fix(test/random): set a random seed for random unit test

GitOrigin-RevId: ad4b01eac7238d7a71f8b71075c37bd4b3e58235

imperative/python/test/unit/random/test_rng.py

@@ -27,13 +27,16 @@ from megengine.core.ops.builtin import (
     UniformRNG,
 )
 from megengine.device import get_device_count
-from megengine.random import RNG, seed, uniform
+from megengine.random import RNG
+from megengine.random import seed as set_global_seed
+from megengine.random import uniform
 
 
 @pytest.mark.skipif(
     get_device_count("xpu") <= 2, reason="xpu counts need > 2",
 )
 def test_gaussian_op():
+    set_global_seed(1024)
     shape = (
         8,
         9,
@@ -64,6 +67,7 @@ def test_gaussian_op():
     get_device_count("xpu") <= 2, reason="xpu counts need > 2",
 )
 def test_uniform_op():
+    set_global_seed(1024)
     shape = (
         8,
         9,
@@ -92,6 +96,7 @@ def test_uniform_op():
     get_device_count("xpu") <= 2, reason="xpu counts need > 2",
 )
 def test_gamma_op():
+    set_global_seed(1024)
     _shape, _scale = 2, 0.8
     _expected_mean, _expected_std = _shape * _scale, np.sqrt(_shape) * _scale
 
@@ -120,6 +125,7 @@ def test_gamma_op():
     get_device_count("xpu") <= 2, reason="xpu counts need > 2",
 )
 def test_beta_op():
+    set_global_seed(1024)
     _alpha, _beta = 2, 0.8
     _expected_mean = _alpha / (_alpha + _beta)
     _expected_std = np.sqrt(
@@ -151,6 +157,7 @@ def test_beta_op():
     get_device_count("xpu") <= 2, reason="xpu counts need > 2",
 )
 def test_poisson_op():
+    set_global_seed(1024)
     lam = F.full([8, 9, 11, 12], value=2, dtype="float32")
     op = PoissonRNG(seed=get_global_rng_seed())
     (output,) = apply(op, lam)
@@ -174,6 +181,7 @@ def test_poisson_op():
     get_device_count("xpu") <= 2, reason="xpu counts need > 2",
 )
 def test_permutation_op():
+    set_global_seed(1024)
     n = 1000
 
     def test_permutation_op_dtype(dtype):
@@ -390,22 +398,23 @@ def test_PermutationRNG():
 
 
 def test_seed():
-    seed(10)
+    set_global_seed(10)
     out1 = uniform(size=[10, 10])
     out2 = uniform(size=[10, 10])
     assert not (out1.numpy() == out2.numpy()).all()
 
-    seed(10)
+    set_global_seed(10)
     out3 = uniform(size=[10, 10])
     np.testing.assert_equal(out1.numpy(), out3.numpy())
 
-    seed(11)
+    set_global_seed(11)
     out4 = uniform(size=[10, 10])
     assert not (out1.numpy() == out4.numpy()).all()
 
 
 @pytest.mark.parametrize("is_symbolic", [None, False, True])
 def test_rng_empty_tensor(is_symbolic):
+    set_global_seed(1024)
     shapes = [
         (0,),
         (0, 0, 0),