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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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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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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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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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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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92e6bff6dc
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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
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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
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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
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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
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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
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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
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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
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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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04e983b86b
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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
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ugh
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2024-10-12 10:41:35 -05:00 |
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d0ab7d755a
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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
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added experimental entropix sampling support
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2024-10-11 21:18:26 -05:00 |
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75a4c866d6
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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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acdce66d4e
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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
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faster
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2024-10-04 22:30:47 -05:00 |
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a507b769a1
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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
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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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d33a906119
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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
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fixes, again
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2024-09-06 11:41:41 -05:00 |
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94cf81d38c
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tweak
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2024-09-05 23:21:18 -05:00 |
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413097f5f7
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fixes
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2024-09-05 21:42:59 -05:00 |
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54547b74d8
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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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32287710a2
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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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2a1794c084
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ughghghhhh
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2024-08-09 21:15:01 -05:00 |
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ed373957e2
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maybe not
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2024-08-09 11:38:08 -05:00 |
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debcc93e7e
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add adapted MixtralAttention for when I make a bad decision to actually train a MoE
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2024-08-04 22:03:22 -05:00 |
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10aaf840e7
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added export option to convert Llama to MixtralMoE for another dumb experiment
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2024-08-04 20:25:06 -05:00 |
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3a65cc4b22
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fix issue with sft and shared tensors...
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2024-08-04 19:56:21 -05:00 |
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6a733eb2ed
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changed torch.Tensor().to(device, dtype) to just torch.tensor(..., device, dtype) because it's been bothering my autism that I'm creating tensors then converting rather than creating with the right device/dtype, some 'optimization' to compile the model but it doesnt seem to do anything useful
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2024-08-03 22:10:21 -05:00 |
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11fa3da665
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some cleanup, fixed the wrapper attention to explicitly use other sdpa backends
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2024-08-03 19:51:00 -05:00 |
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97c5241bef
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fixes, throw an exception when using NAR only model with non-unified position IDs, since for some reason it outputs garbage for the NAR
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2024-08-02 22:25:49 -05:00 |
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443422ecb5
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ugh, finally got some form of offloading working (need to test if it works on different GPUs, but GPU and CPU offloading seems to work in the test trainer)
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2024-08-01 22:43:39 -05:00 |
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c9ec6b28ef
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it actually wasn't working because Engines.__init__() automatically moves the entire module to the requested device, which was being called after offloading the model in the test trainer (and it seems I cant do it without injecting a bunch of shit in modeling_llama.py)
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2024-08-01 20:56:28 -05:00 |
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b4c895114c
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naive model offloading support (handles automatically splitting parts of the model to requested device per memory constraints, either inferred or requested in the yaml, input tensors are automatically migrated to the right device, it SEEMS to work for training under the test trainer when split between GPU and CPU) (this was specifically only because that Flux imagegen model released so I can test it there)
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2024-08-01 20:12:06 -05:00 |
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07f8e2ad06
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added option to set the causal size (how many tokens to sample per AR step), but requires the model to be trained for this (which explains why recurrent chunk sampling just doesn't work for the retnet tests, obvious in hindsight)
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2024-07-30 20:53:51 -05:00 |
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c2f5b916fc
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added what I think is DRY sampling
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2024-07-29 19:15:07 -05:00 |
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ce8bb1e4f7
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sanity cleanups with weird off-by-one-ness, cleaned up and validated vall_e.models.experimental works again
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2024-07-27 15:36:05 -05:00 |
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06e948aec1
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suppress warning on exit about distributed not being cleaned up (because I updated my system)
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2024-07-25 16:50:47 -05:00 |
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1acb0e9c84
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added experimental training setting to perform token dropout to MAYBE compensate for errors from the preceding RVQ level (two types: token error offset, token dropout embedding replace)
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2024-07-24 19:35:17 -05:00 |
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75b04686f8
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added prom-less training / inferencing, some other things
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2024-07-22 19:36:07 -05:00 |
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e19aa643a6
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cleaned up demo page creation, added option to pass in RVQ level sampling distribution for training
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2024-07-21 19:12:03 -05:00 |
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d87b492295
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added rudimentary demo page creator (currently just embeds base64 wavs into the page, need to test not doing that)
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2024-07-19 20:49:40 -05:00 |
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39f961abcd
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test trainer (vall_e.models.ar_nar) tests some SpeechX features
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2024-07-18 18:46:45 -05:00 |
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