Difference between view, reshape and permute autograd
  
  
    reshape tries to return a view if possible, otherwise copies to data to a contiguous tensor and returns the view on it. From the docs: 

Returns a tensor with the same data and number of elements as input , but with the specified shape. When possible, the returned tensor will be a view of input . Otherwise, it will be a copy. Contiguous inputs and inputs with compatible strides can be reshaped without copying, but you should not depend on the copying vs. viewing behavior. 
See  torch.Tensor.view…
  



  
    
    
    Difference between 2 reshaping operations (reshape vs permute) nlp
  
  
    Im not sure about reshape. 
Permute is a multidimensional rotation saying somehow. It keeps the data ordering. View (which is another reshaping method) maps from one dimensionality to another sequentially reading data from the upper dimensions to the lower ones. 
So if you want fuse two dimensions into one, you have to apply it over contiguous dimensions or u will modify the data ordering