[Computer-go] CNN with 54% prediction on KGS 6d+ data

David Ongaro david.ongaro at hamburg.de
Tue Dec 8 10:31:07 PST 2015


Did everyone forget the fact that stronger playouts don't necessarily lead to an better evaluation function? (Yes, that what playouts essential are, a dynamic evaluation function.) This is even under the assumption that we can reach the same number of playouts per move.


> On 08 Dec 2015, at 10:21, Álvaro Begué <alvaro.begue at gmail.com> wrote:
> 
> I don't think the CPU-GPU communication is what's going to kill this idea. The latency in actually computing the feed-forward pass of the CNN is going to be in the order of 0.1 seconds (I am guessing here), which means finishing the first playout will take many seconds.
> 
> So perhaps it would be interesting to do something like this for correspondence games, but not for regular games.
> 
> 
> Álvaro.
> 
> 
> 
> On Tue, Dec 8, 2015 at 12:03 PM, Petr Baudis <pasky at ucw.cz <mailto:pasky at ucw.cz>> wrote:
>   Hi!
> 
>   Well, for this to be practical the entire playout would have to be
> executed on the GPU, with no round-trips to the CPU.  That's what my
> email was aimed at.
> 
> On Tue, Dec 08, 2015 at 04:37:05PM +0000, Josef Moudrik wrote:
> > Regarding full CNN playouts, I think that problem is that a playout is a
> > long serial process, given 200-300 moves a game. You need to construct
> > planes and transfer them to GPU for each move and read result back (at
> > least with current CNN implementations afaik), so my guess would be that
> > such playout would take time in order of seconds. So there seems to be a
> > tradeoff, CNN playouts are (probably much) better (at "playing better
> > games") than e.g. distribution playouts, but whether this is worth the
> > implied (probably much) lower height of the MC tree is a question.
> >
> > Maybe if you had really a lot of GPUs and very high thinking time, this
> > could be the way.
> >
> > Josef
> >
> > On Tue, Dec 8, 2015 at 5:17 PM Petr Baudis <pasky at ucw.cz <mailto:pasky at ucw.cz>> wrote:
> >
> > >   Hi!
> > >
> > >   In case someone is looking for a starting point to actually implement
> > > Go rules etc. on GPU, you may find useful:
> > >
> > >
> > > https://www.mail-archive.com/computer-go@computer-go.org/msg12485.html <https://www.mail-archive.com/computer-go@computer-go.org/msg12485.html>
> > >
> > >   I wonder if you can easily integrate caffe GPU kernels in another GPU
> > > kernel like this?  But without training, reimplementing the NN could be
> > > pretty straightforward.
> > >
> > > On Tue, Dec 08, 2015 at 04:53:14PM +0100, Michael Markefka wrote:
> > > > Hello Detlef,
> > > >
> > > > I've got a question regarding CNN-based Go engines I couldn't find
> > > > anything about on this list. As I've been following your posts here, I
> > > > thought you might be the right person to ask.
> > > >
> > > > Have you ever tried using the CNN for complete playouts? I know that
> > > > CNNs have been tried for move prediction, immediate scoring and move
> > > > generation to be used in an MC evaluator, but couldn't find anything
> > > > about CNN-based playouts.
> > > >
> > > > It might only be feasible to play out the CNN's first choice move for
> > > > evaluation purposes, but considering how well the performance of batch
> > > > sizes scales, especially on GPU-based CNN applications, it might be
> > > > possible to setup something like 10 candidate moves, 10 reply
> > > > candidate moves and then have the CNN play out the first choice move
> > > > for those 100 board positions until the end and then sum up scores
> > > > again for move evaluation (and/or possibly apply some other tried and
> > > > tested methods like minimax). Given that the number of 10 moves is
> > > > supposed to be illustrative rather than representative, other
> > > > configurations of depth and width in position generation and
> > > > evaluation would be possible.
> > > >
> > > > It feels like CNN can provide a very focused, high-quality width in
> > > > move generation, but it might also be possible to apply that quality
> > > > to depth of evaluation.
> > > >
> > > > Any thoughts to share?
> > > >
> > > >
> > > > All the best
> > > >
> > > > Michael
> > > >
> > > > On Tue, Dec 8, 2015 at 4:13 PM, Detlef Schmicker <ds2 at physik.de <mailto:ds2 at physik.de>> wrote:
> > > > > -----BEGIN PGP SIGNED MESSAGE-----
> > > > > Hash: SHA1
> > > > >
> > > > > Hi,
> > > > >
> > > > > as somebody ask I will offer my actual CNN for testing.
> > > > >
> > > > > It has 54% prediction on KGS 6d+ data (which I thought would be state
> > > > > of the art when I started training, but it is not anymore:).
> > > > >
> > > > > it has:
> > > > > 1
> > > > > 2
> > > > > 3
> > > > >> 4 libs playing color
> > > > > 1
> > > > > 2
> > > > > 3
> > > > >> 4 libs opponent color
> > > > > Empty points
> > > > > last move
> > > > > second last move
> > > > > third last move
> > > > > forth last move
> > > > >
> > > > > input layers, and it is fully convolutional, so with just editing the
> > > > > golast19.prototxt file you can use it for 13x13 as well, as I did on
> > > > > last sunday. It was used in November tournament as well.
> > > > >
> > > > > You can find it
> > > > > http://physik.de/CNNlast.tar.gz <http://physik.de/CNNlast.tar.gz>
> > > > >
> > > > >
> > > > >
> > > > > If you try here some points I like to get discussion:
> > > > >
> > > > > - - it seems to me, that the playouts get much more important with such
> > > > > a strong move prediction. Often the move prediction seems better the
> > > > > playouts (I use 8000 at the moment against pachi 32000 with about 70%
> > > > > winrate on 19x19, but with an extremely focused progressive widening
> > > > > (a=400, a=20 was usual).
> > > > >
> > > > > - - live and death becomes worse. My interpretation is, that the strong
> > > > > CNN does not play moves, which obviously do not help to get a group
> > > > > life, but would help the playouts to recognize the group is dead.
> > > > > (http://physik.de/example.sgf <http://physik.de/example.sgf> top black group was with weaker move
> > > > > prediction read very dead, with good CNN it was 30% alive or so :(
> > > > >
> > > > >
> > > > > OK, hope you try it, as you know our engine oakfoam is open source :)
> > > > > We just merged all the CNN stuff into the main branch!
> > > > > https://bitbucket.org/francoisvn/oakfoam/wiki/Home <https://bitbucket.org/francoisvn/oakfoam/wiki/Home>
> > > > > http://oakfoam.com <http://oakfoam.com/>
> > > > >
> > > > >
> > > > > Do the very best with the CNN
> > > > >
> > > > > Detlef
> > > > >
> > > > >
> > > > >
> > > > >
> > > > > code:
> > > > > if (col==Go::BLACK) {
> > > > >           for (int j=0;j<size;j++)
> > > > >             for (int k=0;k<size;k++)
> > > > >                   {
> > > > >         for (int l=0;l<caffe_test_net_input_dim;l++)
> > > > > data[l*size*size+size*j+k]=0;
> > > > >         //fprintf(stderr,"%d %d %d\n",i,j,k);
> > > > >         int pos=Go::Position::xy2pos(j,k,size);
> > > > >         int libs=0;
> > > > >         if (board->inGroup(pos))
> > > > > libs=board->getGroup(pos)->numRealLibs()-1;
> > > > >         if (libs>3) libs=3;
> > > > >         if (board->getColor(pos)==Go::BLACK)
> > > > >                   {
> > > > >                           data[(0+libs)*size*size + size*j + k]=1.0;
> > > > >                           //data[size*size+size*j+k]=0.0;
> > > > >                           }
> > > > >               else if (board->getColor(pos)==Go::WHITE)
> > > > >                       {
> > > > >                           //data[j*size+k]=0.0;
> > > > >                           data[(4+libs)*size*size + size*j + k]=1.0;
> > > > >                           }
> > > > >               else if
> > > > > (board->getColor(Go::Position::xy2pos(j,k,size))==Go::EMPTY)
> > > > >               {
> > > > >                             data[8*size*size + size*j + k]=1.0;
> > > > >                           }
> > > > >             }
> > > > >         }
> > > > >         if (col==Go::WHITE) {
> > > > >           for (int j=0;j<size;j++)
> > > > >             for (int k=0;k<size;k++)
> > > > >                   {//fprintf(stderr,"%d %d %d\n",i,j,k);
> > > > >         for (int l=0;l<caffe_test_net_input_dim;l++)
> > > > > data[l*size*size+size*j+k]=0;
> > > > >         //fprintf(stderr,"%d %d %d\n",i,j,k);
> > > > >         int pos=Go::Position::xy2pos(j,k,size);
> > > > >         int libs=0;
> > > > >         if (board->inGroup(pos))
> > > > > libs=board->getGroup(pos)->numRealLibs()-1;
> > > > >         if (libs>3) libs=3;
> > > > >         if (board->getColor(pos)==Go::BLACK)
> > > > >                   {
> > > > >                           data[(4+libs)*size*size + size*j + k]=1.0;
> > > > >                           //data[size*size+size*j+k]=0.0;
> > > > >                           }
> > > > >               else if (board->getColor(pos)==Go::WHITE)
> > > > >                       {
> > > > >                           //data[j*size+k]=0.0;
> > > > >                           data[(0+libs)*size*size + size*j + k]=1.0;
> > > > >                           }
> > > > >               else if (board->getColor(pos)==Go::EMPTY)
> > > > >               {
> > > > >                             data[8*size*size + size*j + k]=1.0;
> > > > >                           }
> > > > >     }
> > > > >         }
> > > > > if (caffe_test_net_input_dim > 9) {
> > > > >   if (board->getLastMove().isNormal()) {
> > > > >     int j=Go::Position::pos2x(board->getLastMove().getPosition(),size);
> > > > >     int k=Go::Position::pos2y(board->getLastMove().getPosition(),size);
> > > > >     data[9*size*size+size*j+k]=1.0;
> > > > >   }
> > > > >   if (board->getSecondLastMove().isNormal()) {
> > > > >     int
> > > > > j=Go::Position::pos2x(board->getSecondLastMove().getPosition(),size);
> > > > >     int
> > > > > k=Go::Position::pos2y(board->getSecondLastMove().getPosition(),size);
> > > > >     data[10*size*size+size*j+k]=1.0;
> > > > >   }
> > > > >   if (board->getThirdLastMove().isNormal()) {
> > > > >     int
> > > > > j=Go::Position::pos2x(board->getThirdLastMove().getPosition(),size);
> > > > >     int
> > > > > k=Go::Position::pos2y(board->getThirdLastMove().getPosition(),size);
> > > > >     data[11*size*size+size*j+k]=1.0;
> > > > >   }
> > > > >   if (board->getForthLastMove().isNormal()) {
> > > > >     int
> > > > > j=Go::Position::pos2x(board->getForthLastMove().getPosition(),size);
> > > > >     int
> > > > > k=Go::Position::pos2y(board->getForthLastMove().getPosition(),size);
> > > > >     data[12*size*size+size*j+k]=1.0;
> > > > >   }
> > > > > }
> > > > >
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> > >
> > > --
> > >                                 Petr Baudis
> > >         If you have good ideas, good data and fast computers,
> > >         you can do almost anything. -- Geoffrey Hinton
> > > _______________________________________________
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> 
> --
>                                 Petr Baudis
>         If you have good ideas, good data and fast computers,
>         you can do almost anything. -- Geoffrey Hinton
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