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4073656293
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oops
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2024-06-05 20:53:10 -05:00 |
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48cd1054f9
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madness
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2024-06-04 23:48:51 -05:00 |
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406ff7bbe1
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re-implemented config.model.interleave for the HF-compat experimental method
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2024-06-04 14:19:52 -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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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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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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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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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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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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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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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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2437a86efa
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ugh
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2024-05-12 13:02:15 -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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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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0b6499601b
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sanitizing
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2024-05-11 16:31:05 -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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1547de5020
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haha...
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2024-05-09 23:15:52 -05:00 |
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b7bd885651
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some possible sanity with deepspeed config
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2024-05-09 22:48:42 -05:00 |
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b6131565ad
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autotune?
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2024-05-09 21:25:40 -05:00 |
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6ed6ab8c03
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a bit more cleanup for deepspeed ds_cfg creation
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2024-05-09 21:00:26 -05:00 |
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0d5d545a40
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crammed in DAdaptation (doesn't seem worth it) and ScheduleFree (forgot I wanted to weeks ago, seems promising), optimization wrapper cleanup, test trainer changes, etc.
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2024-05-09 20:28:20 -05:00 |
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215800484d
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correcting my wrong of assuming I could just use raw 24Khz audio in the 44Khz DAC without too much of an issue (there are issues)
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2024-05-04 23:49:15 -05:00 |
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33b7f81b94
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small cleanups
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2024-05-04 22:37:22 -05:00 |
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ffa200eec7
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added option to specify frames per second for the given audio representation (Encodec is 75Hz, DAC is 41Hz (at 24K sources))
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2024-05-04 12:05:41 -05:00 |
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c494894261
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simple DDP wrapper (for my NVlink test)
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2024-05-04 11:48:26 -05:00 |
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a7b43b98b5
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renamed cfg.bitsandbytes to cfg.optimizations (and having it serve as cfg.optimizations.bitsandbytes)
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2024-05-02 20:08:59 -05:00 |
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b5d1456a09
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backwards compat for my shitty old weights (was testing if disabling AudioEmbedding summing magically made things better (it did not))
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2024-04-29 22:14:01 -05:00 |
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5120ffdda7
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god it would be nice to know the best way to handle audio embeddings, because I genuinely don't know without skimming through papers or devoting X amount of GPU hours in training
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2024-04-29 18:24:05 -05:00 |
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caad7ee3c9
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final tweaks, hopefully
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2024-04-28 22:28:29 -05:00 |
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071fb97777
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dataset preparation script updates, caved and am using HF tokenizer now
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2024-04-21 14:49:18 -05:00 |
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a8ffa88844
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it slipped my mind that technically DAC can be used at any sample rate, since it models waveforms; make it a config YAML option to allow this behavior
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2024-04-19 18:36:54 -05:00 |
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4f5c9e518a
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actually use the passed-through sample rate from encode for DAC because it does its own resampling I guess
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2024-04-18 13:32:41 -05:00 |
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5ff2b4aab5
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finally swallowing the Descript-Audio-Codec pill (I guess I'm going to have to regenerate my entire dataset)
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2024-04-17 20:39:35 -05:00 |
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b0bd88833c
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refractor cleanup, had a revelation on how I can handle a batch of varying tasks
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2024-04-16 21:04:48 -05:00 |
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aa1e25fbf5
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backwards compat for old YAMLs with models, option to set flash attention 2 for Llama (and derivatives), included syncdoth/RetNets torchscale retnet for shits and grins, etc.
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2024-04-16 10:02:31 -05:00 |
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545162195b
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deprecate sole AR/NAR model by only keeping the AR+NAR (the beauty of no one using this is that I can break compat as much as I want), add tone token for when I classify my dataset with tone/emotion in the future, some other things
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2024-04-15 19:54:32 -05:00 |
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789bb5d11b
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add an optional label override for model loading (used for easy testing between 12/16/20/24 layered model)
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2024-04-13 12:43:35 -05:00 |
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f0c4baeb25
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added Adagrad (experimenting with it), added 'extended' model size (16 layers instead of 12, experimenting with it)
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2024-04-09 22:04:01 -05:00 |
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9d97eb5104
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added FP8 support through NVIDIA/TransformerEngine, added RetNet_HF through syncdoth/RetNet (as an alternative to branch away from torchscale)
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2024-04-08 20:14:51 -05:00 |
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7075c2a5f0
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added an option to allow injecting embeddings from another model, because it dawned upon me how valuable embeddings from a good model can be for subsequent trainings (defined under cfg.models._embeddings as a relative path to the yaml)
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2024-04-04 19:11:49 -05:00 |
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47435207f7
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Added cfg.bitsandbytes.replace as a less intrusive alternative to cfg.bitsandbytes.inject to replace all Linear modules in a model
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2024-03-01 19:20:10 -06:00 |
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0427d8d076
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logger broke for some reason, added flag to just tqdm.write instead, make cfg.bitsandbytes.bitnet==True yamls denoted since I'm sure they're not interoperable
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2024-03-01 10:32:35 -06:00 |
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35d78a2bb0
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Yet Another Underlying Transformer Implementation (BitNet, will give it a few days to see how it fares)
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2024-02-29 20:29:17 -06:00 |
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c690aa509d
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fixes and compat (MoE-fying an existing model and retraining from there just ruins it after a second of audio...)
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2023-12-25 21:20:32 -06:00 |
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9c198eb75a
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added torchscale XMOE integration (because Mixtral 8x7B seems very promising and I want to see if it works)
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2023-12-20 18:45:58 -06:00 |
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32d4271ca8
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fixed issue with training from scratch (oops)
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2023-10-21 09:55:38 -05:00 |
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