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The memory allocation is C contiguous if the rows are stored next to each other like this: This is what PyTorch considers contiguous. >>> t.is_contiguous() True PyTorch's Tensor class method stride() gives the number of bytes to skip to get the next element in each dimension >>> t.stride() (4, 1)
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Beta3. contiguous() 在 pytorch 中只有很少几个操作是不改变tensor内容本身的，大多数操作不进行数据拷贝和数据的改变，变的是原数据。 例如：narrow()、view()、expand()和 transpose() 等。. 例如使用transpose()进行转置操作时，pytorch并不会创建新的、转置后的 tensor ，而是修改了tensor中的一些属性（也就是原数据 ...
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Betatorch.Tensor.contiguous¶ Tensor. contiguous (memory_format = torch.contiguous_format) → Tensor ¶ Returns a contiguous in memory tensor containing the same data as self tensor. If self tensor is already in the specified memory format, this function returns the self tensor. Parameters
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BetaOkay, now we finished the introduction of contiguous view, and also learned how the strides works in a N dimensional tensor in Pytorch. Now let’s take a look at what the non-contiguous data is like.
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BetaThe key difference revolves around how PyTorch handles the underlying memory of the tensor.Non-Contiguous Memory If a tensor has been manipulated (e.g., by transposing or slicing), its data might be scattered in memory ... Contiguity Always be mindful of contiguity when using view(). Use contiguous() if needed. python pytorch ...
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BetaCall .contiguous() before .view(). So let’s call .contiguous() before the view call: y = y.contiguous() print(y.stride()) > (4, 1) y = y.view(-1) Now the memory layout is contiguous once again (have a look at the strides) and the view works just fine. I’m not completely sure, but I assume the contiguous call copies the memory to make it ...
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Beta文章浏览阅读8.5k次，点赞7次，收藏23次。本文详细解析了PyTorch中view函数的使用方法及其与contiguous函数的关系。解释了为何某些情况下需要使用contiguous确保tensor在内存中连续分布，以便正确执行view操作。同时介绍了view函数中-1参数的含义，以及如何利用它进行灵活的张量重塑。
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BetaOutput: torch.Size([6, 4]) In most cases, .reshape() is more flexible and safer to use, but .view() is more efficient when working with contiguous tensors. Conclusion. The .view() function in PyTorch is a powerful tool for reshaping tensors efficiently. It allows you to alter the shape of a tensor without changing its data, provided that the tensor is contiguous.
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BetaNormally some changes like view(..), transpose(...) or permute(..) would just change the metadata (being lazy) and not the underlying storage. This create issues with parallel computations. Inorder to consolidate it into a contiguous memory as expected by other ops, contiguous() is called.
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BetaI really want to know that in Pytorch, functions such like view(), permute(), contiguous() operate the Tensor in-place or they will allocate new memory block to store the result. Recently, I encounter that my data needs lot of memory, so I need to reduce memory usage in the forward process, and knowing the answer of the question above is of great help. Thanks a lot
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