support long for mx.random.seed (apache#14314)

* support long for mx.random.seed

* update test_random

* reorder

* use mx.random.uniform

* trigger CI

* retrigger CI

python/mxnet/random.py

@@ -22,7 +22,7 @@
 from __future__ import absolute_import
 
 import ctypes
-from .base import _LIB, check_call
+from .base import _LIB, check_call, integer_types
 from .ndarray.random import *
 from .context import Context
 
@@ -90,9 +90,9 @@ def seed(seed_state, ctx="all"):
     [[ 2.5020072 -1.6884501]
      [-0.7931333 -1.4218881]]
     """
-    if not isinstance(seed_state, int):
+    if not isinstance(seed_state, integer_types):
         raise ValueError('seed_state must be int')
-    seed_state = ctypes.c_int(seed_state)
+    seed_state = ctypes.c_int(int(seed_state))
     if ctx == "all":
         check_call(_LIB.MXRandomSeed(seed_state))
     else:

tests/python/unittest/test_random.py

@@ -336,6 +336,7 @@ def test_parallel_random_seed_setting():
         # Avoid excessive test cpu runtimes
         num_temp_seeds = 25 if ctx.device_type == 'gpu' else 1
         # To flush out a possible race condition, run multiple times
+
         for _ in range(20):
             # Create enough samples such that we get a meaningful distribution.
             shape = (200, 200)
@@ -670,7 +671,7 @@ def gen_data(seed=None):
         with random_seed(seed):
             python_data = [rnd.random() for _ in range(size)]
             np_data = np.random.rand(size)
-            mx_data = mx.nd.random_uniform(shape=shape, ctx=ctx).asnumpy()
+            mx_data = mx.random.uniform(shape=shape, ctx=ctx).asnumpy()
         return (seed, python_data, np_data, mx_data)
 
     # check data, expecting them to be the same or different based on the seeds
@@ -712,6 +713,32 @@ def check_data(a, b):
         for j in range(i+1, num_seeds):
             check_data(data[i],data[j])
 
+@with_seed()
+def test_random_seed():
+    shape = (5, 5)
+    seed = rnd.randint(-(1 << 31), (1 << 31))
+
+    def _assert_same_mx_arrays(a, b):
+        assert len(a) == len(b)
+        for a_i, b_i in zip(a, b):
+            assert (a_i.asnumpy() == b_i.asnumpy()).all()
+
+    N = 100
+    mx.random.seed(seed)
+    v1 = [mx.random.uniform(shape=shape) for _ in range(N)]
+
+    mx.random.seed(seed)
+    v2 = [mx.random.uniform(shape=shape) for _ in range(N)]
+    _assert_same_mx_arrays(v1, v2)
+
+    try:
+        long
+        mx.random.seed(long(seed))
+        v3 = [mx.random.uniform(shape=shape) for _ in range(N)]
+        _assert_same_mx_arrays(v1, v3)
+    except NameError:
+        pass
+
 @with_seed()
 def test_unique_zipfian_generator():
     ctx = mx.context.current_context()