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2dd80a03ff
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stuff for interfacing with the loss scaler value (because I want to cap it)
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2025-03-06 17:07:29 -06:00 |
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a30dffcca7
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wandb additions (to-do eventually, upload samples as artifacts)
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2025-03-06 15:44:40 -06:00 |
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1cd24f3381
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a birdie tells me i should probably use a different optimizer (also preliminary support for native sparse attention but I don't know if I'll use it)
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2025-03-04 14:53:02 -06:00 |
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56f8be4d62
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lol
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2025-02-28 22:15:37 -06:00 |
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ddc49c89c5
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the learning rate scheduler pill is a tough pill to swallow
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2025-02-28 22:12:19 -06:00 |
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b97faa8173
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fixes...
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2025-02-28 18:53:07 -06:00 |
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ceecac6ffe
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I think I made resp_parallel_training=True faster with loss factoring?
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2025-02-26 23:13:32 -06:00 |
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7d2e64630c
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lol
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2025-02-26 10:49:06 -06:00 |
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95da4e9405
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made muon actually work by actually utilizing param groups (thanks APOLLO for reminding me this is the sane way to handle this split)
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2025-02-26 10:39:13 -06:00 |
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f593ee98fc
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ugh
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2025-02-23 21:20:36 -06:00 |
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cbf6b84e27
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fixed grad norm and loss scale not reporting for local trainer
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2025-02-23 19:08:26 -06:00 |
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b640fabab5
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borrowed muon since it might better work under deepspeed and not require cruft (even though it really does not like the masked-NAR, also make the masked-NAR faux-causal since it might better help out for cfg.model.version >= 7
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2025-02-23 17:23:24 -06:00 |
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3019c88799
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separate mask token and stop token because this might cause issues
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2025-02-23 11:36:32 -06:00 |
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6634d07576
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added muon optimizer through kludge hacks because it necessitates a second optimizer in tandum that seems to only sometimes work with deepspeed
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2025-02-23 11:22:13 -06:00 |
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ab0abd2b12
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fixes fixes fixes (a quarter of my recently processed audio returned zero'd tensors......)
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2025-02-22 09:07:33 -06:00 |
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a65c8144f4
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with the amount of tweaks I keep making I could have probably had the nvidia/audio-codec-44khz model realized already......
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2025-02-13 18:38:40 -06:00 |
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e8f182b634
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cleaned up loss calc code (it REALLY hates ignore_loss_for_inputs, but is fine with splitting with loss factors)
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2025-02-13 09:35:27 -06:00 |
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b52c5c5d80
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this seems to work in testing
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2025-02-12 16:16:04 -06:00 |
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e029a8804d
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ironically none of this cruft gets the loss lower than the original way
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2025-02-12 11:17:00 -06:00 |
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e5916ea519
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for my sanity it seems having extraneous tokens in the embedding/classifier has the loss/acc a little higher than it should
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2025-02-11 14:47:35 -06:00 |
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497bdfc67b
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more work (the wall is non-causal decoding......)
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2024-12-22 20:11:31 -06:00 |
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5f289db275
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ugh
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2024-12-22 16:15:24 -06:00 |
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353e478e68
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agony
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2024-12-21 22:52:10 -06:00 |
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4800e7179a
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remove nan checks because it causes problems in distributed training because I'm not syncing between GPUs (and nan losses gets ignored anyways with loss scaling)
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2024-12-15 09:42:54 -06:00 |
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3dd31e74d1
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finally figured out a clean way to handle "resuming" the tqdm bar
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2024-12-14 18:44:43 -06:00 |
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09804ecc16
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APOLLO tweaks to make it work with deepspeed
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2024-12-13 23:03:52 -06:00 |
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64c67160a3
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tweaks
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2024-12-13 19:00:35 -06:00 |
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0fbfb8bbe8
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actually save the optimizer for the local engine backend because safetensors doesn't save it
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2024-12-12 17:12:59 -06:00 |
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f41251f648
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more fixes for local engine backend
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2024-12-12 14:38:42 -06:00 |
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6b237ae5e3
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tweaks for the local engine orchestrator (that I never caught since I always used the deepspeed backend)
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2024-12-12 13:37:38 -06:00 |
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9a62e3b824
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APOLLO cringe (doesn't want to work with deepspeed)
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2024-12-12 00:31:58 -06:00 |
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8568a93dad
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added WER/SIM-O metrics, added APOLLO but I need to test it
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2024-12-10 20:13:21 -06:00 |
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61ed662856
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ACTUALLY actually fix KD-loss (the -inf in the logits was caused by cringecode)
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2024-12-07 12:31:54 -06:00 |
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23d402bf01
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added knowledge distillation in the trainer (sadly it is not agnostic because of the grave mistake of further processing the batch within the forward pass, so subsequent calls do not match......)
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2024-12-05 23:05:52 -06:00 |
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3fc0540f49
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m
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2024-11-21 15:07:46 -06:00 |
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dfdba3f190
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oops
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2024-11-20 19:21:03 -06:00 |
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cd6e9ba2f2
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oops
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2024-11-20 16:27:51 -06:00 |
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1a73ac6a20
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I cannot believe it's not actually called Wand DB (added wandb logging support since I think it would have been a much better way to look at my metrics)
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2024-11-20 16:10:47 -06:00 |
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190a917b3e
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I did it.
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2024-11-19 12:24:33 -06:00 |
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e412e98125
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ugh
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2024-11-14 07:34:22 -06:00 |
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269648605e
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move NAR-len rvq level 0 to separate embedding
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2024-11-13 11:38:58 -06:00 |
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48490757da
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fixes
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2024-11-10 20:37:50 -06:00 |
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9cb0b6901b
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unified nar.py into ar_nar.py
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2024-11-10 12:19:48 -06:00 |
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e108c54daf
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new NAR-len training paradigm......
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2024-11-07 11:32:11 -06:00 |
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c83670c38c
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Windows specific fixes (to-do: find libespeak-ng.dll automatically because it cannot be trusted to do it by default)
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2024-11-03 19:19:15 -06:00 |
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62fe5b0943
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ughh
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2024-11-01 22:36:48 -05:00 |
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ef1c17430f
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skip step on nan loss (ironically I have not had a nan loss after adding this), throw exception with invalid cfg.dataset.sample_type and sample_order combination (because I was tricked by this in my yaml and had inconsistent vram usage)
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2024-11-01 20:54:53 -05:00 |
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4049f51ba9
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added option to load lora directly from the model file itself with --lora
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2024-10-26 00:13:10 -05:00 |
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ccf71dc1b6
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added option to load from a model state dict directly instead of a yaml (to-do: do this for LoRAs too), automatically download the default model if none is provided
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2024-10-25 22:15:15 -05:00 |
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75b90be325
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cleaned up unused config flags, allow less strict yaml by pruning missing keys, renamed some dataset configs to be more unified
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2024-10-17 17:06:48 -05:00 |
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