chore: import upstream snapshot with attribution

example/utils/pytorch_util.py

@@ -0,0 +1,48 @@
+# Copyright 2022 Twitter, Inc and Zhendong Wang.
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+import numpy as np
+
+
+def soft_update_from_to(source, target, tau):
+    for target_param, param in zip(target.parameters(), source.parameters()):
+        target_param.data.copy_(
+            target_param.data * (1.0 - tau) + param.data * tau
+        )
+
+
+def copy_model_params_from_to(source, target):
+    for target_param, param in zip(target.parameters(), source.parameters()):
+        target_param.data.copy_(param.data)
+
+
+def fanin_init(tensor, scale=1):
+    size = tensor.size()
+    if len(size) == 2:
+        fan_in = size[0]
+    elif len(size) > 2:
+        fan_in = np.prod(size[1:])
+    else:
+        raise Exception("Shape must be have dimension at least 2.")
+    bound = scale / np.sqrt(fan_in)
+    return tensor.data.uniform_(-bound, bound)
+
+
+def orthogonal_init(tensor, gain=0.01):
+    torch.nn.init.orthogonal_(tensor, gain=gain)
+
+
+def fanin_init_weights_like(tensor):
+    size = tensor.size()
+    if len(size) == 2:
+        fan_in = size[0]
+    elif len(size) > 2:
+        fan_in = np.prod(size[1:])
+    else:
+        raise Exception("Shape must be have dimension at least 2.")
+    bound = 1. / np.sqrt(fan_in)
+    new_tensor = torch.FloatTensor(tensor.size())
+    new_tensor.uniform_(-bound, bound)
+    return new_tensor
+