diff --git a/.download-backup.log b/.download-backup.log new file mode 100644 index 0000000000000000000000000000000000000000..a836a0515b562e276a0e9f4126780571a20f8b2f --- /dev/null +++ b/.download-backup.log @@ -0,0 +1,18 @@ +START 2026-08-01T00:45:09+08:00 +=== [00:45:09] backup-0727 === + Total 55 objects,155.094 GiB, Copy... done:(14 files,202.347 KiB), 0.082% Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 0.865% Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 1.718% Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 2.635% Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 4.021%, avg 3.054 GiB/s Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 4.922%, avg 3.002 GiB/s Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 5.757%, avg 2.933 GiB/s Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 6.409%, avg 2.803 GiB/s Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 7.299%, avg 2.798 GiB/s Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 8.885%, avg 3.033 GiB/s Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 9.725%, avg 2.991 GiB/s Total 55 objects,155.094 GiB, Copy... done:(15 files,2.413 MiB), 10.429%, avg 2.917 GiB/s Total 55 objects,155.094 GiB, Copy... done:(17 files,1.804 GiB), 11.293%, avg 2.897 GiB/s Total 55 objects,155.094 GiB, Copy... done:(33 files,9.011 GiB), 12.424%, avg 2.944 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 13.297%, avg 2.928 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 13.952%, avg 2.868 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 14.459%, avg 2.787 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 15.710%, avg 2.851 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 16.408%, avg 2.813 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 17.019%, avg 2.765 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 17.590%, avg 2.715 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 18.699%, avg 2.749 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 19.505%, avg 2.738 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 20.099%, avg 2.699 GiB/s Total 55 objects,155.094 GiB, Copy... done:(40 files,10.817 GiB), 20.643%, avg 2.657 GiB/s Total 55 objects,155.094 GiB, Copy... done:(42 files,12.619 GiB), 21.578%, avg 2.667 GiB/s Total 55 objects,155.094 GiB, Copy... done:(44 files,14.421 GiB), 22.386%, avg 2.660 GiB/s Total 55 objects,155.094 GiB, Copy... done:(44 files,14.421 GiB), 22.847%, avg 2.615 GiB/s Total 55 objects,155.094 GiB, Copy... done:(44 files,14.421 GiB), 23.332%, avg 2.575 GiB/s Total 55 objects,155.094 GiB, Copy... done:(44 files,14.421 GiB), 24.197%, avg 2.579 GiB/s Total 55 objects,155.094 GiB, Copy... done:(44 files,14.421 GiB), 24.816%, avg 2.557 GiB/s Total 55 objects,155.094 GiB, Copy... done:(44 files,14.421 GiB), 25.157%, avg 2.509 GiB/s Total 55 objects,155.094 GiB, Copy... done:(45 files,16.824 GiB), 25.652%, avg 2.478 GiB/s Total 55 objects,155.094 GiB, Copy... done:(45 files,16.824 GiB), 26.501%, avg 2.483 GiB/s Total 55 objects,155.094 GiB, Copy... done:(46 files,19.432 GiB), 27.021%, avg 2.457 GiB/s Total 55 objects,155.094 GiB, Copy... done:(46 files,19.432 GiB), 27.352%, avg 2.416 GiB/s Total 55 objects,155.094 GiB, Copy... done:(46 files,19.432 GiB), 27.871%, avg 2.394 GiB/s Total 55 objects,155.094 GiB, Copy... done:(46 files,19.432 GiB), 28.468%, avg 2.379 GiB/s Total 55 objects,155.094 GiB, Copy... done:(46 files,19.432 GiB), 29.002%, avg 2.360 GiB/s Total 55 objects,155.094 GiB, Copy... done:(46 files,19.432 GiB), 29.369%, avg 2.329 GiB/s Total 55 objects,155.094 GiB, Copy... done:(46 files,19.432 GiB), 29.818%, avg 2.306 GiB/s Total 55 objects,155.094 GiB, Copy... done:(46 files,19.432 GiB), 30.484%, avg 2.300 GiB/s Total 55 objects,155.094 GiB, Copy... done:(46 files,19.432 GiB), 30.940%, avg 2.279 GiB/s Total 55 objects,155.094 GiB, Copy... done:(47 files,21.835 GiB), 31.330%, avg 2.254 GiB/s Total 55 objects,155.094 GiB, Copy... done:(47 files,21.835 GiB), 31.863%, avg 2.240 GiB/s Total 55 objects,155.094 GiB, Copy... done:(48 files,25.439 GiB), 32.378%, avg 2.226 GiB/s Total 55 objects,155.094 GiB, Copy... done:(48 files,25.439 GiB), 32.751%, avg 2.202 GiB/s Total 55 objects,155.094 GiB, Copy... done:(49 files,29.045 GiB), 33.054%, avg 2.176 GiB/s Total 55 objects,155.094 GiB, Copy... done:(49 files,29.045 GiB), 33.411%, avg 2.153 GiB/s Total 55 objects,155.094 GiB, Copy... done:(49 files,29.045 GiB), 33.917%, avg 2.141 GiB/s Total 55 objects,155.094 GiB, Copy... done:(49 files,29.045 GiB), 34.159%, avg 2.114 GiB/s Total 55 objects,155.094 GiB, Copy... done:(49 files,29.045 GiB), 34.543%, avg 2.095 GiB/s Total 55 objects,155.094 GiB, Copy... done:(49 files,29.045 GiB), 34.941%, avg 2.079 GiB/s Total 55 objects,155.094 GiB, Copy... done:(49 files,29.045 GiB), 35.358%, avg 2.064 GiB/s Total 55 objects,155.094 GiB, Copy... done:(49 files,29.045 GiB), 35.641%, avg 2.042 GiB/s Total 55 objects,155.094 GiB, Copy... done:(49 files,29.045 GiB), 35.918%, avg 2.020 GiB/s Total 55 objects,155.094 GiB, Copy... done:(49 files,29.045 GiB), 36.369%, avg 2.009 GiB/s Total 55 objects,155.094 GiB, Copy... done:(50 files,32.650 GiB), 36.736%, avg 1.994 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 37.013%, avg 1.974 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 37.370%, avg 1.960 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 37.607%, avg 1.860 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 37.783%, avg 1.837 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 38.196%, avg 1.827 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 38.433%, avg 1.809 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 38.670%, avg 1.737 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 38.967%, avg 1.723 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 39.264%, avg 1.709 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 39.561%, avg 1.696 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 39.733%, avg 1.575 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 39.969%, avg 1.560 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 40.267%, avg 1.549 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 40.564%, avg 1.538 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 40.814%, avg 1.459 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 41.172%, avg 1.453 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 41.302%, avg 1.435 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 41.627%, avg 1.428 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 41.938%, avg 1.348 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 42.235%, avg 1.340 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 42.486%, avg 1.331 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 42.648%, avg 1.317 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 43.108%, avg 1.244 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 43.284%, avg 1.231 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 43.521%, avg 1.223 GiB/s Total 55 objects,155.094 GiB, Copy... done:(51 files,36.254 GiB), 43.834%, avg 1.218 GiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 44.136%, avg 1.152 GiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 44.257%, avg 1.138 GiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 44.499%, avg 1.131 GiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 44.741%, avg 1.125 GiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 44.862%, avg 1.063 GiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 45.103%, avg 1.058 GiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 45.345%, avg 1.053 GiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 45.466%, avg 1.041 GiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 45.708%, avg 1011.048 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 45.950%, avg 1006.564 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 46.191%, avg 1002.399 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 46.373%, avg 995.175 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 46.554%, avg 948.400 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 46.735%, avg 941.871 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 47.038%, avg 942.182 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 47.159%, avg 932.600 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 47.400%, avg 880.404 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 47.642%, avg 878.433 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 47.944%, avg 879.836 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 48.005%, avg 868.187 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 48.247%, avg 840.608 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 48.488%, avg 839.334 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 48.670%, avg 834.796 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 48.730%, avg 823.911 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 49.032%, avg 798.500 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 49.274%, avg 797.964 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 49.395%, avg 790.831 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 49.697%, avg 793.675 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 49.879%, avg 765.543 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 50.060%, avg 762.218 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 50.181%, avg 755.701 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 50.483%, avg 759.159 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 50.785%, avg 746.758 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 50.846%, avg 737.034 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 51.027%, avg 734.259 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 51.269%, avg 734.828 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 51.511%, avg 727.635 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 51.632%, avg 721.611 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 51.813%, avg 719.102 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 52.055%, avg 719.928 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 52.297%, avg 711.937 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 52.478%, avg 709.544 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 52.659%, avg 707.210 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 52.961%, avg 711.489 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 53.082%, avg 695.245 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 53.264%, avg 693.146 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 53.506%, avg 694.434 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 53.747%, avg 695.678 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 53.868%, avg 676.111 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 54.050%, avg 674.355 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 54.291%, avg 675.966 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 54.594%, avg 680.688 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 54.714%, avg 657.764 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 54.956%, avg 659.695 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 55.198%, avg 661.405 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 55.379%, avg 659.957 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 55.500%, avg 643.886 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 55.742%, avg 646.059 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 55.984%, avg 648.158 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 56.105%, avg 643.684 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 56.347%, avg 650.485 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 56.588%, avg 652.542 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 56.830%, avg 654.529 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 56.951%, avg 649.948 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 57.253%, avg 653.909 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 57.374%, avg 649.180 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 57.555%, avg 647.918 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 57.737%, avg 646.699 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 57.979%, avg 647.243 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 58.160%, avg 645.992 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 58.281%, avg 641.478 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 58.644%, avg 650.118 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 58.764%, avg 643.834 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 58.946%, avg 642.644 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 59.127%, avg 641.495 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 59.429%, avg 646.886 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 59.611%, avg 643.860 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 59.732%, avg 639.304 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 59.913%, avg 638.116 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 60.215%, avg 643.563 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 60.396%, avg 643.758 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 60.578%, avg 642.569 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 60.820%, avg 644.728 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 61.061%, avg 646.814 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 61.182%, avg 640.350 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 61.424%, avg 642.585 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 61.787%, avg 651.357 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 61.847%, avg 643.578 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 61.908%, avg 630.077 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 62.270%, avg 639.222 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 62.512%, avg 641.438 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 62.573%, avg 633.828 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 62.754%, avg 636.926 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 63.056%, avg 642.586 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 63.237%, avg 641.431 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 63.358%, avg 637.072 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 63.600%, avg 640.344 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 63.842%, avg 642.579 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 64.023%, avg 641.431 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 64.205%, avg 640.236 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 64.386%, avg 640.291 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 64.749%, avg 649.255 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 64.749%, avg 638.074 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 64.990%, avg 640.272 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 65.353%, avg 650.427 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 65.474%, avg 645.759 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 65.716%, avg 647.574 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 65.897%, avg 646.225 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 66.199%, avg 650.071 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 66.199%, avg 638.688 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 66.441%, avg 640.912 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 66.743%, avg 646.308 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 66.925%, avg 646.805 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 67.046%, avg 642.202 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 67.287%, avg 644.150 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 67.529%, avg 646.243 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 67.650%, avg 639.985 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 67.771%, avg 635.501 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 67.952%, avg 634.475 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 68.255%, avg 639.982 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 68.376%, avg 629.539 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 68.496%, avg 625.132 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 68.799%, avg 630.891 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 69.161%, avg 639.703 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 69.222%, avg 636.271 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 69.403%, avg 635.214 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 69.584%, avg 634.194 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 69.826%, avg 636.456 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 70.008%, avg 636.346 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 70.189%, avg 635.288 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 70.370%, avg 634.208 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 70.612%, avg 636.470 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 70.733%, avg 629.444 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 70.975%, avg 631.868 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 71.217%, avg 634.209 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 71.398%, avg 633.224 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 71.579%, avg 632.966 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 71.821%, avg 635.329 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 72.063%, avg 637.610 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 72.184%, avg 633.308 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 72.425%, avg 636.375 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 72.607%, avg 635.316 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 72.849%, avg 637.597 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 73.030%, avg 636.554 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 73.211%, avg 639.797 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 73.332%, avg 635.316 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 73.574%, avg 637.598 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 73.816%, avg 639.802 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 73.997%, avg 636.374 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 74.299%, avg 642.038 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 74.420%, avg 637.597 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 74.602%, avg 636.553 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 74.722%, avg 629.537 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 75.025%, avg 635.321 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 75.266%, avg 637.602 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 75.327%, avg 630.064 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 75.448%, avg 626.203 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 75.750%, avg 632.047 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 75.992%, avg 634.385 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 76.173%, avg 633.396 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 76.355%, avg 622.917 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 76.657%, avg 628.818 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 76.838%, avg 627.908 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 76.899%, avg 620.531 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 77.201%, avg 623.343 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 77.443%, avg 625.876 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 77.624%, avg 624.814 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 77.805%, avg 623.987 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 78.047%, avg 629.983 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 78.228%, avg 629.036 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 78.349%, avg 624.815 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 78.712%, avg 633.737 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 78.893%, avg 637.051 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 79.075%, avg 635.980 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 79.196%, avg 631.638 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 79.558%, avg 640.448 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 79.740%, avg 644.084 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 79.860%, avg 639.523 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 80.102%, avg 641.482 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 80.404%, avg 646.874 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 80.525%, avg 640.509 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 80.646%, avg 636.012 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 80.767%, avg 631.670 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 81.130%, avg 640.479 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 81.251%, avg 637.083 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 81.372%, avg 632.646 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 81.734%, avg 641.583 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 81.916%, avg 640.471 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 81.976%, avg 637.136 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 82.097%, avg 632.697 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 82.399%, avg 638.333 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 82.641%, avg 640.528 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 82.701%, avg 630.283 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 82.822%, avg 625.965 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 83.185%, avg 635.025 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 83.366%, avg 633.889 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 83.487%, avg 626.729 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 83.669%, avg 625.838 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 84.031%, avg 634.823 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 84.152%, avg 630.575 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 84.334%, avg 630.075 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 84.394%, avg 622.397 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 84.817%, avg 634.822 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 84.998%, avg 633.824 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 85.180%, avg 633.499 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 85.422%, avg 635.854 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 85.603%, avg 634.822 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 85.784%, avg 633.824 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 85.966%, avg 636.922 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 86.207%, avg 639.218 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 86.389%, avg 638.128 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 86.449%, avg 630.574 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 86.751%, avg 640.337 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 87.114%, avg 649.301 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 87.175%, avg 641.424 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 87.295%, avg 637.065 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 87.598%, avg 636.913 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 87.779%, avg 635.844 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 87.960%, avg 634.812 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 88.142%, avg 633.815 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 88.504%, avg 640.337 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 88.625%, avg 635.844 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 88.807%, avg 634.390 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 89.230%, avg 646.394 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 89.351%, avg 640.111 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 89.472%, avg 635.623 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 89.955%, avg 650.894 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 90.076%, avg 646.373 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 90.197%, avg 639.874 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 90.378%, avg 638.754 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 90.801%, avg 650.738 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 90.801%, avg 639.730 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 91.043%, avg 639.720 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 91.285%, avg 641.883 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 91.527%, avg 643.899 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 91.708%, avg 642.740 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 91.889%, avg 643.153 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 92.131%, avg 645.337 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 92.313%, avg 644.143 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 92.494%, avg 642.988 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 92.796%, avg 653.416 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 92.857%, avg 645.337 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 93.038%, avg 644.142 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 93.219%, avg 642.988 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 93.522%, avg 653.423 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 93.763%, avg 655.428 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 93.945%, avg 654.048 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 94.005%, avg 646.232 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 94.307%, avg 656.379 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 94.549%, avg 658.310 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 94.670%, avg 653.592 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 94.912%, avg 655.525 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 95.214%, avg 663.343 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 95.395%, avg 661.814 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 95.577%, avg 660.337 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 95.758%, avg 658.889 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 96.060%, avg 663.304 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 96.181%, avg 658.416 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 96.363%, avg 656.997 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 96.725%, avg 665.277 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 96.907%, avg 663.214 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 97.088%, avg 661.593 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 97.330%, avg 663.421 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 97.571%, avg 665.186 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 97.753%, avg 666.537 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 97.995%, avg 668.311 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 98.236%, avg 670.023 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 98.357%, avg 665.188 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 98.478%, avg 663.131 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 98.780%, avg 668.322 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 99.022%, avg 670.034 MiB/s Total 55 objects,155.094 GiB, Copy... done:(52 files,44.078 GiB), 99.308%, avg 674.042 MiB/s Total 55 objects,155.094 GiB, Copy... done:(53 files,81.083 GiB), 99.412%, avg 668.091 MiB/s Total 55 objects,155.094 GiB, Copy... done:(54 files,118.089 GiB), 99.593%, avg 666.478 MiB/s Total 55 objects,155.094 GiB, Copy... done:(54 files,118.089 GiB), 99.654%, avg 658.319 MiB/s Total 55 objects,155.094 GiB, Copy... done:(54 files,118.089 GiB), 99.714%, avg 650.437 MiB/s Total 55 objects,155.094 GiB, Copy... done:(54 files,118.089 GiB), 99.714%, avg 633.908 MiB/s Total 55 objects,155.094 GiB, Copy... done:(54 files,118.089 GiB), 99.835%, avg 629.535 MiB/s Success: Total 55 objects, 166531170396 B, Download done:(55 files, 166531170396 B), avg 874.286 MiB/s + +181.656196(s) elapsed +=== [00:48:11] backup-260717 === + Total 62 objects,64.344 GiB, Copy... done:(26 files,9.744 MiB), 1.299% Total 62 objects,64.344 GiB, Copy... done:(26 files,9.744 MiB), 3.760% Total 62 objects,64.344 GiB, Copy... done:(26 files,9.744 MiB), 5.934% Total 62 objects,64.344 GiB, Copy... done:(26 files,9.744 MiB), 8.350% Total 62 objects,64.344 GiB, Copy... done:(26 files,9.744 MiB), 9.932%, avg 2.776 GiB/s Total 62 objects,64.344 GiB, Copy... done:(26 files,9.744 MiB), 12.512%, avg 2.885 GiB/s Total 62 objects,64.344 GiB, Copy... done:(26 files,9.744 MiB), 15.225%, avg 2.986 GiB/s Total 62 objects,64.344 GiB, Copy... done:(26 files,9.744 MiB), 17.098%, avg 2.904 GiB/s Total 62 objects,64.344 GiB, Copy... done:(26 files,9.744 MiB), 19.672%, avg 2.955 GiB/s Total 62 objects,64.344 GiB, Copy... done:(26 files,9.744 MiB), 21.624%, avg 2.905 GiB/s Total 62 objects,64.344 GiB, Copy... done:(27 files,755.831 MiB), 23.518%, avg 2.859 GiB/s Total 62 objects,64.344 GiB, Copy... done:(27 files,755.831 MiB), 26.195%, avg 2.912 GiB/s Total 62 objects,64.344 GiB, Copy... done:(27 files,755.831 MiB), 28.776%, avg 2.946 GiB/s Total 62 objects,64.344 GiB, Copy... done:(27 files,755.831 MiB), 30.824%, avg 2.922 GiB/s Total 62 objects,64.344 GiB, Copy... done:(27 files,755.831 MiB), 33.244%, avg 2.936 GiB/s Total 62 objects,64.344 GiB, Copy... done:(27 files,755.831 MiB), 35.681%, avg 2.949 GiB/s Total 62 objects,64.344 GiB, Copy... done:(28 files,1.939 GiB), 37.357%, avg 2.900 GiB/s Total 62 objects,64.344 GiB, Copy... done:(37 files,9.147 GiB), 39.622%, avg 2.900 GiB/s Total 62 objects,64.344 GiB, Copy... done:(49 files,13.953 GiB), 41.139%, avg 2.847 GiB/s Total 62 objects,64.344 GiB, Copy... done:(49 files,13.953 GiB), 42.830%, avg 2.812 GiB/s Total 62 objects,64.344 GiB, Copy... done:(49 files,13.953 GiB), 44.451%, avg 2.776 GiB/s Total 62 objects,64.344 GiB, Copy... done:(49 files,13.953 GiB), 46.211%, avg 2.751 GiB/s Total 62 objects,64.344 GiB, Copy... done:(49 files,13.953 GiB), 47.468%, avg 2.700 GiB/s Total 62 objects,64.344 GiB, Copy... done:(49 files,13.953 GiB), 49.037%, avg 2.670 GiB/s Total 62 objects,64.344 GiB, Copy... done:(49 files,13.953 GiB), 50.588%, avg 2.642 GiB/s Total 62 objects,64.344 GiB, Copy... done:(49 files,13.953 GiB), 52.334%, avg 2.626 GiB/s Total 62 objects,64.344 GiB, Copy... done:(49 files,13.953 GiB), 53.782%, avg 2.597 GiB/s Total 62 objects,64.344 GiB, Copy... done:(49 files,13.953 GiB), 55.533%, avg 2.584 GiB/s Total 62 objects,64.344 GiB, Copy... done:(50 files,15.154 GiB), 56.732%, avg 2.547 GiB/s Total 62 objects,64.344 GiB, Copy... done:(50 files,15.154 GiB), 58.183%, avg 2.523 GiB/s Total 62 objects,64.344 GiB, Copy... done:(50 files,15.154 GiB), 59.746%, avg 2.506 GiB/s Total 62 objects,64.344 GiB, Copy... done:(50 files,15.154 GiB), 61.431%, avg 2.495 GiB/s Total 62 objects,64.344 GiB, Copy... done:(50 files,15.154 GiB), 62.867%, avg 2.475 GiB/s Total 62 objects,64.344 GiB, Copy... done:(51 files,16.356 GiB), 64.309%, avg 2.456 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 65.568%, avg 2.432 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 66.955%, avg 2.413 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 68.428%, avg 2.399 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 69.472%, avg 2.370 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 70.905%, avg 2.356 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 72.146%, avg 2.337 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 73.266%, avg 2.314 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 74.775%, avg 2.305 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 76.132%, avg 2.292 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 77.236%, avg 2.272 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 78.657%, avg 2.262 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 79.889%, avg 2.247 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 81.237%, avg 2.235 GiB/s Total 62 objects,64.344 GiB, Copy... done:(52 files,17.557 GiB), 82.530%, avg 2.223 GiB/s Total 62 objects,64.344 GiB, Copy... done:(54 files,22.363 GiB), 83.759%, avg 2.210 GiB/s Total 62 objects,64.344 GiB, Copy... done:(54 files,22.363 GiB), 84.782%, avg 2.192 GiB/s Total 62 objects,64.344 GiB, Copy... done:(54 files,22.363 GiB), 85.929%, avg 2.177 GiB/s Total 62 objects,64.344 GiB, Copy... done:(54 files,22.363 GiB), 86.800%, avg 2.157 GiB/s Total 62 objects,64.344 GiB, Copy... done:(54 files,22.363 GiB), 87.924%, avg 2.143 GiB/s Total 62 objects,64.344 GiB, Copy... done:(54 files,22.363 GiB), 89.025%, avg 2.129 GiB/s Total 62 objects,64.344 GiB, Copy... done:(54 files,22.363 GiB), 90.054%, avg 2.114 GiB/s Total 62 objects,64.344 GiB, Copy... done:(54 files,22.363 GiB), 91.071%, avg 2.100 GiB/s Total 62 objects,64.344 GiB, Copy... done:(54 files,22.363 GiB), 92.216%, avg 2.088 GiB/s Total 62 objects,64.344 GiB, Copy... done:(55 files,26.973 GiB), 93.213%, avg 2.074 GiB/s Total 62 objects,64.344 GiB, Copy... done:(55 files,26.973 GiB), 94.020%, avg 2.056 GiB/s Total 62 objects,64.344 GiB, Copy... done:(55 files,26.973 GiB), 95.061%, avg 2.044 GiB/s Total 62 objects,64.344 GiB, Copy... done:(55 files,26.973 GiB), 95.996%, avg 1.976 GiB/s Total 62 objects,64.344 GiB, Copy... done:(55 files,26.973 GiB), 96.995%, avg 1.964 GiB/s Total 62 objects,64.344 GiB, Copy... done:(55 files,26.973 GiB), 97.866%, avg 1.950 GiB/s Total 62 objects,64.344 GiB, Copy... done:(55 files,26.973 GiB), 98.992%, avg 1.941 GiB/s Total 62 objects,64.344 GiB, Copy... done:(58 files,42.989 GiB), 99.767%, avg 1.839 GiB/s Success: Total 62 objects, 69089286527 B, Download done:(62 files, 69089286527 B), avg 1.954 GiB/s + +32.935278(s) elapsed +=== [00:48:43] backup-260721 === + Total 3 objects,111.017 GiB, Copy... done:(0 files), 0.000% Total 3 objects,111.017 GiB, Copy... done:(0 files), 0.000% Total 3 objects,111.017 GiB, Copy... done:(0 files), 0.000% Total 3 objects,111.017 GiB, Copy... done:(0 files), 0.760% Total 3 objects,111.017 GiB, Copy... done:(0 files), 1.013%, avg 575.916 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 1.013%, avg 460.737 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 1.182%, avg 447.942 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 1.689%, avg 548.496 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 2.027%, avg 575.892 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 2.111%, avg 533.238 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 2.280%, avg 518.311 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 2.702%, avg 558.453 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 3.040%, avg 575.513 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 3.293%, avg 575.547 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 3.631%, avg 589.278 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 3.716%, avg 562.806 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 4.138%, avg 587.611 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 4.476%, avg 598.209 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 4.729%, avg 596.972 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 5.151%, avg 616.063 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 5.236%, avg 594.868 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 5.658%, avg 612.244 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 5.911%, avg 610.593 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 6.165%, avg 609.086 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 6.502%, avg 615.702 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 6.925%, avg 629.463 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 7.178%, avg 627.404 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 7.431%, avg 625.498 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 7.685%, avg 623.729 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 7.938%, avg 622.081 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 8.191%, avg 620.543 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 8.529%, avg 625.295 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 8.951%, avg 635.747 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 9.120%, avg 628.119 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 9.458%, avg 632.229 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 9.711%, avg 630.621 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 9.965%, avg 629.103 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 10.387%, avg 638.041 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 10.556%, avg 631.355 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 10.978%, avg 639.777 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 11.147%, avg 633.302 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 11.569%, avg 641.264 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 11.823%, avg 639.710 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 12.076%, avg 638.227 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 12.414%, avg 641.173 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 12.751%, avg 643.988 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 13.005%, avg 642.509 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 13.343%, avg 645.176 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 13.512%, avg 639.735 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 13.849%, avg 642.351 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 14.018%, avg 637.184 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 14.187%, avg 632.219 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 14.694%, avg 642.081 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 15.116%, avg 648.074 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 15.285%, avg 643.186 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 15.623%, avg 645.452 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 15.792%, avg 640.785 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 16.129%, avg 643.014 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 16.467%, avg 644.731 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 16.720%, avg 643.567 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 16.974%, avg 647.187 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 17.481%, avg 656.030 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 17.734%, avg 654.651 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 17.987%, avg 653.318 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 18.156%, avg 654.034 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 18.663%, avg 662.728 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 18.916%, avg 661.233 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 19.085%, avg 656.539 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 19.507%, avg 667.802 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 19.845%, avg 669.557 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 19.929%, avg 661.331 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 20.183%, avg 659.885 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 20.605%, avg 667.795 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 20.858%, avg 666.186 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 21.112%, avg 664.630 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 21.534%, avg 669.630 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 21.872%, avg 674.645 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 22.041%, avg 669.550 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 22.378%, avg 671.244 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 22.885%, avg 679.382 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 22.885%, avg 664.371 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 23.307%, avg 669.349 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 23.561%, avg 667.740 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 23.898%, avg 669.436 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 24.067%, avg 664.168 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 24.574%, avg 672.713 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 24.912%, avg 674.353 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 24.996%, avg 666.187 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 25.419%, avg 667.592 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 25.841%, avg 672.520 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 26.010%, avg 667.552 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 26.347%, avg 669.251 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 26.516%, avg 670.820 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 26.939%, avg 675.883 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 27.107%, avg 670.857 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 27.445%, avg 672.500 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 27.699%, avg 670.880 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 28.205%, avg 679.306 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 28.374%, avg 674.023 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 28.627%, avg 672.364 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 28.965%, avg 670.669 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 29.303%, avg 672.372 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 29.556%, avg 670.712 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 29.979%, avg 675.604 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 30.148%, avg 674.091 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 30.401%, avg 672.371 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 30.739%, avg 674.015 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 30.992%, avg 672.355 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 31.330%, avg 674.215 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 31.752%, avg 679.038 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 31.921%, avg 673.960 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 32.174%, avg 672.301 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 32.428%, avg 674.031 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 32.765%, avg 675.595 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 33.103%, avg 677.183 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 33.441%, avg 678.718 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 33.779%, avg 681.267 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 34.032%, avg 679.421 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 34.201%, avg 674.333 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 34.454%, avg 672.668 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 34.792%, avg 674.421 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 35.214%, avg 679.422 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 35.383%, avg 674.335 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 35.721%, avg 675.616 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 36.143%, avg 674.133 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 36.312%, avg 669.051 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 36.566%, avg 667.449 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 36.903%, avg 668.903 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 37.241%, avg 673.874 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 37.494%, avg 672.157 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 37.832%, avg 673.805 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 38.086%, avg 672.149 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 38.339%, avg 667.031 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 38.761%, avg 672.157 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 38.930%, avg 667.198 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 39.268%, avg 668.902 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 39.521%, avg 673.871 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 39.774%, avg 672.157 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 40.197%, avg 677.108 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 40.281%, avg 668.897 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 40.703%, avg 674.071 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 40.957%, avg 672.352 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 41.295%, avg 673.996 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 41.548%, avg 672.337 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 41.886%, avg 667.227 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 42.223%, avg 668.990 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 42.392%, avg 664.085 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 42.815%, avg 669.089 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 43.068%, avg 670.845 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 43.321%, avg 669.182 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 43.575%, avg 667.576 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 43.997%, avg 672.475 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 44.335%, avg 674.216 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 44.588%, avg 672.494 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 44.926%, avg 674.136 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 45.179%, avg 672.474 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 45.432%, avg 667.428 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 45.939%, avg 675.913 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 46.192%, avg 674.192 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 46.361%, avg 669.280 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 46.699%, avg 670.851 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 47.121%, avg 675.859 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 47.375%, avg 674.135 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 47.544%, avg 669.205 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 47.881%, avg 670.772 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 48.135%, avg 669.111 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 48.473%, avg 670.811 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 48.810%, avg 672.454 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 49.148%, avg 677.801 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 49.486%, avg 679.380 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 49.739%, avg 677.599 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 50.077%, avg 679.126 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 50.161%, avg 664.187 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 50.584%, avg 669.368 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 50.921%, avg 671.065 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 51.175%, avg 669.455 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 51.513%, avg 677.883 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 51.850%, avg 679.460 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 51.935%, avg 671.066 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 52.188%, avg 669.455 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 52.610%, avg 667.927 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 52.948%, avg 669.679 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 53.202%, avg 668.041 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 53.539%, avg 669.565 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 53.708%, avg 667.978 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 53.962%, avg 666.363 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 54.299%, avg 668.113 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 54.553%, avg 666.551 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 54.806%, avg 667.931 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 55.228%, avg 673.023 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 55.566%, avg 674.657 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 55.735%, avg 669.734 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 55.988%, avg 667.905 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 56.411%, avg 673.023 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 56.579%, avg 668.043 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 57.002%, avg 672.904 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 57.171%, avg 667.825 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 57.593%, avg 672.942 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 57.762%, avg 667.964 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 58.015%, avg 666.405 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 58.437%, avg 671.247 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 58.691%, avg 669.575 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 59.028%, avg 671.269 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 59.282%, avg 669.655 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 59.620%, avg 671.247 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 59.873%, avg 669.571 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 60.126%, avg 667.958 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 60.548%, avg 672.901 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 60.717%, avg 664.443 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 61.055%, avg 666.257 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 61.308%, avg 664.701 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 61.562%, avg 663.084 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 61.900%, avg 667.902 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 62.153%, avg 666.290 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 62.491%, avg 668.040 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 62.744%, avg 666.450 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 63.082%, avg 664.445 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 63.420%, avg 666.258 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 63.842%, avg 671.316 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 63.926%, avg 663.199 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 64.264%, avg 664.524 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 64.602%, avg 666.335 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 64.855%, avg 664.778 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 65.109%, avg 663.273 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 65.446%, avg 661.067 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 65.784%, avg 662.734 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 65.953%, avg 657.933 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 66.291%, avg 659.795 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 66.629%, avg 667.709 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 66.882%, avg 666.100 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 67.135%, avg 664.520 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 67.473%, avg 666.187 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 67.895%, avg 664.169 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 68.064%, avg 659.259 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 68.318%, avg 657.824 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 68.655%, avg 659.688 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 68.909%, avg 660.744 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 69.331%, avg 665.986 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 69.500%, avg 660.846 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 69.922%, avg 665.905 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 70.260%, avg 670.931 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 70.513%, avg 669.267 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 70.851%, avg 670.964 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 71.273%, avg 675.854 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 71.358%, avg 670.979 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 71.611%, avg 669.313 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 71.949%, avg 671.011 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 72.371%, avg 675.900 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 72.624%, avg 674.428 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 72.962%, avg 676.056 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 73.300%, avg 677.637 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 73.384%, avg 669.416 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 73.975%, avg 681.352 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 74.144%, avg 676.141 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 74.313%, avg 671.109 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 74.651%, avg 672.748 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 74.989%, avg 671.084 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 75.242%, avg 669.416 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 75.411%, avg 664.501 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 75.918%, avg 672.752 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 76.256%, avg 674.509 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 76.424%, avg 669.418 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 76.593%, avg 664.502 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 77.016%, avg 669.502 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 77.353%, avg 674.514 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 77.522%, avg 669.423 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 77.944%, avg 674.425 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 78.198%, avg 672.752 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 78.451%, avg 671.141 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 78.705%, avg 669.472 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 78.958%, avg 667.861 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 79.380%, avg 672.805 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 79.633%, avg 671.173 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 79.971%, avg 672.844 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 80.309%, avg 674.481 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 80.478%, avg 669.563 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 80.900%, avg 674.573 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 81.153%, avg 672.837 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 81.491%, avg 674.474 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 81.660%, avg 669.556 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 81.998%, avg 671.174 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 82.420%, avg 676.181 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 82.589%, avg 671.148 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 82.758%, avg 666.286 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 83.011%, avg 664.483 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 83.434%, avg 669.661 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 83.687%, avg 667.958 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 83.856%, avg 663.149 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 84.278%, avg 664.507 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 84.616%, avg 666.320 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 84.869%, avg 664.762 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 85.291%, avg 669.760 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 85.629%, avg 678.208 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 85.714%, avg 669.684 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 86.051%, avg 671.375 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 86.474%, avg 676.262 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 86.643%, avg 664.802 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 86.896%, avg 663.243 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 87.234%, avg 665.047 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 87.656%, avg 669.988 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 87.825%, avg 668.172 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 88.163%, avg 669.745 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 88.585%, avg 674.744 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 88.669%, avg 666.552 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 88.923%, avg 661.128 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 89.345%, avg 666.365 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 89.683%, avg 668.114 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 89.936%, avg 666.552 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 90.274%, avg 661.137 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 90.696%, avg 666.375 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 90.696%, avg 654.894 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 91.034%, avg 656.808 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 91.287%, avg 661.137 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 91.709%, avg 666.373 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 91.963%, avg 664.815 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 92.216%, avg 663.238 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 92.554%, avg 661.033 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 92.723%, avg 656.177 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 93.060%, avg 658.101 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 93.314%, avg 656.710 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 93.736%, avg 664.458 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 93.989%, avg 662.906 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 94.243%, avg 661.221 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 94.749%, avg 669.410 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 95.003%, avg 671.058 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 95.172%, avg 666.027 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 95.341%, avg 661.170 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 95.763%, avg 666.207 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 96.016%, avg 664.191 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 96.269%, avg 662.644 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 96.692%, avg 667.733 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 96.945%, avg 666.179 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 97.283%, avg 664.184 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 97.536%, avg 662.637 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 97.789%, avg 661.144 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 98.212%, avg 666.101 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 98.296%, avg 660.662 MiB/s Total 3 objects,111.017 GiB, Copy... done:(0 files), 98.803%, avg 669.261 MiB/s Total 3 objects,111.017 GiB, Copy... done:(1 files,37.006 GiB), 99.117%, avg 670.052 MiB/s Total 3 objects,111.017 GiB, Copy... done:(1 files,37.006 GiB), 99.286%, avg 665.210 MiB/s Total 3 objects,111.017 GiB, Copy... done:(1 files,37.006 GiB), 99.455%, avg 666.614 MiB/s Total 3 objects,111.017 GiB, Copy... done:(1 files,37.006 GiB), 99.854%, avg 670.810 MiB/s Success: Total 3 objects, 119203084740 B, Download done:(3 files, 119203084740 B), avg 663.031 MiB/s + +171.460470(s) elapsed +=== [00:51:35] backup-260729 === + Total 3 objects,18.295 GiB, Copy... done:(0 files), 0.347% Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 1.372% Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 2.396% Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 3.677% Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 4.830%, avg 419.932 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 6.496%, avg 459.806 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 7.521%, avg 446.754 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 8.546%, avg 437.802 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 9.570%, avg 430.891 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 10.595%, avg 424.279 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 11.620%, avg 420.265 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 12.901%, avg 425.665 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 14.438%, avg 438.116 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 15.591%, avg 437.644 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 16.616%, avg 433.811 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 17.641%, avg 430.499 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 18.666%, avg 427.439 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 19.947%, avg 430.007 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 20.972%, avg 427.365 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 21.997%, avg 424.482 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 23.278%, avg 427.239 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 24.303%, avg 425.189 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 25.328%, avg 423.285 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 26.737%, avg 427.810 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 28.018%, avg 429.710 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 29.043%, avg 427.753 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 30.196%, avg 427.856 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 31.220%, avg 426.239 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 32.373%, avg 426.337 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 33.398%, avg 424.838 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 34.807%, avg 427.691 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 36.217%, avg 430.733 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 37.242%, avg 429.280 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 38.266%, avg 427.914 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 39.291%, avg 426.605 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 40.316%, avg 425.394 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 41.469%, avg 425.575 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 42.622%, avg 425.746 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 43.647%, avg 424.652 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 44.800%, avg 424.838 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 46.081%, avg 426.208 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 47.234%, avg 426.342 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 48.515%, avg 427.612 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 49.668%, avg 427.584 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 50.949%, avg 428.682 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 52.102%, avg 428.754 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 53.127%, avg 427.785 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 54.408%, avg 428.741 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 55.305%, avg 426.818 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 56.586%, avg 427.896 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 57.739%, avg 427.977 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 58.379%, avg 424.308 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 59.660%, avg 425.361 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 60.941%, avg 426.383 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 61.966%, avg 425.527 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 63.503%, avg 428.249 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 64.400%, avg 426.608 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 65.169%, avg 424.186 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 66.322%, avg 424.320 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 67.731%, avg 426.068 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 68.500%, avg 424.049 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 69.653%, avg 424.186 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 70.806%, avg 424.313 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 71.702%, avg 422.823 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 72.599%, avg 419.765 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 73.624%, avg 419.139 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 74.777%, avg 419.359 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 76.186%, avg 421.191 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 77.339%, avg 419.135 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 78.364%, avg 418.519 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 79.645%, avg 419.566 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 80.798%, avg 419.775 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 81.823%, avg 420.884 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 82.976%, avg 421.078 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 84.385%, avg 422.785 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 85.282%, avg 421.319 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 86.435%, avg 421.137 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 87.459%, avg 420.487 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 88.869%, avg 422.333 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 89.894%, avg 421.682 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 90.918%, avg 419.552 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 91.943%, avg 418.929 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 92.968%, avg 418.327 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 94.249%, avg 419.369 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 95.402%, avg 420.543 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 96.683%, avg 421.581 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 97.836%, avg 421.759 MiB/s Total 3 objects,18.295 GiB, Copy... done:(1 files,40.948 MiB), 99.117%, avg 422.743 MiB/s Success: Total 3 objects, 19644369512 B, Download done:(3 files, 19644369512 B), avg 421.183 MiB/s + +44.484081(s) elapsed +END 2026-08-01T00:52:19+08:00 diff --git a/.download.log b/.download.log new file mode 100644 index 0000000000000000000000000000000000000000..8e7da18d3ebaef7068ae13b0cfd3d32793f33ab4 --- /dev/null +++ b/.download.log @@ -0,0 +1,2 @@ +START 2026-08-01T00:40:47+08:00 + Scanning... found:(3000 objects,86.847 GiB), Copy... done:(43 files,37.799 MiB) Scanning... found:(10000 objects,188.180 GiB), Copy... done:(121 files,212.016 MiB) Scanning... found:(16000 objects,375.395 GiB), Copy... done:(206 files,433.456 MiB) Scanning... found:(24000 objects,671.465 GiB), Copy... done:(286 files,700.972 MiB) Estimated 29137 objects,1.207 TiB, Copy... done:(357 files,982.329 MiB), 0.221%, avg 1.343 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(446 files,1.178 GiB), 0.280%, avg 1.365 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(542 files,1.312 GiB), 0.329%, avg 1.341 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(578 files,1.386 GiB), 0.417%, avg 1.459 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(613 files,1.444 GiB), 0.526%, avg 1.612 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(641 files,1.488 GiB), 0.610%, avg 1.665 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(679 files,1.540 GiB), 0.685%, avg 1.683 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(712 files,3.071 GiB), 0.807%, avg 1.803 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(759 files,3.169 GiB), 0.906%, avg 1.856 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(802 files,3.252 GiB), 0.987%, avg 1.868 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(845 files,3.392 GiB), 1.086%, avg 1.908 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(898 files,3.487 GiB), 1.179%, avg 1.934 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(941 files,3.568 GiB), 1.270%, avg 1.953 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1011 files,5.869 GiB), 1.362%, avg 1.973 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1077 files,6.007 GiB), 1.452%, avg 1.986 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1130 files,6.125 GiB), 1.513%, avg 1.961 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1208 files,6.270 GiB), 1.605%, avg 1.976 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1267 files,6.446 GiB), 1.685%, avg 1.976 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1333 files,6.592 GiB), 1.763%, avg 1.974 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1397 files,6.745 GiB), 1.857%, avg 1.989 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1458 files,6.892 GiB), 1.951%, avg 2.003 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1512 files,7.024 GiB), 2.026%, avg 1.997 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1583 files,7.198 GiB), 2.118%, avg 2.008 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1641 files,7.322 GiB), 2.190%, avg 1.999 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1685 files,7.411 GiB), 2.274%, avg 2.002 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1745 files,7.537 GiB), 2.365%, avg 2.011 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1812 files,7.684 GiB), 2.440%, avg 2.005 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1873 files,7.831 GiB), 2.516%, avg 2.001 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(1935 files,8.014 GiB), 2.601%, avg 2.004 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2031 files,8.099 GiB), 2.687%, avg 2.007 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2090 files,8.211 GiB), 2.756%, avg 1.998 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2176 files,8.318 GiB), 2.845%, avg 2.004 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2207 files,8.339 GiB), 2.926%, avg 2.004 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2249 files,8.378 GiB), 2.983%, avg 1.988 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2335 files,8.446 GiB), 3.067%, avg 1.991 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2419 files,8.508 GiB), 3.141%, avg 1.986 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2477 files,8.627 GiB), 3.214%, avg 1.982 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2527 files,8.896 GiB), 3.319%, avg 1.996 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2568 files,9.166 GiB), 3.417%, avg 2.006 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2607 files,9.351 GiB), 3.485%, avg 1.999 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2652 files,9.568 GiB), 3.576%, avg 2.005 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2689 files,9.800 GiB), 3.661%, avg 2.007 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2755 files,10.042 GiB), 3.762%, avg 2.017 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2821 files,10.171 GiB), 3.852%, avg 2.022 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2886 files,10.278 GiB), 3.913%, avg 2.011 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(2972 files,10.379 GiB), 3.983%, avg 2.005 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3020 files,10.511 GiB), 4.059%, avg 2.002 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3075 files,10.778 GiB), 4.163%, avg 2.013 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3115 files,10.981 GiB), 4.243%, avg 2.013 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3156 files,11.272 GiB), 4.344%, avg 2.022 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3220 files,11.469 GiB), 4.436%, avg 2.027 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3273 files,11.575 GiB), 4.487%, avg 2.013 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3353 files,11.710 GiB), 4.580%, avg 2.018 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3426 files,11.818 GiB), 4.666%, avg 2.020 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3483 files,12.009 GiB), 4.752%, avg 2.021 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3535 files,16.877 GiB), 4.845%, avg 2.026 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3580 files,21.805 GiB), 4.940%, avg 2.081 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3637 files,21.973 GiB), 4.994%, avg 2.067 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3721 files,22.145 GiB), 5.075%, avg 2.066 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3815 files,26.882 GiB), 5.138%, avg 2.058 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3897 files,31.686 GiB), 5.203%, avg 2.062 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(3970 files,36.662 GiB), 5.282%, avg 2.060 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4054 files,37.089 GiB), 5.358%, avg 2.057 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4146 files,37.275 GiB), 5.406%, avg 2.043 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4270 files,37.458 GiB), 5.463%, avg 2.009 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4347 files,37.730 GiB), 5.527%, avg 2.002 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4411 files,38.120 GiB), 5.604%, avg 2.000 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4499 files,38.426 GiB), 5.667%, avg 1.993 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4597 files,38.617 GiB), 5.709%, avg 1.958 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4714 files,38.797 GiB), 5.768%, avg 1.949 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4782 files,38.905 GiB), 5.831%, avg 1.942 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4881 files,39.094 GiB), 5.911%, avg 1.943 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(4964 files,39.191 GiB), 5.965%, avg 1.923 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5058 files,39.801 GiB), 6.037%, avg 1.920 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5106 files,40.167 GiB), 6.141%, avg 1.931 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5156 files,40.283 GiB), 6.248%, avg 1.944 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5192 files,40.420 GiB), 6.328%, avg 1.930 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5252 files,40.821 GiB), 6.437%, avg 1.944 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5288 files,41.003 GiB), 6.516%, avg 1.944 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5348 files,41.100 GiB), 6.620%, avg 1.954 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5376 files,41.266 GiB), 6.720%, avg 1.961 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5421 files,41.391 GiB), 6.793%, avg 1.958 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5477 files,41.526 GiB), 6.898%, avg 1.969 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5527 files,42.389 GiB), 6.983%, avg 1.971 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5589 files,43.168 GiB), 7.082%, avg 1.977 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5598 files,43.180 GiB), 7.215%, avg 2.000 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5606 files,43.200 GiB), 7.330%, avg 2.014 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5612 files,43.212 GiB), 7.445%, avg 2.028 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5628 files,44.250 GiB), 7.574%, avg 2.050 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5642 files,44.277 GiB), 7.698%, avg 2.068 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5656 files,44.296 GiB), 7.768%, avg 2.062 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5671 files,44.319 GiB), 7.912%, avg 2.087 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5686 files,44.344 GiB), 8.059%, avg 2.137 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5698 files,44.363 GiB), 8.151%, avg 2.139 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5716 files,44.387 GiB), 8.266%, avg 2.151 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5734 files,45.871 GiB), 8.368%, avg 2.157 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5761 files,46.660 GiB), 8.486%, avg 2.166 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5795 files,46.736 GiB), 8.615%, avg 2.184 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5831 files,46.793 GiB), 8.747%, avg 2.202 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5857 files,46.865 GiB), 8.794%, avg 2.185 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5886 files,46.966 GiB), 8.920%, avg 2.209 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5919 files,47.027 GiB), 9.029%, avg 2.217 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5949 files,47.080 GiB), 9.122%, avg 2.219 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(5988 files,47.153 GiB), 9.250%, avg 2.234 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6025 files,47.214 GiB), 9.368%, avg 2.261 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6054 files,47.267 GiB), 9.442%, avg 2.253 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6092 files,47.339 GiB), 9.573%, avg 2.270 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6123 files,47.412 GiB), 9.656%, avg 2.266 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6156 files,47.487 GiB), 9.784%, avg 2.296 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6196 files,47.573 GiB), 9.914%, avg 2.312 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6232 files,58.329 GiB), 9.988%, avg 2.303 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6277 files,58.424 GiB), 10.104%, avg 2.313 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6314 files,58.529 GiB), 10.201%, avg 2.321 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6364 files,69.318 GiB), 10.328%, avg 2.335 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6400 files,69.397 GiB), 10.408%, avg 2.329 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6453 files,69.519 GiB), 10.524%, avg 2.338 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6510 files,80.341 GiB), 10.625%, avg 2.392 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6548 files,91.110 GiB), 10.706%, avg 2.385 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6611 files,91.266 GiB), 10.810%, avg 2.388 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6664 files,91.380 GiB), 10.883%, avg 2.378 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6737 files,102.206 GiB), 10.975%, avg 2.432 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6809 files,102.362 GiB), 11.067%, avg 2.429 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6873 files,102.504 GiB), 11.159%, avg 2.426 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(6936 files,102.657 GiB), 11.237%, avg 2.417 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7010 files,102.825 GiB), 11.324%, avg 2.477 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7095 files,102.910 GiB), 11.393%, avg 2.463 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7163 files,103.051 GiB), 11.479%, avg 2.458 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7226 files,103.111 GiB), 11.563%, avg 2.451 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7323 files,103.196 GiB), 11.647%, avg 2.506 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7404 files,103.251 GiB), 11.716%, avg 2.492 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7497 files,103.343 GiB), 11.805%, avg 2.487 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7549 files,108.130 GiB), 11.881%, avg 2.477 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7596 files,113.033 GiB), 11.958%, avg 2.483 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7643 files,113.287 GiB), 12.042%, avg 2.476 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7691 files,113.501 GiB), 12.117%, avg 2.465 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7746 files,113.843 GiB), 12.207%, avg 2.462 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7811 files,114.062 GiB), 12.270%, avg 2.448 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7894 files,114.236 GiB), 12.348%, avg 2.439 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(7982 files,114.333 GiB), 12.412%, avg 2.424 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8063 files,114.480 GiB), 12.489%, avg 2.415 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8119 files,114.766 GiB), 12.572%, avg 2.421 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8171 files,115.014 GiB), 12.664%, avg 2.419 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8219 files,119.970 GiB), 12.731%, avg 2.406 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8316 files,124.784 GiB), 12.796%, avg 2.392 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8413 files,129.559 GiB), 12.853%, avg 2.328 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8514 files,129.734 GiB), 12.914%, avg 2.314 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8589 files,130.112 GiB), 12.984%, avg 2.304 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8660 files,130.546 GiB), 13.059%, avg 2.296 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8751 files,130.717 GiB), 13.106%, avg 2.227 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8866 files,130.894 GiB), 13.165%, avg 2.213 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(8948 files,131.092 GiB), 13.220%, avg 2.198 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9013 files,131.429 GiB), 13.294%, avg 2.192 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9032 files,131.579 GiB), 13.349%, avg 2.145 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9087 files,131.853 GiB), 13.408%, avg 2.133 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9176 files,132.019 GiB), 13.456%, avg 2.117 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9301 files,132.211 GiB), 13.525%, avg 2.110 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9382 files,132.450 GiB), 13.575%, avg 2.053 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9449 files,132.849 GiB), 13.657%, avg 2.053 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9529 files,133.184 GiB), 13.728%, avg 2.048 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9630 files,133.389 GiB), 13.778%, avg 2.034 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9762 files,133.587 GiB), 13.839%, avg 1.972 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9841 files,133.718 GiB), 13.898%, avg 1.963 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(9925 files,133.879 GiB), 13.958%, avg 1.955 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(10027 files,134.000 GiB), 14.028%, avg 1.951 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(10142 files,134.305 GiB), 14.097%, avg 1.902 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(10374 files,134.889 GiB), 14.153%, avg 1.893 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(10637 files,134.891 GiB), 14.205%, avg 1.883 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(10902 files,134.893 GiB), 14.250%, avg 1.869 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(11192 files,135.185 GiB), 14.294%, avg 1.806 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(11525 files,135.187 GiB), 14.350%, avg 1.798 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(11740 files,135.258 GiB), 14.402%, avg 1.789 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(11912 files,135.264 GiB), 14.460%, avg 1.783 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(11940 files,135.316 GiB), 14.555%, avg 1.733 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(11976 files,135.388 GiB), 14.675%, avg 1.755 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12013 files,135.454 GiB), 14.725%, avg 1.746 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12041 files,135.483 GiB), 14.848%, avg 1.768 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12041 files,135.483 GiB), 14.996%, avg 1.774 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12041 files,135.483 GiB), 15.112%, avg 1.793 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12041 files,135.483 GiB), 15.234%, avg 1.814 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12043 files,136.496 GiB), 15.347%, avg 1.831 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12043 files,136.496 GiB), 15.463%, avg 1.825 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12047 files,137.224 GiB), 15.611%, avg 1.858 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12058 files,138.685 GiB), 15.734%, avg 1.878 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12058 files,138.685 GiB), 15.829%, avg 1.886 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12058 files,138.685 GiB), 15.967%, avg 1.906 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12058 files,138.685 GiB), 16.062%, avg 1.913 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12064 files,139.414 GiB), 16.180%, avg 1.931 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12079 files,140.168 GiB), 16.325%, avg 1.959 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12093 files,140.203 GiB), 16.462%, avg 1.987 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12105 files,150.904 GiB), 16.540%, avg 1.986 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12126 files,150.948 GiB), 16.683%, avg 2.013 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12147 files,150.985 GiB), 16.759%, avg 2.010 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12171 files,151.023 GiB), 16.901%, avg 2.043 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12200 files,162.477 GiB), 17.030%, avg 2.063 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12240 files,162.548 GiB), 17.155%, avg 2.080 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12265 files,162.590 GiB), 17.222%, avg 2.073 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12306 files,162.660 GiB), 17.343%, avg 2.105 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12334 files,162.712 GiB), 17.455%, avg 2.116 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12372 files,173.452 GiB), 17.551%, avg 2.121 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12414 files,173.528 GiB), 17.674%, avg 2.136 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12467 files,184.293 GiB), 17.788%, avg 2.177 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12508 files,184.398 GiB), 17.848%, avg 2.165 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12552 files,184.530 GiB), 17.968%, avg 2.179 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12605 files,195.319 GiB), 18.060%, avg 2.180 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12656 files,195.406 GiB), 18.126%, avg 2.214 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12726 files,195.517 GiB), 18.228%, avg 2.220 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12784 files,195.587 GiB), 18.326%, avg 2.223 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12802 files,195.609 GiB), 18.458%, avg 2.241 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12814 files,195.636 GiB), 18.556%, avg 2.297 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12831 files,195.657 GiB), 18.673%, avg 2.307 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12847 files,195.674 GiB), 18.790%, avg 2.317 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12864 files,195.696 GiB), 18.933%, avg 2.338 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12875 files,195.751 GiB), 19.076%, avg 2.427 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12883 files,195.789 GiB), 19.158%, avg 2.420 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12893 files,195.837 GiB), 19.296%, avg 2.437 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12902 files,195.887 GiB), 19.399%, avg 2.438 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12913 files,195.905 GiB), 19.545%, avg 2.517 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12939 files,197.401 GiB), 19.672%, avg 2.528 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12955 files,197.428 GiB), 19.773%, avg 2.527 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12979 files,197.484 GiB), 19.894%, avg 2.535 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(12995 files,197.581 GiB), 19.989%, avg 2.599 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13018 files,197.672 GiB), 20.122%, avg 2.611 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13046 files,197.720 GiB), 20.211%, avg 2.604 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13077 files,197.764 GiB), 20.339%, avg 2.613 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13092 files,197.858 GiB), 20.433%, avg 2.708 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13108 files,197.956 GiB), 20.541%, avg 2.707 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13134 files,198.026 GiB), 20.673%, avg 2.717 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13162 files,198.071 GiB), 20.770%, avg 2.711 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13188 files,198.118 GiB), 20.864%, avg 2.784 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13207 files,198.206 GiB), 21.003%, avg 2.795 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13236 files,202.900 GiB), 21.117%, avg 2.795 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13281 files,212.174 GiB), 21.212%, avg 2.787 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13309 files,212.198 GiB), 21.338%, avg 2.798 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13316 files,212.210 GiB), 21.421%, avg 2.785 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13317 files,216.821 GiB), 21.570%, avg 2.800 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13317 files,216.821 GiB), 21.714%, avg 2.813 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13319 files,221.431 GiB), 21.827%, avg 2.808 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13319 files,221.431 GiB), 21.975%, avg 2.823 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13319 files,221.431 GiB), 22.088%, avg 2.822 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13319 files,221.431 GiB), 22.242%, avg 2.839 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13319 files,221.431 GiB), 22.346%, avg 2.814 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13319 files,221.431 GiB), 22.501%, avg 2.832 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13323 files,226.770 GiB), 22.588%, avg 2.820 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13339 files,227.521 GiB), 22.729%, avg 2.831 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13353 files,227.540 GiB), 22.845%, avg 2.816 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13374 files,228.294 GiB), 22.965%, avg 2.818 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13394 files,228.321 GiB), 23.049%, avg 2.805 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13424 files,228.362 GiB), 23.189%, avg 2.817 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13448 files,228.394 GiB), 23.330%, avg 2.836 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13468 files,228.448 GiB), 23.448%, avg 2.838 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13486 files,228.519 GiB), 23.587%, avg 2.848 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13504 files,229.197 GiB), 23.676%, avg 2.837 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13526 files,229.291 GiB), 23.792%, avg 2.845 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13548 files,229.379 GiB), 23.936%, avg 2.857 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13568 files,229.462 GiB), 24.071%, avg 2.866 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13595 files,229.531 GiB), 24.148%, avg 2.849 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13625 files,229.582 GiB), 24.277%, avg 2.863 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13652 files,229.623 GiB), 24.376%, avg 2.855 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13682 files,229.672 GiB), 24.510%, avg 2.863 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13713 files,229.722 GiB), 24.628%, avg 2.864 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13733 files,229.768 GiB), 24.716%, avg 2.907 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13761 files,229.832 GiB), 24.847%, avg 2.913 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13791 files,229.885 GiB), 24.974%, avg 2.917 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13822 files,229.947 GiB), 25.101%, avg 2.921 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13841 files,230.014 GiB), 25.162%, avg 2.914 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13868 files,230.094 GiB), 25.303%, avg 2.924 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13910 files,239.388 GiB), 25.422%, avg 2.925 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13950 files,239.457 GiB), 25.507%, avg 2.910 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(13993 files,239.532 GiB), 25.620%, avg 2.887 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14033 files,239.606 GiB), 25.715%, avg 2.877 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14080 files,239.700 GiB), 25.815%, avg 2.871 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14122 files,239.798 GiB), 25.942%, avg 2.875 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14134 files,239.824 GiB), 26.069%, avg 2.878 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14160 files,239.880 GiB), 26.147%, avg 2.862 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14210 files,239.988 GiB), 26.269%, avg 2.864 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14251 files,240.074 GiB), 26.354%, avg 2.851 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14283 files,240.169 GiB), 26.488%, avg 2.867 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14327 files,240.272 GiB), 26.611%, avg 2.870 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14364 files,240.351 GiB), 26.696%, avg 2.857 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14397 files,240.429 GiB), 26.808%, avg 2.855 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14434 files,240.513 GiB), 26.924%, avg 2.863 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14479 files,240.627 GiB), 27.043%, avg 2.865 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14514 files,240.714 GiB), 27.130%, avg 2.853 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14552 files,240.808 GiB), 27.260%, avg 2.859 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14597 files,240.915 GiB), 27.376%, avg 2.872 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14626 files,240.978 GiB), 27.452%, avg 2.855 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14667 files,241.066 GiB), 27.569%, avg 2.856 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14709 files,241.150 GiB), 27.665%, avg 2.847 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14753 files,241.242 GiB), 27.784%, avg 2.844 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14796 files,241.337 GiB), 27.906%, avg 2.847 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14839 files,256.717 GiB), 28.016%, avg 2.845 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14890 files,261.448 GiB), 28.078%, avg 2.823 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(14950 files,276.918 GiB), 28.183%, avg 2.804 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15051 files,287.691 GiB), 28.253%, avg 2.785 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15133 files,287.842 GiB), 28.336%, avg 2.773 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15238 files,287.940 GiB), 28.417%, avg 2.759 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15353 files,298.717 GiB), 28.485%, avg 2.709 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15446 files,298.784 GiB), 28.533%, avg 2.682 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15523 files,309.656 GiB), 28.607%, avg 2.667 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15578 files,309.992 GiB), 28.675%, avg 2.650 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15648 files,310.363 GiB), 28.755%, avg 2.608 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15716 files,321.415 GiB), 28.837%, avg 2.597 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15806 files,321.715 GiB), 28.895%, avg 2.577 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(15923 files,321.918 GiB), 28.947%, avg 2.555 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16036 files,322.078 GiB), 29.008%, avg 2.505 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16111 files,322.440 GiB), 29.073%, avg 2.489 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16170 files,322.795 GiB), 29.138%, avg 2.474 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16252 files,323.106 GiB), 29.206%, avg 2.460 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16369 files,323.327 GiB), 29.265%, avg 2.414 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16478 files,323.496 GiB), 29.325%, avg 2.398 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16542 files,323.773 GiB), 29.383%, avg 2.381 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16606 files,324.202 GiB), 29.462%, avg 2.374 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16704 files,324.455 GiB), 29.519%, avg 2.313 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16800 files,324.634 GiB), 29.578%, avg 2.298 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16897 files,324.783 GiB), 29.633%, avg 2.281 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(16967 files,325.096 GiB), 29.690%, avg 2.267 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17024 files,325.485 GiB), 29.765%, avg 2.228 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17125 files,325.752 GiB), 29.819%, avg 2.212 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17226 files,325.937 GiB), 29.875%, avg 2.197 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17337 files,326.141 GiB), 29.938%, avg 2.187 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17401 files,326.475 GiB), 30.009%, avg 2.138 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17459 files,326.856 GiB), 30.080%, avg 2.132 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17569 files,327.103 GiB), 30.142%, avg 2.121 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17672 files,327.260 GiB), 30.184%, avg 2.103 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17773 files,338.114 GiB), 30.250%, avg 2.042 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17864 files,348.968 GiB), 30.311%, avg 2.033 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(17950 files,349.074 GiB), 30.360%, avg 2.019 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18087 files,349.456 GiB), 30.419%, avg 2.009 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18127 files,350.054 GiB), 30.511%, avg 1.960 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18160 files,350.092 GiB), 30.630%, avg 1.977 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18189 files,350.127 GiB), 30.690%, avg 1.968 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 30.812%, avg 1.986 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 30.945%, avg 1.966 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 31.070%, avg 1.986 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 31.191%, avg 2.003 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 31.322%, avg 2.024 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 31.421%, avg 1.984 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 31.574%, avg 2.016 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 31.710%, avg 2.039 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 31.793%, avg 2.039 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 31.947%, avg 2.017 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18223 files,350.456 GiB), 32.042%, avg 2.023 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18224 files,351.469 GiB), 32.197%, avg 2.054 GiB/s Estimated 29137 objects,1.207 TiB, Copy... done:(18224 files,351.469 GiB), 32.326%, avg 2.073 GiB/s Estimated 29137 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b/backup-0727/output/0224_stage4_self_rollout_dmd/backup/checkpoint-5900/checkpoint-5900/config.json @@ -0,0 +1,356 @@ +{ + "_gradient_checkpointing": true, + "ar_state_text_system_prompt": "You are a helpful assistant, capable of perceiving auditory and textual inputs, as well as generating text responses.", + "ar_target_debug": true, + "ar_target_do_sample": true, + "ar_target_max_new_tokens": 64, + "ar_target_temperature": 0.9, + "ar_target_top_k": 50, + "ar_target_top_p": 0.9, + "ar_target_use_v2": true, + "architectures": [ + "Stage4DMDSelfRolloutMetaQuery" + ], + "attn_implementation": "auto", + "audio_context_noise": 0.5, + "audio_lora_dict": { + "adapter_name": "s2_ovi_audio_lora", + "enable": true, + "lora_dropout": 0.0, + "network_alpha": 128, + "rank": 128, + "target_modules": [ + "q", + "k", + "v", + "o", + "ffn.0", + "ffn.2" + ] + }, + "audio_loss_weight": 0.5, + "audio_negative_prompt": "robotic, muffled, echo, distorted", + "audio_vae_path": "./pretrained_models/MMAudio/ext_weights/", + "block_causal": { + "a2a_lookahead": -1, + "attention_backend": "sdpa", + "audio_kv_cache_size": 4, + "enable": true, + "frame_per_block": 3, + "teacher_forcing": true, + "v2a_lookahead": 1, + "video_kv_cache_size": 4 + }, + "bsmntw_weighting": true, + "connector_num_hidden_layers": 8, + "diffusion_forcing": { + "enable": false, + "prefix_only": false, + "prob": 0.9, + "start_step": 0, + "temp_chunk": 3 + }, + "diffusion_model_id": "Lightricks/LTX-Video", + "diffusion_model_path": "./pretrained_models/Ovi/model_960x960_10s.safetensors", + "distillation": {}, + "dmd": { + "audio_loss_weight": 0.5, + "critic_audio_loss_weight": 0.5, + "critic_video_loss_weight": 1.0, + "denoising_step_lists": { + "1": [ + 1000 + ], + "2": [ + 1000, + 500 + ], + "3": [ + 1000, + 750, + 500 + ], + "4": [ + 1000, + 750, + 500, + 250 + ] + }, + "denoising_step_lists_first_chunk": { + "1": [ + 1000, + 750, + 500, + 250 + ], + "2": [ + 1000, + 750, + 500, + 250 + ], + "3": [ + 1000, + 750, + 500, + 250 + ], + "4": [ + 1000, + 750, + 500, + 250 + ] + }, + "detach_fusion_audio": false, + "detach_fusion_video": false, + "detach_mllm_mq": true, + "ema_start_step": 200, + "ema_weight": 0.99, + "fake_guidance_scale": 0.0, + "fake_score_learning_rate": 5e-06, + "fake_score_update_interval": 5, + "flowmatch_shift": 5.0, + "gan_audio_start_step": 0, + "gan_critic_checkpoint_blocks": 30, + "gan_feature_depths": [ + 0.1, + 0.5, + 0.9 + ], + "gan_loss_weight_audio": 0.0, + "gan_loss_weight_video": 0.0, + "gan_t_max": 1000, + "gan_t_min": 0, + "gan_video_start_step": 0, + "generator_adam_beta1": 0.0, + "generator_audio_loss_weight": 0.5, + "generator_gt_audio_listening_weight_multiplier": 1.0, + "generator_gt_audio_loss_weight": 0.05, + "generator_gt_video_loss_weight": 0.0, + "generator_video_loss_weight": 1.0, + "gt_score_regularize_start_step": 93000, + "gt_score_regularize_weight": 0.5, + "lookahead_weight": 2.0, + "lookahead_weight_position": 1, + "min_score_timestep": 0, + "normalize_gradient": true, + "num_inference_steps": 2, + "num_train_timestep": 1000, + "plan_commit_consistency_enable": false, + "plan_commit_gt_weight": 1.0, + "plan_commit_loss_weight": 3.0, + "purify_draft_static_margin": 4.0, + "purify_draft_static_mask_enable": false, + "purify_draft_static_quantile": 0.2, + "purify_draft_weight": 0.0, + "purify_start_step": 0, + "purify_t_max": 100, + "purify_t_min": 0, + "purify_weight": 0.0, + "real_audio_scale": 4.5, + "real_guidance_scale": 3.5, + "real_score_gt_x0_ratio": 0.5, + "real_score_gt_x0_start_step": 36000, + "real_slg_layer": 11, + "score_flowmatch_shift": 5.0, + "trainer": "dmd_self_rollout", + "ts_schedule": false, + "ts_schedule_max": false, + "validation_score_timestep": 250, + "video_loss_weight": 1.0, + "warp_denoising_step": true + }, + "fake_score_learning_rate": 5e-06, + "fake_score_lora_checkpointing": { + "audio_adapter_name": "s4_fake_ovi_audio_lora", + "block_causal": { + "enable": false + }, + "merge_from_checkpoint": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss", + "merge_load_non_lora": true, + "resume_from_checkpoint": null, + "resume_gan_head": false, + "resume_lora_only": false, + "video_adapter_name": "s4_fake_ovi_vid_lora" + }, + "flowmatch_shift": 5.0, + "gen_fps": 25.0, + "generator_lora_checkpointing": { + "audio_adapter_name": "s2_ovi_audio_lora", + "merge_from_checkpoint": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss", + "merge_load_non_lora": true, + "prefer_ema": true, + "resume_from_checkpoint": "./output/0222_stage3_causal_cd_frame3", + "resume_lora_only": true, + "video_adapter_name": "s2_ovi_vid_lora" + }, + "has_context_prompt": { + "enable": true, + "gt_t5_transcript_ratio": 1.0, + "no_mq_context_uncond_ratio": 0.0, + "skip_mllm_encode_ratio": 0.3 + }, + "in_channels": 16, + "infer": false, + "latent_spatial_size": 28, + "latent_temporal_size": 25, + "listening_loss_weight": 1.0, + "listening_prompt": "not speaking, closed mouth, listening, silent", + "local_files_only": true, + "lora_checkpointing": { + "audio_adapter_name": "s2_ovi_audio_lora", + "merge_from_checkpoint": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss", + "merge_load_non_lora": true, + "prefer_ema": true, + "resume_from_checkpoint": "./output/0222_stage3_causal_cd_frame3", + "resume_lora_only": true, + "video_adapter_name": "s2_ovi_vid_lora" + }, + "lora_dict": {}, + "max_input_text_tokens": 256, + "mllm_ce_only": false, + "mllm_id": "./pretrained_models/Qwen2.5-Omni-7B", + "mllm_local_path": null, + "mllm_lora_dict": { + "alpha": 64, + "dropout": 0.0, + "enable": false, + "rank": 32, + "target_modules": [ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj" + ] + }, + "model_type": "stage4_dmd_self_rollout_metaquery", + "modules_to_freeze": [ + "vae_model_video", + "vae_model_audio", + "text_model", + "fusion_model", + "mllm.mllm_backbone", + "mllm.connector" + ], + "modules_to_unfreeze": [], + "negative_text_prompt": "", + "noise_scheduler_id": "Lightricks/LTX-Video", + "num_metaqueries": 128, + "ovi_gradient_checkpointing_block_count": null, + "prev_temp_chunk": 1, + "real_score_lora_checkpointing": { + "audio_adapter_name": "s4_real_ovi_audio_lora", + "block_causal": { + "enable": false + }, + "merge_from_checkpoint": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss", + "merge_load_non_lora": true, + "resume_from_checkpoint": null, + "resume_lora_only": false, + "video_adapter_name": "s4_real_ovi_vid_lora" + }, + "rollout": { + "agent_context_source": "gt", + "aud_kv_gt_ratio": 0.0, + "audio_exit_replay_grad": false, + "audio_kv_cache_size": 4, + "audio_kv_grad_blocks": 0, + "context_noise": 0, + "decode_context_min_video_latents": 12, + "enable_ode_step": false, + "exit_index_sync": "broadcast", + "extra_kv_clean_forward": false, + "kv_cache_size": 4, + "kv_shift_step": 0, + "last_step_only": false, + "listening_output_fade_samples": 1536, + "listening_silence_enable": false, + "num_windows_schedule": [ + [ + 0, + [ + [ + 1 + ], + [ + 1 + ] + ] + ], + [ + 500, + [ + [ + 1, + 2, + 3 + ], + [ + 0.4, + 0.4, + 0.2 + ] + ] + ] + ], + "real_score_bridge_use_gt": false, + "same_step_across_blocks": true, + "score_bridge_vid_latents": 5, + "share_video_noise_weight": 0, + "use_kv_cache": true, + "user_context_source": "gt", + "validation_decode": true, + "validation_max_samples": 1, + "validation_num_windows": 3, + "validation_seed": 920, + "vid_kv_gt_ratio": 0.0, + "video_kv_cache_size": 4, + "video_latents_per_window": 24, + "window_duration_sec": 3.84 + }, + "scheduler_id": "Lightricks/LTX-Video", + "stage": 4, + "stage_name": "dmd_self_rollout", + "student_checkpoint": null, + "student_lora_checkpointing": {}, + "system_prompt": "You are a speech-video clip narrator for a text-to-audio-video (T2AV) model.\nThe input includes audio-visual dialogue between the Interlocutor and the Agent. The dialogue context is provided ONLY to infer what occurs next and must NOT be described, referenced, or summarized explicitly.\nYour task is to write a narration describing the NEXT recorded speech and video clip that shows ONLY the AGENT. Follow these rules precisely:\nVISUAL RULES: - Provide a HIGH-DETAIL appearance sentence, covering: (a) Gender + precise clothing (material/color/style, e.g., \"velvet head covering with ruffled accent\"), (b) Distinctive features (eyeliner/beard/accessories), (c) Scene composition (framing/lighting/color tone/backdrop, e.g., \"stark black and white lighting against plain curtains\"). - Describe ONLY visible physical actions in strict chronological order: (a) Facial muscles (e.g., \"lips curve upward forming a subtle smile\"), (b) Eye movements (e.g., \"gaze drifts downward for two seconds\"), (c) Posture/gestures (e.g., \"thumb brushes beard thoughtfully\"). - Emotions MUST derive from observable cues: CORRECT: \"eyes crinkling at the corners in a warm smile\" (visible) WRONG: \"she feels happy\" (internal) or \"looks thoughtful\" (abstract).\nSPEECH RULES: - Determine speech occurrence based on PROVIDED TARGETS or NATURAL CONVERSATION FLOW: - IF TARGETS ARE PROVIDED: (a) If \"[Target State]: Listening\" is provided in the context, describe silent actions ONLY without any speech content and do not use tags. (b) If \"[Target Speech]: [Text]\" is provided, use the provided [Text] exactly as full speech content. - IF TARGETS ARE NOT PROVIDED: (a) If dialogue context implies continued listening or non-verbal reaction, describe silent actions ONLY without any speech content and do not use tags. (b) If dialogue context implies the agent WILL SPEAK NEXT (even after brief listening actions), generate speech. (c) When speech needs to be generated: (1) Infer the speech content EXCLUSIVELY from dialogue context. (2) The speech content must be a RESPONSE that move forward the conversation, but word overlap with the end of the agent\u2019s speech context is STRICTLY FORBIDDEN. - GENERAL SPEECH STYLE (Only for speech existing in the narration): (b) Wrap each segment in and (speech contents are broken down into multiple segments when there are too many words for one segment) in the MIDDLE of the narration. (c) For each speech segment, embed it NATURALLY within action descriptions BEFORE and AFTER speech: CORRECT: \"meeting the camera, I have considered your proposal he brushes his thumb against his beard.\" WRONG: separate speech/action blocks.\nAUDIO RULES: - Add \"Audio: [description]\" at the VERY END of the paragraph, separated by a single space. - If no speech occurs (means no tags exist), use \"SILENT\" for the audio description. - Otherwise, describe ONLY the agent\u2019s audio characteristics: (a) Voice traits: Describe the voice of the AGENT only. If the agent has spoken in the prior context, describe that voice in detail (pitch/tone/pace/texture, e.g., \"low-pitched with slight rasp\"). If the agent has NOT yet spoken in the context, you MUST ignore the interlocutor\u2019s voice entirely and infer a new, plausible voice for the AGENT EXCLUSIVELY based on his visual appearance (age, gender, physique). (b) Emotional progression (e.g., \"reflective shifting to joyful tone\"). (c) Recording environment ONLY if it affects vocal quality (e.g., \"dry and direct, quiet room\"). - NEVER mention background sounds, other voices, or specific locations (e.g., \"gift shop\"). - Never mix or attribute the interlocutor\u2019s vocal traits to the agent.\nTEMPORAL & STYLE RULES: - Chronological flow: [Full appearance sentence] \u2192 [Pre-speech actions] \u2192 [Speech Content] \u2192 [Post-speech actions] \u2192 (repeat for multiple speech segments if any) \u2192 Audio line - Appearance sentence must start with appearance-based identifier (e.g., \"A woman in a blue dress...\"), then use pronouns (she/he/they). - Use PRESENT TENSE throughout (e.g., \"she wears\", \"he gestures\"). - Write in THIRD-PERSON, single continuous paragraph. NO bullet points, headings, or line breaks. - NEVER use: agent, assistant, interlocutor, respond, reply, listen, understand.\nOUTPUT EXAMPLES (CRITICAL FOR FORMATTING): EXAMPLE 1 (Natural pause before speech, no target provided): \"A man in a grey sweatshirt stands before dark red curtains. His eyes track the interlocutor for two seconds, meeting the camera with a steady gaze, I have been considering your proposal all week. straightening his shoulders as his thumb brushes his beard thoughtfully. Audio: Calm low-pitched male voice with steady rhythm and slight rasp, recorded in a quiet room.\"\nEXAMPLE 2 (Silent reaction only, no target provided): \"A woman in a dark velvet head covering sits in stark black and white lighting. Her gaze drifts downward for three seconds, eyelids lowering slowly. Fingers tap once on the armrest as her head tilts upward with a subtle smile, eyes crinkling at the corners while maintaining silence. Audio: SILENT.\"\nEXAMPLE 3 (Multiple speech segments with emotional progression, no target provided):\nEXAMPLE 4 (Target provided and speech break into multiple segments, [Target Speech]: \"I completely understand your frustration with the current situation, but we need to stay focused on the long-term goals of the project.\"): \"A woman in a silk blouse sits against a bright window. She nods slowly as the interlocutor finishes, then takes a deep breath. Meeting the camera with a steady gaze, I completely understand your frustration with the current situation, her expression softens. Pausing briefly to adjust her posture, she continues, but we need to stay focused on the long-term goals of the project. She remains attentive, her eyes reflecting deep thought while awaiting a reply. Audio: Articulate female voice with a calm, reassuring tone and steady pace, recorded in a quiet environment.\"\nEXAMPLE 5 (Target provided andcontains [Target State]: Listening): \"A man in a flannel shirt leans back against a brick wall. He tilts his head slightly to the right, eyes tracking the interlocutor\u2019s movements. His brow furrows momentarily in concentration, lips pressed together in a neutral line. He maintains a steady, receptive posture, nodding once in acknowledgement while remaining silent. Audio: SILENT.\"\nNEVER OUTPUT LIKE THIS (common failures): - \"...last words of agent\u2019s context speech. Last words of context, then new content.\" // Repeating context - \"The agent looks thoughtful after hearing the question.\" // Banned words + internal state - \"I agree But I don\u2019t think it\u2019s a good idea\" // Short speech broken down into multiple segments and not embedded in actions - \"Audio: Quiet room with distant traffic.\" // Illegal background sound - \"A man speaks about his recovery.\" // Invented topic (must derive from context) - \"She has a kind face.\" // Abstract trait without visible cues", + "target_same_timestep_across_blocks": false, + "teacher_checkpoint": null, + "teacher_lora_checkpointing": {}, + "text_encoder_id": "./pretrained_models/Wan2.2-TI2V-5B", + "text_encoder_path": "./pretrained_models/Wan2.2-TI2V-5B/models_t5_umt5-xxl-enc-bf16.pth", + "torch_dtype": "bfloat16", + "transformers_version": "4.52.3", + "use_ref_as_sink": true, + "use_ref_audio_concat": { + "enable": true, + "uncond_ratio": 0.0 + }, + "vae_downsample_f": 16, + "vae_downsample_t": 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b/backup-0727/output/0224_stage4_self_rollout_dmd/backup/checkpoint-5900/checkpoint-5900/training_args.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:314102a8d6a1a76859191599d91c549087e90c8cb0a8219096bf3ddeb91436a1 +size 8337 diff --git a/backup-0727/output/0224_stage4_self_rollout_dmd/backup/checkpoint-5900/checkpoint-5900/zero_to_fp32.py b/backup-0727/output/0224_stage4_self_rollout_dmd/backup/checkpoint-5900/checkpoint-5900/zero_to_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..5995d6e6f04e43b989587aa9022a3aef0c66d694 --- /dev/null +++ b/backup-0727/output/0224_stage4_self_rollout_dmd/backup/checkpoint-5900/checkpoint-5900/zero_to_fp32.py @@ -0,0 +1,760 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: +# python zero_to_fp32.py . output_dir/ +# or +# python zero_to_fp32.py . output_dir/ --safe_serialization + +import argparse +import torch +import glob +import math +import os +import re +import gc +import json +import numpy as np +from tqdm import tqdm +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device, weights_only=False) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + total_files = len(files) + state_dicts = [] + for f in tqdm(files, desc='Loading checkpoint shards'): + state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +class GatheredTensor: + """ + A pseudo tensor that collects partitioned weights. + It is more memory efficient when there are multiple groups. + """ + + def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape): + self.flat_groups = flat_groups + self.flat_groups_offset = flat_groups_offset + self.offset = offset + self.partitioned_numel = partitioned_numel + self.shape = shape + self.dtype = self.flat_groups[0][0].dtype + + def contiguous(self): + """ + Merge partitioned weights from flat_groups into a single tensor. + """ + end_idx = self.offset + self.partitioned_numel + world_size = len(self.flat_groups) + pad_flat_param_chunks = [] + + for rank_i in range(world_size): + # for each rank, we need to collect weights from related group/groups + flat_groups_at_rank_i = self.flat_groups[rank_i] + start_group_id = None + end_group_id = None + for group_id in range(len(self.flat_groups_offset)): + if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]: + start_group_id = group_id + if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]: + end_group_id = group_id + break + # collect weights from related group/groups + for group_id in range(start_group_id, end_group_id + 1): + flat_tensor = flat_groups_at_rank_i[group_id] + start_offset = self.offset - self.flat_groups_offset[group_id] + end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id] + pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset]) + + # collect weights from all ranks + pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0) + param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous() + return param + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size + + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]])) + for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'): + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # memory efficient tensor + tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape) + state_dict[name] = tensor + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def to_torch_tensor(state_dict, return_empty_tensor=False): + """ + Convert state_dict of GatheredTensor to torch tensor + """ + torch_state_dict = {} + converted_tensors = {} + for name, tensor in state_dict.items(): + tensor_id = id(tensor) + if tensor_id in converted_tensors: # shared tensors + shared_tensor = torch_state_dict[converted_tensors[tensor_id]] + torch_state_dict[name] = shared_tensor + else: + converted_tensors[tensor_id] = name + if return_empty_tensor: + torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype) + else: + torch_state_dict[name] = tensor.contiguous() + return torch_state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag=None, + exclude_frozen_parameters=False, + lazy_mode=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient. + Convert the pesduo tensor to torch tensor by ``.contiguous()`` + + Returns: + - pytorch ``state_dict`` + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + Note: the above usage may not work if your application doesn't have sufficient free CPU memory. + You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. Or you can load state_dict in lazy mode :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu + for name, lazy_tensor in state_dict.item(): + tensor = lazy_tensor.contiguous() # to cpu + print(name, tensor) + # del tensor to release memory if it no longer in use + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters) + if lazy_mode: + return state_dict + else: + return to_torch_tensor(state_dict) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, + output_dir, + max_shard_size="5GB", + safe_serialization=False, + tag=None, + exclude_frozen_parameters=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_dir``: directory to the pytorch fp32 state_dict output files + - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB + - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + """ + + # Dependency pre-check + if safe_serialization: + try: + from safetensors.torch import save_file + except ImportError: + print('If you want to use `safe_serialization`, please `pip install safetensors`') + raise + if max_shard_size is not None: + try: + from huggingface_hub import split_torch_state_dict_into_shards + except ImportError: + print('If you want to use `max_shard_size`, please `pip install huggingface_hub`') + raise + + # Convert zero checkpoint to state_dict + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag, + exclude_frozen_parameters, + lazy_mode=True) + + # Shard the model if it is too big. + weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin" + if max_shard_size is not None: + filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors") + # an memory-efficient approach for sharding + empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True) + state_dict_split = split_torch_state_dict_into_shards(empty_state_dict, + filename_pattern=filename_pattern, + max_shard_size=max_shard_size) + else: + from collections import namedtuple + StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"]) + state_dict_split = StateDictSplit(is_sharded=False, + filename_to_tensors={weights_name: list(state_dict.keys())}) + + # Save the model by shard + os.makedirs(output_dir, exist_ok=True) + filename_to_tensors = state_dict_split.filename_to_tensors.items() + for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"): + shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors} + shard_state_dict = to_torch_tensor(shard_state_dict) + output_path = os.path.join(output_dir, shard_file) + if safe_serialization: + save_file(shard_state_dict, output_path, metadata={"format": "pt"}) + else: + torch.save(shard_state_dict, output_path) + # release the memory of current shard + for tensor_name in list(shard_state_dict.keys()): + del state_dict[tensor_name] + del shard_state_dict[tensor_name] + del shard_state_dict + gc.collect() + + # Save index if sharded + if state_dict_split.is_sharded: + index = { + "metadata": state_dict_split.metadata, + "weight_map": state_dict_split.tensor_to_filename, + } + save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json" + save_index_file = os.path.join(output_dir, save_index_file) + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info("Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info("Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument("output_dir", + type=str, + help="directory to the pytorch fp32 state_dict output files" + "(e.g. path/checkpoint-12-output/)") + parser.add_argument( + "--max_shard_size", + type=str, + default="5GB", + help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size" + "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`" + "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances" + "without CPU OOM issues.") + parser.add_argument( + "--safe_serialization", + default=False, + action='store_true', + help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, + args.output_dir, + max_shard_size=args.max_shard_size, + safe_serialization=args.safe_serialization, + tag=args.tag, + exclude_frozen_parameters=args.exclude_frozen_parameters) diff --git a/backup-0727/output/0224_stage4_self_rollout_dmd/checkpoint-9900/checkpoint-9900/config.json b/backup-0727/output/0224_stage4_self_rollout_dmd/checkpoint-9900/checkpoint-9900/config.json new file mode 100644 index 0000000000000000000000000000000000000000..c71d0aacd435eac446b3de4dd33ba19eb96495e3 --- /dev/null +++ b/backup-0727/output/0224_stage4_self_rollout_dmd/checkpoint-9900/checkpoint-9900/config.json @@ -0,0 +1,356 @@ +{ + "_gradient_checkpointing": true, + "ar_state_text_system_prompt": "You are a helpful assistant, capable of perceiving auditory and textual inputs, as well as generating text responses.", + "ar_target_debug": true, + "ar_target_do_sample": true, + "ar_target_max_new_tokens": 64, + "ar_target_temperature": 0.9, + "ar_target_top_k": 50, + "ar_target_top_p": 0.9, + "ar_target_use_v2": true, + "architectures": [ + "Stage4DMDSelfRolloutMetaQuery" + ], + "attn_implementation": "auto", + "audio_context_noise": 0.5, + "audio_lora_dict": { + "adapter_name": "s2_ovi_audio_lora", + "enable": true, + "lora_dropout": 0.0, + "network_alpha": 128, + "rank": 128, + "target_modules": [ + "q", + "k", + "v", + "o", + "ffn.0", + "ffn.2" + ] + }, + "audio_loss_weight": 0.5, + "audio_negative_prompt": "robotic, muffled, echo, distorted", + "audio_vae_path": "./pretrained_models/MMAudio/ext_weights/", + "block_causal": { + "a2a_lookahead": -1, + "attention_backend": "sdpa", + "audio_kv_cache_size": 4, + "enable": true, + "frame_per_block": 3, + "teacher_forcing": true, + "v2a_lookahead": 1, + "video_kv_cache_size": 4 + }, + "bsmntw_weighting": true, + "connector_num_hidden_layers": 8, + "diffusion_forcing": { + "enable": false, + "prefix_only": false, + "prob": 0.9, + "start_step": 0, + "temp_chunk": 3 + }, + "diffusion_model_id": "Lightricks/LTX-Video", + "diffusion_model_path": "./pretrained_models/Ovi/model_960x960_10s.safetensors", + "distillation": {}, + "dmd": { + "audio_loss_weight": 0.5, + "critic_audio_loss_weight": 0.5, + "critic_video_loss_weight": 1.0, + "denoising_step_lists": { + "1": [ + 1000 + ], + "2": [ + 1000, + 500 + ], + "3": [ + 1000, + 750, + 500 + ], + "4": [ + 1000, + 750, + 500, + 250 + ] + }, + "denoising_step_lists_first_chunk": { + "1": [ + 1000, + 750, + 500, + 250 + ], + "2": [ + 1000, + 750, + 500, + 250 + ], + "3": [ + 1000, + 750, + 500, + 250 + ], + "4": [ + 1000, + 750, + 500, + 250 + ] + }, + "detach_fusion_audio": false, + "detach_fusion_video": false, + "detach_mllm_mq": true, + "ema_start_step": 200, + "ema_weight": 0.99, + "fake_guidance_scale": 0.0, + "fake_score_learning_rate": 5e-06, + "fake_score_update_interval": 5, + "flowmatch_shift": 5.0, + "gan_audio_start_step": 0, + "gan_critic_checkpoint_blocks": 30, + "gan_feature_depths": [ + 0.1, + 0.5, + 0.9 + ], + "gan_loss_weight_audio": 0.0, + "gan_loss_weight_video": 0.0, + "gan_t_max": 1000, + "gan_t_min": 0, + "gan_video_start_step": 0, + "generator_adam_beta1": 0.0, + "generator_audio_loss_weight": 0.5, + "generator_gt_audio_listening_weight_multiplier": 1.0, + "generator_gt_audio_loss_weight": 0.05, + "generator_gt_video_loss_weight": 0.0, + "generator_video_loss_weight": 1.0, + "gt_score_regularize_start_step": 93000, + "gt_score_regularize_weight": 0.5, + "lookahead_weight": 2.0, + "lookahead_weight_position": 1, + "min_score_timestep": 0, + "normalize_gradient": true, + "num_inference_steps": 2, + "num_train_timestep": 1000, + "plan_commit_consistency_enable": false, + "plan_commit_gt_weight": 1.0, + "plan_commit_loss_weight": 3.0, + "purify_draft_static_margin": 4.0, + "purify_draft_static_mask_enable": false, + "purify_draft_static_quantile": 0.2, + "purify_draft_weight": 0.0, + "purify_start_step": 0, + "purify_t_max": 100, + "purify_t_min": 0, + "purify_weight": 0.0, + "real_audio_scale": 4.5, + "real_guidance_scale": 3.5, + "real_score_gt_x0_ratio": 0.5, + "real_score_gt_x0_start_step": 36000, + "real_slg_layer": 11, + "score_flowmatch_shift": 5.0, + "trainer": "dmd_self_rollout", + "ts_schedule": false, + "ts_schedule_max": false, + "validation_score_timestep": 250, + "video_loss_weight": 1.0, + "warp_denoising_step": true + }, + "fake_score_learning_rate": 5e-06, + "fake_score_lora_checkpointing": { + "audio_adapter_name": "s4_fake_ovi_audio_lora", + "block_causal": { + "enable": false + }, + "merge_from_checkpoint": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss", + "merge_load_non_lora": true, + "resume_from_checkpoint": null, + "resume_gan_head": false, + "resume_lora_only": false, + "video_adapter_name": "s4_fake_ovi_vid_lora" + }, + "flowmatch_shift": 5.0, + "gen_fps": 25.0, + "generator_lora_checkpointing": { + "audio_adapter_name": "s2_ovi_audio_lora", + "merge_from_checkpoint": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss", + "merge_load_non_lora": true, + "prefer_ema": true, + "resume_from_checkpoint": "./output/0222_stage3_causal_cd_frame3", + "resume_lora_only": true, + "video_adapter_name": "s2_ovi_vid_lora" + }, + "has_context_prompt": { + "enable": true, + "gt_t5_transcript_ratio": 1.0, + "no_mq_context_uncond_ratio": 0.0, + "skip_mllm_encode_ratio": 0.3 + }, + "in_channels": 16, + "infer": false, + "latent_spatial_size": 28, + "latent_temporal_size": 25, + "listening_loss_weight": 1.0, + "listening_prompt": "not speaking, closed mouth, listening, silent", + "local_files_only": true, + "lora_checkpointing": { + "audio_adapter_name": "s2_ovi_audio_lora", + "merge_from_checkpoint": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss", + "merge_load_non_lora": true, + "prefer_ema": true, + "resume_from_checkpoint": "./output/0222_stage3_causal_cd_frame3", + "resume_lora_only": true, + "video_adapter_name": "s2_ovi_vid_lora" + }, + "lora_dict": {}, + "max_input_text_tokens": 256, + "mllm_ce_only": false, + "mllm_id": "./pretrained_models/Qwen2.5-Omni-7B", + "mllm_local_path": null, + "mllm_lora_dict": { + "alpha": 64, + "dropout": 0.0, + "enable": false, + "rank": 32, + "target_modules": [ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj" + ] + }, + "model_type": "stage4_dmd_self_rollout_metaquery", + "modules_to_freeze": [ + "vae_model_video", + "vae_model_audio", + "text_model", + "fusion_model", + "mllm.mllm_backbone", + "mllm.connector" + ], + "modules_to_unfreeze": [], + "negative_text_prompt": "", + "noise_scheduler_id": "Lightricks/LTX-Video", + "num_metaqueries": 128, + "ovi_gradient_checkpointing_block_count": null, + "prev_temp_chunk": 1, + "real_score_lora_checkpointing": { + "audio_adapter_name": "s4_real_ovi_audio_lora", + "block_causal": { + "enable": false + }, + "merge_from_checkpoint": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss", + "merge_load_non_lora": true, + "resume_from_checkpoint": null, + "resume_lora_only": false, + "video_adapter_name": "s4_real_ovi_vid_lora" + }, + "rollout": { + "agent_context_source": "gt", + "aud_kv_gt_ratio": 0.0, + "audio_exit_replay_grad": false, + "audio_kv_cache_size": 4, + "audio_kv_grad_blocks": 0, + "context_noise": 0, + "decode_context_min_video_latents": 12, + "enable_ode_step": false, + "exit_index_sync": "broadcast", + "extra_kv_clean_forward": false, + "kv_cache_size": 4, + "kv_shift_step": 0, + "last_step_only": false, + "listening_output_fade_samples": 1536, + "listening_silence_enable": false, + "num_windows_schedule": [ + [ + 0, + [ + [ + 1 + ], + [ + 1 + ] + ] + ], + [ + 500, + [ + [ + 1, + 2, + 3 + ], + [ + 0.4, + 0.4, + 0.2 + ] + ] + ] + ], + "real_score_bridge_use_gt": false, + "same_step_across_blocks": true, + "score_bridge_vid_latents": 5, + "share_video_noise_weight": 0, + "use_kv_cache": true, + "user_context_source": "gt", + "validation_decode": true, + "validation_max_samples": 1, + "validation_num_windows": 3, + "validation_seed": 920, + "vid_kv_gt_ratio": 0.0, + "video_kv_cache_size": 4, + "video_latents_per_window": 24, + "window_duration_sec": 3.84 + }, + "scheduler_id": "Lightricks/LTX-Video", + "stage": 4, + "stage_name": "dmd_self_rollout", + "student_checkpoint": null, + "student_lora_checkpointing": {}, + "system_prompt": "You are a speech-video clip narrator for a text-to-audio-video (T2AV) model.\nThe input includes audio-visual dialogue between the Interlocutor and the Agent. The dialogue context is provided ONLY to infer what occurs next and must NOT be described, referenced, or summarized explicitly.\nYour task is to write a narration describing the NEXT recorded speech and video clip that shows ONLY the AGENT. Follow these rules precisely:\nVISUAL RULES: - Provide a HIGH-DETAIL appearance sentence, covering: (a) Gender + precise clothing (material/color/style, e.g., \"velvet head covering with ruffled accent\"), (b) Distinctive features (eyeliner/beard/accessories), (c) Scene composition (framing/lighting/color tone/backdrop, e.g., \"stark black and white lighting against plain curtains\"). - Describe ONLY visible physical actions in strict chronological order: (a) Facial muscles (e.g., \"lips curve upward forming a subtle smile\"), (b) Eye movements (e.g., \"gaze drifts downward for two seconds\"), (c) Posture/gestures (e.g., \"thumb brushes beard thoughtfully\"). - Emotions MUST derive from observable cues: CORRECT: \"eyes crinkling at the corners in a warm smile\" (visible) WRONG: \"she feels happy\" (internal) or \"looks thoughtful\" (abstract).\nSPEECH RULES: - Determine speech occurrence based on PROVIDED TARGETS or NATURAL CONVERSATION FLOW: - IF TARGETS ARE PROVIDED: (a) If \"[Target State]: Listening\" is provided in the context, describe silent actions ONLY without any speech content and do not use tags. (b) If \"[Target Speech]: [Text]\" is provided, use the provided [Text] exactly as full speech content. - IF TARGETS ARE NOT PROVIDED: (a) If dialogue context implies continued listening or non-verbal reaction, describe silent actions ONLY without any speech content and do not use tags. (b) If dialogue context implies the agent WILL SPEAK NEXT (even after brief listening actions), generate speech. (c) When speech needs to be generated: (1) Infer the speech content EXCLUSIVELY from dialogue context. (2) The speech content must be a RESPONSE that move forward the conversation, but word overlap with the end of the agent\u2019s speech context is STRICTLY FORBIDDEN. - GENERAL SPEECH STYLE (Only for speech existing in the narration): (b) Wrap each segment in and (speech contents are broken down into multiple segments when there are too many words for one segment) in the MIDDLE of the narration. (c) For each speech segment, embed it NATURALLY within action descriptions BEFORE and AFTER speech: CORRECT: \"meeting the camera, I have considered your proposal he brushes his thumb against his beard.\" WRONG: separate speech/action blocks.\nAUDIO RULES: - Add \"Audio: [description]\" at the VERY END of the paragraph, separated by a single space. - If no speech occurs (means no tags exist), use \"SILENT\" for the audio description. - Otherwise, describe ONLY the agent\u2019s audio characteristics: (a) Voice traits: Describe the voice of the AGENT only. If the agent has spoken in the prior context, describe that voice in detail (pitch/tone/pace/texture, e.g., \"low-pitched with slight rasp\"). If the agent has NOT yet spoken in the context, you MUST ignore the interlocutor\u2019s voice entirely and infer a new, plausible voice for the AGENT EXCLUSIVELY based on his visual appearance (age, gender, physique). (b) Emotional progression (e.g., \"reflective shifting to joyful tone\"). (c) Recording environment ONLY if it affects vocal quality (e.g., \"dry and direct, quiet room\"). - NEVER mention background sounds, other voices, or specific locations (e.g., \"gift shop\"). - Never mix or attribute the interlocutor\u2019s vocal traits to the agent.\nTEMPORAL & STYLE RULES: - Chronological flow: [Full appearance sentence] \u2192 [Pre-speech actions] \u2192 [Speech Content] \u2192 [Post-speech actions] \u2192 (repeat for multiple speech segments if any) \u2192 Audio line - Appearance sentence must start with appearance-based identifier (e.g., \"A woman in a blue dress...\"), then use pronouns (she/he/they). - Use PRESENT TENSE throughout (e.g., \"she wears\", \"he gestures\"). - Write in THIRD-PERSON, single continuous paragraph. NO bullet points, headings, or line breaks. - NEVER use: agent, assistant, interlocutor, respond, reply, listen, understand.\nOUTPUT EXAMPLES (CRITICAL FOR FORMATTING): EXAMPLE 1 (Natural pause before speech, no target provided): \"A man in a grey sweatshirt stands before dark red curtains. His eyes track the interlocutor for two seconds, meeting the camera with a steady gaze, I have been considering your proposal all week. straightening his shoulders as his thumb brushes his beard thoughtfully. Audio: Calm low-pitched male voice with steady rhythm and slight rasp, recorded in a quiet room.\"\nEXAMPLE 2 (Silent reaction only, no target provided): \"A woman in a dark velvet head covering sits in stark black and white lighting. Her gaze drifts downward for three seconds, eyelids lowering slowly. Fingers tap once on the armrest as her head tilts upward with a subtle smile, eyes crinkling at the corners while maintaining silence. Audio: SILENT.\"\nEXAMPLE 3 (Multiple speech segments with emotional progression, no target provided):\nEXAMPLE 4 (Target provided and speech break into multiple segments, [Target Speech]: \"I completely understand your frustration with the current situation, but we need to stay focused on the long-term goals of the project.\"): \"A woman in a silk blouse sits against a bright window. She nods slowly as the interlocutor finishes, then takes a deep breath. Meeting the camera with a steady gaze, I completely understand your frustration with the current situation, her expression softens. Pausing briefly to adjust her posture, she continues, but we need to stay focused on the long-term goals of the project. She remains attentive, her eyes reflecting deep thought while awaiting a reply. Audio: Articulate female voice with a calm, reassuring tone and steady pace, recorded in a quiet environment.\"\nEXAMPLE 5 (Target provided andcontains [Target State]: Listening): \"A man in a flannel shirt leans back against a brick wall. He tilts his head slightly to the right, eyes tracking the interlocutor\u2019s movements. His brow furrows momentarily in concentration, lips pressed together in a neutral line. He maintains a steady, receptive posture, nodding once in acknowledgement while remaining silent. Audio: SILENT.\"\nNEVER OUTPUT LIKE THIS (common failures): - \"...last words of agent\u2019s context speech. Last words of context, then new content.\" // Repeating context - \"The agent looks thoughtful after hearing the question.\" // Banned words + internal state - \"I agree But I don\u2019t think it\u2019s a good idea\" // Short speech broken down into multiple segments and not embedded in actions - \"Audio: Quiet room with distant traffic.\" // Illegal background sound - \"A man speaks about his recovery.\" // Invented topic (must derive from context) - \"She has a kind face.\" // Abstract trait without visible cues", + "target_same_timestep_across_blocks": false, + "teacher_checkpoint": null, + "teacher_lora_checkpointing": {}, + "text_encoder_id": "./pretrained_models/Wan2.2-TI2V-5B", + "text_encoder_path": "./pretrained_models/Wan2.2-TI2V-5B/models_t5_umt5-xxl-enc-bf16.pth", + "torch_dtype": "bfloat16", + "transformers_version": "4.52.3", + "use_ref_as_sink": true, + "use_ref_audio_concat": { + "enable": true, + "uncond_ratio": 0.0 + }, + "vae_downsample_f": 16, + "vae_downsample_t": 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b/backup-0727/output/0224_stage4_self_rollout_dmd/checkpoint-9900/checkpoint-9900/training_args.bin new file mode 100644 index 0000000000000000000000000000000000000000..8806fcdeb4b95760b3cb356ed2c97e49777fccc4 --- /dev/null +++ b/backup-0727/output/0224_stage4_self_rollout_dmd/checkpoint-9900/checkpoint-9900/training_args.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:314102a8d6a1a76859191599d91c549087e90c8cb0a8219096bf3ddeb91436a1 +size 8337 diff --git a/backup-0727/output/0224_stage4_self_rollout_dmd/checkpoint-9900/checkpoint-9900/zero_to_fp32.py b/backup-0727/output/0224_stage4_self_rollout_dmd/checkpoint-9900/checkpoint-9900/zero_to_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..5995d6e6f04e43b989587aa9022a3aef0c66d694 --- /dev/null +++ b/backup-0727/output/0224_stage4_self_rollout_dmd/checkpoint-9900/checkpoint-9900/zero_to_fp32.py @@ -0,0 +1,760 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: +# python zero_to_fp32.py . output_dir/ +# or +# python zero_to_fp32.py . output_dir/ --safe_serialization + +import argparse +import torch +import glob +import math +import os +import re +import gc +import json +import numpy as np +from tqdm import tqdm +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device, weights_only=False) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + total_files = len(files) + state_dicts = [] + for f in tqdm(files, desc='Loading checkpoint shards'): + state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +class GatheredTensor: + """ + A pseudo tensor that collects partitioned weights. + It is more memory efficient when there are multiple groups. + """ + + def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape): + self.flat_groups = flat_groups + self.flat_groups_offset = flat_groups_offset + self.offset = offset + self.partitioned_numel = partitioned_numel + self.shape = shape + self.dtype = self.flat_groups[0][0].dtype + + def contiguous(self): + """ + Merge partitioned weights from flat_groups into a single tensor. + """ + end_idx = self.offset + self.partitioned_numel + world_size = len(self.flat_groups) + pad_flat_param_chunks = [] + + for rank_i in range(world_size): + # for each rank, we need to collect weights from related group/groups + flat_groups_at_rank_i = self.flat_groups[rank_i] + start_group_id = None + end_group_id = None + for group_id in range(len(self.flat_groups_offset)): + if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]: + start_group_id = group_id + if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]: + end_group_id = group_id + break + # collect weights from related group/groups + for group_id in range(start_group_id, end_group_id + 1): + flat_tensor = flat_groups_at_rank_i[group_id] + start_offset = self.offset - self.flat_groups_offset[group_id] + end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id] + pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset]) + + # collect weights from all ranks + pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0) + param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous() + return param + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size + + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]])) + for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'): + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # memory efficient tensor + tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape) + state_dict[name] = tensor + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def to_torch_tensor(state_dict, return_empty_tensor=False): + """ + Convert state_dict of GatheredTensor to torch tensor + """ + torch_state_dict = {} + converted_tensors = {} + for name, tensor in state_dict.items(): + tensor_id = id(tensor) + if tensor_id in converted_tensors: # shared tensors + shared_tensor = torch_state_dict[converted_tensors[tensor_id]] + torch_state_dict[name] = shared_tensor + else: + converted_tensors[tensor_id] = name + if return_empty_tensor: + torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype) + else: + torch_state_dict[name] = tensor.contiguous() + return torch_state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag=None, + exclude_frozen_parameters=False, + lazy_mode=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient. + Convert the pesduo tensor to torch tensor by ``.contiguous()`` + + Returns: + - pytorch ``state_dict`` + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + Note: the above usage may not work if your application doesn't have sufficient free CPU memory. + You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. Or you can load state_dict in lazy mode :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu + for name, lazy_tensor in state_dict.item(): + tensor = lazy_tensor.contiguous() # to cpu + print(name, tensor) + # del tensor to release memory if it no longer in use + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters) + if lazy_mode: + return state_dict + else: + return to_torch_tensor(state_dict) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, + output_dir, + max_shard_size="5GB", + safe_serialization=False, + tag=None, + exclude_frozen_parameters=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_dir``: directory to the pytorch fp32 state_dict output files + - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB + - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + """ + + # Dependency pre-check + if safe_serialization: + try: + from safetensors.torch import save_file + except ImportError: + print('If you want to use `safe_serialization`, please `pip install safetensors`') + raise + if max_shard_size is not None: + try: + from huggingface_hub import split_torch_state_dict_into_shards + except ImportError: + print('If you want to use `max_shard_size`, please `pip install huggingface_hub`') + raise + + # Convert zero checkpoint to state_dict + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag, + exclude_frozen_parameters, + lazy_mode=True) + + # Shard the model if it is too big. + weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin" + if max_shard_size is not None: + filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors") + # an memory-efficient approach for sharding + empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True) + state_dict_split = split_torch_state_dict_into_shards(empty_state_dict, + filename_pattern=filename_pattern, + max_shard_size=max_shard_size) + else: + from collections import namedtuple + StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"]) + state_dict_split = StateDictSplit(is_sharded=False, + filename_to_tensors={weights_name: list(state_dict.keys())}) + + # Save the model by shard + os.makedirs(output_dir, exist_ok=True) + filename_to_tensors = state_dict_split.filename_to_tensors.items() + for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"): + shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors} + shard_state_dict = to_torch_tensor(shard_state_dict) + output_path = os.path.join(output_dir, shard_file) + if safe_serialization: + save_file(shard_state_dict, output_path, metadata={"format": "pt"}) + else: + torch.save(shard_state_dict, output_path) + # release the memory of current shard + for tensor_name in list(shard_state_dict.keys()): + del state_dict[tensor_name] + del shard_state_dict[tensor_name] + del shard_state_dict + gc.collect() + + # Save index if sharded + if state_dict_split.is_sharded: + index = { + "metadata": state_dict_split.metadata, + "weight_map": state_dict_split.tensor_to_filename, + } + save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json" + save_index_file = os.path.join(output_dir, save_index_file) + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info("Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info("Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument("output_dir", + type=str, + help="directory to the pytorch fp32 state_dict output files" + "(e.g. path/checkpoint-12-output/)") + parser.add_argument( + "--max_shard_size", + type=str, + default="5GB", + help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size" + "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`" + "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances" + "without CPU OOM issues.") + parser.add_argument( + "--safe_serialization", + default=False, + action='store_true', + help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, + args.output_dir, + max_shard_size=args.max_shard_size, + safe_serialization=args.safe_serialization, + tag=args.tag, + exclude_frozen_parameters=args.exclude_frozen_parameters) diff --git a/backup-260717/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss-checkpoint-29400/config.json b/backup-260717/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss-checkpoint-29400/config.json new file mode 100644 index 0000000000000000000000000000000000000000..6ed88a1d371caf63eb0be387491cdf525ae9937e --- /dev/null +++ b/backup-260717/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss-checkpoint-29400/config.json @@ -0,0 +1,154 @@ +{ + "_gradient_checkpointing": true, + "ar_state_text_system_prompt": "You are a helpful assistant, capable of perceiving auditory and textual inputs, as well as generating text responses.", + "ar_target_debug": true, + "ar_target_do_sample": true, + "ar_target_max_new_tokens": 64, + "ar_target_temperature": 0.9, + "ar_target_top_k": 50, + "ar_target_top_p": 0.9, + "ar_target_use_v2": true, + "architectures": [ + "MetaQuery" + ], + "attn_implementation": "auto", + "audio_context_noise": 0.0, + "audio_lora_dict": { + "adapter_name": "s1_ovi_audio_lora", + "enable": true, + "lora_dropout": 0.0, + "network_alpha": 128, + "rank": 128, + "target_modules": [ + "q", + "k", + "v", + "o", + "ffn.0", + "ffn.2" + ] + }, + "audio_loss_weight": 0.5, + "audio_negative_prompt": "robotic, muffled, echo, distorted", + "audio_vae_path": "./pretrained_models/MMAudio/ext_weights/", + "block_causal": { + "enable": false, + "frame_per_block": 1 + }, + "bsmntw_weighting": true, + "connector_num_hidden_layers": 8, + "diffusion_forcing": { + "enable": true, + "prefix_only": true, + "prob": 0.65, + "start_step": 4000, + "temp_chunk": 3 + }, + "diffusion_model_id": "Lightricks/LTX-Video", + "diffusion_model_path": "./pretrained_models/Ovi/model_960x960_10s.safetensors", + "distillation": {}, + "dmd": {}, + "fake_score_learning_rate": null, + "fake_score_lora_checkpointing": {}, + "flowmatch_shift": 5.0, + "gen_fps": 25.0, + "generator_lora_checkpointing": {}, + "has_context_prompt": { + "enable": true, + "gt_t5_transcript_ratio": 1.0, + "no_mq_context_uncond_ratio": 0.25, + "skip_mllm_encode_ratio": 0.4 + }, + "in_channels": 16, + "infer": false, + "latent_spatial_size": 28, + "latent_temporal_size": 29, + "listening_loss_weight": 1.0, + "listening_prompt": "not speaking, closed mouth, listening, silent", + "local_files_only": true, + "lora_checkpointing": { + "merge_from_checkpoint": null, + "merge_load_non_lora": true, + "resume_from_checkpoint": null, + "resume_lora_only": false + }, + "lora_dict": {}, + "max_input_text_tokens": 256, + "mllm_ce_only": false, + "mllm_id": "./pretrained_models/Qwen2.5-Omni-7B", + "mllm_local_path": null, + "mllm_lora_dict": { + "alpha": 64, + "dropout": 0.0, + "enable": false, + "rank": 32, + "target_modules": [ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj" + ] + }, + "model_type": "metaquery", + "modules_to_freeze": [ + "vae_model_video", + "vae_model_audio", + "text_model", + "fusion_model", + "mllm.mllm_backbone" + ], + "modules_to_unfreeze": [ + "mllm.connector", + "mllm.mllm_backbone.model.embed_tokens" + ], + "negative_text_prompt": "jitter, bad hands, blur, distortion, robotic, muffled, echo, distorted", + "noise_scheduler_id": "Lightricks/LTX-Video", + "num_metaqueries": 128, + "ovi_gradient_checkpointing_block_count": 25, + "prev_temp_chunk": 5, + "real_score_lora_checkpointing": {}, + "rollout": {}, + "scheduler_id": "Lightricks/LTX-Video", + "stage": 1, + "stage_name": null, + "student_checkpoint": null, + "student_lora_checkpointing": {}, + "system_prompt": "You are a speech-video clip narrator for a text-to-audio-video (T2AV) model.\nThe input includes audio-visual dialogue between the Interlocutor and the Agent. The dialogue context is provided ONLY to infer what occurs next and must NOT be described, referenced, or summarized explicitly.\nYour task is to write a narration describing the NEXT recorded speech and video clip that shows ONLY the AGENT. Follow these rules precisely:\nVISUAL RULES: - Provide a HIGH-DETAIL appearance sentence, covering: (a) Gender + precise clothing (material/color/style, e.g., \"velvet head covering with ruffled accent\"), (b) Distinctive features (eyeliner/beard/accessories), (c) Scene composition (framing/lighting/color tone/backdrop, e.g., \"stark black and white lighting against plain curtains\"). - Describe ONLY visible physical actions in strict chronological order: (a) Facial muscles (e.g., \"lips curve upward forming a subtle smile\"), (b) Eye movements (e.g., \"gaze drifts downward for two seconds\"), (c) Posture/gestures (e.g., \"thumb brushes beard thoughtfully\"). - Emotions MUST derive from observable cues: CORRECT: \"eyes crinkling at the corners in a warm smile\" (visible) WRONG: \"she feels happy\" (internal) or \"looks thoughtful\" (abstract).\nSPEECH RULES: - Determine speech occurrence based on PROVIDED TARGETS or NATURAL CONVERSATION FLOW: - IF TARGETS ARE PROVIDED: (a) If \"[Target State]: Listening\" is provided in the context, describe silent actions ONLY without any speech content and do not use tags. (b) If \"[Target Speech]: [Text]\" is provided, use the provided [Text] exactly as full speech content. - IF TARGETS ARE NOT PROVIDED: (a) If dialogue context implies continued listening or non-verbal reaction, describe silent actions ONLY without any speech content and do not use tags. (b) If dialogue context implies the agent WILL SPEAK NEXT (even after brief listening actions), generate speech. (c) When speech needs to be generated: (1) Infer the speech content EXCLUSIVELY from dialogue context. (2) The speech content must be a RESPONSE that move forward the conversation, but word overlap with the end of the agent\u2019s speech context is STRICTLY FORBIDDEN. - GENERAL SPEECH STYLE (Only for speech existing in the narration): (b) Wrap each segment in and (speech contents are broken down into multiple segments when there are too many words for one segment) in the MIDDLE of the narration. (c) For each speech segment, embed it NATURALLY within action descriptions BEFORE and AFTER speech: CORRECT: \"meeting the camera, I have considered your proposal he brushes his thumb against his beard.\" WRONG: separate speech/action blocks.\nAUDIO RULES: - Add \"Audio: [description]\" at the VERY END of the paragraph, separated by a single space. - If no speech occurs (means no tags exist), use \"SILENT\" for the audio description. - Otherwise, describe ONLY the agent\u2019s audio characteristics: (a) Voice traits: Describe the voice of the AGENT only. If the agent has spoken in the prior context, describe that voice in detail (pitch/tone/pace/texture, e.g., \"low-pitched with slight rasp\"). If the agent has NOT yet spoken in the context, you MUST ignore the interlocutor\u2019s voice entirely and infer a new, plausible voice for the AGENT EXCLUSIVELY based on his visual appearance (age, gender, physique). (b) Emotional progression (e.g., \"reflective shifting to joyful tone\"). (c) Recording environment ONLY if it affects vocal quality (e.g., \"dry and direct, quiet room\"). - NEVER mention background sounds, other voices, or specific locations (e.g., \"gift shop\"). - Never mix or attribute the interlocutor\u2019s vocal traits to the agent.\nTEMPORAL & STYLE RULES: - Chronological flow: [Full appearance sentence] \u2192 [Pre-speech actions] \u2192 [Speech Content] \u2192 [Post-speech actions] \u2192 (repeat for multiple speech segments if any) \u2192 Audio line - Appearance sentence must start with appearance-based identifier (e.g., \"A woman in a blue dress...\"), then use pronouns (she/he/they). - Use PRESENT TENSE throughout (e.g., \"she wears\", \"he gestures\"). - Write in THIRD-PERSON, single continuous paragraph. NO bullet points, headings, or line breaks. - NEVER use: agent, assistant, interlocutor, respond, reply, listen, understand.\nOUTPUT EXAMPLES (CRITICAL FOR FORMATTING): EXAMPLE 1 (Natural pause before speech, no target provided): \"A man in a grey sweatshirt stands before dark red curtains. His eyes track the interlocutor for two seconds, meeting the camera with a steady gaze, I have been considering your proposal all week. straightening his shoulders as his thumb brushes his beard thoughtfully. Audio: Calm low-pitched male voice with steady rhythm and slight rasp, recorded in a quiet room.\"\nEXAMPLE 2 (Silent reaction only, no target provided): \"A woman in a dark velvet head covering sits in stark black and white lighting. Her gaze drifts downward for three seconds, eyelids lowering slowly. Fingers tap once on the armrest as her head tilts upward with a subtle smile, eyes crinkling at the corners while maintaining silence. Audio: SILENT.\"\nEXAMPLE 3 (Multiple speech segments with emotional progression, no target provided):\nEXAMPLE 4 (Target provided and speech break into multiple segments, [Target Speech]: \"I completely understand your frustration with the current situation, but we need to stay focused on the long-term goals of the project.\"): \"A woman in a silk blouse sits against a bright window. She nods slowly as the interlocutor finishes, then takes a deep breath. Meeting the camera with a steady gaze, I completely understand your frustration with the current situation, her expression softens. Pausing briefly to adjust her posture, she continues, but we need to stay focused on the long-term goals of the project. She remains attentive, her eyes reflecting deep thought while awaiting a reply. Audio: Articulate female voice with a calm, reassuring tone and steady pace, recorded in a quiet environment.\"\nEXAMPLE 5 (Target provided andcontains [Target State]: Listening): \"A man in a flannel shirt leans back against a brick wall. He tilts his head slightly to the right, eyes tracking the interlocutor\u2019s movements. His brow furrows momentarily in concentration, lips pressed together in a neutral line. He maintains a steady, receptive posture, nodding once in acknowledgement while remaining silent. Audio: SILENT.\"\nNEVER OUTPUT LIKE THIS (common failures): - \"...last words of agent\u2019s context speech. Last words of context, then new content.\" // Repeating context - \"The agent looks thoughtful after hearing the question.\" // Banned words + internal state - \"I agree But I don\u2019t think it\u2019s a good idea\" // Short speech broken down into multiple segments and not embedded in actions - \"Audio: Quiet room with distant traffic.\" // Illegal background sound - \"A man speaks about his recovery.\" // Invented topic (must derive from context) - \"She has a kind face.\" // Abstract trait without visible cues", + "target_same_timestep_across_blocks": false, + "teacher_checkpoint": null, + "teacher_lora_checkpointing": {}, + "text_encoder_id": "./pretrained_models/Wan2.2-TI2V-5B", + "text_encoder_path": "./pretrained_models/Wan2.2-TI2V-5B/models_t5_umt5-xxl-enc-bf16.pth", + "torch_dtype": "bfloat16", + "transformers_version": "4.52.3", + "use_ref_as_sink": true, + "use_ref_audio_concat": { + "enable": true, + "uncond_ratio": 0.1 + }, + "vae_downsample_f": 16, + "vae_downsample_t": 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a/backup-260717/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss-checkpoint-29400/zero_to_fp32.py b/backup-260717/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss-checkpoint-29400/zero_to_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..5995d6e6f04e43b989587aa9022a3aef0c66d694 --- /dev/null +++ b/backup-260717/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss-checkpoint-29400/zero_to_fp32.py @@ -0,0 +1,760 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: +# python zero_to_fp32.py . output_dir/ +# or +# python zero_to_fp32.py . output_dir/ --safe_serialization + +import argparse +import torch +import glob +import math +import os +import re +import gc +import json +import numpy as np +from tqdm import tqdm +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device, weights_only=False) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + total_files = len(files) + state_dicts = [] + for f in tqdm(files, desc='Loading checkpoint shards'): + state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +class GatheredTensor: + """ + A pseudo tensor that collects partitioned weights. + It is more memory efficient when there are multiple groups. + """ + + def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape): + self.flat_groups = flat_groups + self.flat_groups_offset = flat_groups_offset + self.offset = offset + self.partitioned_numel = partitioned_numel + self.shape = shape + self.dtype = self.flat_groups[0][0].dtype + + def contiguous(self): + """ + Merge partitioned weights from flat_groups into a single tensor. + """ + end_idx = self.offset + self.partitioned_numel + world_size = len(self.flat_groups) + pad_flat_param_chunks = [] + + for rank_i in range(world_size): + # for each rank, we need to collect weights from related group/groups + flat_groups_at_rank_i = self.flat_groups[rank_i] + start_group_id = None + end_group_id = None + for group_id in range(len(self.flat_groups_offset)): + if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]: + start_group_id = group_id + if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]: + end_group_id = group_id + break + # collect weights from related group/groups + for group_id in range(start_group_id, end_group_id + 1): + flat_tensor = flat_groups_at_rank_i[group_id] + start_offset = self.offset - self.flat_groups_offset[group_id] + end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id] + pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset]) + + # collect weights from all ranks + pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0) + param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous() + return param + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size + + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]])) + for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'): + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # memory efficient tensor + tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape) + state_dict[name] = tensor + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def to_torch_tensor(state_dict, return_empty_tensor=False): + """ + Convert state_dict of GatheredTensor to torch tensor + """ + torch_state_dict = {} + converted_tensors = {} + for name, tensor in state_dict.items(): + tensor_id = id(tensor) + if tensor_id in converted_tensors: # shared tensors + shared_tensor = torch_state_dict[converted_tensors[tensor_id]] + torch_state_dict[name] = shared_tensor + else: + converted_tensors[tensor_id] = name + if return_empty_tensor: + torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype) + else: + torch_state_dict[name] = tensor.contiguous() + return torch_state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag=None, + exclude_frozen_parameters=False, + lazy_mode=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient. + Convert the pesduo tensor to torch tensor by ``.contiguous()`` + + Returns: + - pytorch ``state_dict`` + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + Note: the above usage may not work if your application doesn't have sufficient free CPU memory. + You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. Or you can load state_dict in lazy mode :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu + for name, lazy_tensor in state_dict.item(): + tensor = lazy_tensor.contiguous() # to cpu + print(name, tensor) + # del tensor to release memory if it no longer in use + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters) + if lazy_mode: + return state_dict + else: + return to_torch_tensor(state_dict) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, + output_dir, + max_shard_size="5GB", + safe_serialization=False, + tag=None, + exclude_frozen_parameters=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_dir``: directory to the pytorch fp32 state_dict output files + - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB + - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + """ + + # Dependency pre-check + if safe_serialization: + try: + from safetensors.torch import save_file + except ImportError: + print('If you want to use `safe_serialization`, please `pip install safetensors`') + raise + if max_shard_size is not None: + try: + from huggingface_hub import split_torch_state_dict_into_shards + except ImportError: + print('If you want to use `max_shard_size`, please `pip install huggingface_hub`') + raise + + # Convert zero checkpoint to state_dict + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag, + exclude_frozen_parameters, + lazy_mode=True) + + # Shard the model if it is too big. + weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin" + if max_shard_size is not None: + filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors") + # an memory-efficient approach for sharding + empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True) + state_dict_split = split_torch_state_dict_into_shards(empty_state_dict, + filename_pattern=filename_pattern, + max_shard_size=max_shard_size) + else: + from collections import namedtuple + StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"]) + state_dict_split = StateDictSplit(is_sharded=False, + filename_to_tensors={weights_name: list(state_dict.keys())}) + + # Save the model by shard + os.makedirs(output_dir, exist_ok=True) + filename_to_tensors = state_dict_split.filename_to_tensors.items() + for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"): + shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors} + shard_state_dict = to_torch_tensor(shard_state_dict) + output_path = os.path.join(output_dir, shard_file) + if safe_serialization: + save_file(shard_state_dict, output_path, metadata={"format": "pt"}) + else: + torch.save(shard_state_dict, output_path) + # release the memory of current shard + for tensor_name in list(shard_state_dict.keys()): + del state_dict[tensor_name] + del shard_state_dict[tensor_name] + del shard_state_dict + gc.collect() + + # Save index if sharded + if state_dict_split.is_sharded: + index = { + "metadata": state_dict_split.metadata, + "weight_map": state_dict_split.tensor_to_filename, + } + save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json" + save_index_file = os.path.join(output_dir, save_index_file) + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info("Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info("Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument("output_dir", + type=str, + help="directory to the pytorch fp32 state_dict output files" + "(e.g. path/checkpoint-12-output/)") + parser.add_argument( + "--max_shard_size", + type=str, + default="5GB", + help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size" + "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`" + "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances" + "without CPU OOM issues.") + parser.add_argument( + "--safe_serialization", + default=False, + action='store_true', + help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, + args.output_dir, + max_shard_size=args.max_shard_size, + safe_serialization=args.safe_serialization, + tag=args.tag, + exclude_frozen_parameters=args.exclude_frozen_parameters) diff --git a/backup-260717/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1-checkpoint-8800/config.json b/backup-260717/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1-checkpoint-8800/config.json new file mode 100644 index 0000000000000000000000000000000000000000..114b20eee5e919156f9ee947a976242a88bcc287 --- /dev/null +++ b/backup-260717/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1-checkpoint-8800/config.json @@ -0,0 +1,158 @@ +{ + "_gradient_checkpointing": true, + "ar_state_text_system_prompt": "You are a helpful assistant, capable of perceiving auditory and textual inputs, as well as generating text responses.", + "ar_target_debug": true, + "ar_target_do_sample": true, + "ar_target_max_new_tokens": 64, + "ar_target_temperature": 0.9, + "ar_target_top_k": 50, + "ar_target_top_p": 0.9, + "ar_target_use_v2": true, + "architectures": [ + "Stage2MetaQuery" + ], + "attn_implementation": "auto", + "audio_context_noise": 0.5, + "audio_lora_dict": { + "adapter_name": "s2_ovi_audio_lora", + "enable": true, + "lora_dropout": 0.0, + "network_alpha": 128, + "rank": 128, + "target_modules": [ + "q", + "k", + "v", + "o", + "ffn.0", + "ffn.2" + ] + }, + "audio_loss_weight": 0.5, + "audio_negative_prompt": "robotic, muffled, echo, distorted", + "audio_vae_path": "./pretrained_models/MMAudio/ext_weights/", + "block_causal": { + "a2a_lookahead": -1, + "attention_backend": "auto", + "audio_kv_cache_size": 4, + "enable": true, + "frame_per_block": 3, + "teacher_forcing": true, + "v2a_lookahead": 1, + "video_kv_cache_size": 4 + }, + "bsmntw_weighting": true, + "connector_num_hidden_layers": 8, + "diffusion_forcing": { + "enable": false, + "prefix_only": false, + "prob": 0.9, + "start_step": 0, + "temp_chunk": 3 + }, + "diffusion_model_id": "Lightricks/LTX-Video", + "diffusion_model_path": "./pretrained_models/Ovi/model_960x960_10s.safetensors", + "distillation": {}, + "dmd": {}, + "fake_score_learning_rate": null, + "fake_score_lora_checkpointing": {}, + "flowmatch_shift": 5.0, + "gen_fps": 25.0, + "generator_lora_checkpointing": {}, + "has_context_prompt": { + "enable": true, + "gt_t5_transcript_ratio": 1.0, + "no_mq_context_uncond_ratio": 0.25, + "skip_mllm_encode_ratio": 0.4 + }, + "in_channels": 16, + "infer": false, + "latent_spatial_size": 28, + "latent_temporal_size": 25, + "listening_loss_weight": 1.0, + "listening_prompt": "not speaking, closed mouth, listening, silent", + "local_files_only": true, + "lora_checkpointing": { + "merge_from_checkpoint": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss", + "merge_load_non_lora": true, + "resume_from_checkpoint": null, + "resume_lora_only": false + }, + "lora_dict": {}, + "max_input_text_tokens": 256, + "mllm_ce_only": false, + "mllm_id": "./pretrained_models/Qwen2.5-Omni-7B", + "mllm_local_path": null, + "mllm_lora_dict": { + "alpha": 64, + "dropout": 0.0, + "enable": false, + "rank": 32, + "target_modules": [ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj" + ] + }, + "model_type": "stage2_metaquery", + "modules_to_freeze": [ + "vae_model_video", + "vae_model_audio", + "text_model", + "fusion_model", + "mllm.mllm_backbone", + "mllm.connector" + ], + "modules_to_unfreeze": [], + "negative_text_prompt": "", + "noise_scheduler_id": "Lightricks/LTX-Video", + "num_metaqueries": 128, + "ovi_gradient_checkpointing_block_count": null, + "prev_temp_chunk": 1, + "real_score_lora_checkpointing": {}, + "rollout": {}, + "scheduler_id": "Lightricks/LTX-Video", + "stage": 1, + "stage_name": null, + "student_checkpoint": null, + "student_lora_checkpointing": {}, + "system_prompt": "You are a speech-video clip narrator for a text-to-audio-video (T2AV) model.\nThe input includes audio-visual dialogue between the Interlocutor and the Agent. The dialogue context is provided ONLY to infer what occurs next and must NOT be described, referenced, or summarized explicitly.\nYour task is to write a narration describing the NEXT recorded speech and video clip that shows ONLY the AGENT. Follow these rules precisely:\nVISUAL RULES: - Provide a HIGH-DETAIL appearance sentence, covering: (a) Gender + precise clothing (material/color/style, e.g., \"velvet head covering with ruffled accent\"), (b) Distinctive features (eyeliner/beard/accessories), (c) Scene composition (framing/lighting/color tone/backdrop, e.g., \"stark black and white lighting against plain curtains\"). - Describe ONLY visible physical actions in strict chronological order: (a) Facial muscles (e.g., \"lips curve upward forming a subtle smile\"), (b) Eye movements (e.g., \"gaze drifts downward for two seconds\"), (c) Posture/gestures (e.g., \"thumb brushes beard thoughtfully\"). - Emotions MUST derive from observable cues: CORRECT: \"eyes crinkling at the corners in a warm smile\" (visible) WRONG: \"she feels happy\" (internal) or \"looks thoughtful\" (abstract).\nSPEECH RULES: - Determine speech occurrence based on PROVIDED TARGETS or NATURAL CONVERSATION FLOW: - IF TARGETS ARE PROVIDED: (a) If \"[Target State]: Listening\" is provided in the context, describe silent actions ONLY without any speech content and do not use tags. (b) If \"[Target Speech]: [Text]\" is provided, use the provided [Text] exactly as full speech content. - IF TARGETS ARE NOT PROVIDED: (a) If dialogue context implies continued listening or non-verbal reaction, describe silent actions ONLY without any speech content and do not use tags. (b) If dialogue context implies the agent WILL SPEAK NEXT (even after brief listening actions), generate speech. (c) When speech needs to be generated: (1) Infer the speech content EXCLUSIVELY from dialogue context. (2) The speech content must be a RESPONSE that move forward the conversation, but word overlap with the end of the agent\u2019s speech context is STRICTLY FORBIDDEN. - GENERAL SPEECH STYLE (Only for speech existing in the narration): (b) Wrap each segment in and (speech contents are broken down into multiple segments when there are too many words for one segment) in the MIDDLE of the narration. (c) For each speech segment, embed it NATURALLY within action descriptions BEFORE and AFTER speech: CORRECT: \"meeting the camera, I have considered your proposal he brushes his thumb against his beard.\" WRONG: separate speech/action blocks.\nAUDIO RULES: - Add \"Audio: [description]\" at the VERY END of the paragraph, separated by a single space. - If no speech occurs (means no tags exist), use \"SILENT\" for the audio description. - Otherwise, describe ONLY the agent\u2019s audio characteristics: (a) Voice traits: Describe the voice of the AGENT only. If the agent has spoken in the prior context, describe that voice in detail (pitch/tone/pace/texture, e.g., \"low-pitched with slight rasp\"). If the agent has NOT yet spoken in the context, you MUST ignore the interlocutor\u2019s voice entirely and infer a new, plausible voice for the AGENT EXCLUSIVELY based on his visual appearance (age, gender, physique). (b) Emotional progression (e.g., \"reflective shifting to joyful tone\"). (c) Recording environment ONLY if it affects vocal quality (e.g., \"dry and direct, quiet room\"). - NEVER mention background sounds, other voices, or specific locations (e.g., \"gift shop\"). - Never mix or attribute the interlocutor\u2019s vocal traits to the agent.\nTEMPORAL & STYLE RULES: - Chronological flow: [Full appearance sentence] \u2192 [Pre-speech actions] \u2192 [Speech Content] \u2192 [Post-speech actions] \u2192 (repeat for multiple speech segments if any) \u2192 Audio line - Appearance sentence must start with appearance-based identifier (e.g., \"A woman in a blue dress...\"), then use pronouns (she/he/they). - Use PRESENT TENSE throughout (e.g., \"she wears\", \"he gestures\"). - Write in THIRD-PERSON, single continuous paragraph. NO bullet points, headings, or line breaks. - NEVER use: agent, assistant, interlocutor, respond, reply, listen, understand.\nOUTPUT EXAMPLES (CRITICAL FOR FORMATTING): EXAMPLE 1 (Natural pause before speech, no target provided): \"A man in a grey sweatshirt stands before dark red curtains. His eyes track the interlocutor for two seconds, meeting the camera with a steady gaze, I have been considering your proposal all week. straightening his shoulders as his thumb brushes his beard thoughtfully. Audio: Calm low-pitched male voice with steady rhythm and slight rasp, recorded in a quiet room.\"\nEXAMPLE 2 (Silent reaction only, no target provided): \"A woman in a dark velvet head covering sits in stark black and white lighting. Her gaze drifts downward for three seconds, eyelids lowering slowly. Fingers tap once on the armrest as her head tilts upward with a subtle smile, eyes crinkling at the corners while maintaining silence. Audio: SILENT.\"\nEXAMPLE 3 (Multiple speech segments with emotional progression, no target provided):\nEXAMPLE 4 (Target provided and speech break into multiple segments, [Target Speech]: \"I completely understand your frustration with the current situation, but we need to stay focused on the long-term goals of the project.\"): \"A woman in a silk blouse sits against a bright window. She nods slowly as the interlocutor finishes, then takes a deep breath. Meeting the camera with a steady gaze, I completely understand your frustration with the current situation, her expression softens. Pausing briefly to adjust her posture, she continues, but we need to stay focused on the long-term goals of the project. She remains attentive, her eyes reflecting deep thought while awaiting a reply. Audio: Articulate female voice with a calm, reassuring tone and steady pace, recorded in a quiet environment.\"\nEXAMPLE 5 (Target provided andcontains [Target State]: Listening): \"A man in a flannel shirt leans back against a brick wall. He tilts his head slightly to the right, eyes tracking the interlocutor\u2019s movements. His brow furrows momentarily in concentration, lips pressed together in a neutral line. He maintains a steady, receptive posture, nodding once in acknowledgement while remaining silent. Audio: SILENT.\"\nNEVER OUTPUT LIKE THIS (common failures): - \"...last words of agent\u2019s context speech. Last words of context, then new content.\" // Repeating context - \"The agent looks thoughtful after hearing the question.\" // Banned words + internal state - \"I agree But I don\u2019t think it\u2019s a good idea\" // Short speech broken down into multiple segments and not embedded in actions - \"Audio: Quiet room with distant traffic.\" // Illegal background sound - \"A man speaks about his recovery.\" // Invented topic (must derive from context) - \"She has a kind face.\" // Abstract trait without visible cues", + "target_same_timestep_across_blocks": false, + "teacher_checkpoint": null, + "teacher_lora_checkpointing": {}, + "text_encoder_id": "./pretrained_models/Wan2.2-TI2V-5B", + "text_encoder_path": "./pretrained_models/Wan2.2-TI2V-5B/models_t5_umt5-xxl-enc-bf16.pth", + "torch_dtype": "bfloat16", + "transformers_version": "4.52.3", + "use_ref_as_sink": true, + "use_ref_audio_concat": { + "enable": true, + "uncond_ratio": 0.1 + }, + "vae_downsample_f": 16, + "vae_downsample_t": 4, + "vae_id": "Lightricks/LTX-Video", + "vae_path": "./pretrained_models/Wan2.2-TI2V-5B/Wan2.2_VAE.pth", + "video_context_noise": 0.5, + "video_lora_dict": { + "adapter_name": "s2_ovi_vid_lora", + "enable": true, + "lora_dropout": 0.0, + "network_alpha": 128, + "rank": 128, + "target_modules": [ + "q", + "k", + "v", + "o", + "ffn.0", + "ffn.2" + ] + }, + "video_negative_prompt": "jitter, bad hands, blur, distortion", + "window": {} +} diff --git a/backup-260717/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1-checkpoint-8800/latest b/backup-260717/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1-checkpoint-8800/latest new file mode 100644 index 0000000000000000000000000000000000000000..b4486d8007daedc972bdd6b4bd5bdd15b4d76ad9 --- /dev/null +++ b/backup-260717/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1-checkpoint-8800/latest @@ -0,0 +1 @@ +global_step8800 \ No newline at end of file diff --git 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"should_epoch_stop": false, + "should_evaluate": false, + "should_log": false, + "should_save": true, + "should_training_stop": false + }, + "attributes": {} + } + }, + "total_flos": 0.0, + "train_batch_size": 6, + "trial_name": null, + "trial_params": null +} diff --git a/backup-260717/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1-checkpoint-8800/zero_to_fp32.py b/backup-260717/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1-checkpoint-8800/zero_to_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..5995d6e6f04e43b989587aa9022a3aef0c66d694 --- /dev/null +++ b/backup-260717/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1-checkpoint-8800/zero_to_fp32.py @@ -0,0 +1,760 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: +# python zero_to_fp32.py . output_dir/ +# or +# python zero_to_fp32.py . output_dir/ --safe_serialization + +import argparse +import torch +import glob +import math +import os +import re +import gc +import json +import numpy as np +from tqdm import tqdm +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device, weights_only=False) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + total_files = len(files) + state_dicts = [] + for f in tqdm(files, desc='Loading checkpoint shards'): + state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +class GatheredTensor: + """ + A pseudo tensor that collects partitioned weights. + It is more memory efficient when there are multiple groups. + """ + + def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape): + self.flat_groups = flat_groups + self.flat_groups_offset = flat_groups_offset + self.offset = offset + self.partitioned_numel = partitioned_numel + self.shape = shape + self.dtype = self.flat_groups[0][0].dtype + + def contiguous(self): + """ + Merge partitioned weights from flat_groups into a single tensor. + """ + end_idx = self.offset + self.partitioned_numel + world_size = len(self.flat_groups) + pad_flat_param_chunks = [] + + for rank_i in range(world_size): + # for each rank, we need to collect weights from related group/groups + flat_groups_at_rank_i = self.flat_groups[rank_i] + start_group_id = None + end_group_id = None + for group_id in range(len(self.flat_groups_offset)): + if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]: + start_group_id = group_id + if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]: + end_group_id = group_id + break + # collect weights from related group/groups + for group_id in range(start_group_id, end_group_id + 1): + flat_tensor = flat_groups_at_rank_i[group_id] + start_offset = self.offset - self.flat_groups_offset[group_id] + end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id] + pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset]) + + # collect weights from all ranks + pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0) + param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous() + return param + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size + + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]])) + for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'): + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # memory efficient tensor + tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape) + state_dict[name] = tensor + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def to_torch_tensor(state_dict, return_empty_tensor=False): + """ + Convert state_dict of GatheredTensor to torch tensor + """ + torch_state_dict = {} + converted_tensors = {} + for name, tensor in state_dict.items(): + tensor_id = id(tensor) + if tensor_id in converted_tensors: # shared tensors + shared_tensor = torch_state_dict[converted_tensors[tensor_id]] + torch_state_dict[name] = shared_tensor + else: + converted_tensors[tensor_id] = name + if return_empty_tensor: + torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype) + else: + torch_state_dict[name] = tensor.contiguous() + return torch_state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag=None, + exclude_frozen_parameters=False, + lazy_mode=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient. + Convert the pesduo tensor to torch tensor by ``.contiguous()`` + + Returns: + - pytorch ``state_dict`` + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + Note: the above usage may not work if your application doesn't have sufficient free CPU memory. + You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. Or you can load state_dict in lazy mode :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu + for name, lazy_tensor in state_dict.item(): + tensor = lazy_tensor.contiguous() # to cpu + print(name, tensor) + # del tensor to release memory if it no longer in use + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters) + if lazy_mode: + return state_dict + else: + return to_torch_tensor(state_dict) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, + output_dir, + max_shard_size="5GB", + safe_serialization=False, + tag=None, + exclude_frozen_parameters=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_dir``: directory to the pytorch fp32 state_dict output files + - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB + - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + """ + + # Dependency pre-check + if safe_serialization: + try: + from safetensors.torch import save_file + except ImportError: + print('If you want to use `safe_serialization`, please `pip install safetensors`') + raise + if max_shard_size is not None: + try: + from huggingface_hub import split_torch_state_dict_into_shards + except ImportError: + print('If you want to use `max_shard_size`, please `pip install huggingface_hub`') + raise + + # Convert zero checkpoint to state_dict + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag, + exclude_frozen_parameters, + lazy_mode=True) + + # Shard the model if it is too big. + weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin" + if max_shard_size is not None: + filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors") + # an memory-efficient approach for sharding + empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True) + state_dict_split = split_torch_state_dict_into_shards(empty_state_dict, + filename_pattern=filename_pattern, + max_shard_size=max_shard_size) + else: + from collections import namedtuple + StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"]) + state_dict_split = StateDictSplit(is_sharded=False, + filename_to_tensors={weights_name: list(state_dict.keys())}) + + # Save the model by shard + os.makedirs(output_dir, exist_ok=True) + filename_to_tensors = state_dict_split.filename_to_tensors.items() + for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"): + shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors} + shard_state_dict = to_torch_tensor(shard_state_dict) + output_path = os.path.join(output_dir, shard_file) + if safe_serialization: + save_file(shard_state_dict, output_path, metadata={"format": "pt"}) + else: + torch.save(shard_state_dict, output_path) + # release the memory of current shard + for tensor_name in list(shard_state_dict.keys()): + del state_dict[tensor_name] + del shard_state_dict[tensor_name] + del shard_state_dict + gc.collect() + + # Save index if sharded + if state_dict_split.is_sharded: + index = { + "metadata": state_dict_split.metadata, + "weight_map": state_dict_split.tensor_to_filename, + } + save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json" + save_index_file = os.path.join(output_dir, save_index_file) + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info("Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info("Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument("output_dir", + type=str, + help="directory to the pytorch fp32 state_dict output files" + "(e.g. path/checkpoint-12-output/)") + parser.add_argument( + "--max_shard_size", + type=str, + default="5GB", + help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size" + "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`" + "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances" + "without CPU OOM issues.") + parser.add_argument( + "--safe_serialization", + default=False, + action='store_true', + help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, + args.output_dir, + max_shard_size=args.max_shard_size, + safe_serialization=args.safe_serialization, + tag=args.tag, + exclude_frozen_parameters=args.exclude_frozen_parameters) diff --git a/backup-260717/0222_stage3_causal_cd_frame3-checkpoint-5200/config.json b/backup-260717/0222_stage3_causal_cd_frame3-checkpoint-5200/config.json new file mode 100644 index 0000000000000000000000000000000000000000..5a7ba62c0b73deea1a658c630c1c77ca7a8c0f86 --- /dev/null +++ b/backup-260717/0222_stage3_causal_cd_frame3-checkpoint-5200/config.json @@ -0,0 +1,246 @@ +{ + "_gradient_checkpointing": true, + "ar_state_text_system_prompt": "You are a helpful assistant, capable of perceiving auditory and textual inputs, as well as generating text responses.", + "ar_target_debug": true, + "ar_target_do_sample": true, + "ar_target_max_new_tokens": 64, + "ar_target_temperature": 0.9, + "ar_target_top_k": 50, + "ar_target_top_p": 0.9, + "ar_target_use_v2": true, + "architectures": [ + "Stage3CausalCDMetaQuery" + ], + "attn_implementation": "auto", + "audio_context_noise": 0.5, + "audio_lora_dict": { + "adapter_name": "s2_ovi_audio_lora", + "enable": true, + "lora_dropout": 0.0, + "network_alpha": 128, + "rank": 128, + "target_modules": [ + "q", + "k", + "v", + "o", + "ffn.0", + "ffn.2" + ] + }, + "audio_loss_weight": 0.5, + "audio_negative_prompt": "robotic, muffled, echo, distorted", + "audio_vae_path": "./pretrained_models/MMAudio/ext_weights/", + "block_causal": { + "a2a_lookahead": -1, + "attention_backend": "auto", + "audio_kv_cache_size": 4, + "enable": true, + "frame_per_block": 3, + "teacher_forcing": true, + "v2a_lookahead": 1, + "video_kv_cache_size": 4 + }, + "bsmntw_weighting": true, + "connector_num_hidden_layers": 8, + "diffusion_forcing": { + "enable": false, + "prefix_only": false, + "prob": 0.9, + "start_step": 0, + "temp_chunk": 3 + }, + "diffusion_model_id": "Lightricks/LTX-Video", + "diffusion_model_path": "./pretrained_models/Ovi/model_960x960_10s.safetensors", + "distillation": { + "audio_loss_weight": 0.5, + "audio_scale": 4.5, + "denoising_step_lists": { + "1": [ + 1000 + ], + "2": [ + 1000, + 500 + ], + "3": [ + 1000, + 750, + 500 + ], + "4": [ + 1000, + 750, + 500, + 250 + ] + }, + "denoising_step_lists_first_chunk": { + "1": [ + 1000, + 750, + 500, + 250 + ], + "2": [ + 1000, + 750, + 500, + 250 + ], + "3": [ + 1000, + 750, + 500, + 250 + ], + "4": [ + 1000, + 750, + 500, + 250 + ] + }, + "detach_mllm_mq": true, + "discrete_cd_n": 48, + "ema_decay": 0.99, + "flowmatch_shift": 5.0, + "guidance_scale": 3.5, + "id_scale": 4.5, + "listening_loss_weight": 1.0, + "num_inference_steps": 4, + "num_train_timestep": 1000, + "slg_layer": 11, + "trainer": "causal_consistency_distillation", + "video_loss_weight": 1.0, + "warp_denoising_step": true + }, + "dmd": {}, + "fake_score_learning_rate": null, + "fake_score_lora_checkpointing": {}, + "flowmatch_shift": 5.0, + "gen_fps": 25.0, + "generator_lora_checkpointing": {}, + "has_context_prompt": { + "enable": true, + "gt_t5_transcript_ratio": 1.0, + "no_mq_context_uncond_ratio": 0.0, + "skip_mllm_encode_ratio": 0.3 + }, + "in_channels": 16, + "infer": false, + "latent_spatial_size": 28, + "latent_temporal_size": 25, + "listening_loss_weight": 1.0, + "listening_prompt": "not speaking, closed mouth, listening, silent", + "local_files_only": true, + "lora_checkpointing": { + "audio_adapter_name": "s2_ovi_audio_lora", + "merge_from_checkpoints": [ + { + "load_non_lora": true, + "path": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss" + } + ], + "merge_load_non_lora": true, + "resume_from_checkpoint": "./output/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1", + "resume_lora_only": true, + "video_adapter_name": "s2_ovi_vid_lora" + }, + "lora_dict": {}, + "max_input_text_tokens": 256, + "mllm_ce_only": false, + "mllm_id": "./pretrained_models/Qwen2.5-Omni-7B", + "mllm_local_path": null, + "mllm_lora_dict": { + "alpha": 64, + "dropout": 0.0, + "enable": false, + "rank": 32, + "target_modules": [ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj" + ] + }, + "model_type": "stage3_causal_cd_metaquery", + "modules_to_freeze": [ + "vae_model_video", + "vae_model_audio", + "text_model", + "fusion_model", + "mllm.mllm_backbone", + "mllm.connector" + ], + "modules_to_unfreeze": [], + "negative_text_prompt": "", + "noise_scheduler_id": "Lightricks/LTX-Video", + "num_metaqueries": 128, + "ovi_gradient_checkpointing_block_count": null, + "prev_temp_chunk": 1, + "real_score_lora_checkpointing": {}, + "rollout": {}, + "scheduler_id": "Lightricks/LTX-Video", + "stage": 3, + "stage_name": "causal_cd", + "student_checkpoint": null, + "student_lora_checkpointing": { + "audio_adapter_name": "s2_ovi_audio_lora", + "merge_from_checkpoints": [ + { + "load_non_lora": true, + "path": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss" + } + ], + "merge_load_non_lora": true, + "resume_from_checkpoint": "./output/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1", + "resume_lora_only": true, + "video_adapter_name": "s2_ovi_vid_lora" + }, + "system_prompt": "You are a speech-video clip narrator for a text-to-audio-video (T2AV) model.\nThe input includes audio-visual dialogue between the Interlocutor and the Agent. The dialogue context is provided ONLY to infer what occurs next and must NOT be described, referenced, or summarized explicitly.\nYour task is to write a narration describing the NEXT recorded speech and video clip that shows ONLY the AGENT. Follow these rules precisely:\nVISUAL RULES: - Provide a HIGH-DETAIL appearance sentence, covering: (a) Gender + precise clothing (material/color/style, e.g., \"velvet head covering with ruffled accent\"), (b) Distinctive features (eyeliner/beard/accessories), (c) Scene composition (framing/lighting/color tone/backdrop, e.g., \"stark black and white lighting against plain curtains\"). - Describe ONLY visible physical actions in strict chronological order: (a) Facial muscles (e.g., \"lips curve upward forming a subtle smile\"), (b) Eye movements (e.g., \"gaze drifts downward for two seconds\"), (c) Posture/gestures (e.g., \"thumb brushes beard thoughtfully\"). - Emotions MUST derive from observable cues: CORRECT: \"eyes crinkling at the corners in a warm smile\" (visible) WRONG: \"she feels happy\" (internal) or \"looks thoughtful\" (abstract).\nSPEECH RULES: - Determine speech occurrence based on PROVIDED TARGETS or NATURAL CONVERSATION FLOW: - IF TARGETS ARE PROVIDED: (a) If \"[Target State]: Listening\" is provided in the context, describe silent actions ONLY without any speech content and do not use tags. (b) If \"[Target Speech]: [Text]\" is provided, use the provided [Text] exactly as full speech content. - IF TARGETS ARE NOT PROVIDED: (a) If dialogue context implies continued listening or non-verbal reaction, describe silent actions ONLY without any speech content and do not use tags. (b) If dialogue context implies the agent WILL SPEAK NEXT (even after brief listening actions), generate speech. (c) When speech needs to be generated: (1) Infer the speech content EXCLUSIVELY from dialogue context. (2) The speech content must be a RESPONSE that move forward the conversation, but word overlap with the end of the agent\u2019s speech context is STRICTLY FORBIDDEN. - GENERAL SPEECH STYLE (Only for speech existing in the narration): (b) Wrap each segment in and (speech contents are broken down into multiple segments when there are too many words for one segment) in the MIDDLE of the narration. (c) For each speech segment, embed it NATURALLY within action descriptions BEFORE and AFTER speech: CORRECT: \"meeting the camera, I have considered your proposal he brushes his thumb against his beard.\" WRONG: separate speech/action blocks.\nAUDIO RULES: - Add \"Audio: [description]\" at the VERY END of the paragraph, separated by a single space. - If no speech occurs (means no tags exist), use \"SILENT\" for the audio description. - Otherwise, describe ONLY the agent\u2019s audio characteristics: (a) Voice traits: Describe the voice of the AGENT only. If the agent has spoken in the prior context, describe that voice in detail (pitch/tone/pace/texture, e.g., \"low-pitched with slight rasp\"). If the agent has NOT yet spoken in the context, you MUST ignore the interlocutor\u2019s voice entirely and infer a new, plausible voice for the AGENT EXCLUSIVELY based on his visual appearance (age, gender, physique). (b) Emotional progression (e.g., \"reflective shifting to joyful tone\"). (c) Recording environment ONLY if it affects vocal quality (e.g., \"dry and direct, quiet room\"). - NEVER mention background sounds, other voices, or specific locations (e.g., \"gift shop\"). - Never mix or attribute the interlocutor\u2019s vocal traits to the agent.\nTEMPORAL & STYLE RULES: - Chronological flow: [Full appearance sentence] \u2192 [Pre-speech actions] \u2192 [Speech Content] \u2192 [Post-speech actions] \u2192 (repeat for multiple speech segments if any) \u2192 Audio line - Appearance sentence must start with appearance-based identifier (e.g., \"A woman in a blue dress...\"), then use pronouns (she/he/they). - Use PRESENT TENSE throughout (e.g., \"she wears\", \"he gestures\"). - Write in THIRD-PERSON, single continuous paragraph. NO bullet points, headings, or line breaks. - NEVER use: agent, assistant, interlocutor, respond, reply, listen, understand.\nOUTPUT EXAMPLES (CRITICAL FOR FORMATTING): EXAMPLE 1 (Natural pause before speech, no target provided): \"A man in a grey sweatshirt stands before dark red curtains. His eyes track the interlocutor for two seconds, meeting the camera with a steady gaze, I have been considering your proposal all week. straightening his shoulders as his thumb brushes his beard thoughtfully. Audio: Calm low-pitched male voice with steady rhythm and slight rasp, recorded in a quiet room.\"\nEXAMPLE 2 (Silent reaction only, no target provided): \"A woman in a dark velvet head covering sits in stark black and white lighting. Her gaze drifts downward for three seconds, eyelids lowering slowly. Fingers tap once on the armrest as her head tilts upward with a subtle smile, eyes crinkling at the corners while maintaining silence. Audio: SILENT.\"\nEXAMPLE 3 (Multiple speech segments with emotional progression, no target provided):\nEXAMPLE 4 (Target provided and speech break into multiple segments, [Target Speech]: \"I completely understand your frustration with the current situation, but we need to stay focused on the long-term goals of the project.\"): \"A woman in a silk blouse sits against a bright window. She nods slowly as the interlocutor finishes, then takes a deep breath. Meeting the camera with a steady gaze, I completely understand your frustration with the current situation, her expression softens. Pausing briefly to adjust her posture, she continues, but we need to stay focused on the long-term goals of the project. She remains attentive, her eyes reflecting deep thought while awaiting a reply. Audio: Articulate female voice with a calm, reassuring tone and steady pace, recorded in a quiet environment.\"\nEXAMPLE 5 (Target provided andcontains [Target State]: Listening): \"A man in a flannel shirt leans back against a brick wall. He tilts his head slightly to the right, eyes tracking the interlocutor\u2019s movements. His brow furrows momentarily in concentration, lips pressed together in a neutral line. He maintains a steady, receptive posture, nodding once in acknowledgement while remaining silent. Audio: SILENT.\"\nNEVER OUTPUT LIKE THIS (common failures): - \"...last words of agent\u2019s context speech. Last words of context, then new content.\" // Repeating context - \"The agent looks thoughtful after hearing the question.\" // Banned words + internal state - \"I agree But I don\u2019t think it\u2019s a good idea\" // Short speech broken down into multiple segments and not embedded in actions - \"Audio: Quiet room with distant traffic.\" // Illegal background sound - \"A man speaks about his recovery.\" // Invented topic (must derive from context) - \"She has a kind face.\" // Abstract trait without visible cues", + "target_same_timestep_across_blocks": false, + "teacher_checkpoint": null, + "teacher_lora_checkpointing": { + "audio_adapter_name": "s2_ovi_audio_lora", + "merge_from_checkpoint": "./output/0217_s1_qwen2p5omni7b_ovi1p1_mq_nonverbal_context_and_T5_response_talking80_vision_audio_loss_0p5_ICL_voice_prefix_sink_loss", + "merge_load_non_lora": true, + "resume_from_checkpoint": "./output/0220_stage2_ar_tf_frame3_context_noise_0p5_aud_lookahead_a2a_all_v2a_1", + "resume_lora_only": true, + "video_adapter_name": "s2_ovi_vid_lora" + }, + "text_encoder_id": "./pretrained_models/Wan2.2-TI2V-5B", + "text_encoder_path": "./pretrained_models/Wan2.2-TI2V-5B/models_t5_umt5-xxl-enc-bf16.pth", + "torch_dtype": "bfloat16", + "transformers_version": "4.52.3", + "use_ref_as_sink": true, + "use_ref_audio_concat": { + "enable": true, + "uncond_ratio": 0.0 + }, + "vae_downsample_f": 16, + "vae_downsample_t": 4, + "vae_id": "Lightricks/LTX-Video", + "vae_path": "./pretrained_models/Wan2.2-TI2V-5B/Wan2.2_VAE.pth", + "video_context_noise": 0.5, + "video_lora_dict": { + "adapter_name": "s2_ovi_vid_lora", + "enable": true, + "lora_dropout": 0.0, + "network_alpha": 128, + "rank": 128, + "target_modules": [ + "q", + "k", + "v", + "o", + "ffn.0", + "ffn.2" + ] + }, + "video_negative_prompt": "jitter, bad hands, blur, distortion", + "window": {} +} diff --git a/backup-260717/0222_stage3_causal_cd_frame3-checkpoint-5200/latest 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{ + "TrainerControl": { + "args": { + "should_epoch_stop": false, + "should_evaluate": false, + "should_log": false, + "should_save": true, + "should_training_stop": false + }, + "attributes": {} + } + }, + "total_flos": 0.0, + "train_batch_size": 6, + "trial_name": null, + "trial_params": null +} diff --git a/backup-260717/0222_stage3_causal_cd_frame3-checkpoint-5200/zero_to_fp32.py b/backup-260717/0222_stage3_causal_cd_frame3-checkpoint-5200/zero_to_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..5995d6e6f04e43b989587aa9022a3aef0c66d694 --- /dev/null +++ b/backup-260717/0222_stage3_causal_cd_frame3-checkpoint-5200/zero_to_fp32.py @@ -0,0 +1,760 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: +# python zero_to_fp32.py . output_dir/ +# or +# python zero_to_fp32.py . output_dir/ --safe_serialization + +import argparse +import torch +import glob +import math +import os +import re +import gc +import json +import numpy as np +from tqdm import tqdm +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device, weights_only=False) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + total_files = len(files) + state_dicts = [] + for f in tqdm(files, desc='Loading checkpoint shards'): + state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +class GatheredTensor: + """ + A pseudo tensor that collects partitioned weights. + It is more memory efficient when there are multiple groups. + """ + + def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape): + self.flat_groups = flat_groups + self.flat_groups_offset = flat_groups_offset + self.offset = offset + self.partitioned_numel = partitioned_numel + self.shape = shape + self.dtype = self.flat_groups[0][0].dtype + + def contiguous(self): + """ + Merge partitioned weights from flat_groups into a single tensor. + """ + end_idx = self.offset + self.partitioned_numel + world_size = len(self.flat_groups) + pad_flat_param_chunks = [] + + for rank_i in range(world_size): + # for each rank, we need to collect weights from related group/groups + flat_groups_at_rank_i = self.flat_groups[rank_i] + start_group_id = None + end_group_id = None + for group_id in range(len(self.flat_groups_offset)): + if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]: + start_group_id = group_id + if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]: + end_group_id = group_id + break + # collect weights from related group/groups + for group_id in range(start_group_id, end_group_id + 1): + flat_tensor = flat_groups_at_rank_i[group_id] + start_offset = self.offset - self.flat_groups_offset[group_id] + end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id] + pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset]) + + # collect weights from all ranks + pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0) + param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous() + return param + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size + + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]])) + for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'): + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # memory efficient tensor + tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape) + state_dict[name] = tensor + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def to_torch_tensor(state_dict, return_empty_tensor=False): + """ + Convert state_dict of GatheredTensor to torch tensor + """ + torch_state_dict = {} + converted_tensors = {} + for name, tensor in state_dict.items(): + tensor_id = id(tensor) + if tensor_id in converted_tensors: # shared tensors + shared_tensor = torch_state_dict[converted_tensors[tensor_id]] + torch_state_dict[name] = shared_tensor + else: + converted_tensors[tensor_id] = name + if return_empty_tensor: + torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype) + else: + torch_state_dict[name] = tensor.contiguous() + return torch_state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag=None, + exclude_frozen_parameters=False, + lazy_mode=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient. + Convert the pesduo tensor to torch tensor by ``.contiguous()`` + + Returns: + - pytorch ``state_dict`` + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + Note: the above usage may not work if your application doesn't have sufficient free CPU memory. + You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. Or you can load state_dict in lazy mode :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu + for name, lazy_tensor in state_dict.item(): + tensor = lazy_tensor.contiguous() # to cpu + print(name, tensor) + # del tensor to release memory if it no longer in use + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters) + if lazy_mode: + return state_dict + else: + return to_torch_tensor(state_dict) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, + output_dir, + max_shard_size="5GB", + safe_serialization=False, + tag=None, + exclude_frozen_parameters=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_dir``: directory to the pytorch fp32 state_dict output files + - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB + - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + """ + + # Dependency pre-check + if safe_serialization: + try: + from safetensors.torch import save_file + except ImportError: + print('If you want to use `safe_serialization`, please `pip install safetensors`') + raise + if max_shard_size is not None: + try: + from huggingface_hub import split_torch_state_dict_into_shards + except ImportError: + print('If you want to use `max_shard_size`, please `pip install huggingface_hub`') + raise + + # Convert zero checkpoint to state_dict + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, + tag, + exclude_frozen_parameters, + lazy_mode=True) + + # Shard the model if it is too big. + weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin" + if max_shard_size is not None: + filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors") + # an memory-efficient approach for sharding + empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True) + state_dict_split = split_torch_state_dict_into_shards(empty_state_dict, + filename_pattern=filename_pattern, + max_shard_size=max_shard_size) + else: + from collections import namedtuple + StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"]) + state_dict_split = StateDictSplit(is_sharded=False, + filename_to_tensors={weights_name: list(state_dict.keys())}) + + # Save the model by shard + os.makedirs(output_dir, exist_ok=True) + filename_to_tensors = state_dict_split.filename_to_tensors.items() + for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"): + shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors} + shard_state_dict = to_torch_tensor(shard_state_dict) + output_path = os.path.join(output_dir, shard_file) + if safe_serialization: + save_file(shard_state_dict, output_path, metadata={"format": "pt"}) + else: + torch.save(shard_state_dict, output_path) + # release the memory of current shard + for tensor_name in list(shard_state_dict.keys()): + del state_dict[tensor_name] + del shard_state_dict[tensor_name] + del shard_state_dict + gc.collect() + + # Save index if sharded + if state_dict_split.is_sharded: + index = { + "metadata": state_dict_split.metadata, + "weight_map": state_dict_split.tensor_to_filename, + } + save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json" + save_index_file = os.path.join(output_dir, save_index_file) + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info("Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info("Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument("output_dir", + type=str, + help="directory to the pytorch fp32 state_dict output files" + "(e.g. path/checkpoint-12-output/)") + parser.add_argument( + "--max_shard_size", + type=str, + default="5GB", + help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size" + "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`" + "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances" + "without CPU OOM issues.") + parser.add_argument( + "--safe_serialization", + default=False, + action='store_true', + help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, + args.output_dir, + max_shard_size=args.max_shard_size, + safe_serialization=args.safe_serialization, + tag=args.tag, + exclude_frozen_parameters=args.exclude_frozen_parameters)