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39096f8ff3
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redid loss calculation to be cleaner, and position ID generation, and other things (I might need to train the NAR-len from scratch and not resume from an existing checkpoint.........)
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2024-11-14 22:17:47 -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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c00fc18b62
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actually use the right embedding for nar-len
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2024-11-13 18:04:04 -06:00 |
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3ea8a610d6
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fix STT
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2024-11-13 14:27:15 -06:00 |
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910033343c
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overhauled how the right resp level / classifier gets picked to avoid cringemath
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2024-11-13 13:31:17 -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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be83ddabaa
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better causal-ness for split loss calc, and also do masking for NAR-len for it
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2024-11-13 10:17:52 -06:00 |
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6b76419123
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ugh
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2024-11-13 09:54:20 -06:00 |
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ad7cfffc00
|
NAR-len RVQ-0 was being trained causally.............
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2024-11-13 09:43:50 -06:00 |
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8286aa54c8
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do not pass timestep token/embedding since it doesn't seem to matter at all after all, fixed training masking rate to 80% because a paper said so
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2024-11-13 09:07:10 -06:00 |
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0f2584eba7
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new meme sampler PogChamp new meme sampler PogChamp (it sort of helps?)
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2024-11-12 22:30:09 -06:00 |
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663f07038d
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haha... (do not create a token dropout/noise mask when not training (this sadly didnt fix NAR-len output))
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2024-11-12 16:41:58 -06:00 |
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b09328069e
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actually do CFG sampling for base AR+NAR tasks
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2024-11-12 13:42:39 -06:00 |
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2495a7ef67
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Fixed STT in the web UI
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2024-11-12 12:49:53 -06:00 |
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8927bad7bc
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actually fixed rep pen (for ar and nar, it seems to help with nar unmasking)
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2024-11-11 21:40:19 -06:00 |
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b1f4db39c8
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threw in CFG sampling for normal model as well to experiment with
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2024-11-11 20:27:38 -06:00 |
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2f56696506
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overhauled inference/sampler kwargs to stop being a bloated mess
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2024-11-11 20:21:16 -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
|
This better work
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2024-11-09 18:04:59 -06:00 |
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8b3d1cf70a
|
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
|
I forgot about the time embedding...
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2024-11-08 22:46:26 -06:00 |
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811b15d280
|
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
|
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
|
ugh
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2024-11-07 09:19:21 -06:00 |
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d13ab00ad8
|
one more note
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2024-11-07 09:11:21 -06:00 |
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5698188824
|
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
|
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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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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aefe8fcdad
|
UGH
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2024-11-05 22:13:58 -06:00 |
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9e65e05e83
|
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
|
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
|
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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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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fb8faa295b
|
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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9b6c57bc57
|
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
|
off-by-one...
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2024-10-31 13:24:48 -05:00 |
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b63293cbbe
|
ugh
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2024-10-30 22:49:11 -05:00 |
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a22534e8f4
|
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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ccf71dc1b6
|
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
|
adjusted how i want to pass eval kwargs
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2024-10-25 20:38:09 -05:00 |
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92e6bff6dc
|
actually ar temp 0.5 with rep pen 1.125 seems to have the benefits of better outputs without it degrading some of the time but not all the time
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2024-10-23 00:03:35 -05:00 |
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8920e5e86b
|
actually have beam_width in the webUI work
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2024-10-22 22:06:22 -05:00 |
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910571ad34
|
too brainlet to diagnose why low temp / greedy sampling is randomly unstable some of the time
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2024-10-22 20:13:54 -05:00 |
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8eb9a4056b
|
modified default arguments (ar temp = 0 and rep pen = 1.125 seems to be stable, at least given the few things i tested), do not pass top k/top p/min p to NAR even though technically none of those things should matter when greedy sampling
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2024-10-22 18:12:39 -05:00 |
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1a02cd5bce
|
modify demo template to say F5 instead of YourTTS, swap LoRA comparison around to make the lora'd the base file, and the no-lora the suffix'd file
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2024-10-21 19:52:02 -05:00 |
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71731ed785
|
added prefixing with silence (was to test something, currently hidden under cfg.experimental=True)
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2024-10-18 17:19:52 -05:00 |
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fc8dfd8617
|
made greedy AR sampling viable (and preferable), with caveats (per comment in vall_e.models.ar_nar)
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2024-10-18 16:55:00 -05:00 |
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75b90be325
|
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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84005c5b00
|
entropix apparently processes the entire sequence of logits but it falls apart when doing that
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2024-10-13 12:01:12 -05:00 |
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c800d28bb8
|
respect attention defined in the yaml for web UI (which might explain why theres been a discrepancy in outputs for me)
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2024-10-13 11:02:24 -05:00 |
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ed6b7a690f
|
ugh.........
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2024-10-13 00:26:46 -05:00 |
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d405f243d4
|
at wits end in trying to output the right attention scores
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2024-10-12 23:53:13 -05:00 |
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70cf694cfd
|
output attention scores for SDPA/flash, since naive attention seems broken
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2024-10-12 12:09:17 -05:00 |
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04e983b86b
|
modified demo page to be more modular with demoing comparisons, actually provide a path to use modified naive attention, entropix sampling is not tied to an experimental yaml flag now
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2024-10-12 11:27:55 -05:00 |
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666e8038fb
|
ugh
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2024-10-12 10:41:35 -05:00 |
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3d6ef9666b
|
overridden naive llama attention to get the right score values that entropix needs
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2024-10-12 10:05:47 -05:00 |
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d6f7c86a5c
|
entropix tweaks (it doesn't output garbage but it loves to go for silence)
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2024-10-12 09:46:18 -05:00 |
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d0ab7d755a
|
added min-p (really does not seem useful since it's very sensitive), more tweaks to entropix
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2024-10-11 22:36:06 -05:00 |
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bef43a0c18
|
added experimental entropix sampling support
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2024-10-11 21:18:26 -05:00 |
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75a4c866d6
|
more demo page tweaks, added arg to force enable/disable LoRAs for inferencing (to-do: setup arg flags to handle this, and checkbox in web UI)
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2024-10-10 19:04:12 -05:00 |
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2ea978f318
|
added --eval-random-text-prompts to use random text prompts for eval pass, added --random-prompts for demo page and --lora to use a sample with the lora disabled, probably finally fixed validation dataloader breaking on eval
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2024-10-10 13:40:25 -05:00 |
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acdce66d4e
|
readme tweaks, set the (unused) default model download URL back to the base ar+nar-llama-8 model, as ar+nar-tts+stt-llama-8 was renamed back to it since it performs well
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2024-10-05 22:53:53 -05:00 |
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84c7419001
|
faster
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2024-10-04 22:30:47 -05:00 |
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a507b769a1
|
sped up inferencing by not doing .tolist() for rep pen / length pen (and a bug fix in the web UI from prev commit)
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2024-10-04 22:18:20 -05:00 |
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4a8e3ccf06
|
README tweaks, added --input-prompt-prefix as an experiment (its literally better to just not do this, but i'll retain it in case i have a revelation on how to improve it)
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2024-10-04 18:57:19 -05:00 |
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31e8b7edb8
|
tweaks and fixes for lora stuffs
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2024-09-08 18:05:21 -05:00 |
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54203c059d
|
validated rep pen for STT (sometimes needed to wrangle the model)
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2024-09-08 08:30:30 -05:00 |
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6a967f91b9
|
oops
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2024-09-07 22:13:49 -05:00 |
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4bd9bb39c8
|
webui for STT (still need to bake the model to handle it better, a few hours so far has it generate what looks like a normal transcription but does not correlate to the audio right now)
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2024-09-06 15:13:04 -05:00 |
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d33a906119
|
cleanup for AR_NAR inferencing to allow both TTS and STT tasks simultaneously (need to have training eval do this to though)
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2024-09-06 14:30:12 -05:00 |
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341e19162b
|
fixes, again
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2024-09-06 11:41:41 -05:00 |
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94cf81d38c
|
tweak
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2024-09-05 23:21:18 -05:00 |
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413097f5f7
|
fixes
|
2024-09-05 21:42:59 -05:00 |
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54547b74d8
|
experimental implementation of STT (need to actually test on a model, test trainer seems to work)
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2024-09-05 20:43:20 -05:00 |
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168e203942
|
ugh
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2024-08-30 14:39:07 -05:00 |
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685f4faec0
|
ugh
|
2024-08-30 10:46:26 -05:00 |
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32287710a2
|
moved prints to use logger, edited readme (fused_attn doesnt seem stable for training)
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2024-08-29 13:27:16 -05:00 |
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d423bc03c2
|
fixed attentions for MoE
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2024-08-27 17:02:42 -05:00 |
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b7b99a25f1
|
added ability to specify attention backend for CLI and webui (because im tired of editing the yaml)
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2024-08-26 19:33:51 -05:00 |
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0d706ec6a1
|
added fused_attn (triton-based fused attention) and simply just query for flash_attn under rocm
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2024-08-26 19:13:34 -05:00 |
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6b0891448c
|
pain (some shit to try and get some flash attention for ROCm (gfx1100) through triton fused attention but no good)
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2024-08-25 20:07:27 -05:00 |
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40e1799adc
|
fixed xformers and flash_attn to actually work now
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2024-08-19 01:03:35 -05:00 |
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29c35528e5
|
the sooner I accept there's no FA for V100s the sooner I'll go to bed
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2024-08-18 23:54:33 -05:00 |
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d636edd3a2
|
added flash_attn LlamaAttention (including flash_attn==1.0.9)
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2024-08-18 20:51:14 -05:00 |
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2a1794c084
|
ughghghhhh
|
2024-08-09 21:15:01 -05:00 |
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ed373957e2
|
maybe not
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2024-08-09 11:38:08 -05:00 |
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