feat(model): implement uniform_init for tensor. (InternLM#252)

* Implement uniform_init for tensor.

* Fix functinal calling bugs: normal->uniform.

* Format editting: remove unused torch importing.

internlm/initialize/initialize_tensor.py

@@ -3,16 +3,15 @@
 
 import math
 
-import torch
 from torch import Tensor, nn
 
 
-def scaled_init_method_normal(sigma, num_layers):
+def scaled_init_method_normal(sigma: float = 1.0, num_layers: int = 1):
     """Init method based on N(0, sigma/sqrt(2*num_layers)."""
     std = sigma / math.sqrt(2.0 * num_layers)
 
     def init_(tensor):
-        return torch.nn.init.normal_(tensor, mean=0.0, std=std)
+        return nn.init.normal_(tensor, mean=0.0, std=std)
 
     return init_
 
@@ -32,3 +31,33 @@ def initializer(tensor: Tensor):
         return nn.init.normal_(tensor, mean, std)
 
     return initializer
+
+
+def scaled_init_method_uniform(sigma: float = 1.0, num_layers: int = 1):
+    """Init method based on p(x)=Uniform(-a, a) where std(x)=sigma/sqrt(2*num_layers)."""
+    std = sigma / math.sqrt(2.0 * num_layers)
+    a = math.sqrt(3.0 * std)
+
+    def init_(tensor):
+        return nn.init.uniform_(tensor, -a, a)
+
+    return init_
+
+
+def uniform_(mean: float = 0.0, std: float = 1.0):
+    r"""Return the initializer filling the input Tensor with values drawn from the uniform distribution
+
+     .. math::
+        \mathcal{U}(mean-a, mean+a), where a satisfies \mathcal{U}_{std}=std.
+
+    Args:
+        mean (float): the mean of the uniform distribution. Defaults 0.0.
+        std (float): the standard deviation of the uniform distribution. Defaults 1.0.
+    """
+
+    a = math.sqrt(3.0 * std)
+
+    def initializer(tensor: Tensor):
+        return nn.init.uniform_(tensor, mean - a, mean + a)
+
+    return initializer