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e50edc3b48
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added a flag to convert to a HF compatible model on export by stitching things
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2024-06-03 22:34:47 -05:00 |
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934672252b
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feverish cleanup
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2024-06-03 21:28:49 -05:00 |
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7feeb944a0
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probably insane with even entertaining going this route
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2024-06-03 20:26:27 -05:00 |
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c2a436d368
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somehow between training sessions grad_norm = None even though it worked before
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2024-06-02 08:29:27 -05:00 |
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c1fcd889d5
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reverted automatically disabling split loss calc, since it seems that it's actually cacling loss on prom causes the oddities, maybe
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2024-06-01 12:34:59 -05:00 |
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8cf176ab46
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ugh
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2024-06-01 10:46:42 -05:00 |
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827cf632e7
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report current loss scale and adjust grad norm by loss scale (for deepspeed)
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2024-06-01 10:44:32 -05:00 |
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d0ebce6bac
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ugh
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2024-06-01 10:30:13 -05:00 |
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39bc019142
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actually save per-rank sampler states
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2024-06-01 09:46:32 -05:00 |
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74df2f5332
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split sampler dict by global_rank, also handle splitting dataset paths by global_rank if sampler_type == path (because I do not trust DistributedSampler) (need to test)
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2024-06-01 09:29:49 -05:00 |
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31785f4eeb
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actually don't default to compute split losses, test bitnet model doesn't seem to be doing things right (despite debug printouts showing theyre roughly the same logit/loss sequences, could just be bitnet linears being not up to par on actual models)
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2024-06-01 09:12:51 -05:00 |
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e9c87060df
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oops
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2024-05-31 22:22:28 -05:00 |
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b482ca19ff
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added model config option to set KV head count for MQA/GQA instead of MHA for llama-based models (i think its very negligible both ways on such a small model size)
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2024-05-31 19:32:37 -05:00 |
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e15c6c74c3
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correctness
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2024-05-30 20:50:45 -05:00 |
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da473295b7
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better way to compute per-segment losses
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2024-05-28 19:29:54 -05:00 |
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6c49ad06a3
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forgot to reinclude mult by loss factors
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2024-05-27 20:40:21 -05:00 |
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b82f0d5c0c
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finally nailed the issue that caused logging to break on one machine but not another (bitnet includes zetascale which is a parasite that will break logging)
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2024-05-27 19:47:58 -05:00 |
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c0ac84c795
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uh
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2024-05-27 19:05:56 -05:00 |
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197d517181
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ugh
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2024-05-27 17:09:35 -05:00 |
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5af6f41c94
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added loss calcs against prom (requires the right settings for not shit results, disabled by default)
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2024-05-27 08:43:00 -05:00 |
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05cd8b797e
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nevermind it breaks training
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2024-05-25 18:03:43 -05:00 |
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85f9684720
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some cleanup
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2024-05-25 17:46:52 -05:00 |
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d760924719
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added kludgy eval only so I don't have to start training, type eval, stop training, then delete the logs for that session
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2024-05-25 17:39:51 -05:00 |
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ddbacde0d1
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DAC just doesn't work well enough......
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2024-05-25 11:07:52 -05:00 |
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e3ef89f5aa
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100x better for subtrain/eval to be by group instead
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2024-05-19 16:40:14 -05:00 |
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458b95d196
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added option to split between text loss and audio loss (to-do: document this better), because it may or may not be a problem with LLaMA-backed models because my loss hovers around 3.9 / 56% accuracy despite sounding decent at the moment
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2024-05-19 11:23:56 -05:00 |
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74e531d391
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ugh
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2024-05-18 12:02:56 -05:00 |
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59ef9461f8
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ugh
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2024-05-18 10:13:58 -05:00 |
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4bc7e5a6d1
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fix loading without needing an hdf5 dataset already prepped (and some other incidental speedups during dataloader prep)
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2024-05-18 07:14:26 -05:00 |
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d88a5ca183
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ugh
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2024-05-16 07:25:33 -05:00 |
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d9aabfa3ae
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final tweaks, hopefully, again
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2024-05-15 23:04:19 -05:00 |
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8d79f78e0a
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god I need to replace omegaconf
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2024-05-12 14:01:52 -05:00 |
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5eb5db7f7f
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just don't use DAC 24Khz, it's bad
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2024-05-12 13:41:17 -05:00 |
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230da8b559
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should be the final things to scramble around for, DAC's 24KHz model is unusable for this, but both encodec's 24KHz and DAC's 44KHz work
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2024-05-12 13:22:08 -05:00 |
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2437a86efa
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ugh
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2024-05-12 13:02:15 -05:00 |
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4f1593c8db
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a bunch of shit to salvage my old encodec-quantized audio because dac-encoded audio just does not want to converge
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2024-05-12 10:17:29 -05:00 |
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917eeb40d2
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ughhh
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2024-05-12 08:22:39 -05:00 |
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9910c75d5a
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checkpointing for bitnet impl
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2024-05-12 07:52:54 -05:00 |
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14709ac67f
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ughh
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2024-05-12 07:30:59 -05:00 |
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3774fcbdee
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ugh
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2024-05-11 22:58:38 -05:00 |
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856545f8bb
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nan loss detection (should have added it earlier), loss scaling for local backend + fp16
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2024-05-11 22:23:29 -05:00 |
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a755eb3c62
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ugh
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2024-05-11 17:34:45 -05:00 |
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88e9b9caff
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local ddp fix
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2024-05-11 17:29:01 -05:00 |
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3337c69e5a
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leverage between xformers and torch.backends.cuda.sdp_kernel for attention
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2024-05-11 17:14:05 -05:00 |
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d33c7bb7cf
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ugh
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2024-05-11 16:47:19 -05:00 |
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0b6499601b
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sanitizing
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2024-05-11 16:31:05 -05:00 |
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71e373064f
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remove redundant loss, tweak readme
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2024-05-11 15:02:47 -05:00 |
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04a80d6b55
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maybe it's better to be more explicit in deepspeed configs
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2024-05-11 13:57:43 -05:00 |
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4d93a16ef7
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might just be better to explicitly define prompt duration ranges, especially under a "train small contexts then increase it" training paradigm
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2024-05-11 09:50:54 -05:00 |
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bd0a36ba8d
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I swear I keep seeing tqdm flicker back a number
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2024-05-10 18:36:01 -05:00 |
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