torch-inplace.txt

torch-inplace.txt

In [1]: print('hello world')
hello world

In [2]: print('hello world!')
hello world!

In [3]: import torch

In [4]: print(torch.__version__)
0.4.1

In [5]: l = torch.nn.Linear(1, 1)

In [6]: chart = torch.FloatTensor(10, 5)

In [7]: x = torch.FloatTensor(10, 1)

In [8]: chart.fill_(0)
Out[8]:
tensor([[0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0.]])

In [9]: x.normal_()
Out[9]:
tensor([[ 0.5913],
        [-1.0685],
        [-0.1202],
        [-0.9325],
        [-0.8090],
        [-1.5605],
        [-0.1662],
        [-0.2653],
        [ 0.5720],
        [ 0.0766]])

In [10]: chart[:, 0:1] = x

In [11]: chart
Out[11]:
tensor([[ 0.5913,  0.0000,  0.0000,  0.0000,  0.0000],
        [-1.0685,  0.0000,  0.0000,  0.0000,  0.0000],
        [-0.1202,  0.0000,  0.0000,  0.0000,  0.0000],
        [-0.9325,  0.0000,  0.0000,  0.0000,  0.0000],
        [-0.8090,  0.0000,  0.0000,  0.0000,  0.0000],
        [-1.5605,  0.0000,  0.0000,  0.0000,  0.0000],
        [-0.1662,  0.0000,  0.0000,  0.0000,  0.0000],
        [-0.2653,  0.0000,  0.0000,  0.0000,  0.0000],
        [ 0.5720,  0.0000,  0.0000,  0.0000,  0.0000],
        [ 0.0766,  0.0000,  0.0000,  0.0000,  0.0000]])

In [12]: chart[:, 1:2] = l(chart[:, 0:1])

In [13]: chart
Out[13]:
tensor([[ 0.5913,  1.3746,  0.0000,  0.0000,  0.0000],
        [-1.0685, -0.2462,  0.0000,  0.0000,  0.0000],
        [-0.1202,  0.6798,  0.0000,  0.0000,  0.0000],
        [-0.9325, -0.1135,  0.0000,  0.0000,  0.0000],
        [-0.8090,  0.0071,  0.0000,  0.0000,  0.0000],
        [-1.5605, -0.7267,  0.0000,  0.0000,  0.0000],
        [-0.1662,  0.6349,  0.0000,  0.0000,  0.0000],
        [-0.2653,  0.5381,  0.0000,  0.0000,  0.0000],
        [ 0.5720,  1.3558,  0.0000,  0.0000,  0.0000],
        [ 0.0766,  0.8720,  0.0000,  0.0000,  0.0000]], grad_fn=<CopySlices>)

In [14]: chart[:, 3:4] = l(chart[:, 1:2])

In [15]: chart
Out[15]:
tensor([[ 0.5913,  1.3746,  0.0000,  2.1396,  0.0000],
        [-1.0685, -0.2462,  0.0000,  0.5568,  0.0000],
        [-0.1202,  0.6798,  0.0000,  1.4611,  0.0000],
        [-0.9325, -0.1135,  0.0000,  0.6864,  0.0000],
        [-0.8090,  0.0071,  0.0000,  0.8042,  0.0000],
        [-1.5605, -0.7267,  0.0000,  0.0875,  0.0000],
        [-0.1662,  0.6349,  0.0000,  1.4172,  0.0000],
        [-0.2653,  0.5381,  0.0000,  1.3226,  0.0000],
        [ 0.5720,  1.3558,  0.0000,  2.1212,  0.0000],
        [ 0.0766,  0.8720,  0.0000,  1.6487,  0.0000]], grad_fn=<CopySlices>)

In [16]: loss = chart.norm()

In [17]: loss.backward()
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-17-52a0569421b1> in <module>
----> 1 loss.backward()

~/anaconda2/envs/diora-1/lib/python3.6/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph)
     91                 products. Defaults to ``False``.
     92         """
---> 93         torch.autograd.backward(self, gradient, retain_graph, create_graph)
     94
     95     def register_hook(self, hook):

~/anaconda2/envs/diora-1/lib/python3.6/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
     88     Variable._execution_engine.run_backward(
     89         tensors, grad_tensors, retain_graph, create_graph,
---> 90         allow_unreachable=True)  # allow_unreachable flag
     91
     92

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation