Re: [eigen] Re: Eigen tensor module

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Tensorflow works around this limitation by "reinterpreting" memory buffers as Eigen Tensors of different rank at runtime: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/tensor.h#L414-L442

On Tue, Oct 29, 2019 at 10:33 AM Rasmus Munk Larsen <rmlarsen@xxxxxxxxxx> wrote:
This was an early design decision. Benoit Steiner did have some plans to add support for dynamic tensors, but it was never implemented. There are no current plans to add this feature, but you are of course welcome to contribute it.

Rasmus

On Tue, Oct 29, 2019 at 10:23 AM William Tambellini <wtambellini@xxxxxxx> wrote:
Hello Tom
I would also favor ranking to be a runtime thing : that s also the choice made by xtensor and ArrayFire :
Now, are eigen::tensor imposing ranking to be template/compil-time for performance reasons ?
Regards
W.

 
From: Peterka, Thomas <tpeterka@xxxxxxxxxxx>
Sent: Friday, October 25, 2019 9:12 AM
To: eigen@xxxxxxxxxxxxxxxxxxx <eigen@xxxxxxxxxxxxxxxxxxx>
Subject: [eigen] Eigen tensor module
 
Hello,

Is there any way to give the rank of an Eigen tensor at run time, or are there any plans to make this a dynamic run-time parameter? The tensor module would be extremely useful for me if not for the limitation that the rank needs to be specified as compile-time template parameter. I am unable to use Eigen tensors for a generic n-dimensional application where I don’t know the tensor rank in advance.

Please let me know if I’m missing something or if there are any future plans to change this? The boost tensor module, for example, has dynamic ranks and sizes:



Thanks,
Tom
-----------------
Tom Peterka
Mathematics and Computer Science Division
Argonne National Laboratory
9700 S. Cass Ave.
Lemont, IL 60439
Bldg 240, Rm 2148
e-mail: tpeterka@xxxxxxxxxxx
web: www.mcs..anl.gov/~tpeterka
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