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Pytorch Note

  • Pytorch and Chainer are in great resemblance, from architecture to API grammer.

  • x_cuda = x.cuda(device_id=0) returns a new tensor on gpu0, meanwhile x is kept as it was; i.e., this is copy op. After this, change x has no effect on x_cuda and vice versa. Use .cpu() to copy the tensor from GPU to

  • Pytorch variable has no .zero_grad() function as Chainer, use .data.zero_() to do the job (In Pytorch, only nn.Module class has .zero_grad() attribute)

  • Pytorch use Module.train() & Model.eval() calls with empty param to switch between training mode and evaluation mode

  • torch.Tensor() is just an alias of torch.FloatTensor(), not what I expected as an universal constructer which would determine the dtype according to input ndarray. torch.from_numpy() does this job meanwhile.

  • For RNN with multiple layers and dropout enabled, the pytorch implementation does not apply dropout for the last layer.

  • The difference between LSTM and LSTMCell lies in the input shape: input of LSTM is 3D (B, T, D) whereas input of LSTMCell is 2D (B, D), i.e., LSTMCell is used for just one time step.

  • The LSTM implementation of Pytorch has two problems: 1) no peepholes 2) two biases in i_t, f_t, g_t, o_t, which is nonsense (according to Facebook's comment, it's from CuDNN's convention)

  • Pytorch does not support numpy-style broadcasting, so to do element-wise multiplication, for example X (3, 50) and y (50), you need do .unsqueeze and then .expand: X * y.unsqueeze(0).expand_as(X)

  • Even with batch_first=True, the hiddens returned by LSTM(GRU, etc) are still of size (num_layers * num_directions, batch, hidden_size)

  • The CNN implementation of Pytorch does not do filter flipping by default; and its speed is comparable to Theano's CNN, produces exactly the same result, meanwhile convolve2d() of scipy is about 2 * times slower, and result slightly different (within 10 eps); source code of Pytorch's convolution resides in pytorch/torch/csrc/autograd/functions/convolution.cpp

  • Pytorch does not do weight initialization automatically, you have to define a reset_parameters() function yourself, and call it at model initialization.

  • Tensor.numpy() return a numpy array sharing the memory. In another word, it just returns the memory pointer. So we can use this mechanism to MODIFY the value of a tensor, though not intuitive.

  • To set a model/module to switch between train/predict mode, call nn.Module.train(True/False). This function will recursively set every child module's mode. NEVER use self.training=True/False, this only applies to the current module without effecting its children.