Merge pull request #1426 from IntelPython/implement-product

Implement product over axis

Author: oleksandr-pavlyk

dpctl/tensor/__init__.py

@@ -160,7 +160,7 @@
     tanh,
     trunc,
 )
-from ._reduction import argmax, argmin, max, min, sum
+from ._reduction import argmax, argmin, max, min, prod, sum
 from ._testing import allclose
 
 __all__ = [
@@ -313,4 +313,5 @@
     "min",
     "argmax",
     "argmin",
+    "prod",
 ]

dpctl/tensor/_reduction.py

@@ -144,12 +144,12 @@ def _reduction_over_axis(
 def sum(x, axis=None, dtype=None, keepdims=False):
     """sum(x, axis=None, dtype=None, keepdims=False)
 
-    Calculates the sum of the input array `x`.
+    Calculates the sum of elements in the input array `x`.
 
     Args:
         x (usm_ndarray):
             input array.
-        axis (Optional[int, Tuple[int,...]]):
+        axis (Optional[int, Tuple[int, ...]]):
             axis or axes along which sums must be computed. If a tuple
             of unique integers, sums are computed over multiple axes.
             If `None`, the sum is computed over the entire array.
@@ -202,6 +202,67 @@ def sum(x, axis=None, dtype=None, keepdims=False):
     )
 
 
+def prod(x, axis=None, dtype=None, keepdims=False):
+    """prod(x, axis=None, dtype=None, keepdims=False)
+
+    Calculates the product of elements in the input array `x`.
+
+    Args:
+        x (usm_ndarray):
+            input array.
+        axis (Optional[int, Tuple[int, ...]]):
+            axis or axes along which products must be computed. If a tuple
+            of unique integers, products are computed over multiple axes.
+            If `None`, the product is computed over the entire array.
+            Default: `None`.
+        dtype (Optional[dtype]):
+            data type of the returned array. If `None`, the default data
+            type is inferred from the "kind" of the input array data type.
+                * If `x` has a real-valued floating-point data type,
+                  the returned array will have the default real-valued
+                  floating-point data type for the device where input
+                  array `x` is allocated.
+                * If x` has signed integral data type, the returned array
+                  will have the default signed integral type for the device
+                  where input array `x` is allocated.
+                * If `x` has unsigned integral data type, the returned array
+                  will have the default unsigned integral type for the device
+                  where input array `x` is allocated.
+                * If `x` has a complex-valued floating-point data typee,
+                  the returned array will have the default complex-valued
+                  floating-pointer data type for the device where input
+                  array `x` is allocated.
+                * If `x` has a boolean data type, the returned array will
+                  have the default signed integral type for the device
+                  where input array `x` is allocated.
+            If the data type (either specified or resolved) differs from the
+            data type of `x`, the input array elements are cast to the
+            specified data type before computing the product. Default: `None`.
+        keepdims (Optional[bool]):
+            if `True`, the reduced axes (dimensions) are included in the result
+            as singleton dimensions, so that the returned array remains
+            compatible with the input arrays according to Array Broadcasting
+            rules. Otherwise, if `False`, the reduced axes are not included in
+            the returned array. Default: `False`.
+    Returns:
+        usm_ndarray:
+            an array containing the products. If the product was computed over
+            the entire array, a zero-dimensional array is returned. The returned
+            array has the data type as described in the `dtype` parameter
+            description above.
+    """
+    return _reduction_over_axis(
+        x,
+        axis,
+        dtype,
+        keepdims,
+        ti._prod_over_axis,
+        ti._prod_over_axis_dtype_supported,
+        _default_reduction_dtype,
+        _identity=1,
+    )
+
+
 def _comparison_over_axis(x, axis, keepdims, _reduction_fn):
     if not isinstance(x, dpt.usm_ndarray):
         raise TypeError(f"Expected dpctl.tensor.usm_ndarray, got {type(x)}")
@@ -253,7 +314,7 @@ def max(x, axis=None, keepdims=False):
     Args:
         x (usm_ndarray):
             input array.
-        axis (Optional[int, Tuple[int,...]]):
+        axis (Optional[int, Tuple[int, ...]]):
             axis or axes along which maxima must be computed. If a tuple
             of unique integers, the maxima are computed over multiple axes.
             If `None`, the max is computed over the entire array.
@@ -281,7 +342,7 @@ def min(x, axis=None, keepdims=False):
     Args:
         x (usm_ndarray):
             input array.
-        axis (Optional[int, Tuple[int,...]]):
+        axis (Optional[int, Tuple[int, ...]]):
             axis or axes along which minima must be computed. If a tuple
             of unique integers, the minima are computed over multiple axes.
             If `None`, the min is computed over the entire array.