Move ti.maximum()/minimum()/multiply() and reuse them

Author: vlad-perevezentsev

dpctl_ext/tensor/CMakeLists.txt

@@ -118,9 +118,9 @@ set(_elementwise_sources
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/logical_not.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/logical_or.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/logical_xor.cpp
-    #${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/maximum.cpp
-    #${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/minimum.cpp
-    #${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/multiply.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/maximum.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/minimum.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/multiply.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/negative.cpp
     #${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/nextafter.cpp
     #${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions/not_equal.cpp

dpctl_ext/tensor/__init__.py

@@ -135,6 +135,9 @@
     logical_not,
     logical_or,
     logical_xor,
+    maximum,
+    minimum,
+    multiply,
     negative,
     positive,
     proj,
@@ -256,9 +259,12 @@
     "log2",
     "log10",
     "max",
+    "maximum",
     "meshgrid",
     "min",
+    "minimum",
     "moveaxis",
+    "multiply",
     "permute_dims",
     "matmul",
     "matrix_transpose",

dpctl_ext/tensor/_elementwise_funcs.py

@@ -1368,6 +1368,106 @@
 )
 del _logical_not_docstring
 
+# B26: ==== MAXIMUM    (x1, x2)
+_maximum_docstring_ = r"""
+maximum(x1, x2, /, \*, out=None, order='K')
+
+Compares two input arrays `x1` and `x2` and returns a new array containing the
+element-wise maxima.
+
+Args:
+    x1 (usm_ndarray):
+        First input array. May have any data type.
+    x2 (usm_ndarray):
+        Second input array. May have any data type.
+    out (Union[usm_ndarray, None], 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".
+
+Returns:
+    usm_ndarray:
+        An array containing the element-wise maxima. The data type of
+        the returned array is determined by the Type Promotion Rules.
+"""
+maximum = BinaryElementwiseFunc(
+    "maximum",
+    ti._maximum_result_type,
+    ti._maximum,
+    _maximum_docstring_,
+)
+del _maximum_docstring_
+
+# B27: ==== MINIMUM    (x1, x2)
+_minimum_docstring_ = r"""
+minimum(x1, x2, /, \*, out=None, order='K')
+
+Compares two input arrays `x1` and `x2` and returns a new array containing the
+element-wise minima.
+
+Args:
+    x1 (usm_ndarray):
+        First input array. May have any data type.
+    x2 (usm_ndarray):
+        Second input array. May have any data type.
+    out (Union[usm_ndarray, None], 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".
+
+Returns:
+    usm_ndarray:
+        An array containing the element-wise minima. The data type of
+        the returned array is determined by the Type Promotion Rules.
+"""
+minimum = BinaryElementwiseFunc(
+    "minimum",
+    ti._minimum_result_type,
+    ti._minimum,
+    _minimum_docstring_,
+)
+del _minimum_docstring_
+
+# B19: ==== MULTIPLY    (x1, x2)
+_multiply_docstring_ = r"""
+multiply(x1, x2, /, \*, out=None, order='K')
+
+Calculates the product for each element `x1_i` of the input array `x1` with the
+respective element `x2_i` of the input array `x2`.
+
+Args:
+    x1 (usm_ndarray):
+        First input array. May have any data type.
+    x2 (usm_ndarray):
+        Second input array. May have any data type.
+    out (Union[usm_ndarray, None], 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".
+
+Returns:
+    usm_ndarray:
+        An array containing the element-wise products. The data type of
+        the returned array is determined by the Type Promotion Rules.
+"""
+multiply = BinaryElementwiseFunc(
+    "multiply",
+    ti._multiply_result_type,
+    ti._multiply,
+    _multiply_docstring_,
+    binary_inplace_fn=ti._multiply_inplace,
+)
+del _multiply_docstring_
+
 # U25: ==== NEGATIVE    (x)
 _negative_docstring_ = r"""
 negative(x, /, \*, out=None, order='K')

dpctl_ext/tensor/libtensor/include/kernels/elementwise_functions/maximum.hpp

@@ -25,6 +25,8 @@
 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 // THE POSSIBILITY OF SUCH DAMAGE.
 //*****************************************************************************
+//
+//===---------------------------------------------------------------------===//
 ///
 /// \file
 /// This file defines kernels for elementwise evaluation of MAXIMUM(x1, x2)
@@ -53,6 +55,7 @@
 
 namespace dpctl::tensor::kernels::maximum
 {
+
 using dpctl::tensor::ssize_t;
 namespace td_ns = dpctl::tensor::type_dispatch;
 namespace tu_ns = dpctl::tensor::type_utils;

dpctl_ext/tensor/libtensor/include/kernels/elementwise_functions/minimum.hpp

@@ -25,6 +25,8 @@
 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
 // THE POSSIBILITY OF SUCH DAMAGE.
 //*****************************************************************************
+//
+//===---------------------------------------------------------------------===//
 ///
 /// \file
 /// This file defines kernels for elementwise evaluation of MINIMUM(x1, x2)
@@ -52,6 +54,7 @@
 
 namespace dpctl::tensor::kernels::minimum
 {
+
 using dpctl::tensor::ssize_t;
 namespace td_ns = dpctl::tensor::type_dispatch;
 namespace tu_ns = dpctl::tensor::type_utils;