Leverage dpctl.tensor.all() and dpctl.tensor.any() implementations

.github/workflows/conda-package.yml

@@ -17,13 +17,15 @@ env:
       test_dparray.py
       test_fft.py
       test_linalg.py
+      test_logic.py
       test_mathematical.py
       test_random_state.py
       test_sort.py
       test_special.py
       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

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  */

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<double, bool>};
 
-    fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_BLN][eft_BLN] = {
-        eft_BLN, (void *)dpnp_all_ext_c<bool, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_INT][eft_INT] = {
-        eft_INT, (void *)dpnp_all_ext_c<int32_t, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_LNG][eft_LNG] = {
-        eft_LNG, (void *)dpnp_all_ext_c<int64_t, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_FLT][eft_FLT] = {
-        eft_FLT, (void *)dpnp_all_ext_c<float, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_DBL][eft_DBL] = {
-        eft_DBL, (void *)dpnp_all_ext_c<double, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_C64][eft_C64] = {
-        eft_C64, (void *)dpnp_all_ext_c<std::complex<float>, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ALL_EXT][eft_C128][eft_C128] = {
-        eft_C128, (void *)dpnp_all_ext_c<std::complex<double>, bool>};
-
     fmap[DPNPFuncName::DPNP_FN_ALLCLOSE][eft_INT][eft_INT] = {
         eft_BLN, (void *)dpnp_allclose_default_c<int32_t, int32_t, bool>};
     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<double, bool>};
 
-    fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_BLN][eft_BLN] = {
-        eft_BLN, (void *)dpnp_any_ext_c<bool, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_INT][eft_INT] = {
-        eft_INT, (void *)dpnp_any_ext_c<int32_t, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_LNG][eft_LNG] = {
-        eft_LNG, (void *)dpnp_any_ext_c<int64_t, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_FLT][eft_FLT] = {
-        eft_FLT, (void *)dpnp_any_ext_c<float, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_DBL][eft_DBL] = {
-        eft_DBL, (void *)dpnp_any_ext_c<double, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_C64][eft_C64] = {
-        eft_C64, (void *)dpnp_any_ext_c<std::complex<float>, bool>};
-    fmap[DPNPFuncName::DPNP_FN_ANY_EXT][eft_C128][eft_C128] = {
-        eft_C128, (void *)dpnp_any_ext_c<std::complex<double>, bool>};
-
     func_map_logic_2arg_2type_helper<eft_BLN, eft_INT, eft_LNG, eft_FLT,
                                      eft_DBL>(fmap);
 

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

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 = <c_dpctl.SyclQueue> 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 = <custom_logic_1in_1out_func_ptr_t > 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 = <c_dpctl.SyclQueue> result_sycl_queue
-    cdef c_dpctl.DPCTLSyclQueueRef q_ref = q.get_queue_ref()
-
-    cdef custom_logic_1in_1out_func_ptr_t func = <custom_logic_1in_1out_func_ptr_t > 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,

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
     )