Hi the official Lc0 release with DX backend and LogitQ not available yet.But you can test it with this version:
https://gofile.io/?c=Ntb3Xv
http://www.filedropper.com/lc0-test-windows-gpu-dx22
https://www.chess2u.com/t13501-lc0-v0-2 ... -gpu#92392
For those who can not download from the 2 links above here is the alternative link:
https://www.dropbox.com/s/66fojbhpagc5j ... 2.zip?dl=0
requirements:
OS: updated Windows 10
AMD drivers: latest >= 20.1
DX is 3-4 x faster than opencl but slower than cudnn-fp16,so its recomended only for amd radeon GPU.But you can test is also on Nvidia GPU anyway.
here some benchmarks
Code:
Nvidia RTX 2060
===============
Network Net-Size OpenCL cudnn-fp32 dx-fp32 cudnn-fp16 dx-fp16
----------------------------------------------------------------------------------
T59 59611 128x10 11624 35470 31088 88009 51326
T30 32390 256x20 2225 4972 6459 15137 17391
T40 42850 256x20 1371 4571 318* 12780 14198
T60 61996 320x24 1555 3421 3317 8418 7669
SV-big-t40-1705 384x30 856 1854 104* 4955 4799
SV-huge-50 512x40 361 802 29* 2241 121*
* - poor performance due to a driver bug (hopefully will be fixed soon).
Nvidia RTX Titan
================
Network Net-Size cudnn-fp32 dx-fp32 cudnn-fp16 dx-fp16
------------------------------------------------------------------------
T59 59611 128x10 70266 50523 123414 73675
T30 32390 256x20 14438 14173 40452 42276
T40 42850 256x20 12932 13113 36753 38388
T60 61996 320x24 8137 7505 17472 18578
SV-big-t40-1705 384x30 4143 3933 11054 13086
SV-huge-50 512x40 1942 1918 4844 7015
Code:
AMD RX 5700XT (Navi)
====================
Network Net-Size OpenCL dx-fp32 dx-fp16
-------------------------------------------------------
T59 59611 128x10 12095 37845 55888
T30 32390 256x20 1505 5814 10198
T40 42850 256x20 900 4666 8041
T60 61996 320x24 1774 2874 5183
SV-big-t40-1705 384x30 . 1479 2801
SV-huge-50 512x40 . * 1080
AMD RX Vega VII
===============
Network Net-Size OpenCL dx-fp32 dx-fp16
-------------------------------------------------------
T59 59611 128x10 14889 46099 59721
T30 32390 256x20 1525 8055 12821
T40 42850 256x20 2754 6168 9156
T60 61996 320x24 2490 3722 6078
SV-big-t40-1705 384x30 1426 2129 3254
SV-huge-50 512x40 . 852 1323
Lc0 v0.24 dev DX backend for AMD Radeon GPU
Moderators: hgm, Rebel, chrisw
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Re: Lc0 v0.24 dev DX backend for AMD Radeon GPU
Thanks because my GPU happens to be AMD Radeon. BTW what does DX mean?
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Re: Lc0 v0.24 dev DX backend for AMD Radeon GPU
Very interesting. On my RTX 2070 this backend is only 10% slower with T40 nets than using cudnn-fp16 backend, only 2% slower with T60 nets, and about 15% faster than cudnn-fp16 with the huge SV net 512x40b.
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Re: Lc0 v0.24 dev DX backend for AMD Radeon GPU
Fantastic news!
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Re: Lc0 v0.24 dev DX backend for AMD Radeon GPU
Does this mean there's an Lc0 version for any GPU you have?
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Re: Lc0 v0.24 dev DX backend for AMD Radeon GPU
Hmmm ... if that is true maybe that means that Lc0 cudnn-fp16 backend needs to be updated for the RTX cards!
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Re: Lc0 v0.24 dev DX backend for AMD Radeon GPU
It seems so. Here I checked at 30s + 0.3s the strength in a sanity check with 384x30b SV net 1538, one of hte strongest nets to LTC on a strong GPU.M ANSARI wrote: ↑Thu Feb 13, 2020 7:12 amHmmm ... if that is true maybe that means that Lc0 cudnn-fp16 backend needs to be updated for the RTX cards!
Score of LargeNet_1538_dx vs LargeNet_1538_cudnn: 115 - 81 - 204 [0.542]
Elo difference: 29.6 +/- 23.8, LOS: 99.2 %, DrawRatio: 51.0 %
400 of 400 games finished.
Normalized Elo (pentanomial): 0.170 +/- 0.050 (1 SD)
DX performs better strength-wise too on these larger nets than cuDNN on an RTX 2070 GPU.
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Re: Lc0 v0.24 dev DX backend for AMD Radeon GPU
Thanks Laskos for testing. It's good to know that the speed increase translates to improvement in playing strength.
The dx backend (that defaults to fp16 precision) uses a different algorithm for convolution (winograd) that scales better with bigger networks compared to what cudnn-fp16 uses (implicit_gemm). We will be adding that path to cudnn-fp16 backend too.
The dx backend (that defaults to fp16 precision) uses a different algorithm for convolution (winograd) that scales better with bigger networks compared to what cudnn-fp16 uses (implicit_gemm). We will be adding that path to cudnn-fp16 backend too.
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Re: Lc0 v0.24 dev DX backend for AMD Radeon GPU
Thanks for the info, I was thinking that cuDNN backend also uses fast Winograd convolutions, at least that was the talk more than year ago, and in fact I used that 10-12 years ago with image processing.ankan wrote: ↑Thu Feb 13, 2020 12:48 pm Thanks Laskos for testing. It's good to know that the speed increase translates to improvement in playing strength.
The dx backend (that defaults to fp16 precision) uses a different algorithm for convolution (winograd) that scales better with bigger networks compared to what cudnn-fp16 uses (implicit_gemm). We will be adding that path to cudnn-fp16 backend too.