1, int decimals): return result -cpdef utils.dpnp_descriptor dpnp_conjugate(utils.dpnp_descriptor x1): - return call_fptr_1in_1out_strides(DPNP_FN_CONJIGUATE_EXT, x1) - - cpdef utils.dpnp_descriptor dpnp_copysign(utils.dpnp_descriptor x1_obj, utils.dpnp_descriptor x2_obj, object dtype=None, diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index 926aa22453d..216e702fa88 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -47,6 +47,7 @@ "dpnp_bitwise_or", "dpnp_bitwise_xor", "dpnp_ceil", + "dpnp_conj", "dpnp_cos", "dpnp_divide", "dpnp_equal", @@ -451,6 +452,58 @@ def dpnp_cos(x, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_conj_docstring = """ +conj(x, out=None, order='K') + +Computes conjugate for each element `x_i` for input array `x`. + +Args: + x (dpnp.ndarray): + Input array, expected to have numeric data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. Array must have the correct + shape and the expected data type. + order ("C","F","A","K", optional): memory layout of the new + output array, if parameter `out` is `None`. + Default: "K". +Return: + dpnp.ndarray: + An array containing the element-wise conjugate. + The returned array has the same data type as `x`. +""" + + +def _call_conj(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_conj_to_call(sycl_queue, src, dst): + # call pybind11 extension for conj() function from OneMKL VM + return vmi._conj(sycl_queue, src, dst, depends) + return ti._conj(src, dst, sycl_queue, depends) + + +conj_func = UnaryElementwiseFunc( + "conj", ti._conj_result_type, _call_conj, _conj_docstring +) + + +def dpnp_conj(x, out=None, order="K"): + """ + Invokes conj() function from pybind11 extension of OneMKL VM if possible. + + Otherwise fully relies on dpctl.tensor implementation for conj() function. + """ + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + res_usm = conj_func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _divide_docstring_ = """ divide(x1, x2, out=None, order="K") diff --git a/dpnp/dpnp_iface_mathematical.py b/dpnp/dpnp_iface_mathematical.py index 724cc6bb643..a716908cd82 100644 --- a/dpnp/dpnp_iface_mathematical.py +++ b/dpnp/dpnp_iface_mathematical.py @@ -52,6 +52,7 @@ check_nd_call_func, dpnp_add, dpnp_ceil, + dpnp_conj, dpnp_divide, dpnp_floor, dpnp_floor_divide, @@ -362,7 +363,17 @@ def ceil( ) -def conjugate(x1, **kwargs): +def conjugate( + x, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, +): """ Return the complex conjugate, element-wise. @@ -371,6 +382,18 @@ def conjugate(x1, **kwargs): For full documentation refer to :obj:`numpy.conjugate`. + Returns + ------- + out : dpnp.ndarray + The conjugate of each element of `x`. + + Limitations + ----------- + Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameters `where`, `dtype` and `subok` are supported with their default values. + Otherwise the function will be executed sequentially on CPU. + Input array data types are limited by supported DPNP :ref:`Data types`. + Examples -------- >>> import dpnp as np @@ -384,13 +407,17 @@ def conjugate(x1, **kwargs): """ - x1_desc = dpnp.get_dpnp_descriptor( - x1, copy_when_strides=False, copy_when_nondefault_queue=False + return check_nd_call_func( + numpy.conjugate, + dpnp_conj, + x, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, ) - if x1_desc and not kwargs: - return dpnp_conjugate(x1_desc).get_pyobj() - - return call_origin(numpy.conjugate, x1, **kwargs) conj = conjugate diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index f1a403d7318..5089a241771 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -12,14 +12,12 @@ tests/test_random.py::TestPermutationsTestShuffle::test_no_miss_numbers[int64] tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.array([])] tests/test_random.py::TestPermutationsTestShuffle::test_shuffle1[lambda x: dpnp.astype(dpnp.asarray(x), dpnp.float32)] -tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-conjugate-data2] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-copy-data3] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-cumprod-data4] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-cumsum-data5] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-ediff1d-data7] tests/test_sycl_queue.py::test_1in_1out[opencl:gpu:0-fabs-data8] -tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-conjugate-data2] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-copy-data3] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-cumprod-data4] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-cumsum-data5] diff --git a/tests/test_usm_type.py b/tests/test_usm_type.py index 239cf887ab4..d61331ffa08 100644 --- a/tests/test_usm_type.py +++ b/tests/test_usm_type.py @@ -250,8 +250,9 @@ def test_meshgrid(usm_type_x, usm_type_y): [ pytest.param("ceil", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), pytest.param("floor", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), - pytest.param("trunc", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), + pytest.param("conjugate", [[1.0 + 1.0j, 0.0], [0.0, 1.0 + 1.0j]]), pytest.param("sqrt", [1.0, 3.0, 9.0]), + pytest.param("trunc", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), ], ) @pytest.mark.parametrize("usm_type", list_of_usm_types, ids=list_of_usm_types) From 04aa87843cabed690d286ec5059e1ff3a2a15860 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Fri, 18 Aug 2023 11:35:18 -0500 Subject: [PATCH 48/58] address comments --- dpnp/dpnp_algo/dpnp_elementwise_common.py | 62 +++++++++---------- dpnp/dpnp_array.py | 4 +- dpnp/dpnp_iface_mathematical.py | 2 +- .../cupy/math_tests/test_arithmetic.py | 19 +++++- 4 files changed, 51 insertions(+), 36 deletions(-) diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index 216e702fa88..7735dae3634 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -420,38 +420,6 @@ def dpnp_ceil(x, out=None, order="K"): """ -def _call_cos(src, dst, sycl_queue, depends=None): - """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" - - if depends is None: - depends = [] - - if vmi._mkl_cos_to_call(sycl_queue, src, dst): - # call pybind11 extension for cos() function from OneMKL VM - return vmi._cos(sycl_queue, src, dst, depends) - return ti._cos(src, dst, sycl_queue, depends) - - -cos_func = UnaryElementwiseFunc( - "cos", ti._cos_result_type, _call_cos, _cos_docstring -) - - -def dpnp_cos(x, out=None, order="K"): - """ - Invokes cos() function from pybind11 extension of OneMKL VM if possible. - - Otherwise fully relies on dpctl.tensor implementation for cos() function. - """ - - # dpctl.tensor only works with usm_ndarray - x1_usm = dpnp.get_usm_ndarray(x) - out_usm = None if out is None else dpnp.get_usm_ndarray(out) - - res_usm = cos_func(x1_usm, out=out_usm, order=order) - return dpnp_array._create_from_usm_ndarray(res_usm) - - _conj_docstring = """ conj(x, out=None, order='K') @@ -490,6 +458,36 @@ def _call_conj(src, dst, sycl_queue, depends=None): ) +def dpnp_cos(x, out=None, order="K"): + """ + Invokes cos() function from pybind11 extension of OneMKL VM if possible. + + Otherwise fully relies on dpctl.tensor implementation for cos() function. + + """ + + def _call_cos(src, dst, sycl_queue, depends=None): + """A callback to register in UnaryElementwiseFunc class of dpctl.tensor""" + + if depends is None: + depends = [] + + if vmi._mkl_cos_to_call(sycl_queue, src, dst): + # call pybind11 extension for cos() function from OneMKL VM + return vmi._cos(sycl_queue, src, dst, depends) + return ti._cos(src, dst, sycl_queue, depends) + + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + func = UnaryElementwiseFunc( + "cos", ti._cos_result_type, _call_cos, _cos_docstring + ) + res_usm = func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + def dpnp_conj(x, out=None, order="K"): """ Invokes conj() function from pybind11 extension of OneMKL VM if possible. diff --git a/dpnp/dpnp_array.py b/dpnp/dpnp_array.py index 8b2a8c90716..717cb9c4f52 100644 --- a/dpnp/dpnp_array.py +++ b/dpnp/dpnp_array.py @@ -622,7 +622,7 @@ def conj(self): """ - if not dpnp.issubsctype(self.dtype, dpnp.complex_): + if not dpnp.issubsctype(self.dtype, dpnp.complexfloating): return self else: return dpnp.conjugate(self) @@ -635,7 +635,7 @@ def conjugate(self): """ - if not dpnp.issubsctype(self.dtype, dpnp.complex_): + if not dpnp.issubsctype(self.dtype, dpnp.complexfloating): return self else: return dpnp.conjugate(self) diff --git a/dpnp/dpnp_iface_mathematical.py b/dpnp/dpnp_iface_mathematical.py index a716908cd82..a6850b0bce4 100644 --- a/dpnp/dpnp_iface_mathematical.py +++ b/dpnp/dpnp_iface_mathematical.py @@ -397,7 +397,7 @@ def conjugate( Examples -------- >>> import dpnp as np - >>> np.conjugate(1+2j) + >>> np.conjugate(np.array(1+2j)) (1-2j) >>> x = np.eye(2) + 1j * np.eye(2) diff --git a/tests/third_party/cupy/math_tests/test_arithmetic.py b/tests/third_party/cupy/math_tests/test_arithmetic.py index de37826cd3b..7a8cf0ffc08 100644 --- a/tests/third_party/cupy/math_tests/test_arithmetic.py +++ b/tests/third_party/cupy/math_tests/test_arithmetic.py @@ -26,7 +26,12 @@ testing.product( { "nargs": [1], - "name": ["reciprocal", "angle"], + "name": [ + "reciprocal", + "conj", + "conjugate", + "angle", + ], } ) + testing.product( @@ -68,6 +73,18 @@ def test_raises_with_numpy_input(self): @testing.parameterize( *( testing.product( + { + "arg1": ( + [ + testing.shaped_arange((2, 3), numpy, dtype=d) + for d in all_types + ] + + [0, 0.0j, 0j, 2, 2.0, 2j, True, False] + ), + "name": ["conj", "conjugate"], + } + ) + + testing.product( { "arg1": ( [ From eef2a2741d157e6f336558a807681915c266da3f Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Fri, 18 Aug 2023 13:49:19 -0500 Subject: [PATCH 49/58] rebase and resolve conflicts --- dpnp/backend/extensions/vm/types_matrix.hpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/dpnp/backend/extensions/vm/types_matrix.hpp b/dpnp/backend/extensions/vm/types_matrix.hpp index 4f93307f073..e710a5b4673 100644 --- a/dpnp/backend/extensions/vm/types_matrix.hpp +++ b/dpnp/backend/extensions/vm/types_matrix.hpp @@ -81,7 +81,7 @@ struct CeilOutputType dpctl_td_ns::TypeMapResultEntry, dpctl_td_ns::TypeMapResultEntry, dpctl_td_ns::DefaultResultEntry>::result_type; -} +}; /** * @brief A factory to define pairs of supported types for which @@ -89,6 +89,7 @@ struct CeilOutputType * * @tparam T Type of input vector `a` and of result vector `y`. */ +template struct ConjOutputType { using value_type = typename std::disjunction< From 653ce2e4710383ccbd67a339a6561ceb8771d734 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Fri, 18 Aug 2023 15:35:46 -0500 Subject: [PATCH 50/58] update skipped tests files --- tests/skipped_tests.tbl | 2 +- tests/skipped_tests_gpu.tbl | 2 +- tests/skipped_tests_gpu_no_fp64.tbl | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/skipped_tests.tbl b/tests/skipped_tests.tbl index a8604e0d318..f5f0f62c28e 100644 --- a/tests/skipped_tests.tbl +++ b/tests/skipped_tests.tbl @@ -559,7 +559,7 @@ tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_para tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary2_param_545_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]]), dtype=float64, name='fmod', use_dtype=False}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticModf::test_modf -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_1_{name='angle', nargs=1}::test_raises_with_numpy_input +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_3_{name='angle', nargs=1}::test_raises_with_numpy_input tests/third_party/cupy/math_tests/test_explog.py::TestExplog::test_logaddexp tests/third_party/cupy/math_tests/test_explog.py::TestExplog::test_logaddexp2 diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index 5089a241771..35092f3b07b 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -733,7 +733,7 @@ tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_3_{reps tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_4_{reps=(2, 3)}::test_array_tile tests/third_party/cupy/manipulation_tests/test_tiling.py::TestTile_param_5_{reps=(2, 3, 4, 5)}::test_array_tile -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_1_{name='angle', nargs=1}::test_raises_with_numpy_input +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticRaisesWithNumpyInput_param_3_{name='angle', nargs=1}::test_raises_with_numpy_input tests/third_party/cupy/math_tests/test_explog.py::TestExplog::test_logaddexp tests/third_party/cupy/math_tests/test_explog.py::TestExplog::test_logaddexp2 diff --git a/tests/skipped_tests_gpu_no_fp64.tbl b/tests/skipped_tests_gpu_no_fp64.tbl index 894f9d7d922..2aca406319d 100644 --- a/tests/skipped_tests_gpu_no_fp64.tbl +++ b/tests/skipped_tests_gpu_no_fp64.tbl @@ -887,7 +887,7 @@ tests/third_party/cupy/logic_tests/test_content.py::TestContent::test_isnan tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_6_{dst_shape=(), src=(1+1j)}::test_copyto -tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticUnary_param_5_{arg1=2.0, name='reciprocal'}::test_unary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticUnary_param_29_{arg1=2.0, name='reciprocal'}::test_unary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_6_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_10_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary From 92e9b1c63051a8ed2204af13ff372b28fa0f30d0 Mon Sep 17 00:00:00 2001 From: vtavana <120411540+vtavana@users.noreply.github.com> Date: Sat, 19 Aug 2023 07:27:03 -0500 Subject: [PATCH 51/58] Modify the tests to align them with the changes in dpctl (#1527) Co-authored-by: Anton <100830759+antonwolfy@users.noreply.github.com> --- tests/test_mathematical.py | 30 +++++++++++++++++++++--------- tests/test_umath.py | 10 +++++----- 2 files changed, 26 insertions(+), 14 deletions(-) diff --git a/tests/test_mathematical.py b/tests/test_mathematical.py index 31fc664a8d5..5c8ca88088e 100644 --- a/tests/test_mathematical.py +++ b/tests/test_mathematical.py @@ -633,7 +633,7 @@ def test_invalid_shape(self, shape, dtype): dp_array = dpnp.arange(10, dtype=dtype) dp_out = dpnp.empty(shape, dtype=dtype) - with pytest.raises(TypeError): + with pytest.raises(ValueError): dpnp.ceil(dp_array, out=dp_out) @@ -673,7 +673,7 @@ def test_invalid_shape(self, shape, dtype): dp_array = dpnp.arange(10, dtype=dtype) dp_out = dpnp.empty(shape, dtype=dtype) - with pytest.raises(TypeError): + with pytest.raises(ValueError): dpnp.floor(dp_array, out=dp_out) @@ -713,7 +713,7 @@ def test_invalid_shape(self, shape, dtype): dp_array = dpnp.arange(10, dtype=dtype) dp_out = dpnp.empty(shape, dtype=dtype) - with pytest.raises(TypeError): + with pytest.raises(ValueError): dpnp.trunc(dp_array, out=dp_out) @@ -765,9 +765,15 @@ def test_out_dtypes(self, dtype): @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True)) def test_out_overlap(self, dtype): size = 1 if dtype == dpnp.bool else 15 + # DPNP dp_a = dpnp.arange(2 * size, dtype=dtype) - with pytest.raises(TypeError): - dpnp.add(dp_a[size::], dp_a[::2], out=dp_a[:size:]) + dpnp.add(dp_a[size::], dp_a[::2], out=dp_a[:size:]) + + # original + np_a = numpy.arange(2 * size, dtype=dtype) + numpy.add(np_a[size::], np_a[::2], out=np_a[:size:]) + + assert_allclose(np_a, dp_a) @pytest.mark.parametrize( "dtype", get_all_dtypes(no_bool=True, no_none=True) @@ -791,7 +797,7 @@ def test_invalid_shape(self, shape): dp_array2 = dpnp.arange(5, 15) dp_out = dpnp.empty(shape) - with pytest.raises(TypeError): + with pytest.raises(ValueError): dpnp.add(dp_array1, dp_array2, out=dp_out) @pytest.mark.parametrize( @@ -854,9 +860,15 @@ def test_out_dtypes(self, dtype): @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True)) def test_out_overlap(self, dtype): size = 1 if dtype == dpnp.bool else 15 + # DPNP dp_a = dpnp.arange(2 * size, dtype=dtype) - with pytest.raises(TypeError): - dpnp.multiply(dp_a[size::], dp_a[::2], out=dp_a[:size:]) + dpnp.multiply(dp_a[size::], dp_a[::2], out=dp_a[:size:]) + + # original + np_a = numpy.arange(2 * size, dtype=dtype) + numpy.multiply(np_a[size::], np_a[::2], out=np_a[:size:]) + + assert_allclose(np_a, dp_a) @pytest.mark.parametrize( "dtype", get_all_dtypes(no_bool=True, no_none=True) @@ -880,7 +892,7 @@ def test_invalid_shape(self, shape): dp_array2 = dpnp.arange(5, 15) dp_out = dpnp.empty(shape) - with pytest.raises(TypeError): + with pytest.raises(ValueError): dpnp.multiply(dp_array1, dp_array2, out=dp_out) @pytest.mark.parametrize( diff --git a/tests/test_umath.py b/tests/test_umath.py index 044510330af..8f1ac15d6ad 100644 --- a/tests/test_umath.py +++ b/tests/test_umath.py @@ -171,7 +171,7 @@ def test_invalid_shape(self, shape): dp_array = dpnp.arange(10) dp_out = dpnp.empty(shape, dtype=dp_array.dtype) - with pytest.raises(TypeError): + with pytest.raises(ValueError): dpnp.sin(dp_array, out=dp_out) @@ -254,7 +254,7 @@ def test_invalid_shape(self, shape): dp_array = dpnp.arange(10) dp_out = dpnp.empty(shape, dtype=dp_array.dtype) - with pytest.raises(TypeError): + with pytest.raises(ValueError): dpnp.cos(dp_array, out=dp_out) @@ -337,7 +337,7 @@ def test_invalid_shape(self, shape): dp_array = dpnp.arange(10) dp_out = dpnp.empty(shape, dtype=dp_array.dtype) - with pytest.raises(TypeError): + with pytest.raises(ValueError): dpnp.log(dp_array, out=dp_out) @@ -613,7 +613,7 @@ def test_invalid_shape(self, shape): dp_array = dpnp.arange(10, dtype=dpnp.float32) dp_out = dpnp.empty(shape, dtype=dpnp.float32) - with pytest.raises(TypeError): + with pytest.raises(ValueError): dpnp.sqrt(dp_array, out=dp_out) @pytest.mark.parametrize( @@ -675,7 +675,7 @@ def test_invalid_shape(self, shape): dp_array = dpnp.arange(10, dtype=dpnp.float32) dp_out = dpnp.empty(shape, dtype=dpnp.float32) - with pytest.raises(TypeError): + with pytest.raises(ValueError): dpnp.square(dp_array, out=dp_out) @pytest.mark.parametrize( From a717c6911c6fcca57d0a3478ae72fb88ae87077b Mon Sep 17 00:00:00 2001 From: vlad-perevezentsev Date: Mon, 21 Aug 2023 14:30:24 +0200 Subject: [PATCH 52/58] Update dpnp fft implementations to run on Iris Xe (#1524) * Impove dpnp_fft impl for Iris Xe * Impove dpnp_rfft impl for Iris Xe * Update test_fft and cupy test * Refresh dpnp_iface_fft.py * Fix cupy test_fft.py * Apply review remarks * Reduce python version to 3.10 for generate_coverage * Raise TypeError for boolean data type * Add a new test for fft funcs --- .github/workflows/generate_coverage.yaml | 2 +- dpnp/backend/kernels/dpnp_krnl_fft.cpp | 45 +- dpnp/fft/dpnp_algo_fft.pyx | 18 +- dpnp/fft/dpnp_iface_fft.py | 488 ++++++++++--------- tests/test_fft.py | 57 ++- tests/third_party/cupy/fft_tests/test_fft.py | 75 ++- 6 files changed, 404 insertions(+), 281 deletions(-) diff --git a/.github/workflows/generate_coverage.yaml b/.github/workflows/generate_coverage.yaml index d3a217a03f8..413835d336c 100644 --- a/.github/workflows/generate_coverage.yaml +++ b/.github/workflows/generate_coverage.yaml @@ -14,7 +14,7 @@ jobs: shell: bash -l {0} env: - python-ver: '3.11' + python-ver: '3.10' CHANNELS: '-c dppy/label/dev -c intel -c conda-forge --override-channels' steps: diff --git a/dpnp/backend/kernels/dpnp_krnl_fft.cpp b/dpnp/backend/kernels/dpnp_krnl_fft.cpp index 25787a7331f..fa88ea92315 100644 --- a/dpnp/backend/kernels/dpnp_krnl_fft.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_fft.cpp @@ -414,6 +414,9 @@ DPCTLSyclEventRef dpnp_fft_fft_c(DPCTLSyclQueueRef q_ref, const size_t norm, const DPCTLEventVectorRef dep_event_vec_ref) { + static_assert(sycl::detail::is_complex<_DataType_output>::value, + "Output data type must be a complex type."); + DPCTLSyclEventRef event_ref = nullptr; if (!shape_size || !array1_in || !result_out) { @@ -476,8 +479,10 @@ DPCTLSyclEventRef dpnp_fft_fft_c(DPCTLSyclQueueRef q_ref, else if constexpr (std::is_same<_DataType_input, int32_t>::value || std::is_same<_DataType_input, int64_t>::value) { - double *array1_copy = reinterpret_cast( - dpnp_memory_alloc_c(q_ref, input_size * sizeof(double))); + using CastType = typename _DataType_output::value_type; + + CastType *array1_copy = reinterpret_cast( + dpnp_memory_alloc_c(q_ref, input_size * sizeof(CastType))); shape_elem_type *copy_strides = reinterpret_cast( dpnp_memory_alloc_c(q_ref, sizeof(shape_elem_type))); @@ -486,15 +491,17 @@ DPCTLSyclEventRef dpnp_fft_fft_c(DPCTLSyclQueueRef q_ref, dpnp_memory_alloc_c(q_ref, sizeof(shape_elem_type))); *copy_shape = input_size; shape_elem_type copy_shape_size = 1; - event_ref = dpnp_copyto_c<_DataType_input, double>( + event_ref = dpnp_copyto_c<_DataType_input, CastType>( q_ref, array1_copy, input_size, copy_shape_size, copy_shape, copy_strides, array1_in, input_size, copy_shape_size, copy_shape, copy_strides, NULL, dep_event_vec_ref); DPCTLEvent_WaitAndThrow(event_ref); DPCTLEvent_Delete(event_ref); - event_ref = dpnp_fft_fft_mathlib_real_to_cmplx_c( + event_ref = dpnp_fft_fft_mathlib_real_to_cmplx_c< + CastType, CastType, + std::conditional_t::value, + desc_dp_real_t, desc_sp_real_t>>( q_ref, array1_copy, result_out, input_shape, result_shape, shape_size, input_size, result_size, inverse, norm, 0); @@ -577,6 +584,8 @@ DPCTLSyclEventRef dpnp_fft_rfft_c(DPCTLSyclQueueRef q_ref, const size_t norm, const DPCTLEventVectorRef dep_event_vec_ref) { + static_assert(sycl::detail::is_complex<_DataType_output>::value, + "Output data type must be a complex type."); DPCTLSyclEventRef event_ref = nullptr; if (!shape_size || !array1_in || !result_out) { @@ -617,8 +626,10 @@ DPCTLSyclEventRef dpnp_fft_rfft_c(DPCTLSyclQueueRef q_ref, else if constexpr (std::is_same<_DataType_input, int32_t>::value || std::is_same<_DataType_input, int64_t>::value) { - double *array1_copy = reinterpret_cast( - dpnp_memory_alloc_c(q_ref, input_size * sizeof(double))); + using CastType = typename _DataType_output::value_type; + + CastType *array1_copy = reinterpret_cast( + dpnp_memory_alloc_c(q_ref, input_size * sizeof(CastType))); shape_elem_type *copy_strides = reinterpret_cast( dpnp_memory_alloc_c(q_ref, sizeof(shape_elem_type))); @@ -627,15 +638,17 @@ DPCTLSyclEventRef dpnp_fft_rfft_c(DPCTLSyclQueueRef q_ref, dpnp_memory_alloc_c(q_ref, sizeof(shape_elem_type))); *copy_shape = input_size; shape_elem_type copy_shape_size = 1; - event_ref = dpnp_copyto_c<_DataType_input, double>( + event_ref = dpnp_copyto_c<_DataType_input, CastType>( q_ref, array1_copy, input_size, copy_shape_size, copy_shape, copy_strides, array1_in, input_size, copy_shape_size, copy_shape, copy_strides, NULL, dep_event_vec_ref); DPCTLEvent_WaitAndThrow(event_ref); DPCTLEvent_Delete(event_ref); - event_ref = dpnp_fft_fft_mathlib_real_to_cmplx_c( + event_ref = dpnp_fft_fft_mathlib_real_to_cmplx_c< + CastType, CastType, + std::conditional_t::value, + desc_dp_real_t, desc_sp_real_t>>( q_ref, array1_copy, result_out, input_shape, result_shape, shape_size, input_size, result_size, inverse, norm, 1); @@ -721,9 +734,11 @@ void func_map_init_fft_func(func_map_t &fmap) dpnp_fft_fft_default_c, std::complex>}; fmap[DPNPFuncName::DPNP_FN_FFT_FFT_EXT][eft_INT][eft_INT] = { - eft_C128, (void *)dpnp_fft_fft_ext_c>}; + eft_C128, (void *)dpnp_fft_fft_ext_c>, + eft_C64, (void *)dpnp_fft_fft_ext_c>}; fmap[DPNPFuncName::DPNP_FN_FFT_FFT_EXT][eft_LNG][eft_LNG] = { - eft_C128, (void *)dpnp_fft_fft_ext_c>}; + eft_C128, (void *)dpnp_fft_fft_ext_c>, + eft_C64, (void *)dpnp_fft_fft_ext_c>}; fmap[DPNPFuncName::DPNP_FN_FFT_FFT_EXT][eft_FLT][eft_FLT] = { eft_C64, (void *)dpnp_fft_fft_ext_c>}; fmap[DPNPFuncName::DPNP_FN_FFT_FFT_EXT][eft_DBL][eft_DBL] = { @@ -748,9 +763,11 @@ void func_map_init_fft_func(func_map_t &fmap) (void *)dpnp_fft_rfft_default_c>}; fmap[DPNPFuncName::DPNP_FN_FFT_RFFT_EXT][eft_INT][eft_INT] = { - eft_C128, (void *)dpnp_fft_rfft_ext_c>}; + eft_C128, (void *)dpnp_fft_rfft_ext_c>, + eft_C64, (void *)dpnp_fft_rfft_ext_c>}; fmap[DPNPFuncName::DPNP_FN_FFT_RFFT_EXT][eft_LNG][eft_LNG] = { - eft_C128, (void *)dpnp_fft_rfft_ext_c>}; + eft_C128, (void *)dpnp_fft_rfft_ext_c>, + eft_C64, (void *)dpnp_fft_rfft_ext_c>}; fmap[DPNPFuncName::DPNP_FN_FFT_RFFT_EXT][eft_FLT][eft_FLT] = { eft_C64, (void *)dpnp_fft_rfft_ext_c>}; fmap[DPNPFuncName::DPNP_FN_FFT_RFFT_EXT][eft_DBL][eft_DBL] = { diff --git a/dpnp/fft/dpnp_algo_fft.pyx b/dpnp/fft/dpnp_algo_fft.pyx index 3424459a527..b751354a1e0 100644 --- a/dpnp/fft/dpnp_algo_fft.pyx +++ b/dpnp/fft/dpnp_algo_fft.pyx @@ -68,9 +68,15 @@ cpdef utils.dpnp_descriptor dpnp_fft(utils.dpnp_descriptor input, input_obj = input.get_array() + # get FPTR function and return type + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + input_obj.sycl_device.has_aspect_fp64) + cdef DPNPFuncType return_type = ret_type_and_func[0] + cdef fptr_dpnp_fft_fft_t func = < fptr_dpnp_fft_fft_t > ret_type_and_func[1] + # ceate result array with type given by FPTR data cdef utils.dpnp_descriptor result = utils.create_output_descriptor(output_shape, - kernel_data.return_type, + return_type, None, device=input_obj.sycl_device, usm_type=input_obj.usm_type, @@ -81,7 +87,6 @@ cpdef utils.dpnp_descriptor dpnp_fft(utils.dpnp_descriptor input, cdef c_dpctl.SyclQueue q = result_sycl_queue cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref() - cdef fptr_dpnp_fft_fft_t func = kernel_data.ptr # call FPTR function cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref, input.get_data(), @@ -122,9 +127,15 @@ cpdef utils.dpnp_descriptor dpnp_rfft(utils.dpnp_descriptor input, input_obj = input.get_array() + # get FPTR function and return type + cdef (DPNPFuncType, void *) ret_type_and_func = utils.get_ret_type_and_func(kernel_data, + input_obj.sycl_device.has_aspect_fp64) + cdef DPNPFuncType return_type = ret_type_and_func[0] + cdef fptr_dpnp_fft_fft_t func = < fptr_dpnp_fft_fft_t > ret_type_and_func[1] + # ceate result array with type given by FPTR data cdef utils.dpnp_descriptor result = utils.create_output_descriptor(output_shape, - kernel_data.return_type, + return_type, None, device=input_obj.sycl_device, usm_type=input_obj.usm_type, @@ -135,7 +146,6 @@ cpdef utils.dpnp_descriptor dpnp_rfft(utils.dpnp_descriptor input, cdef c_dpctl.SyclQueue q = result_sycl_queue cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref() - cdef fptr_dpnp_fft_fft_t func = kernel_data.ptr # call FPTR function cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref, input.get_data(), diff --git a/dpnp/fft/dpnp_iface_fft.py b/dpnp/fft/dpnp_iface_fft.py index d5cb09e5819..0e1a5a98223 100644 --- a/dpnp/fft/dpnp_iface_fft.py +++ b/dpnp/fft/dpnp_iface_fft.py @@ -86,23 +86,28 @@ def get_validated_norm(norm): raise ValueError("Unknown norm value.") -def fft(x1, n=None, axis=-1, norm=None): +def fft(x, n=None, axis=-1, norm=None): """ Compute the one-dimensional discrete Fourier Transform. - Limitations - ----------- - Parameter ``norm`` is unsupported. - Parameter ``axis`` is supported with its default value. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64``, - ``dpnp.complex64`` and ``dpnp.complex128`` datatypes only. - For full documentation refer to :obj:`numpy.fft.fft`. + Limitations + ----------- + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `axis` is supported with its default value. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128`, `dpnp.complex64` data types are supported. + The `dpnp.bool` data type is not supported and will raise a `TypeError` exception. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc: + dt = x_desc.dtype + if dpnp.issubdtype(dt, dpnp.bool): + raise TypeError(f"The `{dt}` data type is unsupported.") + norm_ = get_validated_norm(norm) if axis is None: @@ -111,11 +116,11 @@ def fft(x1, n=None, axis=-1, norm=None): axis_param = axis if n is None: - input_boundarie = x1_desc.shape[axis_param] + input_boundarie = x_desc.shape[axis_param] else: input_boundarie = n - if x1_desc.size < 1: + if x_desc.size < 1: pass # let fallback to handle exception elif input_boundarie < 1: pass # let fallback to handle exception @@ -126,89 +131,93 @@ def fft(x1, n=None, axis=-1, norm=None): else: output_boundarie = input_boundarie return dpnp_fft( - x1_desc, + x_desc, input_boundarie, output_boundarie, axis_param, False, norm_.value, ).get_pyobj() - return call_origin(numpy.fft.fft, x1, n, axis, norm) + return call_origin(numpy.fft.fft, x, n, axis, norm) -def fft2(x1, s=None, axes=(-2, -1), norm=None): +def fft2(x, s=None, axes=(-2, -1), norm=None): """ - Compute the 2-dimensional discrete Fourier Transform + Compute the 2-dimensional discrete Fourier Transform. - Multi-dimensional arrays computed as batch of 1-D arrays + Multi-dimensional arrays computed as batch of 1-D arrays. + + For full documentation refer to :obj:`numpy.fft.fft2`. Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.fft2`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc: if norm is not None: pass else: - return fftn(x1, s, axes, norm) + return fftn(x, s, axes, norm) - return call_origin(numpy.fft.fft2, x1, s, axes, norm) + return call_origin(numpy.fft.fft2, x, s, axes, norm) def fftfreq(n=None, d=1.0): """ Compute the one-dimensional discrete Fourier Transform sample frequencies. + For full documentation refer to :obj:`numpy.fft.fftfreq`. + Limitations ----------- - Parameter ``d`` is unsupported. - - For full documentation refer to :obj:`numpy.fft.fftfreq`. + Parameter `d` is unsupported. """ return call_origin(numpy.fft.fftfreq, n, d) -def fftn(x1, s=None, axes=None, norm=None): +def fftn(x, s=None, axes=None, norm=None): """ Compute the N-dimensional FFT. - Multi-dimensional arrays computed as batch of 1-D arrays + Multi-dimensional arrays computed as batch of 1-D arrays. + + For full documentation refer to :obj:`numpy.fft.fftn`. Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.fftn`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc: if s is None: - boundaries = tuple([x1_desc.shape[i] for i in range(x1_desc.ndim)]) + boundaries = tuple([x_desc.shape[i] for i in range(x_desc.ndim)]) else: boundaries = s if axes is None: - axes_param = tuple([i for i in range(x1_desc.ndim)]) + axes_param = tuple([i for i in range(x_desc.ndim)]) else: axes_param = axes if norm is not None: pass else: - x1_iter = x1 + x_iter = x iteration_list = list(range(len(axes_param))) iteration_list.reverse() # inplace operation for it in iteration_list: @@ -223,41 +232,43 @@ def fftn(x1, s=None, axes=None, norm=None): f"< {len(boundaries)}", ) - x1_iter = fft(x1_iter, n=param_n, axis=param_axis, norm=norm) + x_iter = fft(x_iter, n=param_n, axis=param_axis, norm=norm) - return x1_iter + return x_iter - return call_origin(numpy.fft.fftn, x1, s, axes, norm) + return call_origin(numpy.fft.fftn, x, s, axes, norm) -def fftshift(x1, axes=None): +def fftshift(x, axes=None): """ Shift the zero-frequency component to the center of the spectrum. + For full documentation refer to :obj:`numpy.fft.fftshift`. + Limitations ----------- - Parameter ``axes`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.fftshift`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `axes` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc and 0: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc and 0: norm_ = Norm.backward - if axis is None: + if axes is None: axis_param = -1 # the most right dimension (default value) else: axis_param = axes - if x1_desc.size < 1: + if x_desc.size < 1: pass # let fallback to handle exception else: return dpnp_fft( - x1_desc, + x_desc, input_boundarie, output_boundarie, axis_param, @@ -265,25 +276,27 @@ def fftshift(x1, axes=None): norm_.value, ).get_pyobj() - return call_origin(numpy.fft.fftshift, x1, axes) + return call_origin(numpy.fft.fftshift, x, axes) -def hfft(x1, n=None, axis=-1, norm=None): +def hfft(x, n=None, axis=-1, norm=None): """ Compute the one-dimensional discrete Fourier Transform of a signal that has Hermitian symmetry. + For full documentation refer to :obj:`numpy.fft.hfft`. + Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.hfft`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc and 0: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc and 0: norm_ = get_validated_norm(norm) if axis is None: @@ -292,11 +305,11 @@ def hfft(x1, n=None, axis=-1, norm=None): axis_param = axis if n is None: - input_boundarie = x1_desc.shape[axis_param] + input_boundarie = x_desc.shape[axis_param] else: input_boundarie = n - if x1.size < 1: + if x.size < 1: pass # let fallback to handle exception elif input_boundarie < 1: pass # let fallback to handle exception @@ -306,7 +319,7 @@ def hfft(x1, n=None, axis=-1, norm=None): output_boundarie = input_boundarie return dpnp_fft( - x1_desc, + x_desc, input_boundarie, output_boundarie, axis_param, @@ -314,25 +327,32 @@ def hfft(x1, n=None, axis=-1, norm=None): norm_.value, ).get_pyobj() - return call_origin(numpy.fft.hfft, x1, n, axis, norm) + return call_origin(numpy.fft.hfft, x, n, axis, norm) -def ifft(x1, n=None, axis=-1, norm=None): +def ifft(x, n=None, axis=-1, norm=None): """ Compute the one-dimensional inverse discrete Fourier Transform. + For full documentation refer to :obj:`numpy.fft.ifft`. + Limitations ----------- - Parameter ``axis`` is supported with its default value. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64``, - ``dpnp.complex64``and ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.ifft`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `axis` is supported with its default value. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`,, + `dpnp.complex128`, `dpnp.complex64` data types are supported. + The `dpnp.bool` data type is not supported and will raise a `TypeError` exception. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc: + dt = x_desc.dtype + if dpnp.issubdtype(dt, dpnp.bool): + raise TypeError(f"The `{dt}` data type is unsupported.") + norm_ = get_validated_norm(norm) if axis is None: @@ -341,11 +361,11 @@ def ifft(x1, n=None, axis=-1, norm=None): axis_param = axis if n is None: - input_boundarie = x1_desc.shape[axis_param] + input_boundarie = x_desc.shape[axis_param] else: input_boundarie = n - if x1_desc.size < 1: + if x_desc.size < 1: pass # let fallback to handle exception elif input_boundarie < 1: pass # let fallback to handle exception @@ -354,7 +374,7 @@ def ifft(x1, n=None, axis=-1, norm=None): else: output_boundarie = input_boundarie return dpnp_fft( - x1_desc, + x_desc, input_boundarie, output_boundarie, axis_param, @@ -362,63 +382,73 @@ def ifft(x1, n=None, axis=-1, norm=None): norm_.value, ).get_pyobj() - return call_origin(numpy.fft.ifft, x1, n, axis, norm) + return call_origin(numpy.fft.ifft, x, n, axis, norm) -def ifft2(x1, s=None, axes=(-2, -1), norm=None): +def ifft2(x, s=None, axes=(-2, -1), norm=None): """ - Compute the 2-dimensional inverse discrete Fourier Transform + Compute the 2-dimensional inverse discrete Fourier Transform. + + Multi-dimensional arrays computed as batch of 1-D arrays. - Multi-dimensional arrays computed as batch of 1-D arrays + For full documentation refer to :obj:`numpy.fft.ifft2`. Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.ifft2`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc: if norm is not None: pass else: - return ifftn(x1, s, axes, norm) + return ifftn(x, s, axes, norm) - return call_origin(numpy.fft.ifft2, x1, s, axes, norm) + return call_origin(numpy.fft.ifft2, x, s, axes, norm) -def ifftshift(x1, axes=None): +def ifftshift(x, axes=None): """ Inverse shift the zero-frequency component to the center of the spectrum. + For full documentation refer to :obj:`numpy.fft.ifftshift`. + Limitations ----------- - Parameter ``axes`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.ifftshift`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `axes` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc and 0: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc and 0: norm_ = Norm.backward - if axis is None: + if axes is None: axis_param = -1 # the most right dimension (default value) else: axis_param = axes - if x1_desc.size < 1: + input_boundarie = x_desc.shape[axis_param] + + if x_desc.size < 1: + pass # let fallback to handle exception + elif input_boundarie < 1: pass # let fallback to handle exception else: + output_boundarie = input_boundarie + return dpnp_fft( - x1_desc, + x_desc, input_boundarie, output_boundarie, axis_param, @@ -426,41 +456,43 @@ def ifftshift(x1, axes=None): norm_.value, ).get_pyobj() - return call_origin(numpy.fft.ifftshift, x1, axes) + return call_origin(numpy.fft.ifftshift, x, axes) -def ifftn(x1, s=None, axes=None, norm=None): +def ifftn(x, s=None, axes=None, norm=None): """ Compute the N-dimensional inverse discrete Fourier Transform. - Multi-dimensional arrays computed as batch of 1-D arrays + Multi-dimensional arrays computed as batch of 1-D arrays. + + For full documentation refer to :obj:`numpy.fft.ifftn`. Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.ifftn`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc and 0: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc and 0: if s is None: - boundaries = tuple([x1_desc.shape[i] for i in range(x1_desc.ndim)]) + boundaries = tuple([x_desc.shape[i] for i in range(x_desc.ndim)]) else: boundaries = s if axes is None: - axes_param = tuple([i for i in range(x1_desc.ndim)]) + axes_param = tuple([i for i in range(x_desc.ndim)]) else: axes_param = axes if norm is not None: pass else: - x1_iter = x1 + x_iter = x iteration_list = list(range(len(axes_param))) iteration_list.reverse() # inplace operation for it in iteration_list: @@ -475,35 +507,37 @@ def ifftn(x1, s=None, axes=None, norm=None): f"< {len(boundaries)}", ) - x1_iter_desc = dpnp.get_dpnp_descriptor(x1_iter) - x1_iter = ifft( - x1_iter_desc.get_pyobj(), + x_iter_desc = dpnp.get_dpnp_descriptor(x_iter) + x_iter = ifft( + x_iter_desc.get_pyobj(), n=param_n, axis=param_axis, norm=norm, ) - return x1_iter + return x_iter - return call_origin(numpy.fft.ifftn, x1, s, axes, norm) + return call_origin(numpy.fft.ifftn, x, s, axes, norm) -def ihfft(x1, n=None, axis=-1, norm=None): +def ihfft(x, n=None, axis=-1, norm=None): """ Compute inverse one-dimensional discrete Fourier Transform of a signal that has Hermitian symmetry. + For full documentation refer to :obj:`numpy.fft.ihfft`. + Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.ihfft`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc and 0: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc and 0: norm_ = get_validated_norm(norm) if axis is None: @@ -512,11 +546,11 @@ def ihfft(x1, n=None, axis=-1, norm=None): axis_param = axis if n is None: - input_boundarie = x1_desc.shape[axis_param] + input_boundarie = x_desc.shape[axis_param] else: input_boundarie = n - if x1_desc.size < 1: + if x_desc.size < 1: pass # let fallback to handle exception elif input_boundarie < 1: pass # let fallback to handle exception @@ -528,7 +562,7 @@ def ihfft(x1, n=None, axis=-1, norm=None): output_boundarie = input_boundarie return dpnp_fft( - x1_desc, + x_desc, input_boundarie, output_boundarie, axis_param, @@ -536,25 +570,27 @@ def ihfft(x1, n=None, axis=-1, norm=None): norm_.value, ).get_pyobj() - return call_origin(numpy.fft.ihfft, x1, n, axis, norm) + return call_origin(numpy.fft.ihfft, x, n, axis, norm) -def irfft(x1, n=None, axis=-1, norm=None): +def irfft(x, n=None, axis=-1, norm=None): """ - Compute the one-dimensional inverse discrete Fourier Transform for real input.. + Compute the one-dimensional inverse discrete Fourier Transform for real input. + + For full documentation refer to :obj:`numpy.fft.irfft`. Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.irfft`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc and 0: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc and 0: norm_ = get_validated_norm(norm) if axis is None: @@ -563,11 +599,11 @@ def irfft(x1, n=None, axis=-1, norm=None): axis_param = axis if n is None: - input_boundarie = x1_desc.shape[axis_param] + input_boundarie = x_desc.shape[axis_param] else: input_boundarie = n - if x1_desc.size < 1: + if x_desc.size < 1: pass # let fallback to handle exception elif input_boundarie < 1: pass # let fallback to handle exception @@ -579,7 +615,7 @@ def irfft(x1, n=None, axis=-1, norm=None): output_boundarie = 2 * (input_boundarie - 1) result = dpnp_rfft( - x1_desc, + x_desc, input_boundarie, output_boundarie, axis_param, @@ -592,67 +628,71 @@ def irfft(x1, n=None, axis=-1, norm=None): # tmp[it] = result[it].real return result - return call_origin(numpy.fft.irfft, x1, n, axis, norm) + return call_origin(numpy.fft.irfft, x, n, axis, norm) -def irfft2(x1, s=None, axes=(-2, -1), norm=None): +def irfft2(x, s=None, axes=(-2, -1), norm=None): """ Compute the 2-dimensional inverse discrete Fourier Transform for real input. - Multi-dimensional arrays computed as batch of 1-D arrays + Multi-dimensional arrays computed as batch of 1-D arrays. + + For full documentation refer to :obj:`numpy.fft.irfft2`. Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.irfft2`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc: if norm is not None: pass else: - return irfftn(x1_desc.get_pyobj(), s, axes, norm) + return irfftn(x_desc.get_pyobj(), s, axes, norm) - return call_origin(numpy.fft.irfft2, x1, s, axes, norm) + return call_origin(numpy.fft.irfft2, x, s, axes, norm) -def irfftn(x1, s=None, axes=None, norm=None): +def irfftn(x, s=None, axes=None, norm=None): """ Compute the N-dimensional inverse discrete Fourier Transform for real input. - Multi-dimensional arrays computed as batch of 1-D arrays + Multi-dimensional arrays computed as batch of 1-D arrays. + + For full documentation refer to :obj:`numpy.fft.irfftn`. Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.irfftn`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc and 0: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc and 0: if s is None: - boundaries = tuple([x1_desc.shape[i] for i in range(x1_desc.ndim)]) + boundaries = tuple([x_desc.shape[i] for i in range(x_desc.ndim)]) else: boundaries = s if axes is None: - axes_param = tuple([i for i in range(x1_desc.ndim)]) + axes_param = tuple([i for i in range(x_desc.ndim)]) else: axes_param = axes if norm is not None: pass else: - x1_iter = x1 + x_iter = x iteration_list = list(range(len(axes_param))) iteration_list.reverse() # inplace operation for it in iteration_list: @@ -667,35 +707,41 @@ def irfftn(x1, s=None, axes=None, norm=None): f"< {len(boundaries)}", ) - x1_iter_desc = dpnp.get_dpnp_descriptor(x1_iter) - x1_iter = irfft( - x1_iter_desc.get_pyobj(), + x_iter_desc = dpnp.get_dpnp_descriptor(x_iter) + x_iter = irfft( + x_iter_desc.get_pyobj(), n=param_n, axis=param_axis, norm=norm, ) - return x1_iter + return x_iter - return call_origin(numpy.fft.irfftn, x1, s, axes, norm) + return call_origin(numpy.fft.irfftn, x, s, axes, norm) -def rfft(x1, n=None, axis=-1, norm=None): +def rfft(x, n=None, axis=-1, norm=None): """ Compute the one-dimensional discrete Fourier Transform for real input. - Limitations - ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - For full documentation refer to :obj:`numpy.fft.rfft`. + Limitations + ----------- + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + The `dpnp.bool` data type is not supported and will raise a `TypeError` exception. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc: + dt = x_desc.dtype + if dpnp.issubdtype(dt, dpnp.bool): + raise TypeError(f"The `{dt}` data type is unsupported.") + norm_ = get_validated_norm(norm) if axis is None: @@ -704,11 +750,11 @@ def rfft(x1, n=None, axis=-1, norm=None): axis_param = axis if n is None: - input_boundarie = x1_desc.shape[axis_param] + input_boundarie = x_desc.shape[axis_param] else: input_boundarie = n - if x1_desc.size < 1: + if x_desc.size < 1: pass # let fallback to handle exception elif input_boundarie < 1: pass # let fallback to handle exception @@ -718,14 +764,14 @@ def rfft(x1, n=None, axis=-1, norm=None): pass elif n is not None: pass - elif x1_desc.dtype in (numpy.complex128, numpy.complex64): + elif x_desc.dtype in (numpy.complex128, numpy.complex64): pass else: output_boundarie = ( input_boundarie // 2 + 1 ) # rfft specific requirenment return dpnp_rfft( - x1_desc, + x_desc, input_boundarie, output_boundarie, axis_param, @@ -733,75 +779,79 @@ def rfft(x1, n=None, axis=-1, norm=None): norm_.value, ).get_pyobj() - return call_origin(numpy.fft.rfft, x1, n, axis, norm) + return call_origin(numpy.fft.rfft, x, n, axis, norm) -def rfft2(x1, s=None, axes=(-2, -1), norm=None): +def rfft2(x, s=None, axes=(-2, -1), norm=None): """ Compute the 2-dimensional discrete Fourier Transform for real input. - Multi-dimensional arrays computed as batch of 1-D arrays + Multi-dimensional arrays computed as batch of 1-D arrays. + + For full documentation refer to :obj:`numpy.fft.rfft2`. Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.rfft2`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc: if norm is not None: pass else: - return rfftn(x1_desc.get_pyobj(), s, axes, norm) + return rfftn(x_desc.get_pyobj(), s, axes, norm) - return call_origin(numpy.fft.rfft2, x1, s, axes, norm) + return call_origin(numpy.fft.rfft2, x, s, axes, norm) def rfftfreq(n=None, d=1.0): """ Compute the one-dimensional discrete Fourier Transform sample frequencies. + For full documentation refer to :obj:`numpy.fft.rfftfreq`. + Limitations ----------- Parameter ``d`` is unsupported. - For full documentation refer to :obj:`numpy.fft.rfftfreq`. - """ return call_origin(numpy.fft.rfftfreq, n, d) -def rfftn(x1, s=None, axes=None, norm=None): +def rfftn(x, s=None, axes=None, norm=None): """ Compute the N-dimensional discrete Fourier Transform for real input. - Multi-dimensional arrays computed as batch of 1-D arrays + Multi-dimensional arrays computed as batch of 1-D arrays. + + For full documentation refer to :obj:`numpy.fft.rfftn`. Limitations ----------- - Parameter ``norm`` is unsupported. - Parameter ``x1`` supports ``dpnp.int32``, ``dpnp.int64``, ``dpnp.float32``, ``dpnp.float64`` and - ``dpnp.complex128`` datatypes only. - - For full documentation refer to :obj:`numpy.fft.rfftn`. + Parameter `x` is supported either as :class:`dpnp.ndarray`. + Parameter `norm` is unsupported. + Only `dpnp.float64`, `dpnp.float32`, `dpnp.int64`, `dpnp.int32`, + `dpnp.complex128` data types are supported. + Otherwise the function will be executed sequentially on CPU. """ - x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False) - if x1_desc and 0: + x_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False) + if x_desc and 0: if s is None: - boundaries = tuple([x1_desc.shape[i] for i in range(x1_desc.ndim)]) + boundaries = tuple([x_desc.shape[i] for i in range(x_desc.ndim)]) else: boundaries = s if axes is None: - axes_param = tuple([i for i in range(x1_desc.ndim)]) + axes_param = tuple([i for i in range(x_desc.ndim)]) else: axes_param = axes @@ -810,7 +860,7 @@ def rfftn(x1, s=None, axes=None, norm=None): elif len(axes) < 1: pass # let fallback to handle exception else: - x1_iter = x1 + x_iter = x iteration_list = list(range(len(axes_param))) iteration_list.reverse() # inplace operation for it in iteration_list: @@ -825,16 +875,16 @@ def rfftn(x1, s=None, axes=None, norm=None): f"< {len(boundaries)}", ) - x1_iter_desc = dpnp.get_dpnp_descriptor( - x1_iter, copy_when_nondefault_queue=False + x_iter_desc = dpnp.get_dpnp_descriptor( + x_iter, copy_when_nondefault_queue=False ) - x1_iter = rfft( - x1_iter_desc.get_pyobj(), + x_iter = rfft( + x_iter_desc.get_pyobj(), n=param_n, axis=param_axis, norm=norm, ) - return x1_iter + return x_iter - return call_origin(numpy.fft.rfftn, x1, s, axes, norm) + return call_origin(numpy.fft.rfftn, x, s, axes, norm) diff --git a/tests/test_fft.py b/tests/test_fft.py index ae6835b86ed..0d2ea664b58 100644 --- a/tests/test_fft.py +++ b/tests/test_fft.py @@ -3,20 +3,14 @@ import dpnp -from .helper import assert_dtype_allclose, has_support_aspect64 - -pytestmark = pytest.mark.skipif( - not has_support_aspect64(), reason="Aborted on Iris Xe: SAT-6028" -) +from .helper import assert_dtype_allclose, get_all_dtypes -@pytest.mark.parametrize( - "type", ["complex128", "complex64", "float32", "float64", "int32", "int64"] -) +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True)) @pytest.mark.parametrize("norm", [None, "forward", "ortho"]) -def test_fft(type, norm): +def test_fft(dtype, norm): # 1 dim array - data = numpy.arange(100, dtype=numpy.dtype(type)) + data = numpy.arange(100, dtype=dtype) dpnp_data = dpnp.array(data) np_res = numpy.fft.fft(data, norm=norm) @@ -25,14 +19,12 @@ def test_fft(type, norm): assert_dtype_allclose(dpnp_res, np_res) -@pytest.mark.parametrize( - "type", ["complex128", "complex64", "float32", "float64", "int32", "int64"] -) +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True)) @pytest.mark.parametrize("shape", [(8, 8), (4, 16), (4, 4, 4), (2, 4, 4, 2)]) @pytest.mark.parametrize("norm", [None, "forward", "ortho"]) -def test_fft_ndim(type, shape, norm): - np_data = numpy.arange(64, dtype=numpy.dtype(type)).reshape(shape) - dpnp_data = dpnp.arange(64, dtype=numpy.dtype(type)).reshape(shape) +def test_fft_ndim(dtype, shape, norm): + np_data = numpy.arange(64, dtype=dtype).reshape(shape) + dpnp_data = dpnp.arange(64, dtype=dtype).reshape(shape) np_res = numpy.fft.fft(np_data, norm=norm) dpnp_res = dpnp.fft.fft(dpnp_data, norm=norm) @@ -40,16 +32,14 @@ def test_fft_ndim(type, shape, norm): assert_dtype_allclose(dpnp_res, np_res) -@pytest.mark.parametrize( - "type", ["complex128", "complex64", "float32", "float64", "int32", "int64"] -) +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True)) @pytest.mark.parametrize( "shape", [(64,), (8, 8), (4, 16), (4, 4, 4), (2, 4, 4, 2)] ) @pytest.mark.parametrize("norm", [None, "forward", "ortho"]) -def test_fft_ifft(type, shape, norm): - np_data = numpy.arange(64, dtype=numpy.dtype(type)).reshape(shape) - dpnp_data = dpnp.arange(64, dtype=numpy.dtype(type)).reshape(shape) +def test_fft_ifft(dtype, shape, norm): + np_data = numpy.arange(64, dtype=dtype).reshape(shape) + dpnp_data = dpnp.arange(64, dtype=dtype).reshape(shape) np_res = numpy.fft.ifft(np_data, norm=norm) dpnp_res = dpnp.fft.ifft(dpnp_data, norm=norm) @@ -57,15 +47,30 @@ def test_fft_ifft(type, shape, norm): assert_dtype_allclose(dpnp_res, np_res) -@pytest.mark.parametrize("type", ["float32", "float64", "int32", "int64"]) +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) @pytest.mark.parametrize( "shape", [(64,), (8, 8), (4, 16), (4, 4, 4), (2, 4, 4, 2)] ) -def test_fft_rfft(type, shape): - np_data = numpy.arange(64, dtype=numpy.dtype(type)).reshape(shape) - dpnp_data = dpnp.arange(64, dtype=numpy.dtype(type)).reshape(shape) +def test_fft_rfft(dtype, shape): + np_data = numpy.arange(64, dtype=dtype).reshape(shape) + dpnp_data = dpnp.arange(64, dtype=dtype).reshape(shape) np_res = numpy.fft.rfft(np_data) dpnp_res = dpnp.fft.rfft(dpnp_data) assert_dtype_allclose(dpnp_res, np_res) + + +@pytest.mark.parametrize( + "func_name", + [ + "fft", + "ifft", + "rfft", + ], +) +def test_fft_invalid_dtype(func_name): + a = dpnp.array([True, False, True]) + dpnp_func = getattr(dpnp.fft, func_name) + with pytest.raises(TypeError): + dpnp_func(a) diff --git a/tests/third_party/cupy/fft_tests/test_fft.py b/tests/third_party/cupy/fft_tests/test_fft.py index 0ddd3cc56c1..0cc49200a24 100644 --- a/tests/third_party/cupy/fft_tests/test_fft.py +++ b/tests/third_party/cupy/fft_tests/test_fft.py @@ -6,6 +6,7 @@ import pytest import dpnp as cupy +from tests.helper import has_support_aspect64 from tests.third_party.cupy import testing @@ -14,7 +15,7 @@ { "n": [None, 0, 5, 10, 15], "shape": [(0,), (10, 0), (10,), (10, 10)], - "norm": [None, "ortho", ""], + "norm": [None, "backward", "ortho", "forward", ""], } ) ) @@ -26,8 +27,7 @@ class TestFft(unittest.TestCase): rtol=1e-4, atol=1e-7, accept_error=ValueError, - contiguous_check=False, - type_check=False, + type_check=has_support_aspect64(), ) def test_fft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) @@ -40,8 +40,7 @@ def test_fft(self, xp, dtype): rtol=1e-4, atol=1e-7, accept_error=ValueError, - contiguous_check=False, - type_check=False, + type_check=has_support_aspect64(), ) def test_ifft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) @@ -80,7 +79,10 @@ def test_ifft(self, xp, dtype): class TestFft2(unittest.TestCase): @testing.for_all_dtypes() @testing.numpy_cupy_allclose( - rtol=1e-4, atol=1e-7, accept_error=ValueError, contiguous_check=False + rtol=1e-4, + atol=1e-7, + accept_error=ValueError, + type_check=has_support_aspect64(), ) def test_fft2(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) @@ -90,7 +92,10 @@ def test_fft2(self, xp, dtype): @testing.for_all_dtypes() @testing.numpy_cupy_allclose( - rtol=1e-4, atol=1e-7, accept_error=ValueError, contiguous_check=False + rtol=1e-4, + atol=1e-7, + accept_error=ValueError, + type_check=has_support_aspect64(), ) def test_ifft2(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) @@ -130,7 +135,10 @@ def test_ifft2(self, xp, dtype): class TestFftn(unittest.TestCase): @testing.for_all_dtypes() @testing.numpy_cupy_allclose( - rtol=1e-4, atol=1e-7, accept_error=ValueError, contiguous_check=False + rtol=1e-4, + atol=1e-7, + accept_error=ValueError, + type_check=has_support_aspect64(), ) def test_fftn(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) @@ -140,7 +148,10 @@ def test_fftn(self, xp, dtype): @testing.for_all_dtypes() @testing.numpy_cupy_allclose( - rtol=1e-4, atol=1e-7, accept_error=ValueError, contiguous_check=False + rtol=1e-4, + atol=1e-7, + accept_error=ValueError, + type_check=has_support_aspect64(), ) def test_ifftn(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) @@ -163,7 +174,9 @@ def test_ifftn(self, xp, dtype): class TestRfft(unittest.TestCase): @testing.for_all_dtypes(no_complex=True) @testing.numpy_cupy_allclose( - rtol=1e-4, atol=1e-7, contiguous_check=False, type_check=False + rtol=1e-4, + atol=1e-7, + type_check=has_support_aspect64(), ) def test_rfft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) @@ -172,7 +185,11 @@ def test_rfft(self, xp, dtype): return out @testing.for_all_dtypes() - @testing.numpy_cupy_allclose(rtol=1e-4, atol=1e-7, contiguous_check=False) + @testing.numpy_cupy_allclose( + rtol=1e-4, + atol=1e-7, + type_check=has_support_aspect64(), + ) def test_irfft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) out = xp.fft.irfft(a, n=self.n, norm=self.norm) @@ -237,7 +254,11 @@ def test_irfftn(self, dtype): @testing.gpu class TestHfft(unittest.TestCase): @testing.for_all_dtypes() - @testing.numpy_cupy_allclose(rtol=1e-4, atol=1e-7, contiguous_check=False) + @testing.numpy_cupy_allclose( + rtol=1e-4, + atol=1e-7, + type_check=has_support_aspect64(), + ) def test_hfft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) out = xp.fft.hfft(a, n=self.n, norm=self.norm) @@ -245,7 +266,11 @@ def test_hfft(self, xp, dtype): return out @testing.for_all_dtypes(no_complex=True) - @testing.numpy_cupy_allclose(rtol=1e-4, atol=1e-7, contiguous_check=False) + @testing.numpy_cupy_allclose( + rtol=1e-4, + atol=1e-7, + type_check=has_support_aspect64(), + ) def test_ihfft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) out = xp.fft.ihfft(a, n=self.n, norm=self.norm) @@ -262,14 +287,22 @@ def test_ihfft(self, xp, dtype): @testing.gpu class TestFftfreq(unittest.TestCase): @testing.for_all_dtypes() - @testing.numpy_cupy_allclose(rtol=1e-4, atol=1e-7, contiguous_check=False) + @testing.numpy_cupy_allclose( + rtol=1e-4, + atol=1e-7, + type_check=has_support_aspect64(), + ) def test_fftfreq(self, xp, dtype): out = xp.fft.fftfreq(self.n, self.d) return out @testing.for_all_dtypes() - @testing.numpy_cupy_allclose(rtol=1e-4, atol=1e-7, contiguous_check=False) + @testing.numpy_cupy_allclose( + rtol=1e-4, + atol=1e-7, + type_check=has_support_aspect64(), + ) def test_rfftfreq(self, xp, dtype): out = xp.fft.rfftfreq(self.n, self.d) @@ -289,7 +322,11 @@ def test_rfftfreq(self, xp, dtype): @testing.gpu class TestFftshift(unittest.TestCase): @testing.for_all_dtypes() - @testing.numpy_cupy_allclose(rtol=1e-4, atol=1e-7, contiguous_check=False) + @testing.numpy_cupy_allclose( + rtol=1e-4, + atol=1e-7, + type_check=has_support_aspect64(), + ) def test_fftshift(self, xp, dtype): x = testing.shaped_random(self.shape, xp, dtype) out = xp.fft.fftshift(x, self.axes) @@ -297,7 +334,11 @@ def test_fftshift(self, xp, dtype): return out @testing.for_all_dtypes() - @testing.numpy_cupy_allclose(rtol=1e-4, atol=1e-7, contiguous_check=False) + @testing.numpy_cupy_allclose( + rtol=1e-4, + atol=1e-7, + type_check=has_support_aspect64(), + ) def test_ifftshift(self, xp, dtype): x = testing.shaped_random(self.shape, xp, dtype) out = xp.fft.ifftshift(x, self.axes) From 0dc03bb80a070259937f3452afbc0208b09217c6 Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Mon, 21 Aug 2023 17:38:31 +0200 Subject: [PATCH 53/58] Removed faulty tests from scope of public CI (#1528) --- .github/workflows/conda-package.yml | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/.github/workflows/conda-package.yml b/.github/workflows/conda-package.yml index bc9f64aae4a..7583fee5d91 100644 --- a/.github/workflows/conda-package.yml +++ b/.github/workflows/conda-package.yml @@ -10,9 +10,9 @@ env: PACKAGE_NAME: dpnp MODULE_NAME: dpnp CHANNELS: '-c dppy/label/dev -c intel -c conda-forge --override-channels' + # TODO: to add test_arraymanipulation.py back to the scope once crash on Windows is gone TEST_SCOPE: >- test_arraycreation.py - test_arraymanipulation.py test_dot.py test_dparray.py test_fft.py @@ -121,9 +121,7 @@ jobs: python: ['3.8', '3.9', '3.10', '3.11'] os: [ubuntu-20.04, ubuntu-latest] - experimental: [false] - - continue-on-error: ${{ matrix.experimental }} + continue-on-error: true env: conda-pkgs: '/home/runner/conda_pkgs_dir/' @@ -222,9 +220,8 @@ jobs: strategy: matrix: python: ['3.8', '3.9', '3.10', '3.11'] - experimental: [false] - continue-on-error: ${{ matrix.experimental }} + continue-on-error: true env: conda-pkgs: 'C:\Users\runneradmin\conda_pkgs_dir\' From ff2dd42330f3d4d46ad4b4d9dd9460761dbd2e59 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Wed, 16 Aug 2023 14:35:21 -0500 Subject: [PATCH 54/58] use dpctl.tensor.sign and dpctl.tensor.negative in dpnp --- dpnp/backend/include/dpnp_iface_fptr.hpp | 8 +- dpnp/backend/kernels/dpnp_krnl_elemwise.cpp | 18 --- dpnp/dpnp_algo/dpnp_algo.pxd | 5 - dpnp/dpnp_algo/dpnp_algo_mathematical.pxi | 10 -- dpnp/dpnp_algo/dpnp_elementwise_common.py | 77 +++++++++++++ dpnp/dpnp_iface_mathematical.py | 118 +++++++++++++++----- dpnp/dpnp_iface_trigonometric.py | 48 ++++---- tests/skipped_tests_gpu.tbl | 2 - 8 files changed, 193 insertions(+), 93 deletions(-) diff --git a/dpnp/backend/include/dpnp_iface_fptr.hpp b/dpnp/backend/include/dpnp_iface_fptr.hpp index 418bed21966..733d2d0f215 100644 --- a/dpnp/backend/include/dpnp_iface_fptr.hpp +++ b/dpnp/backend/include/dpnp_iface_fptr.hpp @@ -282,8 +282,6 @@ enum class DPNPFuncName : size_t DPNP_FN_NANVAR_EXT, /**< Used in numpy.nanvar() impl, requires extra parameters */ DPNP_FN_NEGATIVE, /**< Used in numpy.negative() impl */ - DPNP_FN_NEGATIVE_EXT, /**< Used in numpy.negative() impl, requires extra - parameters */ DPNP_FN_NONZERO, /**< Used in numpy.nonzero() impl */ DPNP_FN_NOT_EQUAL_EXT, /**< Used in numpy.not_equal() impl, requires extra parameters */ @@ -441,10 +439,8 @@ enum class DPNPFuncName : size_t DPNP_FN_SEARCHSORTED_EXT, /**< Used in numpy.searchsorted() impl, requires extra parameters */ DPNP_FN_SIGN, /**< Used in numpy.sign() impl */ - DPNP_FN_SIGN_EXT, /**< Used in numpy.sign() impl, requires extra parameters - */ - DPNP_FN_SIN, /**< Used in numpy.sin() impl */ - DPNP_FN_SINH, /**< Used in numpy.sinh() impl */ + DPNP_FN_SIN, /**< Used in numpy.sin() impl */ + DPNP_FN_SINH, /**< Used in numpy.sinh() impl */ DPNP_FN_SINH_EXT, /**< Used in numpy.sinh() impl, requires extra parameters */ DPNP_FN_SORT, /**< Used in numpy.sort() impl */ diff --git a/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp b/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp index d99510fde91..8fb510e9fab 100644 --- a/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp @@ -1061,15 +1061,6 @@ static void func_map_init_elemwise_1arg_1type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_NEGATIVE][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_negative_c_default}; - fmap[DPNPFuncName::DPNP_FN_NEGATIVE_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_negative_c_ext}; - fmap[DPNPFuncName::DPNP_FN_NEGATIVE_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_negative_c_ext}; - fmap[DPNPFuncName::DPNP_FN_NEGATIVE_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_negative_c_ext}; - fmap[DPNPFuncName::DPNP_FN_NEGATIVE_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_negative_c_ext}; - fmap[DPNPFuncName::DPNP_FN_RECIP][eft_INT][eft_INT] = { eft_INT, (void *)dpnp_recip_c_default}; fmap[DPNPFuncName::DPNP_FN_RECIP][eft_LNG][eft_LNG] = { @@ -1097,15 +1088,6 @@ static void func_map_init_elemwise_1arg_1type(func_map_t &fmap) fmap[DPNPFuncName::DPNP_FN_SIGN][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_sign_c_default}; - fmap[DPNPFuncName::DPNP_FN_SIGN_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_sign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_SIGN_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_sign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_SIGN_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_sign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_SIGN_EXT][eft_DBL][eft_DBL] = { - eft_DBL, (void *)dpnp_sign_c_ext}; - fmap[DPNPFuncName::DPNP_FN_SQUARE][eft_INT][eft_INT] = { eft_INT, (void *)dpnp_square_c_default}; fmap[DPNPFuncName::DPNP_FN_SQUARE][eft_LNG][eft_LNG] = { diff --git a/dpnp/dpnp_algo/dpnp_algo.pxd b/dpnp/dpnp_algo/dpnp_algo.pxd index 089e45e7c71..9a4a556435a 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pxd +++ b/dpnp/dpnp_algo/dpnp_algo.pxd @@ -160,8 +160,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_MODF_EXT DPNP_FN_NANVAR DPNP_FN_NANVAR_EXT - DPNP_FN_NEGATIVE - DPNP_FN_NEGATIVE_EXT DPNP_FN_NONZERO DPNP_FN_ONES DPNP_FN_ONES_LIKE @@ -257,8 +255,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_RNG_ZIPF_EXT DPNP_FN_SEARCHSORTED DPNP_FN_SEARCHSORTED_EXT - DPNP_FN_SIGN - DPNP_FN_SIGN_EXT DPNP_FN_SINH DPNP_FN_SINH_EXT DPNP_FN_SORT @@ -427,7 +423,6 @@ cpdef dpnp_descriptor dpnp_maximum(dpnp_descriptor x1_obj, dpnp_descriptor x2_ob dpnp_descriptor out=*, object where=*) cpdef dpnp_descriptor dpnp_minimum(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*, dpnp_descriptor out=*, object where=*) -cpdef dpnp_descriptor dpnp_negative(dpnp_descriptor array1) cpdef dpnp_descriptor dpnp_power(dpnp_descriptor x1_obj, dpnp_descriptor x2_obj, object dtype=*, dpnp_descriptor out=*, object where=*) diff --git a/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi b/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi index 64003efd7ac..86579933b01 100644 --- a/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi +++ b/dpnp/dpnp_algo/dpnp_algo_mathematical.pxi @@ -56,10 +56,8 @@ __all__ += [ "dpnp_nancumsum", "dpnp_nanprod", "dpnp_nansum", - "dpnp_negative", "dpnp_power", "dpnp_prod", - "dpnp_sign", "dpnp_sum", "dpnp_trapz", ] @@ -455,10 +453,6 @@ cpdef utils.dpnp_descriptor dpnp_nansum(utils.dpnp_descriptor x1): return dpnp_sum(result) -cpdef utils.dpnp_descriptor dpnp_negative(dpnp_descriptor x1): - return call_fptr_1in_1out_strides(DPNP_FN_NEGATIVE_EXT, x1) - - cpdef utils.dpnp_descriptor dpnp_power(utils.dpnp_descriptor x1_obj, utils.dpnp_descriptor x2_obj, object dtype=None, @@ -529,10 +523,6 @@ cpdef utils.dpnp_descriptor dpnp_prod(utils.dpnp_descriptor x1, return result -cpdef utils.dpnp_descriptor dpnp_sign(utils.dpnp_descriptor x1): - return call_fptr_1in_1out_strides(DPNP_FN_SIGN_EXT, x1) - - cpdef utils.dpnp_descriptor dpnp_sum(utils.dpnp_descriptor x1, object axis=None, object dtype=None, diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index 7735dae3634..188bfe7d2a4 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -1390,6 +1390,42 @@ def dpnp_multiply(x1, x2, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_negative_docstring = """ +negative(x, out=None, order="K") + +Computes the numerical negative for each element `x_i` of input array `x`. + +Args: + x (dpnp.ndarray): + Input array, expected to have numeric data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + An array containing the negative of `x`. +""" + + +negative_func = UnaryElementwiseFunc( + "negative", ti._negative_result_type, ti._negative, _negative_docstring +) + + +def dpnp_negative(x, out=None, order="K"): + """Invokes negative() from dpctl.tensor implementation for negative() function.""" + + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + res_usm = negative_func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _not_equal_docstring_ = """ not_equal(x1, x2, out=None, order="K") @@ -1525,6 +1561,47 @@ def dpnp_right_shift(x1, x2, out=None, order="K"): return dpnp_array._create_from_usm_ndarray(res_usm) +_sign_docstring = """ +sign(x, out=None, order="K") + +Computes an indication of the sign of each element `x_i` of input array `x` +using the signum function. + +The signum function returns `-1` if `x_i` is less than `0`, +`0` if `x_i` is equal to `0`, and `1` if `x_i` is greater than `0`. + +Args: + x (dpnp.ndarray): + Input array, expected to have numeric data type. + out ({None, dpnp.ndarray}, optional): + Output array to populate. + Array have the correct shape and the expected data type. + order ("C","F","A","K", optional): + Memory layout of the newly output array, if parameter `out` is `None`. + Default: "K". +Returns: + dpnp.ndarray: + An array containing the element-wise results. The data type of the + returned array is determined by the Type Promotion Rules. +""" + + +sign_func = UnaryElementwiseFunc( + "sign", ti._sign_result_type, ti._sign, _sign_docstring +) + + +def dpnp_sign(x, out=None, order="K"): + """Invokes sign() from dpctl.tensor implementation for sign() function.""" + + # dpctl.tensor only works with usm_ndarray + x1_usm = dpnp.get_usm_ndarray(x) + out_usm = None if out is None else dpnp.get_usm_ndarray(out) + + res_usm = sign_func(x1_usm, out=out_usm, order=order) + return dpnp_array._create_from_usm_ndarray(res_usm) + + _sin_docstring = """ sin(x, out=None, order='K') Computes sine for each element `x_i` of input array `x`. diff --git a/dpnp/dpnp_iface_mathematical.py b/dpnp/dpnp_iface_mathematical.py index a6850b0bce4..def99c6adbd 100644 --- a/dpnp/dpnp_iface_mathematical.py +++ b/dpnp/dpnp_iface_mathematical.py @@ -57,7 +57,9 @@ dpnp_floor, dpnp_floor_divide, dpnp_multiply, + dpnp_negative, dpnp_remainder, + dpnp_sign, dpnp_subtract, dpnp_trunc, ) @@ -1486,39 +1488,64 @@ def nansum(x1, **kwargs): return call_origin(numpy.nansum, x1, **kwargs) -def negative(x1, **kwargs): +def negative( + x, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, +): """ Negative element-wise. For full documentation refer to :obj:`numpy.negative`. + Returns + ------- + out : dpnp.ndarray + The numerical negative of each element of `x`. + Limitations ----------- - Parameter `x1` is supported as :class:`dpnp.ndarray`. + Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameters `where`, `dtype` and `subok` are supported with their default values. Keyword arguments `kwargs` are currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. See Also -------- - :obj:`dpnp.copysign` : Change the sign of x1 to that of x2, element-wise. + :obj:`dpnp.copysign` : Change the sign of `x1` to that of `x2`, element-wise. Examples -------- >>> import dpnp as np - >>> result = np.negative([1, -1]) - >>> [x for x in result] - [-1, 1] + >>> np.negative(np.array([1, -1])) + array([-1, 1]) + + The ``-`` operator can be used as a shorthand for ``negative`` on + :class:`dpnp.ndarray`. + >>> x = np.array([1., -1.]) + >>> -x + array([-1., 1.]) """ - x1_desc = dpnp.get_dpnp_descriptor( - x1, copy_when_strides=False, copy_when_nondefault_queue=False + return check_nd_call_func( + numpy.negative, + dpnp_negative, + x, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, ) - if x1_desc and not kwargs: - return dpnp_negative(x1_desc).get_pyobj() - - return call_origin(numpy.negative, x1, **kwargs) def power(x1, x2, /, out=None, *, where=True, dtype=None, subok=True, **kwargs): @@ -1759,35 +1786,70 @@ def round_(a, decimals=0, out=None): return around(a, decimals, out) -def sign(x1, **kwargs): +def sign( + x, + /, + out=None, + *, + order="K", + where=True, + dtype=None, + subok=True, + **kwargs, +): """ Returns an element-wise indication of the sign of a number. For full documentation refer to :obj:`numpy.sign`. + Returns + ------- + out : dpnp.ndarray + The indication of the sign of each element of `x`. + Limitations ----------- - Parameter `x1` is supported as :class:`dpnp.ndarray`. - Keyword arguments `kwargs` are currently unsupported. + Parameters `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. + Parameters `where`, `dtype` and `subok` are supported with their default values. + Keyword argument `kwargs` is currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. Examples -------- >>> import dpnp as np - >>> result = np.sign(np.array([-5., 4.5])) - >>> [x for x in result] - [-1.0, 1.0] - - """ - - x1_desc = dpnp.get_dpnp_descriptor( - x1, copy_when_strides=False, copy_when_nondefault_queue=False - ) - if x1_desc and not kwargs: - return dpnp_sign(x1_desc).get_pyobj() - - return call_origin(numpy.sign, x1, **kwargs) + >>> np.sign(np.array([-5., 4.5])) + array([-1.0, 1.0]) + >>> np.sign(np.array(0)) + array(0) + >>> np.sign(np.array(5-2j)) + array([1+0j]) + + """ + + if numpy.iscomplexobj(x): + return call_origin( + numpy.sign, + x, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, + ) + else: + return check_nd_call_func( + numpy.sign, + dpnp_sign, + x, + out=out, + where=where, + order=order, + dtype=dtype, + subok=subok, + **kwargs, + ) def subtract( diff --git a/dpnp/dpnp_iface_trigonometric.py b/dpnp/dpnp_iface_trigonometric.py index d64359e1db8..dc91fa75eda 100644 --- a/dpnp/dpnp_iface_trigonometric.py +++ b/dpnp/dpnp_iface_trigonometric.py @@ -97,7 +97,7 @@ def arccos(x1): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. + Input array is supported as :class:`dpnp.ndarray`. Input array data types are limited by supported DPNP :ref:`Data types`. See Also @@ -133,7 +133,7 @@ def arccosh(x1): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. + Input array is supported as :class:`dpnp.ndarray`. Input array data types are limited by supported DPNP :ref:`Data types`. See Also @@ -171,8 +171,8 @@ def arcsin(x1, out=None, **kwargs): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. + Input array is supported as :class:`dpnp.ndarray`. + Keyword argument `kwargs` is currently unsupported. Input array data types are limited by supported DPNP :ref:`Data types`. See Also @@ -217,7 +217,7 @@ def arcsinh(x1): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. + Input array is supported as :class:`dpnp.ndarray`. Input array data types are limited by supported DPNP :ref:`Data types`. Examples @@ -247,8 +247,8 @@ def arctan(x1, out=None, **kwargs): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. + Input array is supported as :class:`dpnp.ndarray`. + Keyword argument `kwargs` is currently unsupported. Input array data types are limited by supported DPNP :ref:`Data types`. See Also @@ -289,7 +289,7 @@ def arctanh(x1): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. + Input array is supported as :class:`dpnp.ndarray`. Input array data types are limited by supported DPNP :ref:`Data types`. Examples @@ -319,7 +319,7 @@ def cbrt(x1): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. + Input array is supported as :class:`dpnp.ndarray`. Input array data types are limited by supported DPNP :ref:`Data types`. Examples @@ -349,10 +349,10 @@ def arctan2(x1, x2, dtype=None, out=None, where=True, **kwargs): Limitations ----------- - Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar. - Parameters ``dtype``, ``out`` and ``where`` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. + Parameters `x1` and `x2` are supported as either :obj:`dpnp.ndarray` or scalar. + Parameters `dtype`, `out` and `where` are supported with their default values. + Keyword argument `kwargs` is currently unsupported. + Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. See Also @@ -436,7 +436,7 @@ def cos( ----------- Parameter `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. Parameters `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments `kwargs` are currently unsupported. + Keyword argument `kwargs` is currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -659,10 +659,10 @@ def hypot(x1, x2, dtype=None, out=None, where=True, **kwargs): Limitations ----------- - Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar. - Parameters ``dtype``, ``out`` and ``where`` are supported with their default values. - Keyword arguments ``kwargs`` are currently unsupported. - Otherwise the functions will be executed sequentially on CPU. + Parameters `x1` and `x2` are supported as either :obj:`dpnp.ndarray` or scalar. + Parameters `dtype`, `out` and `where` are supported with their default values. + Keyword argument `kwargs` is currently unsupported. + Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. Examples @@ -889,7 +889,7 @@ def reciprocal(x1, **kwargs): Limitations ----------- Input array is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. + Keyword argument `kwargs` is currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -989,7 +989,7 @@ def sin( ----------- Parameter `x` is only supported as either :class:`dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`. Parameters `where`, `dtype` and `subok` are supported with their default values. - Keyword arguments `kwargs` are currently unsupported. + Keyword argument `kwargs` is currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -1180,8 +1180,8 @@ def tan(x1, out=None, **kwargs): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. - Keyword arguments ``kwargs`` are currently unsupported. + Input array is supported as :class:`dpnp.ndarray`. + Keyword argument `kwargs` is currently unsupported. Input array data types are limited by supported DPNP :ref:`Data types`. Examples @@ -1216,7 +1216,7 @@ def tanh(x1): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. + Input array is supported as :class:`dpnp.ndarray`. Input array data types are limited by supported DPNP :ref:`Data types`. Examples @@ -1246,7 +1246,7 @@ def unwrap(x1): Limitations ----------- - Input array is supported as :obj:`dpnp.ndarray`. + Input array is supported as :class:`dpnp.ndarray`. Input array data types are limited by supported DPNP :ref:`Data types`. See Also diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index 79ca0610e32..6e56c2b9bbd 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -27,9 +27,7 @@ tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-nancumprod-data11] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-nancumsum-data12] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-nanprod-data13] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-nansum-data14] -tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-negative-data15] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-prod-data16] -tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-sign-data17] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-sum-data18] tests/test_sycl_queue.py::test_1in_1out[level_zero:gpu:0-trapz-data19] From 6c2ad267c93a0cd8d26b3b087a8f48add390e26e Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Mon, 21 Aug 2023 15:57:59 -0500 Subject: [PATCH 55/58] address comments --- dpnp/dpnp_algo/dpnp_elementwise_common.py | 11 +++++++ dpnp/dpnp_iface_logic.py | 38 +++++++++++------------ dpnp/dpnp_iface_mathematical.py | 5 +-- dpnp/dpnp_utils/dpnp_algo_utils.pyx | 4 +-- tests/test_mathematical.py | 31 +++++++++++++++++- tests/test_usm_type.py | 4 ++- 6 files changed, 68 insertions(+), 25 deletions(-) diff --git a/dpnp/dpnp_algo/dpnp_elementwise_common.py b/dpnp/dpnp_algo/dpnp_elementwise_common.py index 188bfe7d2a4..dd3a34369e3 100644 --- a/dpnp/dpnp_algo/dpnp_elementwise_common.py +++ b/dpnp/dpnp_algo/dpnp_elementwise_common.py @@ -1418,6 +1418,13 @@ def dpnp_multiply(x1, x2, out=None, order="K"): def dpnp_negative(x, out=None, order="K"): """Invokes negative() from dpctl.tensor implementation for negative() function.""" + # TODO: discuss with dpctl if the check is needed to be moved there + if not dpnp.isscalar(x) and x.dtype == dpnp.bool: + raise TypeError( + "DPNP boolean negative, the `-` operator, is not supported, " + "use the `~` operator or the logical_not function instead." + ) + # dpctl.tensor only works with usm_ndarray x1_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) @@ -1594,6 +1601,10 @@ def dpnp_right_shift(x1, x2, out=None, order="K"): def dpnp_sign(x, out=None, order="K"): """Invokes sign() from dpctl.tensor implementation for sign() function.""" + # TODO: discuss with dpctl if the check is needed to be moved there + if not dpnp.isscalar(x) and x.dtype == dpnp.bool: + raise TypeError("DPNP boolean sign is not supported.") + # dpctl.tensor only works with usm_ndarray x1_usm = dpnp.get_usm_ndarray(x) out_usm = None if out is None else dpnp.get_usm_ndarray(out) diff --git a/dpnp/dpnp_iface_logic.py b/dpnp/dpnp_iface_logic.py index a560b71a1a6..d867bbbd660 100644 --- a/dpnp/dpnp_iface_logic.py +++ b/dpnp/dpnp_iface_logic.py @@ -95,8 +95,8 @@ def all(x, /, axis=None, out=None, keepdims=False, *, where=True): Returns ------- dpnp.ndarray - An array with a data type of `bool` - containing the results of the logical AND reduction. + An array with a data type of `bool` + containing the results of the logical AND reduction. Limitations ----------- @@ -160,8 +160,8 @@ def allclose(x1, x2, rtol=1.0e-5, atol=1.0e-8, **kwargs): Limitations ----------- - Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar. - Keyword arguments ``kwargs`` are currently unsupported. + Parameters `x1` and `x2` are supported as either :class:`dpnp.ndarray` or scalar. + Keyword argument `kwargs` is currently unsupported. Otherwise the functions will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -276,7 +276,7 @@ def equal( Returns ------- out : dpnp.ndarray - Output array of bool type, element-wise comparison of `x1` and `x2`. + Output array of bool type, element-wise comparison of `x1` and `x2`. Limitations ----------- @@ -352,7 +352,7 @@ def greater( Returns ------- out : dpnp.ndarray - Output array of bool type, element-wise comparison of `x1` and `x2`. + Output array of bool type, element-wise comparison of `x1` and `x2`. Limitations ----------- @@ -422,7 +422,7 @@ def greater_equal( Returns ------- out : dpnp.ndarray - Output array of bool type, element-wise comparison of `x1` and `x2`. + Output array of bool type, element-wise comparison of `x1` and `x2`. Limitations ----------- @@ -480,8 +480,8 @@ def isclose(x1, x2, rtol=1e-05, atol=1e-08, equal_nan=False): Limitations ----------- - ``x2`` is supported to be integer if ``x1`` is :obj:`dpnp.ndarray` or - at least either ``x1`` or ``x2`` should be as :obj:`dpnp.ndarray`. + `x2` is supported to be integer if `x1` is :class:`dpnp.ndarray` or + at least either `x1` or `x2` should be as :class:`dpnp.ndarray`. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. @@ -678,7 +678,7 @@ def less( Returns ------- out : dpnp.ndarray - Output array of bool type, element-wise comparison of `x1` and `x2`. + Output array of bool type, element-wise comparison of `x1` and `x2`. Limitations ----------- @@ -748,7 +748,7 @@ def less_equal( Returns ------- out : dpnp.ndarray - Output array of bool type, element-wise comparison of `x1` and `x2`. + Output array of bool type, element-wise comparison of `x1` and `x2`. Limitations ----------- @@ -818,8 +818,8 @@ def logical_and( Returns ------- out : dpnp.ndarray - Boolean result of the logical AND operation applied to the elements - of `x1` and `x2`; the shape is determined by broadcasting. + Boolean result of the logical AND operation applied to the elements + of `x1` and `x2`; the shape is determined by broadcasting. Limitations ----------- @@ -891,8 +891,8 @@ def logical_not( Returns ------- out : dpnp.ndarray - Boolean result with the same shape as `x` of the NOT operation - on elements of `x`. + Boolean result with the same shape as `x` of the NOT operation + on elements of `x`. Limitations ----------- @@ -953,8 +953,8 @@ def logical_or( Returns ------- out : dpnp.ndarray - Boolean result of the logical OR operation applied to the elements - of `x1` and `x2`; the shape is determined by broadcasting. + Boolean result of the logical OR operation applied to the elements + of `x1` and `x2`; the shape is determined by broadcasting. Limitations ----------- @@ -1027,8 +1027,8 @@ def logical_xor( Returns ------- out : dpnp.ndarray - Boolean result of the logical XOR operation applied to the elements - of `x1` and `x2`; the shape is determined by broadcasting. + Boolean result of the logical XOR operation applied to the elements + of `x1` and `x2`; the shape is determined by broadcasting. Limitations ----------- diff --git a/dpnp/dpnp_iface_mathematical.py b/dpnp/dpnp_iface_mathematical.py index def99c6adbd..7552bcd2130 100644 --- a/dpnp/dpnp_iface_mathematical.py +++ b/dpnp/dpnp_iface_mathematical.py @@ -1507,7 +1507,7 @@ def negative( Returns ------- out : dpnp.ndarray - The numerical negative of each element of `x`. + The numerical negative of each element of `x`. Limitations ----------- @@ -1805,7 +1805,7 @@ def sign( Returns ------- out : dpnp.ndarray - The indication of the sign of each element of `x`. + The indication of the sign of each element of `x`. Limitations ----------- @@ -1814,6 +1814,7 @@ def sign( Keyword argument `kwargs` is currently unsupported. Otherwise the function will be executed sequentially on CPU. Input array data types are limited by supported DPNP :ref:`Data types`. + However, if the input array data type is complex, the function will be executed sequentially on CPU. Examples -------- diff --git a/dpnp/dpnp_utils/dpnp_algo_utils.pyx b/dpnp/dpnp_utils/dpnp_algo_utils.pyx index f88b42bde7f..9ec9174d400 100644 --- a/dpnp/dpnp_utils/dpnp_algo_utils.pyx +++ b/dpnp/dpnp_utils/dpnp_algo_utils.pyx @@ -131,8 +131,8 @@ def call_origin(function, *args, **kwargs): if not allow_fallback and config.__DPNP_RAISE_EXCEPION_ON_NUMPY_FALLBACK__ == 1: raise NotImplementedError(f"Requested funtion={function.__name__} with args={args} and kwargs={kwargs} " "isn't currently supported and would fall back on NumPy implementation. " - "Define enviroment variable `DPNP_RAISE_EXCEPION_ON_NUMPY_FALLBACK` to `0` " - "if the fall back is required to be supported without rasing an exception.") + "Define environment variable `DPNP_RAISE_EXCEPION_ON_NUMPY_FALLBACK` to `0` " + "if the fall back is required to be supported without raising an exception.") dpnp_inplace = kwargs.pop("dpnp_inplace", False) sycl_queue = kwargs.pop("sycl_queue", None) diff --git a/tests/test_mathematical.py b/tests/test_mathematical.py index 5c8ca88088e..2ae3494854d 100644 --- a/tests/test_mathematical.py +++ b/tests/test_mathematical.py @@ -392,7 +392,7 @@ def test_nancumsum(array): [[[1.0, -1.0], [0.1, -0.1]], [-2, -1, 0, 1, 2]], ids=["[[1., -1.], [0.1, -0.1]]", "[-2, -1, 0, 1, 2]"], ) -@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True)) def test_negative(data, dtype): np_a = numpy.array(data, dtype=dtype) dpnp_a = dpnp.array(data, dtype=dtype) @@ -402,6 +402,35 @@ def test_negative(data, dtype): assert_allclose(result, expected) +def test_negative_boolean(): + dpnp_a = dpnp.array([True, False]) + + with pytest.raises(TypeError): + dpnp.negative(dpnp_a) + + +@pytest.mark.parametrize( + "data", + [[2, 0, -2], [1.1, -1.1]], + ids=["[2, 0, -2]", "[1.1, -1.1]"], +) +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) +def test_sign(data, dtype): + np_a = numpy.array(data, dtype=dtype) + dpnp_a = dpnp.array(data, dtype=dtype) + + result = dpnp.sign(dpnp_a) + expected = numpy.sign(np_a) + assert_allclose(result, expected) + + +def test_sign_boolean(): + dpnp_a = dpnp.array([True, False]) + + with pytest.raises(TypeError): + dpnp.sign(dpnp_a) + + @pytest.mark.parametrize("val_type", get_all_dtypes(no_none=True)) @pytest.mark.parametrize("data_type", get_all_dtypes()) @pytest.mark.parametrize("val", [1.5, 1, 5], ids=["1.5", "1", "5"]) diff --git a/tests/test_usm_type.py b/tests/test_usm_type.py index d61331ffa08..4bf16022ba9 100644 --- a/tests/test_usm_type.py +++ b/tests/test_usm_type.py @@ -249,8 +249,10 @@ def test_meshgrid(usm_type_x, usm_type_y): "func,data", [ pytest.param("ceil", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), - pytest.param("floor", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), pytest.param("conjugate", [[1.0 + 1.0j, 0.0], [0.0, 1.0 + 1.0j]]), + pytest.param("floor", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), + pytest.param("negative", [1.0, -1.0]), + pytest.param("sign", [-5.0, 4.5]), pytest.param("sqrt", [1.0, 3.0, 9.0]), pytest.param("trunc", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]), ], From 096857cb2f7a12f2d4ced3ccd173f99e4d831332 Mon Sep 17 00:00:00 2001 From: Vahid Tavanashad Date: Tue, 22 Aug 2023 04:33:58 -0500 Subject: [PATCH 56/58] add falls back on numpy for tests --- dpnp/dpnp_iface_bitwise.py | 4 ++-- tests/skipped_tests_gpu.tbl | 1 - tests/skipped_tests_gpu_no_fp64.tbl | 2 ++ tests/test_mathematical.py | 3 ++- tests/third_party/cupy/math_tests/test_misc.py | 1 + 5 files changed, 7 insertions(+), 4 deletions(-) diff --git a/dpnp/dpnp_iface_bitwise.py b/dpnp/dpnp_iface_bitwise.py index f3f0e2cfec8..eded1984809 100644 --- a/dpnp/dpnp_iface_bitwise.py +++ b/dpnp/dpnp_iface_bitwise.py @@ -374,7 +374,7 @@ def left_shift( >>> np.left_shift(x1, x2) array([10, 20, 40]) - The ``<<`` operator can be used as a shorthand for ``right_shift`` on + The ``<<`` operator can be used as a shorthand for ``left_shift`` on :class:`dpnp.ndarray`. >>> x1 << x2 @@ -437,7 +437,7 @@ def right_shift( >>> np.right_shift(x1, x2) array([5, 2, 1]) - The ``>>`` operator can be used as a shorthand for ``left_shift`` on + The ``>>`` operator can be used as a shorthand for ``right_shift`` on :class:`dpnp.ndarray`. >>> x1 >> x2 diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index 6e56c2b9bbd..0b95dde9bb5 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -721,7 +721,6 @@ tests/third_party/cupy/math_tests/test_floating.py::TestFloating::test_signbit tests/third_party/cupy/math_tests/test_misc.py::TestMisc::test_clip_min_max_none tests/third_party/cupy/math_tests/test_misc.py::TestMisc::test_external_clip4 tests/third_party/cupy/math_tests/test_misc.py::TestMisc::test_absolute_negative -tests/third_party/cupy/math_tests/test_misc.py::TestMisc::test_sign_negative tests/third_party/cupy/math_tests/test_misc.py::TestMisc::test_maximum_nan tests/third_party/cupy/math_tests/test_misc.py::TestMisc::test_minimum_nan tests/third_party/cupy/math_tests/test_misc.py::TestMisc::test_fmax_nan diff --git a/tests/skipped_tests_gpu_no_fp64.tbl b/tests/skipped_tests_gpu_no_fp64.tbl index 5fc5b57352c..31c4b499cd0 100644 --- a/tests/skipped_tests_gpu_no_fp64.tbl +++ b/tests/skipped_tests_gpu_no_fp64.tbl @@ -1220,6 +1220,8 @@ tests/third_party/cupy/math_tests/test_hyperbolic.py::TestHyperbolic::test_cosh tests/third_party/cupy/math_tests/test_hyperbolic.py::TestHyperbolic::test_sinh tests/third_party/cupy/math_tests/test_hyperbolic.py::TestHyperbolic::test_tanh +tests/third_party/cupy/math_tests/test_misc.py::TestMisc::test_sign_negative + tests/third_party/cupy/math_tests/test_sumprod.py::TestNansumNanprodLong_param_0_{axis=0, func='nansum', keepdims=True, shape=(2, 3, 4), transpose_axes=True}::test_nansum_all tests/third_party/cupy/math_tests/test_sumprod.py::TestNansumNanprodLong_param_0_{axis=0, func='nansum', keepdims=True, shape=(2, 3, 4), transpose_axes=True}::test_nansum_axis_transposed tests/third_party/cupy/math_tests/test_sumprod.py::TestNansumNanprodLong_param_1_{axis=0, func='nansum', keepdims=True, shape=(2, 3, 4), transpose_axes=False}::test_nansum_all diff --git a/tests/test_mathematical.py b/tests/test_mathematical.py index 2ae3494854d..2b5c98083e2 100644 --- a/tests/test_mathematical.py +++ b/tests/test_mathematical.py @@ -414,7 +414,8 @@ def test_negative_boolean(): [[2, 0, -2], [1.1, -1.1]], ids=["[2, 0, -2]", "[1.1, -1.1]"], ) -@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True)) +@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True)) +@pytest.mark.usefixtures("allow_fall_back_on_numpy") def test_sign(data, dtype): np_a = numpy.array(data, dtype=dtype) dpnp_a = dpnp.array(data, dtype=dtype) diff --git a/tests/third_party/cupy/math_tests/test_misc.py b/tests/third_party/cupy/math_tests/test_misc.py index 9ae4ad058e2..f8e0a32f642 100644 --- a/tests/third_party/cupy/math_tests/test_misc.py +++ b/tests/third_party/cupy/math_tests/test_misc.py @@ -193,6 +193,7 @@ def test_fabs_negative(self, xp, dtype): def test_sign(self): self.check_unary("sign", no_bool=True) + @pytest.mark.usefixtures("allow_fall_back_on_numpy") def test_sign_negative(self): self.check_unary_negative("sign", no_bool=True) From 17d792165173cd2d74c179266074546e6d462f27 Mon Sep 17 00:00:00 2001 From: Anton <100830759+antonwolfy@users.noreply.github.com> Date: Tue, 22 Aug 2023 13:33:57 +0200 Subject: [PATCH 57/58] Reworked dpnp.copyto() implementation through existing calls (#1516) * Reworked dpnp.copyto() implementation through existing calls * Extended public CI scope with two more files * Unmuted tests around dpnp.copyto() * Fixed typos in description * Left part of dpnp_copyto implementations in backend since used by FFT * Removed leftover in cython for DPNP_FN_COPYTO * Removed unnecessary import * Applied review comments * Mute not working tests * Mute tests for dpnp.power() which are failing in CI --- .github/workflows/conda-package.yml | 2 + dpnp/backend/kernels/dpnp_krnl_elemwise.cpp | 53 +- dpnp/dpnp_algo/dpnp_algo.pxd | 2 - dpnp/dpnp_algo/dpnp_algo_manipulation.pxi | 40 -- dpnp/dpnp_iface_manipulation.py | 117 ++-- tests/__init__.py | 31 -- tests/skipped_tests.tbl | 2 + tests/skipped_tests_gpu.tbl | 31 +- tests/skipped_tests_gpu_no_fp64.tbl | 34 +- tests/test_manipulation.py | 23 +- tests/third_party/cupy/fft_tests/test_fft.py | 2 + .../cupy/linalg_tests/test_product.py | 3 +- .../cupy/manipulation_tests/test_basic.py | 228 +++++--- .../cupy/math_tests/test_arithmetic.py | 2 +- tests/third_party/cupy/testing/helper.py | 515 +++++++++++++----- 15 files changed, 673 insertions(+), 412 deletions(-) diff --git a/.github/workflows/conda-package.yml b/.github/workflows/conda-package.yml index 7583fee5d91..2fda6913bc5 100644 --- a/.github/workflows/conda-package.yml +++ b/.github/workflows/conda-package.yml @@ -18,6 +18,7 @@ env: test_fft.py test_linalg.py test_logic.py + test_manipulation.py test_mathematical.py test_random_state.py test_sort.py @@ -26,6 +27,7 @@ env: test_usm_type.py third_party/cupy/linalg_tests/test_product.py third_party/cupy/logic_tests/test_truth.py + third_party/cupy/manipulation_tests/test_basic.py third_party/cupy/manipulation_tests/test_join.py third_party/cupy/math_tests/test_explog.py third_party/cupy/math_tests/test_misc.py diff --git a/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp b/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp index 900f7d95c57..d99510fde91 100644 --- a/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp +++ b/dpnp/backend/kernels/dpnp_krnl_elemwise.cpp @@ -414,59 +414,26 @@ static void func_map_init_elemwise_1arg_2type(func_map_t &fmap) (void *) dpnp_copyto_c_default, std::complex>}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_BLN][eft_BLN] = { - eft_BLN, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_BLN][eft_INT] = { - eft_INT, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_BLN][eft_LNG] = { - eft_LNG, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_BLN][eft_FLT] = { - eft_FLT, (void *)dpnp_copyto_c_ext}; + // dpnp_copyto_c is required by dpnp_fft_fft_c and dpnp_fft_rfft_c fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_BLN][eft_DBL] = { eft_DBL, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_INT][eft_BLN] = { - eft_BLN, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_INT][eft_INT] = { - eft_INT, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_INT][eft_LNG] = { - eft_LNG, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_INT][eft_FLT] = { - eft_FLT, (void *)dpnp_copyto_c_ext}; fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_INT][eft_DBL] = { eft_DBL, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_LNG][eft_BLN] = { - eft_BLN, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_LNG][eft_INT] = { - eft_INT, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_LNG][eft_LNG] = { - eft_LNG, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_LNG][eft_FLT] = { - eft_FLT, (void *)dpnp_copyto_c_ext}; fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_LNG][eft_DBL] = { eft_DBL, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_FLT][eft_BLN] = { - eft_BLN, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_FLT][eft_INT] = { - eft_INT, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_FLT][eft_LNG] = { - eft_LNG, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_FLT][eft_FLT] = { - eft_FLT, (void *)dpnp_copyto_c_ext}; fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_FLT][eft_DBL] = { eft_DBL, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_DBL][eft_BLN] = { - eft_BLN, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_DBL][eft_INT] = { - eft_INT, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_DBL][eft_LNG] = { - eft_LNG, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_DBL][eft_FLT] = { - eft_FLT, (void *)dpnp_copyto_c_ext}; fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_DBL][eft_DBL] = { eft_DBL, (void *)dpnp_copyto_c_ext}; - fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_C128][eft_C128] = { - eft_C128, - (void *)dpnp_copyto_c_ext, std::complex>}; + + fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_BLN][eft_FLT] = { + eft_FLT, (void *)dpnp_copyto_c_ext}; + fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_INT][eft_FLT] = { + eft_FLT, (void *)dpnp_copyto_c_ext}; + fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_LNG][eft_FLT] = { + eft_FLT, (void *)dpnp_copyto_c_ext}; + fmap[DPNPFuncName::DPNP_FN_COPYTO_EXT][eft_FLT][eft_FLT] = { + eft_FLT, (void *)dpnp_copyto_c_ext}; fmap[DPNPFuncName::DPNP_FN_COS][eft_INT][eft_INT] = { eft_DBL, (void *)dpnp_cos_c_default}; diff --git a/dpnp/dpnp_algo/dpnp_algo.pxd b/dpnp/dpnp_algo/dpnp_algo.pxd index c59c7ced636..089e45e7c71 100644 --- a/dpnp/dpnp_algo/dpnp_algo.pxd +++ b/dpnp/dpnp_algo/dpnp_algo.pxd @@ -72,8 +72,6 @@ cdef extern from "dpnp_iface_fptr.hpp" namespace "DPNPFuncName": # need this na DPNP_FN_COPY_EXT DPNP_FN_COPYSIGN DPNP_FN_COPYSIGN_EXT - DPNP_FN_COPYTO - DPNP_FN_COPYTO_EXT DPNP_FN_CORRELATE DPNP_FN_CORRELATE_EXT DPNP_FN_COSH diff --git a/dpnp/dpnp_algo/dpnp_algo_manipulation.pxi b/dpnp/dpnp_algo/dpnp_algo_manipulation.pxi index 94a58b057bf..43b8310578d 100644 --- a/dpnp/dpnp_algo/dpnp_algo_manipulation.pxi +++ b/dpnp/dpnp_algo/dpnp_algo_manipulation.pxi @@ -38,7 +38,6 @@ and the rest of the library __all__ += [ "dpnp_atleast_2d", "dpnp_atleast_3d", - "dpnp_copyto", "dpnp_expand_dims", "dpnp_repeat", "dpnp_reshape", @@ -105,45 +104,6 @@ cpdef utils.dpnp_descriptor dpnp_atleast_3d(utils.dpnp_descriptor arr): return arr -cpdef dpnp_copyto(utils.dpnp_descriptor dst, utils.dpnp_descriptor src, where=True): - # Convert string type names (array.dtype) to C enum DPNPFuncType - cdef DPNPFuncType dst_type = dpnp_dtype_to_DPNPFuncType(dst.dtype) - cdef DPNPFuncType src_type = dpnp_dtype_to_DPNPFuncType(src.dtype) - - cdef shape_type_c dst_shape = dst.shape - cdef shape_type_c dst_strides = utils.strides_to_vector(dst.strides, dst_shape) - - cdef shape_type_c src_shape = src.shape - cdef shape_type_c src_strides = utils.strides_to_vector(src.strides, src_shape) - - # get the FPTR data structure - cdef DPNPFuncData kernel_data = get_dpnp_function_ptr(DPNP_FN_COPYTO_EXT, src_type, dst_type) - - _, _, result_sycl_queue = utils.get_common_usm_allocation(dst, src) - - cdef c_dpctl.SyclQueue q = result_sycl_queue - cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref() - - # Call FPTR function - cdef fptr_1in_1out_strides_t func = kernel_data.ptr - cdef c_dpctl.DPCTLSyclEventRef event_ref = func(q_ref, - dst.get_data(), - dst.size, - dst.ndim, - dst_shape.data(), - dst_strides.data(), - src.get_data(), - src.size, - src.ndim, - src_shape.data(), - src_strides.data(), - NULL, - NULL) # dep_events_ref - - with nogil: c_dpctl.DPCTLEvent_WaitAndThrow(event_ref) - c_dpctl.DPCTLEvent_Delete(event_ref) - - cpdef utils.dpnp_descriptor dpnp_expand_dims(utils.dpnp_descriptor in_array, axis): axis_tuple = utils._object_to_tuple(axis) result_ndim = len(axis_tuple) + in_array.ndim diff --git a/dpnp/dpnp_iface_manipulation.py b/dpnp/dpnp_iface_manipulation.py index 5f5dcb3617d..e29e6b0203e 100644 --- a/dpnp/dpnp_iface_manipulation.py +++ b/dpnp/dpnp_iface_manipulation.py @@ -251,7 +251,7 @@ def concatenate(arrays, /, *, axis=0, out=None, dtype=None, **kwargs): Limitations ----------- Each array in `arrays` is supported as either :class:`dpnp.ndarray` - or :class:`dpctl.tensor.usm_ndarray`. Otherwise ``TypeError`` exeption + or :class:`dpctl.tensor.usm_ndarray`. Otherwise ``TypeError`` exception will be raised. Parameters `out` and `dtype are supported with default value. Keyword argument ``kwargs`` is currently unsupported. @@ -313,64 +313,79 @@ def copyto(dst, src, casting="same_kind", where=True): """ Copies values from one array to another, broadcasting as necessary. + Raises a ``TypeError`` if the `casting` rule is violated, and if + `where` is provided, it selects which elements to copy. + For full documentation refer to :obj:`numpy.copyto`. Limitations ----------- - Input arrays are supported as :obj:`dpnp.ndarray`. - Otherwise the function will be executed sequentially on CPU. - Parameter ``casting`` is supported only with default value ``"same_kind"``. - Parameter ``where`` is supported only with default value ``True``. - Shapes of input arrays are supported to be equal. + The `dst` parameter is supported as either :class:`dpnp.ndarray` + or :class:`dpctl.tensor.usm_ndarray`. + The `where` parameter is supported as either :class:`dpnp.ndarray`, + :class:`dpctl.tensor.usm_ndarray` or scalar. + Otherwise ``TypeError`` exception will be raised. Input array data types are limited by supported DPNP :ref:`Data types`. + Examples + -------- + >>> import dpnp as np + >>> A = np.array([4, 5, 6]) + >>> B = [1, 2, 3] + >>> np.copyto(A, B) + >>> A + array([1, 2, 3]) + + >>> A = np.array([[1, 2, 3], [4, 5, 6]]) + >>> B = [[4, 5, 6], [7, 8, 9]] + >>> np.copyto(A, B) + >>> A + array([[4, 5, 6], + [7, 8, 9]]) + """ - dst_desc = dpnp.get_dpnp_descriptor( - dst, copy_when_strides=False, copy_when_nondefault_queue=False - ) - src_desc = dpnp.get_dpnp_descriptor(src, copy_when_nondefault_queue=False) - if dst_desc and src_desc: - if casting != "same_kind": - pass - elif ( - dst_desc.dtype == dpnp.bool - and src_desc.dtype # due to 'same_kind' casting - in [ - dpnp.int32, - dpnp.int64, - dpnp.float32, - dpnp.float64, - dpnp.complex128, - ] - ): - pass - elif dst_desc.dtype in [ - dpnp.int32, - dpnp.int64, - ] and src_desc.dtype in [ # due to 'same_kind' casting - dpnp.float32, - dpnp.float64, - dpnp.complex128, - ]: - pass - elif ( - dst_desc.dtype in [dpnp.float32, dpnp.float64] - and src_desc.dtype == dpnp.complex128 - ): # due to 'same_kind' casting - pass - elif where is not True: - pass - elif dst_desc.shape != src_desc.shape: - pass - elif dst_desc.strides != src_desc.strides: - pass - else: - return dpnp_copyto(dst_desc, src_desc, where=where) + if not dpnp.is_supported_array_type(dst): + raise TypeError( + "Destination array must be any of supported type, " + f"but got {type(dst)}" + ) + elif not dpnp.is_supported_array_type(src): + src = dpnp.array(src, sycl_queue=dst.sycl_queue) - return call_origin( - numpy.copyto, dst, src, casting, where, dpnp_inplace=True - ) + if not dpt.can_cast(src.dtype, dst.dtype, casting=casting): + raise TypeError( + f"Cannot cast from {src.dtype} to {dst.dtype} " + f"according to the rule {casting}." + ) + + if where is True: + dst[...] = src + elif where is False: + # nothing to copy + pass + else: + if dpnp.isscalar(where): + where = dpnp.array( + where, dtype=dpnp.bool, sycl_queue=dst.sycl_queue + ) + elif not dpnp.is_supported_array_type(where): + raise TypeError( + "`where` array must be any of supported type, " + f"but got {type(where)}" + ) + elif where.dtype != dpnp.bool: + raise TypeError( + "`where` keyword argument must be of boolean type, " + f"but got {where.dtype}" + ) + + dst_usm, src_usm, mask_usm = dpt.broadcast_arrays( + dpnp.get_usm_ndarray(dst), + dpnp.get_usm_ndarray(src), + dpnp.get_usm_ndarray(where), + ) + dst_usm[mask_usm] = src_usm[mask_usm] def expand_dims(x1, axis): @@ -847,7 +862,7 @@ def stack(arrays, /, *, axis=0, out=None, dtype=None, **kwargs): Limitations ----------- Each array in `arrays` is supported as either :class:`dpnp.ndarray` - or :class:`dpctl.tensor.usm_ndarray`. Otherwise ``TypeError`` exeption + or :class:`dpctl.tensor.usm_ndarray`. Otherwise ``TypeError`` exception will be raised. Parameters `out` and `dtype are supported with default value. Keyword argument ``kwargs`` is currently unsupported. diff --git a/tests/__init__.py b/tests/__init__.py index 8650a7c357a..161e4a1a994 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,38 +1,7 @@ import numpy -import dpnp from tests import testing -from tests.third_party.cupy import testing as cupy_testing - -from .helper import has_support_aspect64 numpy.testing.assert_allclose = testing.assert_allclose numpy.testing.assert_array_equal = testing.assert_array_equal numpy.testing.assert_equal = testing.assert_equal - -# patch for shaped_arange func to exclude calls of astype and reshape -# necessary because new data container does not support these functions yet - -orig_shaped_arange = cupy_testing.shaped_arange -orig_shaped_reverse_arange = cupy_testing.shaped_reverse_arange - - -def _shaped_arange(shape, xp=dpnp, dtype=dpnp.float64, order="C"): - if dtype is dpnp.float64: - dtype = dpnp.float32 if not has_support_aspect64() else dtype - res = xp.array( - orig_shaped_arange(shape, xp=numpy, dtype=dtype, order=order), - dtype=dtype, - ) - return res - - -def _shaped_reverse_arange(shape, xp=dpnp, dtype=dpnp.float32): - res = xp.array( - orig_shaped_reverse_arange(shape, xp=numpy, dtype=dtype), dtype=dtype - ) - return res - - -cupy_testing.shaped_arange = _shaped_arange -cupy_testing.shaped_reverse_arange = _shaped_reverse_arange diff --git a/tests/skipped_tests.tbl b/tests/skipped_tests.tbl index f5f0f62c28e..a78de781e3b 100644 --- a/tests/skipped_tests.tbl +++ b/tests/skipped_tests.tbl @@ -265,6 +265,8 @@ tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_ones_like_s tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_zeros_like_subok tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_zeros_strides +tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_copy_order + tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction_from_list tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction_from_tuple tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_extraction_from_nested_list diff --git a/tests/skipped_tests_gpu.tbl b/tests/skipped_tests_gpu.tbl index 35092f3b07b..79ca0610e32 100644 --- a/tests/skipped_tests_gpu.tbl +++ b/tests/skipped_tests_gpu.tbl @@ -212,34 +212,6 @@ tests/third_party/cupy/linalg_tests/test_einsum.py::TestListArgEinSumError::test tests/third_party/cupy/linalg_tests/test_einsum.py::TestListArgEinSumError::test_too_many_dims3 tests/third_party/cupy/linalg_tests/test_product.py::TestProduct::test_reversed_vdot -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_7_{dst_shape=(0,), src=3.2}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_8_{dst_shape=(0,), src=0}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_9_{dst_shape=(0,), src=4}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_10_{dst_shape=(0,), src=-4}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_11_{dst_shape=(0,), src=True}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_12_{dst_shape=(0,), src=False}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_13_{dst_shape=(0,), src=(1+1j)}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_14_{dst_shape=(1,), src=3.2}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_15_{dst_shape=(1,), src=0}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_16_{dst_shape=(1,), src=4}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_17_{dst_shape=(1,), src=-4}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_18_{dst_shape=(1,), src=True}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_19_{dst_shape=(1,), src=False}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_20_{dst_shape=(1,), src=(1+1j)}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_21_{dst_shape=(1, 1), src=3.2}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_22_{dst_shape=(1, 1), src=0}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_23_{dst_shape=(1, 1), src=4}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_24_{dst_shape=(1, 1), src=-4}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_25_{dst_shape=(1, 1), src=True}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_26_{dst_shape=(1, 1), src=False}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_27_{dst_shape=(1, 1), src=(1+1j)}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_28_{dst_shape=(2, 2), src=3.2}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_29_{dst_shape=(2, 2), src=0}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_30_{dst_shape=(2, 2), src=4}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_31_{dst_shape=(2, 2), src=-4}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_32_{dst_shape=(2, 2), src=True}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_33_{dst_shape=(2, 2), src=False}::test_copyto_where -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_34_{dst_shape=(2, 2), src=(1+1j)}::test_copyto_where tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_out_noncontiguous tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum_param_0_{axis=0}::test_cumsum_axis_out_noncontiguous @@ -459,6 +431,9 @@ tests/third_party/cupy/creation_tests/test_ranges.py::TestRanges::test_linspace_ tests/third_party/cupy/creation_tests/test_ranges.py::TestRanges::test_linspace_with_retstep tests/third_party/cupy/creation_tests/test_ranges.py::TestRanges::test_linspace_zero_num_no_endopoint_with_retstep tests/third_party/cupy/creation_tests/test_ranges.py::TestRanges::test_logspace_zero_num + +tests/third_party/cupy/creation_tests/test_from_data.py::TestFromData::test_copy_order + tests/third_party/cupy/fft_tests/test_fft.py::TestFft2_param_1_{axes=None, norm=None, s=(1, None), shape=(3, 4)}::test_fft2 tests/third_party/cupy/fft_tests/test_fft.py::TestFft2_param_7_{axes=(), norm=None, s=None, shape=(3, 4)}::test_fft2 tests/third_party/cupy/fft_tests/test_fft.py::TestFft2_param_7_{axes=(), norm=None, s=None, shape=(3, 4)}::test_ifft2 diff --git a/tests/skipped_tests_gpu_no_fp64.tbl b/tests/skipped_tests_gpu_no_fp64.tbl index 2aca406319d..5fc5b57352c 100644 --- a/tests/skipped_tests_gpu_no_fp64.tbl +++ b/tests/skipped_tests_gpu_no_fp64.tbl @@ -885,28 +885,42 @@ tests/third_party/cupy/logic_tests/test_content.py::TestContent::test_isfinite tests/third_party/cupy/logic_tests/test_content.py::TestContent::test_isinf tests/third_party/cupy/logic_tests/test_content.py::TestContent::test_isnan -tests/third_party/cupy/manipulation_tests/test_basic.py::TestCopytoFromScalar_param_6_{dst_shape=(), src=(1+1j)}::test_copyto - tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticUnary_param_29_{arg1=2.0, name='reciprocal'}::test_unary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_6_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_10_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_10_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_10_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_10_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_14_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_14_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_14_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_14_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_38_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_42_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_42_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_42_{arg1=array([[1., 2., 3.], [4., 5., 6.]]), arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_42_{arg1=array([[1., 2., 3.], [4., 5., 6.]]), arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_46_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_46_{arg1=array([[1., 2., 3.], [4., 5., 6.]], dtype=float32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_46_{arg1=array([[1., 2., 3.], [4., 5., 6.]]), arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_46_{arg1=array([[1., 2., 3.], [4., 5., 6.]]), arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_66_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0., 1., 2.], [3., 4., 5.]], dtype=float32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_66_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0., 1., 2.], [3., 4., 5.]], dtype=float32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_66_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[6., 5., 4.], [3., 2., 1.]], dtype=float32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_66_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[6., 5., 4.], [3., 2., 1.]], dtype=float32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_70_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_70_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_70_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[6., 5., 4.], [3., 2., 1.]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_70_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[6., 5., 4.], [3., 2., 1.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_74_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_74_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_74_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_74_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_78_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_78_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_78_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_78_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_82_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_82_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_86_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=0.0, name='power'}::test_binary @@ -915,12 +929,20 @@ tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_94_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int32), arg2=2.0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_98_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0., 1., 2.], [3., 4., 5.]], dtype=float32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_98_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[0., 1., 2.], [3., 4., 5.]], dtype=float32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_98_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[6., 5., 4.], [3., 2., 1.]], dtype=float32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_98_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[6., 5., 4.], [3., 2., 1.]], dtype=float32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_102_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_102_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_102_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[6., 5., 4.], [3., 2., 1.]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_102_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[6., 5., 4.], [3., 2., 1.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_106_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_106_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_106_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_106_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_110_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_110_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_110_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_110_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_114_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_114_{arg1=array([[1, 2, 3], [4, 5, 6]], dtype=int64), arg2=0, name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_118_{arg1=array([[1, 2, 3], [4, 5, 6]]), arg2=0.0, name='power'}::test_binary @@ -930,16 +952,24 @@ tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_134_{arg1=0, arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_138_{arg1=0, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_138_{arg1=0, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_138_{arg1=0, arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_138_{arg1=0, arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_142_{arg1=0, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_142_{arg1=0, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_142_{arg1=0, arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_142_{arg1=0, arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_166_{arg1=0.0, arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_170_{arg1=0.0, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_174_{arg1=0.0, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_198_{arg1=2, arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_202_{arg1=2, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_202_{arg1=2, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_202_{arg1=2, arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int32), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_202_{arg1=2, arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_206_{arg1=2, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_206_{arg1=2, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int64), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_206_{arg1=2, arg2=array([[6, 5, 4], [3, 2, 1]]), name='power'}::test_binary +tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_206_{arg1=2, arg2=array([[6, 5, 4], [3, 2, 1]], dtype=int64), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_230_{arg1=2.0, arg2=array([[0., 1., 2.], [3., 4., 5.]]), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_234_{arg1=2.0, arg2=array([[0, 1, 2], [3, 4, 5]], dtype=int32), name='power'}::test_binary tests/third_party/cupy/math_tests/test_arithmetic.py::TestArithmeticBinary_param_238_{arg1=2.0, arg2=array([[0, 1, 2], [3, 4, 5]]), name='power'}::test_binary diff --git a/tests/test_manipulation.py b/tests/test_manipulation.py index c40ec0786ff..576229b1d56 100644 --- a/tests/test_manipulation.py +++ b/tests/test_manipulation.py @@ -25,8 +25,6 @@ @pytest.mark.parametrize("in_obj, out_dtype", testdata) def test_copyto_dtype(in_obj, out_dtype): - if out_dtype == dpnp.complex64: - pytest.skip("SAT-6016: dpnp.copyto() do not work with complex64 dtype") ndarr = numpy.array(in_obj) expected = numpy.empty(ndarr.size, dtype=out_dtype) numpy.copyto(expected, ndarr) @@ -38,6 +36,27 @@ def test_copyto_dtype(in_obj, out_dtype): assert_array_equal(result, expected) +@pytest.mark.parametrize("dst", [7, numpy.ones(10), (2, 7), [5], range(3)]) +def test_copyto_dst_raises(dst): + a = dpnp.array(4) + with pytest.raises( + TypeError, + match="Destination array must be any of supported type, but got", + ): + dpnp.copyto(dst, a) + + +@pytest.mark.parametrize("where", [numpy.ones(10), (2, 7), [5], range(3)]) +def test_copyto_where_raises(where): + a = dpnp.empty((2, 3)) + b = dpnp.arange(6).reshape((2, 3)) + + with pytest.raises( + TypeError, match="`where` array must be any of supported type, but got" + ): + dpnp.copyto(a, b, where=where) + + @pytest.mark.usefixtures("allow_fall_back_on_numpy") @pytest.mark.parametrize( "arr", diff --git a/tests/third_party/cupy/fft_tests/test_fft.py b/tests/third_party/cupy/fft_tests/test_fft.py index 0cc49200a24..4650fda557a 100644 --- a/tests/third_party/cupy/fft_tests/test_fft.py +++ b/tests/third_party/cupy/fft_tests/test_fft.py @@ -28,6 +28,7 @@ class TestFft(unittest.TestCase): atol=1e-7, accept_error=ValueError, type_check=has_support_aspect64(), + contiguous_check=False, ) def test_fft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) @@ -41,6 +42,7 @@ def test_fft(self, xp, dtype): atol=1e-7, accept_error=ValueError, type_check=has_support_aspect64(), + contiguous_check=False, ) def test_ifft(self, xp, dtype): a = testing.shaped_random(self.shape, xp, dtype) diff --git a/tests/third_party/cupy/linalg_tests/test_product.py b/tests/third_party/cupy/linalg_tests/test_product.py index 2dc68b648b7..974ea0af377 100644 --- a/tests/third_party/cupy/linalg_tests/test_product.py +++ b/tests/third_party/cupy/linalg_tests/test_product.py @@ -100,7 +100,8 @@ def test_dot_with_out(self, xp, dtype_a, dtype_b, dtype_c): @testing.gpu class TestCrossProduct(unittest.TestCase): @testing.for_all_dtypes_combination(["dtype_a", "dtype_b"]) - @testing.numpy_cupy_allclose() + # TODO: remove 'contiguous_check=False' once fixed in dpnp.cross() + @testing.numpy_cupy_allclose(contiguous_check=False) def test_cross(self, xp, dtype_a, dtype_b): if dtype_a == dtype_b == numpy.bool_: # cross does not support bool-bool inputs. diff --git a/tests/third_party/cupy/manipulation_tests/test_basic.py b/tests/third_party/cupy/manipulation_tests/test_basic.py index 0e5bb07fd39..0712f88a736 100644 --- a/tests/third_party/cupy/manipulation_tests/test_basic.py +++ b/tests/third_party/cupy/manipulation_tests/test_basic.py @@ -1,17 +1,14 @@ -import itertools -import unittest +import warnings +import dpctl import numpy import pytest import dpnp as cupy - -# from cupy import cuda from tests.third_party.cupy import testing -@testing.gpu -class TestBasic(unittest.TestCase): +class TestBasic: @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_copyto(self, xp, dtype): @@ -20,6 +17,14 @@ def test_copyto(self, xp, dtype): xp.copyto(b, a) return b + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_copyto_different_contiguity(self, xp, dtype): + a = testing.shaped_arange((2, 3, 2), xp, dtype).T + b = xp.empty((2, 3, 2), dtype=dtype) + xp.copyto(b, a) + return b + @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_copyto_dtype(self, xp, dtype): @@ -28,7 +33,6 @@ def test_copyto_dtype(self, xp, dtype): xp.copyto(b, a) return b - @pytest.mark.usefixtures("allow_fall_back_on_numpy") @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_copyto_broadcast(self, xp, dtype): @@ -37,7 +41,46 @@ def test_copyto_broadcast(self, xp, dtype): xp.copyto(b, a) return b - @pytest.mark.usefixtures("allow_fall_back_on_numpy") + @pytest.mark.parametrize( + ("dst_shape", "src_shape"), + [ + ((), (2,)), + ((2, 0, 5, 4), (2, 0, 3, 4)), + ((6,), (2, 3)), + ((2, 3), (6,)), + ], + ) + def test_copyto_raises_shape(self, dst_shape, src_shape): + for xp in (numpy, cupy): + dst = xp.zeros(dst_shape, dtype=xp.int64) + src = xp.zeros(src_shape, dtype=xp.int64) + with pytest.raises(ValueError): + xp.copyto(dst, src) + + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_copyto_squeeze(self, xp, dtype): + a = testing.shaped_arange((1, 1, 3, 4), xp, dtype) + b = xp.empty((3, 4), dtype=dtype) + xp.copyto(b, a) + return b + + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_copyto_squeeze_different_contiguity(self, xp, dtype): + a = testing.shaped_arange((1, 1, 3, 4), xp, dtype) + b = xp.empty((4, 3), dtype=dtype).T + xp.copyto(b, a) + return b + + @testing.for_all_dtypes() + @testing.numpy_cupy_array_equal() + def test_copyto_squeeze_broadcast(self, xp, dtype): + a = testing.shaped_arange((1, 2, 1, 4), xp, dtype) + b = xp.empty((2, 3, 4), dtype=dtype) + xp.copyto(b, a) + return b + @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_copyto_where(self, xp, dtype): @@ -47,63 +90,49 @@ def test_copyto_where(self, xp, dtype): xp.copyto(a, b, where=c) return a - # def _check_copyto_where_multigpu_raises(self, dtype, ngpus): - # def get_numpy(): - # a = testing.shaped_arange((2, 3, 4), numpy, dtype) - # b = testing.shaped_reverse_arange((2, 3, 4), numpy, dtype) - # c = testing.shaped_arange((2, 3, 4), numpy, '?') - # numpy.copyto(a, b, where=c) - # return a - # - # for dev1, dev2, dev3, dev4 in itertools.product(*[range(ngpus)] * 4): - # if dev1 == dev2 == dev3 == dev4: - # continue - # if not dev1 <= dev2 <= dev3 <= dev4: - # continue - # - # with cuda.Device(dev1): - # a = testing.shaped_arange((2, 3, 4), cupy, dtype) - # with cuda.Device(dev2): - # b = testing.shaped_reverse_arange((2, 3, 4), cupy, dtype) - # with cuda.Device(dev3): - # c = testing.shaped_arange((2, 3, 4), cupy, '?') - # with cuda.Device(dev4): - # with self.assertRaisesRegex( - # ValueError, - # '^Array device must be same as the current device'): - # cupy.copyto(a, b, where=c) - # - # @testing.multi_gpu(2) - # @testing.for_all_dtypes() - # def test_copyto_where_multigpu_raises(self, dtype): - # self._check_copyto_where_multigpu_raises(dtype, 2) - # - # @testing.multi_gpu(2) - # @testing.for_all_dtypes() - # @testing.numpy_cupy_array_equal() - # def test_copyto_multigpu(self, xp, dtype): - # with cuda.Device(0): - # a = testing.shaped_arange((2, 3, 4), xp, dtype) - # with cuda.Device(1): - # b = xp.empty((2, 3, 4), dtype=dtype) - # xp.copyto(b, a) - # return b - # - # @testing.multi_gpu(2) - # @testing.for_all_dtypes() - # def test_copyto_multigpu_noncontinguous(self, dtype): - # with cuda.Device(0): - # src = testing.shaped_arange((2, 3, 4), cupy, dtype) - # src = src.swapaxes(0, 1) - # with cuda.Device(1): - # dst = cupy.empty_like(src) - # cupy.copyto(dst, src) - # - # expected = testing.shaped_arange((2, 3, 4), numpy, dtype) - # expected = expected.swapaxes(0, 1) - # - # testing.assert_array_equal(expected, src.get()) - # testing.assert_array_equal(expected, dst.get()) + @pytest.mark.parametrize("shape", [(2, 3, 4), (0,)]) + @testing.for_all_dtypes(no_bool=True) + def test_copyto_where_raises(self, dtype, shape): + for xp in (numpy, cupy): + a = testing.shaped_arange(shape, xp, "i") + b = testing.shaped_reverse_arange(shape, xp, "i") + c = testing.shaped_arange(shape, xp, dtype) + with pytest.raises(TypeError): + xp.copyto(a, b, where=c) + + @testing.for_all_dtypes() + def test_copyto_where_multidevice_raises(self, dtype): + a = testing.shaped_arange( + (2, 3, 4), cupy, dtype, device=dpctl.SyclQueue() + ) + b = testing.shaped_reverse_arange( + (2, 3, 4), cupy, dtype, device=dpctl.SyclQueue() + ) + c = testing.shaped_arange( + (2, 3, 4), cupy, "?", device=dpctl.SyclQueue() + ) + with pytest.raises( + dpctl.utils.ExecutionPlacementError, + match="arrays have different associated queues", + ): + cupy.copyto(a, b, where=c) + + @testing.for_all_dtypes() + def test_copyto_noncontinguous(self, dtype): + src = testing.shaped_arange((2, 3, 4), cupy, dtype) + # TODO: replace with + # src = src.swapaxes(0, 1) + # once dpnp.ndarray.swapaxes() is fully implemented + src = cupy.swapaxes(src, 0, 1) + + dst = cupy.empty_like(src) + cupy.copyto(dst, src) + + expected = testing.shaped_arange((2, 3, 4), numpy, dtype) + expected = expected.swapaxes(0, 1) + + testing.assert_array_equal(expected, src) + testing.assert_array_equal(expected, dst) @testing.parameterize( @@ -114,9 +143,7 @@ def test_copyto_where(self, xp, dtype): } ) ) -@pytest.mark.usefixtures("allow_fall_back_on_numpy") -@testing.gpu -class TestCopytoFromScalar(unittest.TestCase): +class TestCopytoFromScalar: @testing.for_all_dtypes() @testing.numpy_cupy_allclose(accept_error=TypeError) def test_copyto(self, xp, dtype): @@ -133,3 +160,68 @@ def test_copyto_where(self, xp, dtype): ) xp.copyto(dst, self.src, where=mask) return dst + + +@pytest.mark.parametrize( + "casting", ["no", "equiv", "safe", "same_kind", "unsafe"] +) +class TestCopytoFromNumpyScalar: + @testing.for_all_dtypes_combination(("dtype1", "dtype2")) + @testing.numpy_cupy_allclose(accept_error=TypeError) + def test_copyto(self, xp, dtype1, dtype2, casting): + if casting == "safe": + pytest.skip( + "NEP50 doesn't work properly in numpy with casting='safe'" + ) + + dst = xp.zeros((2, 3, 4), dtype=dtype1) + src = numpy.array(1, dtype=dtype2) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", numpy.ComplexWarning) + xp.copyto(dst, src, casting) + return dst + + @testing.for_all_dtypes() + @pytest.mark.parametrize( + "make_src", + [lambda dtype: numpy.array([1], dtype=dtype), lambda dtype: dtype(1)], + ) + @testing.numpy_cupy_allclose(accept_error=TypeError) + def test_copyto2(self, xp, make_src, dtype, casting): + dst = xp.zeros((2, 3, 4), dtype=dtype) + src = make_src(dtype) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", numpy.ComplexWarning) + xp.copyto(dst, src, casting) + return dst + + @testing.for_all_dtypes_combination(("dtype1", "dtype2")) + @testing.numpy_cupy_allclose(accept_error=TypeError) + def test_copyto_where(self, xp, dtype1, dtype2, casting): + if casting == "safe": + pytest.skip( + "NEP50 doesn't work properly in numpy with casting='safe'" + ) + + shape = (2, 3, 4) + dst = xp.ones(shape, dtype=dtype1) + src = numpy.array(1, dtype=dtype2) + mask = (testing.shaped_arange(shape, xp, dtype1) % 2).astype(xp.bool_) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", numpy.ComplexWarning) + xp.copyto(dst, src, casting=casting, where=mask) + return dst + + +@pytest.mark.parametrize("shape", [(3, 2), (0,)]) +@pytest.mark.parametrize( + "where", [float(3.2), int(0), int(4), int(-4), True, False, 1 + 1j] +) +@testing.for_all_dtypes() +@testing.numpy_cupy_allclose() +def test_copyto_scalarwhere(xp, dtype, where, shape): + dst = xp.zeros(shape, dtype=dtype) + src = xp.ones(shape, dtype=dtype) + xp.copyto(dst, src, where=where) + return dst diff --git a/tests/third_party/cupy/math_tests/test_arithmetic.py b/tests/third_party/cupy/math_tests/test_arithmetic.py index 7a8cf0ffc08..f15682b9a67 100644 --- a/tests/third_party/cupy/math_tests/test_arithmetic.py +++ b/tests/third_party/cupy/math_tests/test_arithmetic.py @@ -100,7 +100,7 @@ def test_raises_with_numpy_input(self): ) @pytest.mark.usefixtures("allow_fall_back_on_numpy") class TestArithmeticUnary(unittest.TestCase): - @testing.numpy_cupy_allclose(atol=1e-5) + @testing.numpy_cupy_allclose(atol=1e-5, type_check=has_support_aspect64()) def test_unary(self, xp): arg1 = self.arg1 if isinstance(arg1, numpy.ndarray): diff --git a/tests/third_party/cupy/testing/helper.py b/tests/third_party/cupy/testing/helper.py index c78dc07d999..f9f419e91c3 100644 --- a/tests/third_party/cupy/testing/helper.py +++ b/tests/third_party/cupy/testing/helper.py @@ -49,12 +49,16 @@ def _format_exception(exc): return "".join(traceback.TracebackException.from_exception(exc).format()) -def _call_func(self, impl, args, kw): +def _call_func(impl, args, kw): + # Note that `_pytest.outcomes.Skipped` is derived from BaseException. + exceptions = (Exception,) + if _is_pytest_available: + exceptions += (_pytest.outcomes.Skipped,) + try: - result = impl(self, *args, **kw) + result = impl(*args, **kw) error = None - tb = None - except Exception as e: + except exceptions as e: tb = e.__traceback__ if tb.tb_next is None: # failed before impl is called, e.g. invalid kw @@ -62,10 +66,10 @@ def _call_func(self, impl, args, kw): result = None error = e - return result, error, tb + return result, error -def _call_func_cupy(self, impl, args, kw, name, sp_name, scipy_name): +def _call_func_cupy(impl, args, kw, name, sp_name, scipy_name): assert isinstance(name, str) assert sp_name is None or isinstance(sp_name, str) assert scipy_name is None or isinstance(scipy_name, str) @@ -76,12 +80,12 @@ def _call_func_cupy(self, impl, args, kw, name, sp_name, scipy_name): kw[sp_name] = cupyx.scipy.sparse if scipy_name: kw[scipy_name] = cupyx.scipy - kw[name] = dpnp - result, error, tb = _call_func(self, impl, args, kw) - return result, error, tb + kw[name] = cupy + result, error = _call_func(impl, args, kw) + return result, error -def _call_func_numpy(self, impl, args, kw, name, sp_name, scipy_name): +def _call_func_numpy(impl, args, kw, name, sp_name, scipy_name): assert isinstance(name, str) assert sp_name is None or isinstance(sp_name, str) assert scipy_name is None or isinstance(scipy_name, str) @@ -97,29 +101,22 @@ def _call_func_numpy(self, impl, args, kw, name, sp_name, scipy_name): import scipy kw[scipy_name] = scipy - result, error, tb = _call_func(self, impl, args, kw) - return result, error, tb + result, error = _call_func(impl, args, kw) + return result, error -def _call_func_numpy_cupy(self, impl, args, kw, name, sp_name, scipy_name): +def _call_func_numpy_cupy(impl, args, kw, name, sp_name, scipy_name): # Run cupy - cupy_result, cupy_error, cupy_tb = _call_func_cupy( - self, impl, args, kw, name, sp_name, scipy_name + cupy_result, cupy_error = _call_func_cupy( + impl, args, kw, name, sp_name, scipy_name ) # Run numpy - numpy_result, numpy_error, numpy_tb = _call_func_numpy( - self, impl, args, kw, name, sp_name, scipy_name + numpy_result, numpy_error = _call_func_numpy( + impl, args, kw, name, sp_name, scipy_name ) - return ( - cupy_result, - cupy_error, - cupy_tb, - numpy_result, - numpy_error, - numpy_tb, - ) + return (cupy_result, cupy_error, numpy_result, numpy_error) _numpy_errors = [ @@ -139,10 +136,8 @@ def _check_numpy_cupy_error_compatible(cupy_error, numpy_error): """Checks if try/except blocks are equivalent up to public error classes""" return all( - [ - isinstance(cupy_error, err) == isinstance(numpy_error, err) - for err in _numpy_errors - ] + isinstance(cupy_error, err) == isinstance(numpy_error, err) + for err in _numpy_errors ) @@ -250,21 +245,35 @@ def _check_cupy_numpy_error(cupy_error, numpy_error, accept_error=False): ) -def _make_positive_mask(self, impl, args, kw, name, sp_name, scipy_name): +def _signed_counterpart(dtype): + return numpy.dtype(numpy.dtype(dtype).char.lower()).type + + +def _make_positive_masks(impl, args, kw, name, sp_name, scipy_name): # Returns a mask of output arrays that indicates valid elements for # comparison. See the comment at the caller. ks = [k for k, v in kw.items() if v in _unsigned_dtypes] for k in ks: - kw[k] = numpy.intp - result, error, tb = _call_func_cupy( - self, impl, args, kw, name, sp_name, scipy_name - ) + kw[k] = _signed_counterpart(kw[k]) + result, error = _call_func_cupy(impl, args, kw, name, sp_name, scipy_name) assert error is None - return dpnp.asnumpy(result) >= 0 + if not isinstance(result, (tuple, list)): + result = (result,) + return [cupy.asnumpy(r) >= 0 for r in result] def _contains_signed_and_unsigned(kw): - vs = set(kw.values()) + def isdtype(v): + if isinstance(v, numpy.dtype): + return True + elif isinstance(v, str): # expecting dtype character codes + return True + elif isinstance(v, type) and issubclass(v, numpy.number): + return True + else: + return False + + vs = set(v for v in kw.values() if isdtype(v)) return any(d in vs for d in _unsigned_dtypes) and any( d in vs for d in _float_dtypes + _signed_dtypes ) @@ -282,27 +291,36 @@ def decorator(impl): return decorator +def _wraps_partial_xp(wrapped, name, sp_name, scipy_name): + names = [name, sp_name, scipy_name] + names = [n for n in names if n is not None] + return _wraps_partial(wrapped, *names) + + def _make_decorator( - check_func, name, type_check, accept_error, sp_name=None, scipy_name=None + check_func, + name, + type_check, + contiguous_check, + accept_error, + sp_name=None, + scipy_name=None, + check_sparse_format=True, ): assert isinstance(name, str) assert sp_name is None or isinstance(sp_name, str) assert scipy_name is None or isinstance(scipy_name, str) def decorator(impl): - @functools.wraps(impl) - def test_func(self, *args, **kw): + @_wraps_partial_xp(impl, name, sp_name, scipy_name) + def test_func(*args, **kw): # Run cupy and numpy ( cupy_result, cupy_error, - cupy_tb, numpy_result, numpy_error, - numpy_tb, - ) = _call_func_numpy_cupy( - self, impl, args, kw, name, sp_name, scipy_name - ) + ) = _call_func_numpy_cupy(impl, args, kw, name, sp_name, scipy_name) assert cupy_result is not None or cupy_error is not None assert numpy_result is not None or numpy_error is not None @@ -322,39 +340,87 @@ def test_func(self, *args, **kw): assert len(cupy_result) == len(numpy_result) + # Check types + cupy_numpy_result_ndarrays = [ + _convert_output_to_ndarray( + cupy_r, numpy_r, sp_name, check_sparse_format + ) + for cupy_r, numpy_r in zip(cupy_result, numpy_result) + ] + + # Check dtypes if type_check: - for cupy_r, numpy_r in zip(cupy_result, numpy_result): + for cupy_r, numpy_r in cupy_numpy_result_ndarrays: if cupy_r.dtype != numpy_r.dtype: - print( - f"\nERROR:\n\tcupy_r.dtype={cupy_r.dtype},\n\tnumpy_r.dtype={numpy_r.dtype}" + raise AssertionError( + """ndarrays of different dtypes are returned. +cupy: {} +numpy: {}""".format( + cupy_r.dtype, numpy_r.dtype + ) ) - assert cupy_r.dtype == numpy_r.dtype - for cupy_r, numpy_r in zip(cupy_result, numpy_result): - if cupy_r.shape != numpy_r.shape: - print( - f"\nERROR:\n\tcupy_r.shape={cupy_r.shape},\n\tnumpy_r.shape={numpy_r.shape}" - ) + # Check contiguities + if contiguous_check: + for cupy_r, numpy_r in zip(cupy_result, numpy_result): + if isinstance(numpy_r, numpy.ndarray): + if ( + numpy_r.flags.c_contiguous + and not cupy_r.flags.c_contiguous + ): + raise AssertionError( + "The state of c_contiguous flag is false. " + "(cupy_result:{} numpy_result:{})".format( + cupy_r.flags.c_contiguous, + numpy_r.flags.c_contiguous, + ) + ) + if ( + numpy_r.flags.f_contiguous + and not cupy_r.flags.f_contiguous + ): + raise AssertionError( + "The state of f_contiguous flag is false. " + "(cupy_result:{} numpy_result:{})".format( + cupy_r.flags.f_contiguous, + numpy_r.flags.f_contiguous, + ) + ) + + # Check shapes + for cupy_r, numpy_r in cupy_numpy_result_ndarrays: assert cupy_r.shape == numpy_r.shape + masks = [None] * len(cupy_result) + if _contains_signed_and_unsigned(kw): + needs_mask = [ + cupy_r.dtype in _unsigned_dtypes for cupy_r in cupy_result + ] + if any(needs_mask): + masks = _make_positive_masks( + impl, args, kw, name, sp_name, scipy_name + ) + for i, flag in enumerate(needs_mask): + if not flag: + masks[i] = None + + # Check item values + for (cupy_r, numpy_r), mask in zip( + cupy_numpy_result_ndarrays, masks + ): # Behavior of assigning a negative value to an unsigned integer # variable is undefined. # nVidia GPUs and Intel CPUs behave differently. # To avoid this difference, we need to ignore dimensions whose # values are negative. + skip = False - if ( - _contains_signed_and_unsigned(kw) - and cupy_r.dtype in _unsigned_dtypes - ): - mask = _make_positive_mask( - self, impl, args, kw, name, sp_name, scipy_name - ) + if mask is not None: if cupy_r.shape == (): skip = (mask == 0).all() else: - cupy_r = dpnp.asnumpy(cupy_r[mask]) - numpy_r = dpnp.asnumpy(numpy_r[mask]) + cupy_r = cupy_r[mask].get() + numpy_r = numpy_r[mask] if not skip: check_func(cupy_r, numpy_r) @@ -364,6 +430,99 @@ def test_func(self, *args, **kw): return decorator +def _convert_output_to_ndarray(c_out, n_out, sp_name, check_sparse_format): + """Checks type of cupy/numpy results and returns cupy/numpy ndarrays. + + Args: + c_out (cupy.ndarray, cupyx.scipy.sparse matrix, cupy.poly1d or scalar): + cupy result + n_out (numpy.ndarray, scipy.sparse matrix, numpy.poly1d or scalar): + numpy result + sp_name(str or None): Argument name whose value is either + ``scipy.sparse`` or ``cupyx.scipy.sparse`` module. If ``None``, no + argument is given for the modules. + check_sparse_format (bool): If ``True``, consistency of format of + sparse matrix is also checked. Default is ``True``. + + Returns: + The tuple of cupy.ndarray and numpy.ndarray. + """ + if sp_name is not None and cupyx.scipy.sparse.issparse(c_out): + # Sparse output case. + import scipy.sparse + + assert scipy.sparse.issparse(n_out) + if check_sparse_format: + assert c_out.format == n_out.format + return c_out.A, n_out.A + if isinstance(c_out, cupy.ndarray) and isinstance( + n_out, (numpy.ndarray, numpy.generic) + ): + # ndarray output case. + return c_out, n_out + if ( + hasattr(cupy, "poly1d") + and isinstance(c_out, cupy.poly1d) + and hasattr(numpy, "poly1d") + and isinstance(n_out, numpy.poly1d) + ): + # poly1d output case. + assert c_out.variable == n_out.variable + return c_out.coeffs, n_out.coeffs + if isinstance(c_out, numpy.generic) and isinstance(n_out, numpy.generic): + # numpy scalar output case. + return c_out, n_out + if isinstance(c_out, numpy.ndarray) and isinstance(n_out, numpy.ndarray): + # fallback on numpy output case. + return c_out, n_out + if numpy.isscalar(c_out) and numpy.isscalar(n_out): + # python scalar output case. + return cupy.array(c_out), numpy.array(n_out) + raise AssertionError( + "numpy and cupy returns different type of return value:\n" + "cupy: {}\nnumpy: {}".format(type(c_out), type(n_out)) + ) + + +def _check_tolerance_keys(rtol, atol): + def _check(tol): + if isinstance(tol, dict): + for k in tol.keys(): + if type(k) is type: + continue + if type(k) is str and k == "default": + continue + msg = ( + "Keys of the tolerance dictionary need to be type " + "objects as `numpy.float32` and `cupy.float32` or " + "`'default'` string." + ) + raise TypeError(msg) + + _check(rtol) + _check(atol) + + +def _resolve_tolerance(type_check, result, rtol, atol): + def _resolve(dtype, tol): + if isinstance(tol, dict): + tol1 = tol.get(dtype.type) + if tol1 is None: + tol1 = tol.get("default") + if tol1 is None: + raise TypeError( + "Can not find tolerance for {}".format(dtype.type) + ) + return tol1 + else: + return tol + + dtype = result.dtype + rtol1 = _resolve(dtype, rtol) + atol1 = _resolve(dtype, atol) + return rtol1, atol1 + + def numpy_cupy_allclose( rtol=1e-7, atol=0, @@ -375,12 +534,19 @@ def numpy_cupy_allclose( sp_name=None, scipy_name=None, contiguous_check=True, + *, + _check_sparse_format=True, ): """Decorator that checks NumPy results and CuPy ones are close. Args: - rtol(float): Relative tolerance. - atol(float): Absolute tolerance. + rtol(float or dict): Relative tolerance. Besides a float value, a + dictionary that maps a dtypes to a float value can be supplied to + adjust tolerance per dtype. If the dictionary has ``'default'`` + string as its key, its value is used as the default tolerance in + case any dtype keys do not match. + atol(float or dict): Absolute tolerance. Besides a float value, a + dictionary can be supplied as ``rtol``. err_msg(str): The error message to be printed in case of failure. verbose(bool): If ``True``, the conflicting values are appended to the error message. @@ -408,9 +574,8 @@ def numpy_cupy_allclose( should return same value. >>> import unittest - >>> from dpnp import testing - >>> @testing.gpu - ... class TestFoo(unittest.TestCase): + >>> from cupy import testing + >>> class TestFoo(unittest.TestCase): ... ... @testing.numpy_cupy_allclose() ... def test_foo(self, xp): @@ -423,21 +588,31 @@ def numpy_cupy_allclose( .. seealso:: :func:`cupy.testing.assert_allclose` """ + _check_tolerance_keys(rtol, atol) + + # When `type_check` is `False`, cupy result and numpy result may have + # different dtypes so we can not determine the dtype to use from the + # tolerance associations. + if not type_check: + if isinstance(rtol, dict) or isinstance(atol, dict): + raise TypeError( + "When `type_check` is `False`, `rtol` and `atol` " + "must be supplied as float." + ) def check_func(c, n): - c_array = c - n_array = n - if sp_name is not None: - import scipy.sparse - - if cupyx.scipy.sparse.issparse(c): - c_array = c.A - if scipy.sparse.issparse(n): - n_array = n.A - array.assert_allclose(c_array, n_array, rtol, atol, err_msg, verbose) + rtol1, atol1 = _resolve_tolerance(type_check, c, rtol, atol) + array.assert_allclose(c, n, rtol1, atol1, err_msg, verbose) return _make_decorator( - check_func, name, type_check, accept_error, sp_name, scipy_name + check_func, + name, + type_check, + contiguous_check, + accept_error, + sp_name, + scipy_name, + _check_sparse_format, ) @@ -485,7 +660,7 @@ def check_func(x, y): array.assert_array_almost_equal(x, y, decimal, err_msg, verbose) return _make_decorator( - check_func, name, type_check, accept_error, sp_name, scipy_name + check_func, name, type_check, False, accept_error, sp_name, scipy_name ) @@ -522,14 +697,19 @@ def numpy_cupy_array_almost_equal_nulp( (except the type of array module) even if ``xp`` is ``numpy`` or ``cupy``. .. seealso:: :func:`cupy.testing.assert_array_almost_equal_nulp` - """ def check_func(x, y): array.assert_array_almost_equal_nulp(x, y, nulp) return _make_decorator( - check_func, name, type_check, accept_error, sp_name, scipy_name=None + check_func, + name, + type_check, + False, + accept_error, + sp_name, + scipy_name=None, ) @@ -577,7 +757,7 @@ def check_func(x, y): array.assert_array_max_ulp(x, y, maxulp, dtype) return _make_decorator( - check_func, name, type_check, accept_error, sp_name, scipy_name + check_func, name, type_check, False, accept_error, sp_name, scipy_name ) @@ -623,18 +803,10 @@ def numpy_cupy_array_equal( """ def check_func(x, y): - if sp_name is not None: - import scipy.sparse - - if cupyx.scipy.sparse.issparse(x): - x = x.A - if scipy.sparse.issparse(y): - y = y.A - array.assert_array_equal(x, y, err_msg, verbose, strides_check) return _make_decorator( - check_func, name, type_check, accept_error, sp_name, scipy_name + check_func, name, type_check, False, accept_error, sp_name, scipy_name ) @@ -660,13 +832,19 @@ def numpy_cupy_array_list_equal( (except the type of array module) even if ``xp`` is ``numpy`` or ``cupy``. .. seealso:: :func:`cupy.testing.assert_array_list_equal` - """ + warnings.warn( + "numpy_cupy_array_list_equal is deprecated." + " Use numpy_cupy_array_equal instead.", + DeprecationWarning, + ) def check_func(x, y): array.assert_array_equal(x, y, err_msg, verbose) - return _make_decorator(check_func, name, False, False, sp_name, scipy_name) + return _make_decorator( + check_func, name, False, False, False, sp_name, scipy_name + ) def numpy_cupy_array_less( @@ -710,7 +888,7 @@ def check_func(x, y): array.assert_array_less(x, y, err_msg, verbose) return _make_decorator( - check_func, name, type_check, accept_error, sp_name, scipy_name + check_func, name, type_check, False, accept_error, sp_name, scipy_name ) @@ -721,7 +899,7 @@ def numpy_cupy_equal(name="xp", sp_name=None, scipy_name=None): name(str): Argument name whose value is either ``numpy`` or ``cupy`` module. sp_name(str or None): Argument name whose value is either - ``scipy.sparse`` or ``cupyx.sciyp.sparse`` module. If ``None``, no + ``scipy.sparse`` or ``cupyx.scipy.sparse`` module. If ``None``, no argument is given for the modules. scipy_name(str or None): Argument name whose value is either ``scipy`` or ``cupyx.scipy`` module. If ``None``, no argument is given for @@ -732,19 +910,21 @@ def numpy_cupy_equal(name="xp", sp_name=None, scipy_name=None): """ def decorator(impl): - @functools.wraps(impl) - def test_func(self, *args, **kw): + @_wraps_partial_xp(impl, name, sp_name, scipy_name) + def test_func(*args, **kw): # Run cupy and numpy ( cupy_result, cupy_error, - cupy_tb, numpy_result, numpy_error, - numpy_tb, - ) = _call_func_numpy_cupy( - self, impl, args, kw, name, sp_name, scipy_name - ) + ) = _call_func_numpy_cupy(impl, args, kw, name, sp_name, scipy_name) + + if cupy_error or numpy_error: + _check_cupy_numpy_error( + cupy_error, numpy_error, accept_error=False + ) + return if cupy_result != numpy_result: message = """Results are not equal: @@ -784,21 +964,20 @@ def numpy_cupy_raises( Decorated test fixture is required throw same errors even if ``xp`` is ``numpy`` or ``cupy``. """ + warnings.warn( + "cupy.testing.numpy_cupy_raises is deprecated.", DeprecationWarning + ) def decorator(impl): - @functools.wraps(impl) - def test_func(self, *args, **kw): + @_wraps_partial_xp(impl, name, sp_name, scipy_name) + def test_func(*args, **kw): # Run cupy and numpy ( cupy_result, cupy_error, - cupy_tb, numpy_result, numpy_error, - numpy_tb, - ) = _call_func_numpy_cupy( - self, impl, args, kw, name, sp_name, scipy_name - ) + ) = _call_func_numpy_cupy(impl, args, kw, name, sp_name, scipy_name) _check_cupy_numpy_error( cupy_error, numpy_error, accept_error=accept_error @@ -829,7 +1008,7 @@ def test_func(*args, **kw): try: kw[name] = numpy.dtype(dtype).type impl(*args, **kw) - except unittest.SkipTest as e: + except _skip_classes as e: print("skipped: {} = {} ({})".format(name, dtype, e)) except Exception: print(name, "is", dtype) @@ -913,9 +1092,8 @@ def for_all_dtypes( ``dtype`` is an argument inserted by the decorator. >>> import unittest - >>> from dpnp import testing - >>> @testing.gpu - ... class TestNpz(unittest.TestCase): + >>> from cupy import testing + >>> class TestNpz(unittest.TestCase): ... ... @testing.for_all_dtypes() ... def test_pickle(self, dtype): @@ -930,9 +1108,8 @@ def for_all_dtypes( The following is such an example. >>> import unittest - >>> from dpnp import testing - >>> @testing.gpu - ... class TestMean(unittest.TestCase): + >>> from cupy import testing + >>> class TestMean(unittest.TestCase): ... ... @testing.for_all_dtypes() ... @testing.numpy_cupy_allclose() @@ -1013,9 +1190,7 @@ def for_int_dtypes(name="dtype", no_bool=False): .. seealso:: :func:`cupy.testing.for_dtypes`, :func:`cupy.testing.for_all_dtypes` - """ - if no_bool: return for_dtypes(_int_dtypes, name=name) else: @@ -1062,7 +1237,6 @@ def for_dtypes_combination(types, names=("dtype",), full=None): If the value is set to ``'1'``, it behaves as if ``full=True``, and otherwise ``full=False``. """ - types = list(types) if len(types) == 1: @@ -1087,14 +1261,20 @@ def for_dtypes_combination(types, names=("dtype",), full=None): combination = [dict(assoc_list) for assoc_list in set(combination)] def decorator(impl): - @functools.wraps(impl) - def test_func(self, *args, **kw): + @_wraps_partial(impl, *names) + def test_func(*args, **kw): for dtypes in combination: kw_copy = kw.copy() kw_copy.update(dtypes) try: - impl(self, *args, **kw_copy) + impl(*args, **kw_copy) + except _skip_classes as e: + msg = ", ".join( + "{} = {}".format(name, dtype) + for name, dtype in dtypes.items() + ) + print("skipped: {} ({})".format(msg, e)) except Exception: print(dtypes) raise @@ -1143,9 +1323,7 @@ def for_signed_dtypes_combination(names=("dtype",), full=None): (see description in :func:`cupy.testing.for_dtypes_combination`). .. seealso:: :func:`cupy.testing.for_dtypes_combination` - """ - return for_dtypes_combination(_signed_dtypes, names=names, full=full) @@ -1160,9 +1338,7 @@ def for_unsigned_dtypes_combination(names=("dtype",), full=None): (see description in :func:`cupy.testing.for_dtypes_combination`). .. seealso:: :func:`cupy.testing.for_dtypes_combination` - """ - return for_dtypes_combination(_unsigned_dtypes, names=names, full=full) @@ -1179,9 +1355,7 @@ def for_int_dtypes_combination(names=("dtype",), no_bool=False, full=None): (see description in :func:`cupy.testing.for_dtypes_combination`). .. seealso:: :func:`cupy.testing.for_dtypes_combination` - """ - if no_bool: types = _int_dtypes else: @@ -1227,7 +1401,7 @@ def for_CF_orders(name="order"): .. seealso:: :func:`cupy.testing.for_all_dtypes` """ - return for_orders([None, "C", "c"], name) + return for_orders([None, "C", "F", "c", "f"], name) def for_castings(castings=None, name="casting"): @@ -1262,6 +1436,56 @@ def test_func(*args, **kw): return decorator +def for_contiguous_axes(name="axis"): + """Decorator for parametrizing tests with possible contiguous axes. + + Args: + name(str): Argument name to which specified axis are passed. + + .. note:: + 1. Adapted from tests/cupy_tests/fft_tests/test_fft.py. + 2. Example: for ``shape = (1, 2, 3)``, the tested axes are + ``[(2,), (1, 2), (0, 1, 2)]`` for the C order, and + ``[(0,), (0, 1), (0, 1, 2)]`` for the F order. + """ + + def decorator(impl): + @_wraps_partial(impl, name) + def test_func(self, *args, **kw): + ndim = len(self.shape) + order = self.order + for i in range(ndim): + a = () + if order in ("c", "C"): + for j in range(ndim - 1, i - 1, -1): + a = (j,) + a + elif order in ("f", "F"): + for j in range(0, i + 1): + a = a + (j,) + else: + raise ValueError("Please specify the array order.") + try: + kw[name] = a + impl(self, *args, **kw) + except Exception: + print( + name, + "is", + a, + ", ndim is", + ndim, + ", shape is", + self.shape, + ", order is", + order, + ) + raise + + return test_func + + return decorator + + def with_requires(*requirements): """Run a test case only when given requirements are satisfied. @@ -1313,7 +1537,7 @@ def numpy_satisfies(version_range): return True -def shaped_arange(shape, xp=dpnp, dtype=numpy.float64, order="C"): +def shaped_arange(shape, xp=cupy, dtype=numpy.float32, order="C", device=None): """Returns an array with given shape, array module, and dtype. Args: @@ -1321,6 +1545,7 @@ def shaped_arange(shape, xp=dpnp, dtype=numpy.float64, order="C"): xp(numpy or cupy): Array module to use. dtype(dtype): Dtype of returned ndarray. order({'C', 'F'}): Order of returned ndarray. + device(None or device): A device where the output array is created. Returns: numpy.ndarray or cupy.ndarray: @@ -1332,21 +1557,23 @@ def shaped_arange(shape, xp=dpnp, dtype=numpy.float64, order="C"): """ dtype = numpy.dtype(dtype) - a = xp.arange(1, prod(shape) + 1, 1) + kw = {} if xp is numpy else {"device": device} + a = numpy.arange(1, prod(shape) + 1, 1) if dtype == "?": a = a % 2 == 0 elif dtype.kind == "c": a = a + a * 1j - return xp.array(a.astype(dtype).reshape(shape), order=order, dtype=dtype) + return xp.array(a.astype(dtype).reshape(shape), order=order, **kw) -def shaped_reverse_arange(shape, xp=dpnp, dtype=numpy.float32): +def shaped_reverse_arange(shape, xp=cupy, dtype=numpy.float32, device=None): """Returns an array filled with decreasing numbers. Args: shape(tuple of int): Shape of returned ndarray. xp(numpy or cupy): Array module to use. dtype(dtype): Dtype of returned ndarray. + device(None or device): A device where the output array is created. Returns: numpy.ndarray or cupy.ndarray: @@ -1357,17 +1584,19 @@ def shaped_reverse_arange(shape, xp=dpnp, dtype=numpy.float32): ``True`` (resp. ``False``). """ dtype = numpy.dtype(dtype) + kw = {} if xp is numpy else {"device": device} size = prod(shape) - # a = xp.arange(size, 0, -1) - a = xp.arange(0, size) + a = numpy.arange(size, 0, -1) if dtype == "?": a = a % 2 == 0 elif dtype.kind == "c": a = a + a * 1j - return xp.array(a.astype(dtype).reshape(shape)) + return xp.array(a.astype(dtype).reshape(shape), **kw) -def shaped_random(shape, xp=dpnp, dtype=numpy.float64, scale=10, seed=0): +def shaped_random( + shape, xp=cupy, dtype=numpy.float32, scale=10, seed=0, order="C" +): """Returns an array filled with random values. Args: @@ -1379,7 +1608,7 @@ def shaped_random(shape, xp=dpnp, dtype=numpy.float64, scale=10, seed=0): Returns: numpy.ndarray or cupy.ndarray: The array with - given shape, array module, + given shape, array module, If ``dtype`` is ``numpy.bool_``, the elements are independently drawn from ``True`` and ``False`` @@ -1391,14 +1620,14 @@ def shaped_random(shape, xp=dpnp, dtype=numpy.float64, scale=10, seed=0): """ numpy.random.seed(seed) dtype = numpy.dtype(dtype) - if dtype == "?": - return xp.asarray(numpy.random.randint(2, size=shape), dtype=dtype) + a = numpy.random.randint(2, size=shape) elif dtype.kind == "c": - a = dpnp.random.rand(*shape) + 1j * numpy.random.rand(*shape) - return xp.asarray(a * scale, dtype=dtype) + a = numpy.random.rand(*shape) + 1j * numpy.random.rand(*shape) + a *= scale else: - return xp.asarray(numpy.random.rand(*shape) * scale, dtype=dtype) + a = numpy.random.rand(*shape) * scale + return xp.asarray(a, dtype=dtype, order=order) def empty(xp=dpnp, dtype=numpy.float64): From d159067929c7e223d7f8244a822336f099a18987 Mon Sep 17 00:00:00 2001 From: DenisScherbakov Date: Tue, 22 Aug 2023 16:02:44 +0200 Subject: [PATCH 58/58] Update install instruction for pip (#1531) * Update pip install command * Minor update --------- Co-authored-by: Anton <100830759+antonwolfy@users.noreply.github.com> --- README.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 8661b7502f8..04bc0e0bc3b 100644 --- a/README.md +++ b/README.md @@ -29,13 +29,11 @@ cd dpnp python scripts/build_locally.py ``` -## Install Wheel Package from Pypi +## Install Wheel Package via pip Install DPNP ```cmd -python -m pip install --index-url https://pypi.anaconda.org/intel/simple --extra-index-url https://pypi.org/simple dpnp +python -m pip install --index-url https://pypi.anaconda.org/intel/simple dpnp ``` -Note: DPNP wheel package is placed on Pypi, but some of its dependencies (like Intel numpy) are in Anaconda Cloud. -That is why install command requires additional intel Pypi channel from Anaconda Cloud. Set path to Performance Libraries in case of using venv or system Python: ```cmd