Leverage dpctl.tensor.iinfo() and dpctl.tensor.finfo() implementation.

dpnp/dpnp_iface_types.py

@@ -34,8 +34,11 @@
 This module provides public type interface file for the library
 """
 
+import dpctl.tensor as dpt
 import numpy
 
+from dpnp.dpnp_array import dpnp_array
+
 __all__ = [
     "bool",
     "bool_",
@@ -50,12 +53,14 @@
     "dtype",
     "e",
     "euler_gamma",
+    "finfo",
     "float",
     "float_",
     "float16",
     "float32",
     "float64",
     "floating",
+    "iinfo",
     "inexact",
     "Inf",
     "inf",
@@ -140,6 +145,85 @@
 PZERO = numpy.PZERO
 
 
+def finfo(dtype):
+    """
+    Returns machine limits for floating-point data types.
+
+    For full documentation refer to :obj:`numpy.finfo`.
+
+    Parameters
+    ----------
+    dtype : dtype, dpnp_array
+        Floating-point dtype or an array with floating point data type.
+        If complex, the information is about its component data type.
+
+    Returns
+    -------
+    out : finfo_object
+        An object have the following attributes
+        * bits: int
+            number of bits occupied by dtype.
+        * dtype: dtype
+            real-valued floating-point data type.
+        * eps: float
+            difference between 1.0 and the next smallest representable
+            real-valued floating-point number larger than 1.0 according
+            to the IEEE-754 standard.
+        * epsneg: float
+            difference between 1.0 and the next smallest representable real-valued
+            floating-point number smaller than 1.0 according to the IEEE-754
+            standard.
+        * max: float
+            largest representable real-valued number.
+        * min: float
+            smallest representable real-valued number.
+        * precision: float
+            the approximate number of decimal digits to which this kind of
+            floating point type is precise.
+        * resolution: float
+            the approximate decimal resolution of this type.
+        * tiny: float
+            an alias for `smallest_normal`
+        * smallest_normal: float
+            smallest positive real-valued floating-point number with
+            full precision.
+
+    """
+    if isinstance(dtype, dpnp_array):
+        dtype = dtype.dtype
+    return dpt.finfo(dtype)
+
+
+def iinfo(dtype):
+    """
+    Returns machine limits for integer data types.
+
+    For full documentation refer to :obj:`numpy.iinfo`.
+
+    Parameters
+    ----------
+    dtype : dtype, dpnp_array
+        Integer dtype or an array with integer dtype.
+
+    Returns
+    -------
+    out : iinfo_object
+        An object with the following attributes
+        * bits: int
+            number of bits occupied by the data type
+        * dtype: dtype
+            integer data type.
+        * max: int
+            largest representable number.
+        * min: int
+            smallest representable number.
+
+    """
+    if isinstance(dtype, dpnp_array):
+        dtype = dtype.dtype
+    return dpt.iinfo(dtype)
+
+
 def isscalar(obj):
     """
     Returns True if the type of `obj` is a scalar type.

dpnp/random/dpnp_algo_random.pyx

@@ -462,7 +462,7 @@ cdef class MT19937(_Engine):
         if value < 0:
             return False
 
-        max_val = numpy.iinfo(numpy.uint32).max
+        max_val = dpnp.iinfo(numpy.uint32).max
         if isinstance(value, dpnp_array):
             max_val = dpnp.array(max_val, dtype=numpy.uint32)
         return value <= max_val
@@ -499,7 +499,7 @@ cdef class MCG59(_Engine):
         if value < 0:
             return False
 
-        max_val = numpy.iinfo(numpy.uint64).max
+        max_val = dpnp.iinfo(numpy.uint64).max
         if isinstance(value, dpnp_array):
             max_val = dpnp.array(max_val, dtype=numpy.uint64)
         return value <= max_val
@@ -1052,7 +1052,7 @@ cpdef utils.dpnp_descriptor dpnp_rng_negative_binomial(double a, double p, size)
 
     result_shape = utils._object_to_tuple(size)
     if p == 0.0:
-        filled_val = numpy.iinfo(dtype).min
+        filled_val = dpnp.iinfo(dtype).min
         return utils.dpnp_descriptor(dpnp.full(result_shape, filled_val, dtype=dtype))
     elif p == 1.0:
         return utils.dpnp_descriptor(dpnp.full(result_shape, 0, dtype=dtype))

dpnp/random/dpnp_iface_random.py

@@ -675,7 +675,7 @@ def multinomial(n, pvals, size=None):
         d = len(pvals)
         if n < 0:
             pass
-        elif n > numpy.iinfo(dpnp.int32).max:
+        elif n > dpnp.iinfo(dpnp.int32).max:
             pass
         elif pvals_sum > 1.0:
             pass

dpnp/random/dpnp_random_state.py

@@ -81,7 +81,7 @@ def __init__(self, seed=None, device=None, sycl_queue=None):
         is_cpu = self._sycl_device.is_cpu
         if seed is None:
             low = 0
-            high = numpy.iinfo(numpy.int32).max + 1
+            high = dpnp.iinfo(numpy.int32).max + 1
 
             if is_cpu:
                 # ask NumPy to generate an array of three random integers as default seed value
@@ -237,8 +237,8 @@ def normal(
                 dtype = self._validate_float_dtype(
                     dtype, (dpnp.float32, dpnp.float64)
                 )
-                min_floating = numpy.finfo(dtype).min
-                max_floating = numpy.finfo(dtype).max
+                min_floating = dpnp.finfo(dtype).min
+                max_floating = dpnp.finfo(dtype).max
 
                 if (
                     loc >= max_floating or loc <= min_floating
@@ -371,8 +371,8 @@ def randint(self, low, high=None, size=None, dtype=int, usm_type="device"):
                         high = low
                         low = 0
 
-                    min_int = numpy.iinfo("int32").min
-                    max_int = numpy.iinfo("int32").max
+                    min_int = dpnp.iinfo("int32").min
+                    max_int = dpnp.iinfo("int32").max
 
                     if (
                         not self._is_finite_scalar(low)
@@ -587,8 +587,8 @@ def uniform(
             elif not dpnp.isscalar(high):
                 pass
             else:
-                min_double = numpy.finfo("double").min
-                max_double = numpy.finfo("double").max
+                min_double = dpnp.finfo("double").min
+                max_double = dpnp.finfo("double").max
 
                 if (
                     not self._is_finite_scalar(low)

tests/helper.py

@@ -18,7 +18,7 @@ def assert_dtype_allclose(dpnp_arr, numpy_arr, check_type=True):
     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,
+            dpnp.finfo(dpnp_arr).resolution,
             numpy.finfo(numpy_arr.dtype).resolution,
         )
         assert_allclose(dpnp_arr.asnumpy(), numpy_arr, atol=tol, rtol=tol)

tests/test_random.py

@@ -674,10 +674,10 @@ def test_extreme_value(self):
         )
         n = 5
         p = 0.0
-        res = dpnp.asnumpy(dpnp.random.negative_binomial(n=n, p=p, size=10))
-        check_val = numpy.iinfo(res.dtype).min
-        assert len(numpy.unique(res)) == 1
-        assert numpy.unique(res)[0] == check_val
+        res = dpnp.random.negative_binomial(n=n, p=p, size=10)
+        check_val = dpnp.iinfo(res).min
+        assert len(dpnp.unique(res)) == 1
+        assert dpnp.unique(res)[0] == check_val
 
     def test_invalid_args(self):
         n = 10  # parameter `n`, OK