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()