Add clipgrad doc and contiguous

python/oneflow/__init__.py

@@ -303,6 +303,7 @@ def atexit_hook(hook):
 from oneflow.nn.modules.tensor_buffer import (
     tensor_buffer_to_tensor_op as tensor_buffer_to_tensor,
 )
+from oneflow.nn.modules.as_tensor import as_tensor
 from oneflow.nn.modules.tensor_buffer import tensor_to_tensor_buffer
 from oneflow.nn.modules.tile import tile_op as tile
 from oneflow.nn.modules.to import to_op as to
@@ -334,6 +335,7 @@ def atexit_hook(hook):
     zeros_initializer,
 )
 
+
 from . import (
     autograd,
     distributed,

python/oneflow/framework/tensor.py

@@ -160,6 +160,11 @@ def _ne(self, other):
     return self.ne(other)
 
 
+def _contiguous(self):
+    # TODO: support stride mechanism
+    return self
+
+
 def _getstate(self):
     assert self.is_local, "Only support local tensor to pickle"
     return {"data": self.numpy(), "dtype": self.dtype}
@@ -443,6 +448,7 @@ def RegisterMethods():
     Tensor.copy_ = _copy
     Tensor.get_device = _get_device
     Tensor._meta_repr = _meta_repr
+    Tensor.contiguous = _contiguous
 
 
 def register_tensor_op(op_name):

python/oneflow/nn/modules/as_tensor.py

@@ -0,0 +1,22 @@
+"""
+Copyright 2020 The OneFlow Authors. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import oneflow as flow
+
+
+def as_tensor(*args, **kwargs):
+    # TODO: support construct tensor with sharing memory with data
+    return flow.tensor(*args, **kwargs)

python/oneflow/nn/utils/clip_grad.py

@@ -59,7 +59,7 @@ def clip_grad_norm_(
 
         >>> import oneflow as flow
         >>> import numpy as np
-        >>> x1 = flow.Tensor(np.array([[2, 3, 4], [1.5, 2.6, 3.7]]).astype(np.float32), requires_grad=True)
+        >>> x1 = flow.tensor(np.array([[2, 3, 4], [1.5, 2.6, 3.7]]).astype(np.float32), requires_grad=True)
         >>> m1 = flow.nn.ReLU()
         >>> out1 = m1(x1)
         >>> out1 = out1.sum()
@@ -70,7 +70,7 @@ def clip_grad_norm_(
         >>> x1.grad
         tensor([[0.1000, 0.1000, 0.1000],
                 [0.1000, 0.1000, 0.1000]], dtype=oneflow.float32)
-        >>> x2 = flow.Tensor(np.array([[-2, -3, -4], [2.5, 0, 3.2]]).astype(np.float32), requires_grad=True)
+        >>> x2 = flow.tensor(np.array([[-2, -3, -4], [2.5, 0, 3.2]]).astype(np.float32), requires_grad=True)
         >>> out2 = flow.atan(x2)
         >>> out2 = out2.sum()
         >>> out2.backward()