Re: [eigen] Componentwise Operations on an Arbitrary Number of Tensors |

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*To*: eigen@xxxxxxxxxxxxxxxxxxx*Subject*: Re: [eigen] Componentwise Operations on an Arbitrary Number of Tensors*From*: Graham Neubig <gneubig@xxxxxxxxxx>*Date*: Mon, 2 Jan 2017 22:17:17 -0500

Hi Christoph and all,

Thanks for the reference to the issue, and glad to see that this is on the radar. I honestly don't really know where to start on this, but if someone could give some pointers for where in the code I could reference I could take a look and see if I could cook up something.

Graham

On Dec 30, 2016 8:06 AM, "Christoph Hertzberg" <chtz@xxxxxxxxxxxxxxxxxxxxxxxx> wrote:

Hi!

For reference, here is a bugzilla entry for this feature request:

http://eigen.tuxfamily.org/bz/show_bug.cgi?id=984

An idea we had back then was to introduce Eigen::tie and then allow assignments like:

Eigen::ArrayXd A, B, X;

Eigen::tie(A,B) = sin(X), cos(X);

(and of course, one could introduce sincos, minmax, ... -operators which are assignable to tie-expressions).

Christoph

On 28.12.2016 at 22:48, Gael Guennebaud wrote:

Hi,

it seems that what you're looking for is a mean to merge multiple

evaluation loops of the same size into a single one (the fact that they run

on the GPU is not really important here). Actually, this needs already

shows up for stuff like:

a = vec.minCoeff();

b = vec.maxCoeff();

that currently requires two loops. I remember that we already talked about

that with Benoit S., and I don't think there is a general solution

implemented in the Tensor module yet.

Technically, I don't think that's very difficult though. The main

difficulty is perhaps on the API side. We could imagine something like:

auto E1 = (R1.deferred() = expr1);

auto E2 = (R2.deferred() = expr2);

....

merged_eval(E1, E2, ...);

that would essentially generate:

(parallel/GPU/whatever) for loop {

R1[i] = expr1.coeffl(i);

R2[i] = expr2.coeffl(i);

...

}

In Eigen/Core, "R.deferred().operator=(expr)"would return an

Eigen::internal::Assignment _expression_ (without calling run) that would be

merged by the merged_eval function.

gael

On Wed, Dec 28, 2016 at 3:22 PM, Graham Neubig <gneubig@xxxxxxxxxx> wrote:

Hi Eigen Folks,

First, thanks for the great library. We're using it in our machine

learning library DyNet to great success.

I had a quick question about something that seems like it should be

possible, but I haven't found a reference. I currently have code here:

https://github.com/clab/dynet/blob/master/dynet/training.cc# L280

That implements the "Adam" update rule for stochastic gradient descent

found in this paper:

https://arxiv.org/abs/1412.6980

Here, all places with "tvec()" are Eigen one-dimensional Tensors. The

thing that bugs me here is that I'm calling 4 different operations, which

results in 4 different GPU kernel launches, for an operation that is

inherently componentwise. If possible, I'd like to be able to basically

create a single functor that takes 4 floats, and modifies them

appropriately, then pass this in a single GPU operation.

I know this is possible using binaryExpr() for binary expressions, but I

couldn't find it for operations with a larger number of arguments. Is there

any chance that there is an elegant way to do this within Eigen (i.e.

without writing my own kernel)?

Graham

--

Dipl. Inf., Dipl. Math. Christoph Hertzberg

Universität Bremen

FB 3 - Mathematik und Informatik

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**Follow-Ups**:**Re: [eigen] Componentwise Operations on an Arbitrary Number of Tensors***From:*Gael Guennebaud

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