implement dpnp.logsumexp and dpnp.reduce_hypot (#1648)

* implement logsumexp and reduce_hypot

* fix pre-commit

* address comments

doc/reference/math.rst

@@ -23,6 +23,7 @@ Trigonometric functions
    dpnp.unwrap
    dpnp.deg2rad
    dpnp.rad2deg
+   dpnp.reduce_hypot
 
 
 Hyperbolic functions
@@ -94,6 +95,7 @@ Exponents and logarithms
    dpnp.log1p
    dpnp.logaddexp
    dpnp.logaddexp2
+   dpnp.logsumexp
 
 
 Other special functions

dpnp/dpnp_iface_trigonometric.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 (
@@ -98,9 +100,11 @@
     "log1p",
     "log2",
     "logaddexp",
+    "logsumexp",
     "rad2deg",
     "radians",
     "reciprocal",
+    "reduce_hypot",
     "rsqrt",
     "sin",
     "sinh",
@@ -989,6 +993,10 @@ def hypot(
     Otherwise the function will be executed sequentially on CPU.
     Input array data types are limited by supported real-valued data types.
 
+    See Also
+    --------
+    :obj:`dpnp.reduce_hypot` : The square root of the sum of squares of elements in the input array.
+
     Examples
     --------
     >>> import dpnp as np
@@ -1303,6 +1311,7 @@ def logaddexp(
     --------
     :obj:`dpnp.log` : Natural logarithm, element-wise.
     :obj:`dpnp.exp` : Exponential, element-wise.
+    :obj:`dpnp.logsumdexp` : Logarithm of the sum of exponentials of elements in the input array.
 
     Examples
     --------
@@ -1331,6 +1340,81 @@ def logaddexp(
     )
 
 
+def logsumexp(x, axis=None, out=None, dtype=None, keepdims=False):
+    """
+    Calculates the logarithm of the sum of exponentials of elements in the input array.
+
+    Parameters
+    ----------
+    x : {dpnp_array, usm_ndarray}
+        Input array, expected to have a real-valued data type.
+    axis : int or tuple of ints, optional
+        Axis or axes along which values must be computed. If a tuple
+        of unique integers, values are computed over multiple axes.
+        If ``None``, the result is computed over the entire array.
+        Default: ``None``.
+    out : {dpnp_array, usm_ndarray}, optional
+        If provided, the result will be inserted into this array. It should
+        be of the appropriate shape and dtype.
+    dtype : data type, optional
+        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 a boolean or integral data type, the returned array
+                will have the default floating point data type for the device
+                where input array `x` is allocated.
+            * If `x` has a complex-valued floating-point data type,
+                an error is raised.
+        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 result. Default: ``None``.
+    keepdims : 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
+    -------
+    out : dpnp.ndarray
+        An array containing the results. If the result 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.
+
+    Note
+    ----
+    This function is equivalent of `numpy.logaddexp.reduce`.
+
+    See Also
+    --------
+    :obj:`dpnp.log` : Natural logarithm, element-wise.
+    :obj:`dpnp.exp` : Exponential, element-wise.
+    :obj:`dpnp.logaddexp` : Logarithm of the sum of exponentiations of the inputs, element-wise.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> a = np.ones(10)
+    >>> np.logsumexp(a)
+    array(3.30258509)
+    >>> np.log(np.sum(np.exp(a)))
+    array(3.30258509)
+
+    """
+
+    dpt_array = dpnp.get_usm_ndarray(x)
+    result = dpnp_array._create_from_usm_ndarray(
+        dpt.logsumexp(dpt_array, axis=axis, dtype=dtype, keepdims=keepdims)
+    )
+
+    return dpnp.get_result_array(result, out, casting="same_kind")
+
+
 def reciprocal(x1, **kwargs):
     """
     Return the reciprocal of the argument, element-wise.
@@ -1363,6 +1447,79 @@ def reciprocal(x1, **kwargs):
     return call_origin(numpy.reciprocal, x1, **kwargs)
 
 
+def reduce_hypot(x, axis=None, out=None, dtype=None, keepdims=False):
+    """
+    Calculates the square root of the sum of squares of elements in the input array.
+
+    Parameters
+    ----------
+    x : {dpnp_array, usm_ndarray}
+        Input array, expected to have a real-valued data type.
+    axis : int or tuple of ints, optional
+        Axis or axes along which values must be computed. If a tuple
+        of unique integers, values are computed over multiple axes.
+        If ``None``, the result is computed over the entire array.
+        Default: ``None``.
+    out : {dpnp_array, usm_ndarray}, optional
+        If provided, the result will be inserted into this array. It should
+        be of the appropriate shape and dtype.
+    dtype : data type, optional
+            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 a boolean or integral data type, the returned array
+                  will have the default floating point data type for the device
+                  where input array `x` is allocated.
+                * If `x` has a complex-valued floating-point data type,
+                  an error is raised.
+            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 result. Default: ``None``.
+    keepdims : 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
+    -------
+    out : dpnp.ndarray
+        An array containing the results. If the result 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.
+
+    Note
+    ----
+    This function is equivalent of `numpy.hypot.reduce`.
+
+    See Also
+    --------
+    :obj:`dpnp.hypot` : Given the "legs" of a right triangle, return its hypotenuse.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> a = np.ones(10)
+    >>> np.reduce_hypot(a)
+    array(3.16227766)
+    >>> np.sqrt(np.sum(np.square(a)))
+    array(3.16227766)
+
+    """
+
+    dpt_array = dpnp.get_usm_ndarray(x)
+    result = dpnp_array._create_from_usm_ndarray(
+        dpt.reduce_hypot(dpt_array, axis=axis, dtype=dtype, keepdims=keepdims)
+    )
+
+    return dpnp.get_result_array(result, out, casting="same_kind")
+
+
 def rsqrt(
     x,
     /,

tests/helper.py

@@ -34,10 +34,17 @@ def assert_dtype_allclose(
     list_64bit_types = [numpy.float64, numpy.complex128]
     is_inexact = lambda x: dpnp.issubdtype(x.dtype, dpnp.inexact)
     if is_inexact(dpnp_arr) or is_inexact(numpy_arr):
-        tol = 8 * max(
-            dpnp.finfo(dpnp_arr).resolution,
-            numpy.finfo(numpy_arr.dtype).resolution,
+        tol_dpnp = (
+            dpnp.finfo(dpnp_arr).resolution
+            if is_inexact(dpnp_arr)
+            else -dpnp.inf
         )
+        tol_numpy = (
+            numpy.finfo(numpy_arr.dtype).resolution
+            if is_inexact(numpy_arr)
+            else -dpnp.inf
+        )
+        tol = 8 * max(tol_dpnp, tol_numpy)
         assert_allclose(dpnp_arr.asnumpy(), numpy_arr, atol=tol, rtol=tol)
         if check_type:
             numpy_arr_dtype = numpy_arr.dtype

tests/test_mathematical.py

@@ -1752,6 +1752,90 @@ def test_invalid_out(self, out):
         assert_raises(TypeError, numpy.hypot, a.asnumpy(), 2, out)
 
 
+class TestLogSumExp:
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_complex=True))
+    @pytest.mark.parametrize("axis", [None, 2, -1, (0, 1)])
+    @pytest.mark.parametrize("keepdims", [True, False])
+    def test_logsumexp(self, dtype, axis, keepdims):
+        a = dpnp.ones((3, 4, 5, 6, 7), dtype=dtype)
+        res = dpnp.logsumexp(a, axis=axis, keepdims=keepdims)
+        exp_dtype = dpnp.default_float_type(a.device)
+        exp = numpy.logaddexp.reduce(
+            dpnp.asnumpy(a), axis=axis, keepdims=keepdims, dtype=exp_dtype
+        )
+
+        assert_dtype_allclose(res, exp)
+
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_complex=True))
+    @pytest.mark.parametrize("axis", [None, 2, -1, (0, 1)])
+    @pytest.mark.parametrize("keepdims", [True, False])
+    def test_logsumexp_out(self, dtype, axis, keepdims):
+        a = dpnp.ones((3, 4, 5, 6, 7), dtype=dtype)
+        exp_dtype = dpnp.default_float_type(a.device)
+        exp = numpy.logaddexp.reduce(
+            dpnp.asnumpy(a), axis=axis, keepdims=keepdims, dtype=exp_dtype
+        )
+        dpnp_out = dpnp.empty(exp.shape, dtype=exp_dtype)
+        res = dpnp.logsumexp(a, axis=axis, out=dpnp_out, keepdims=keepdims)
+
+        assert res is dpnp_out
+        assert_dtype_allclose(res, exp)
+
+    @pytest.mark.parametrize(
+        "in_dtype", get_all_dtypes(no_bool=True, no_complex=True)
+    )
+    @pytest.mark.parametrize("out_dtype", get_all_dtypes(no_bool=True))
+    def test_logsumexp_dtype(self, in_dtype, out_dtype):
+        a = dpnp.ones(100, dtype=in_dtype)
+        res = dpnp.logsumexp(a, dtype=out_dtype)
+        exp = numpy.logaddexp.reduce(dpnp.asnumpy(a))
+        exp = exp.astype(out_dtype)
+
+        assert_allclose(res, exp, rtol=1e-06)
+
+
+class TestReduceHypot:
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_complex=True))
+    @pytest.mark.parametrize("axis", [None, 2, -1, (0, 1)])
+    @pytest.mark.parametrize("keepdims", [True, False])
+    def test_reduce_hypot(self, dtype, axis, keepdims):
+        a = dpnp.ones((3, 4, 5, 6, 7), dtype=dtype)
+        res = dpnp.reduce_hypot(a, axis=axis, keepdims=keepdims)
+        exp_dtype = dpnp.default_float_type(a.device)
+        exp = numpy.hypot.reduce(
+            dpnp.asnumpy(a), axis=axis, keepdims=keepdims, dtype=exp_dtype
+        )
+
+        assert_dtype_allclose(res, exp)
+
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_complex=True))
+    @pytest.mark.parametrize("axis", [None, 2, -1, (0, 1)])
+    @pytest.mark.parametrize("keepdims", [True, False])
+    def test_reduce_hypot_out(self, dtype, axis, keepdims):
+        a = dpnp.ones((3, 4, 5, 6, 7), dtype=dtype)
+        exp_dtype = dpnp.default_float_type(a.device)
+        exp = numpy.hypot.reduce(
+            dpnp.asnumpy(a), axis=axis, keepdims=keepdims, dtype=exp_dtype
+        )
+        dpnp_out = dpnp.empty(exp.shape, dtype=exp_dtype)
+        res = dpnp.reduce_hypot(a, axis=axis, out=dpnp_out, keepdims=keepdims)
+
+        assert res is dpnp_out
+        assert_dtype_allclose(res, exp)
+
+    @pytest.mark.parametrize(
+        "in_dtype", get_all_dtypes(no_bool=True, no_complex=True)
+    )
+    @pytest.mark.parametrize("out_dtype", get_all_dtypes(no_bool=True))
+    def test_reduce_hypot_dtype(self, in_dtype, out_dtype):
+        a = dpnp.ones(99, dtype=in_dtype)
+        res = dpnp.reduce_hypot(a, dtype=out_dtype)
+        exp = numpy.hypot.reduce(dpnp.asnumpy(a))
+        exp = exp.astype(out_dtype)
+
+        assert_allclose(res, exp, rtol=1e-06)
+
+
 class TestMaximum:
     @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
     def test_maximum(self, dtype):

tests/test_strides.py

@@ -6,7 +6,7 @@
 
 import dpnp
 
-from .helper import get_all_dtypes
+from .helper import assert_dtype_allclose, get_all_dtypes
 
 
 def _getattr(ex, str_):
@@ -99,17 +99,33 @@ def test_strides_1arg(func_name, dtype, shape):
 
 
 @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True))
-def test_strides_rsqrt(dtype):
-    a = numpy.arange(1, 11, dtype=dtype)
-    b = a[::2]
+def test_rsqrt(dtype):
+    a = numpy.arange(1, 11, dtype=dtype)[::2]
+    dpa = dpnp.arange(1, 11, dtype=dtype)[::2]
 
-    dpa = dpnp.arange(1, 11, dtype=dtype)
-    dpb = dpa[::2]
+    result = dpnp.rsqrt(dpa)
+    expected = 1 / numpy.sqrt(a)
+    assert_dtype_allclose(result, expected)
 
-    result = dpnp.rsqrt(dpb)
-    expected = 1 / numpy.sqrt(b)
 
-    assert_allclose(result, expected, rtol=1e-06)
+@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True))
+def test_logsumexp(dtype):
+    a = numpy.arange(10, dtype=dtype)[::2]
+    dpa = dpnp.arange(10, dtype=dtype)[::2]
+
+    result = dpnp.logsumexp(dpa)
+    expected = numpy.logaddexp.reduce(a)
+    assert_allclose(result, expected)
+
+
+@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True))
+def test_reduce_hypot(dtype):
+    a = numpy.arange(10, dtype=dtype)[::2]
+    dpa = dpnp.arange(10, dtype=dtype)[::2]
+
+    result = dpnp.reduce_hypot(dpa)
+    expected = numpy.hypot.reduce(a)
+    assert_allclose(result, expected)
 
 
 @pytest.mark.parametrize(