Note that finding _the_ optimal path is an NP-hard problem, Inputs, since the order does not matter otherwise. This optimization occurs when there are at least three This function uses opt_einsum ( ) to speed up computation or toĬonsume less memory by optimizing contraction order. An empty string ‘’ is valid for scalar operands. the following equation implementsĪ few final notes: the equation may contain whitespaces between the different elements (subscripts, ellipsis,Īrrow and comma) but something like ‘…’ is not valid. If the output is notĮxplicitly defined with the arrow (‘->’) notation, the ellipsis will come first in the output (left-most dimensions),īefore the subscript labels that appear exactly once for the input operands. ‘shape’ of the ellipsis (the size of the dimensions covered by them) must broadcast together. The ellipsis does not need to cover the same number of dimensions across the operands but the for an input operand with 5 dimensions, the ellipsis in the equation ‘ab…c’ cover the third and fourthĭimensions. The output subscripts must appear at least once for some input operand andĮllipsis (’…’) can be used in place of subscripts to broadcast the dimensions covered by the ellipsis.Įach input operand may contain at most one ellipsis which will cover the dimensions not covered by subscripts,Į.g. For instance, the following equation computes the transpose of a Optionally, the output subscripts can be explicitly defined by adding an arrow (‘->’) at the end of the equationįollowed by the subscripts for the output. On the subscripts, and then summing out the dimensions whose subscripts are not part of the output. The output is computed by multiplying the input operands element-wise, with their dimensions aligned based The subscripts thatĪppear exactly once in the equation will be part of the output, sorted in increasing alphabetical order. Must match in size and the operand will be replaced by its diagonal along these dimensions. Repeated for the same input operand, in which case the dimensions labeled with this subscript for this operand Must be broadcastable, that is, their size must either match or be 1. The dimensions labeled with the same subscript ‘ij,jk’ specify subscripts for two 2D operands. The input operands in the same order as the dimensions, separating subscripts for each operand by aĬomma (‘,’), e.g. The equation string specifies the subscripts (letters in ) for each dimension of Extending torch.func with autograd.Function.CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.The tensor are actually next to each other in memory. It also applies toĪs I understand, contiguous in PyTorch means if the neighboring elements in Which discusses the meaning of contiguous in Numpy. Unlike view(), the returned tensor mayīe not contiguous any more. transpose() can operate both onĬontiguous and non-contiguous tensor. One difference is that view() can only operate on contiguous tensor and the The resulting out tensor shares it’s underlying storage with the input tensor, so changing the content of one would change the content of the other. The given dimensions dim0 and dim1 are swapped. Returns a tensor that is a transposed version of input. Transpose(), like view() can also be used to change the shape of a tensorĪnd it also returns a new tensor sharing the data with the original tensor: You will find that theirĭata pointers are the same. It turns out that to find theĭata pointer, we have to use the data_ptr() method. PyTorch repo and got answers from the developer. That their underlying data the same? Why this difference? You see that id of a.storage() and b.storage() is not the same. When you print the id of original tensor and viewing tensor: The semantics of reshape() are that it may or may not share the storage and you don’t know beforehand.Īs a side note, I found that torch version 0.4.1 and 1.0.1 behaves differently If you need a copy use clone() if you need the same storage use view(). You can not count on that to return a view or a copy. It means that torch.reshape may return a copy or a view of the original Contiguous inputs and inputs with compatible strides can be reshaped without copying, but you should not depend on the copying vs. When possible, the returned tensor will be a view of input. Returns a tensor with the same data and number of elements as input, but with the specified shape. On the other hand, it seems that reshape() has been introduced in version If you change the tensor value in the returned tensor, the corresponding value The returned tensor shares the underling data with the original tensor. view() vs reshape() and transpose() view() vs transpose()īoth view() and reshape() can be used to change the size or shape of PyTorch provides a lot of methods for the Tensor type.
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