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f7b8b1e825
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dropped subtrain dataloader since its useless to duplicate
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2024-11-11 17:00:49 -06:00 |
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cf9df71f2c
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use homwbrewed caching system for dataloader paths / durations (I'm pretty sure I am now triggering OOM killers with my entire dataset used)
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2024-11-11 16:32:08 -06:00 |
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a748e223ce
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tweaks
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2024-11-11 12:40:41 -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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9def34cd66
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lol
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2024-11-10 12:48:41 -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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a9d2faf2d7
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all I can do now until I wait for the model to (re)train for pure NAR
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2024-11-09 22:57:34 -06:00 |
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ad7e290a5e
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ugh (ROCm seems to silently clamp any token value >= logits.shape[-1] for loss calculation, while cuda will throw an assert, making it hard to find this dumb fuckup)
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2024-11-09 19:40:02 -06:00 |
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943fe70c10
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I don't know why this fixes an assert thrown but it does
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2024-11-09 19:04:13 -06:00 |
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f50d92ba6c
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Almost made a mistake
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2024-11-09 18:12:54 -06:00 |
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c6a38693a2
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This better work
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2024-11-09 18:04:59 -06:00 |
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8b3d1cf70a
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Something's Wrong
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2024-11-09 15:07:43 -06:00 |
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dcd5fecff3
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some cleanup while I wait for the NAR-len to train to an acceptable state (currently it performs okay, but only on audo after 3 seconds or so)
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2024-11-09 12:12:46 -06:00 |
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69b0b3b854
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set timestep tensor to whatever the time embedding's dtype is because it'll gripe under amp
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2024-11-09 00:11:16 -06:00 |
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5a09a5f6e9
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I forgot about the time embedding...
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2024-11-08 22:46:26 -06:00 |
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811b15d280
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I suppose I just have a shit training method since the sampler is as solid as I can get it...............
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2024-11-08 22:05:41 -06:00 |
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13b54953bd
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agony
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2024-11-08 13:34:39 -06:00 |
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c127c4e488
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'borrowed' a sampling scheduler for NAR-len's RVQ level 0 (better than before, but still not good enough)
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2024-11-07 21:19:14 -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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ed174c589e
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ugh
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2024-11-07 09:19:21 -06:00 |
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d13ab00ad8
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one more note
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2024-11-07 09:11:21 -06:00 |
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5698188824
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あたしって、ほんとバカ
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2024-11-07 09:10:18 -06:00 |
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77ff23e319
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repeat extend the prom to fill the initial tokens for nar-len (it somewhat works, the model just needs to train more)
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2024-11-06 23:29:53 -06:00 |
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a3bc26f7ec
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ugh
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2024-11-06 23:16:28 -06:00 |
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d606a693ff
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eval fix for nar-len
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2024-11-06 23:14:16 -06:00 |
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105ed51159
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I guess I'll fall for the NAR-len meme again (I don't know where my previous weights are, so I need to train it again to test something)
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2024-11-06 19:17:12 -06:00 |
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bcabde3454
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more notes
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2024-11-06 13:51:28 -06:00 |
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bfc5e1d723
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agony
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2024-11-05 22:30:49 -06:00 |
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aefe8fcdad
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UGH
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2024-11-05 22:13:58 -06:00 |
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556d9db0d5
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web UI support for HF ZeroGPU
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2024-11-05 21:38:02 -06:00 |
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e58a9469a3
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move layerskip to experimental settings.......
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2024-11-05 20:37:06 -06:00 |
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bbc2de3713
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ugh
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2024-11-05 11:50:05 -06:00 |
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9e65e05e83
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more windows specific fixes, limit gradio to <5.0.0 on linux (it works on windows, but not on my linux machine tm)
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2024-11-04 18:00:33 -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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d229725c76
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more adjustments (adjustments of early-exit entropy/varentropy thresholds, default rep pen being 1.5, experimental refine-on-stop, etc.)
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2024-11-03 18:31:28 -06:00 |
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aee08b7307
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changed layerskip float16 training warning (since it didnt seem to fry on my 4xV100 system)
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2024-11-03 09:58:29 -06:00 |
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3826f9bae4
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saner mask creation? (it doesnt matter, kv cache wont work)
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2024-11-02 21:00:21 -05:00 |
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ded746e157
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very, very naive layerskip speculative sampling (it just checks if the current layer's state is good enough)
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2024-11-02 11:49:05 -05:00 |
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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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ec79230965
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shuffled web UI options hidden by cfg.experimental to its own tab, expose early exit selection to inferencing (it kinda works naively, still need to implement self-speculation)
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2024-11-01 21:30:06 -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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fb8faa295b
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actually float16(+AMP) and layerskip is bad and will kill the model......
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2024-11-01 18:36:44 -05:00 |
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edf1e66bf9
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layerskip_r=6 fries the model so hard the loss is sub-1...
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2024-11-01 17:06:07 -05:00 |
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9b6c57bc57
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third time's the charm (for some reason it escaped me that I should treat early exit loss as an aux_loss to be used with the normal loss, as if I was training a MoE's router)
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2024-11-01 12:50:37 -05:00 |
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76ebef45dc
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off-by-one...
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2024-10-31 13:24:48 -05:00 |
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b63293cbbe
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ugh
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2024-10-30 22:49:11 -05:00 |
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a22534e8f4
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layer skip training implemented (need to gut the inferencing from the repo, and to actually see if the model can benefit from this)
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2024-10-30 20:05:45 -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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a96f5aee32
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adjusted how i want to pass eval kwargs
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2024-10-25 20:38:09 -05:00 |
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