refactor(mge/module): use mge rng to sample from uniform/normal distribution

GitOrigin-RevId: 6ec8f99af5cc3f82fe70fe357e274f67eb1d4c36

python_module/megengine/module/init.py

@@ -12,7 +12,8 @@ from typing import Optional, Tuple, Union
 
 import numpy as np
 
-from ..core import Tensor
+from ..core import Tensor, Graph
+from ..random import gaussian, uniform
 
 
 def fill_(tensor: Tensor, val: Union[float, int]) -> None:
@@ -48,7 +49,8 @@ def uniform_(tensor: Tensor, a: float = 0.0, b: float = 1.0) -> None:
     :param a: Lower bound of the sampling interval
     :param b: Upper bound of the sampling interval
     """
-    tensor.set_value(np.random.uniform(a, b, tensor.shape).astype(tensor.dtype))
+    with Graph(eager_evaluation=True):
+        tensor.set_value((b - a) * uniform(tensor.shape) + a)
 
 
 def normal_(tensor: Tensor, mean: float = 0.0, std: float = 1.0) -> None:
@@ -59,7 +61,8 @@ def normal_(tensor: Tensor, mean: float = 0.0, std: float = 1.0) -> None:
     :param mean: The mean of the normal distribution
     :param std: The standard deviation of the normal distribution
     """
-    tensor.set_value(np.random.normal(mean, std, tensor.shape).astype(np.float32))
+    with Graph(eager_evaluation=True):
+        tensor.set_value(gaussian(tensor.shape, mean=mean, std=std))
 
 
 def calculate_gain(