Call uniform_full_int in tf.random.Generator.uniform when dtype is integer and both minval and maxval are None, to align with stateless_random_uniform.

PiperOrigin-RevId: 299455876
Change-Id: I7274a15bef63ea349631e2f3053c71af387c2ca1

tensorflow/python/ops/stateful_random_ops.py

@@ -666,6 +666,11 @@ class Generator(tracking.AutoTrackable, composite_tensor.CompositeTensor):
     return gen_stateful_random_ops.stateful_uniform(
         self.state.handle, self.algorithm, shape=shape, dtype=dtype)
 
+  def _uniform_full_int(self, shape, dtype, name=None):
+    return gen_stateful_random_ops.stateful_uniform_full_int(
+        self.state.handle, self.algorithm, shape=shape,
+        dtype=dtype, name=name)
+
   def uniform(self, shape, minval=0, maxval=None,
               dtype=dtypes.float32, name=None):
     """Outputs random values from a uniform distribution.
@@ -684,17 +689,22 @@ class Generator(tracking.AutoTrackable, composite_tensor.CompositeTensor):
     `maxval - minval` significantly smaller than the range of the output (either
     `2**32` or `2**64`).
 
+    For full-range random integers, pass `minval=None` and `maxval=None` with an
+    integer `dtype` (for integer dtypes, `minval` and `maxval` must be both
+    `None` or both not `None`).
+
     Args:
       shape: A 1-D integer Tensor or Python array. The shape of the output
         tensor.
       minval: A Tensor or Python value of type `dtype`, broadcastable with
         `shape` (for integer types, broadcasting is not supported, so it needs
-        to be a scalar). The lower bound on the range of random values to
+        to be a scalar). The lower bound (included) on the range of random
-        generate.  Defaults to 0.
+        values to generate. Pass `None` for full-range integers. Defaults to 0.
       maxval: A Tensor or Python value of type `dtype`, broadcastable with
         `shape` (for integer types, broadcasting is not supported, so it needs
-        to be a scalar). The upper bound on the range of random values to
+        to be a scalar). The upper bound (excluded) on the range of random
-        generate.  Defaults to 1 if `dtype` is floating point.
+        values to generate. Pass `None` for full-range integers. Defaults to 1
+        if `dtype` is floating point.
       dtype: The type of the output.
       name: A name for the operation (optional).
 
@@ -705,13 +715,18 @@ class Generator(tracking.AutoTrackable, composite_tensor.CompositeTensor):
       ValueError: If `dtype` is integral and `maxval` is not specified.
     """
     dtype = dtypes.as_dtype(dtype)
-    if maxval is None:
+    if dtype.is_integer:
-      if dtype.is_integer:
+      if (minval is None) != (maxval is None):
-        raise ValueError("Must specify maxval for integer dtype %r" % dtype)
+        raise ValueError("For integer dtype {}, minval and maxval must be both "
+                         "`None` or both non-`None`; got minval={} and "
+                         "maxval={}".format(dtype, minval, maxval))
+    elif maxval is None:
       maxval = 1
     with ops.name_scope(name, "stateful_uniform",
                         [shape, minval, maxval]) as name:
       shape = _shape_tensor(shape)
+      if dtype.is_integer and minval is None:
+        return self._uniform_full_int(shape=shape, dtype=dtype, name=name)
       minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
       maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
       if dtype.is_integer:
@@ -725,8 +740,8 @@ class Generator(tracking.AutoTrackable, composite_tensor.CompositeTensor):
   def uniform_full_int(self, shape, dtype=dtypes.uint64, name=None):
     """Uniform distribution on an integer type's entire range.
 
-    The other method `uniform` only covers the range [minval, maxval), which
+    This method is the same as setting `minval` and `maxval` to `None` in the
-    cannot be `dtype`'s full range because `maxval` is of type `dtype`.
+    `uniform` method.
 
     Args:
       shape: the shape of the output.
@@ -740,9 +755,7 @@ class Generator(tracking.AutoTrackable, composite_tensor.CompositeTensor):
     with ops.name_scope(name, "stateful_uniform_full_int",
                         [shape]) as name:
       shape = _shape_tensor(shape)
-      return gen_stateful_random_ops.stateful_uniform_full_int(
+      return self._uniform_full_int(shape=shape, dtype=dtype, name=name)
-          self.state.handle, self.algorithm, shape=shape,
-          dtype=dtype, name=name)
 
   def binomial(self, shape, counts, probs, dtype=dtypes.int32, name=None):
     """Outputs random values from a binomial distribution.

tensorflow/python/ops/stateful_random_ops_test.py

@@ -748,6 +748,18 @@ class StatefulRandomOpsTest(test.TestCase, parameterized.TestCase):
       self.assertAllEqual(2, len(local_results))
       self.assertAllDifferent(local_results)
 
+  @test_util.run_v2_only
+  def testUniformFullInt(self):
+    """Tests full-range int uniform.
+    """
+    shape = [3, 4]
+    dtype = dtypes.int32
+    g = random.Generator.from_seed(1)
+    r1 = g.uniform(shape=shape, dtype=dtype, minval=None)
+    g = random.Generator.from_seed(1)
+    r2 = g.uniform_full_int(shape=shape, dtype=dtype)
+    self.assertAllEqual(r1, r2)
+
 
 if __name__ == "__main__":
   test.main()

tensorflow/python/ops/stateless_random_ops.py

@@ -66,7 +66,7 @@ def stateless_random_uniform(shape,
   `maxval - minval` significantly smaller than the range of the output (either
   `2**32` or `2**64`).
 
-  For full full-range (i.e. inclusive of both max and min) random integers, pass
+  For full-range (i.e. inclusive of both max and min) random integers, pass
   `minval=None` and `maxval=None` with an integer `dtype`. For an integer dtype
   either both `minval` and `maxval` must be `None` or neither may be `None`. For
   example: