|
|
d04f6911b4
|
oops
|
2024-08-08 19:38:55 -05:00 |
|
|
|
949339a3fa
|
do not include SDPA attention if there's no available SDPA backends
|
2024-08-06 20:42:39 -05:00 |
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|
|
7cdfa3dc0c
|
updated process_datasets.py, added argparsing so I can mostly stop manually editing things, and some other cleanup
|
2024-08-05 15:59:25 -05:00 |
|
|
|
debcc93e7e
|
add adapted MixtralAttention for when I make a bad decision to actually train a MoE
|
2024-08-04 22:03:22 -05:00 |
|
|
|
10aaf840e7
|
added export option to convert Llama to MixtralMoE for another dumb experiment
|
2024-08-04 20:25:06 -05:00 |
|
|
|
3a65cc4b22
|
fix issue with sft and shared tensors...
|
2024-08-04 19:56:21 -05:00 |
|
|
|
23f3b56fda
|
oops
|
2024-08-04 08:18:57 -05:00 |
|
|
|
6a733eb2ed
|
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
|
2024-08-03 22:10:21 -05:00 |
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|
|
d0a5c7eca2
|
more coping with the NAR len
|
2024-08-03 20:23:36 -05:00 |
|
|
|
11fa3da665
|
some cleanup, fixed the wrapper attention to explicitly use other sdpa backends
|
2024-08-03 19:51:00 -05:00 |
|
|
|
9564ecda43
|
wrapper attention class for other sdpa backends + xformers seems to have broke...
|
2024-08-03 15:12:11 -05:00 |
|
|
|
9e1989be1b
|
tweaked initial NAR pass's initial token embeddings to use a different value, or osmething
|
2024-08-03 09:01:37 -05:00 |
|
|
|
26f74c5739
|
somehow fixed non-unified position IDs for the NAR-len
|
2024-08-03 08:43:42 -05:00 |
|
|
|
66407e5bdb
|
tweaks for the NAR-len model, maybe
|
2024-08-03 08:40:39 -05:00 |
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|
|
97c5241bef
|
fixes, throw an exception when using NAR only model with non-unified position IDs, since for some reason it outputs garbage for the NAR
|
2024-08-02 22:25:49 -05:00 |
|
|
|
443422ecb5
|
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)
|
2024-08-01 22:43:39 -05:00 |
|
|
|
c9ec6b28ef
|
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)
|
2024-08-01 20:56:28 -05:00 |
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|
|
b4c895114c
|
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)
|
2024-08-01 20:12:06 -05:00 |
|
|
|
387358bc8a
|
fixes for the NAR-len model, and documentation some config options, and a better way to handle resizing modules on state_dict load
|
2024-07-31 20:35:09 -05:00 |
|
|
|
07f8e2ad06
|
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)
|
2024-07-30 20:53:51 -05:00 |
|
|
|
ebf848d249
|
possible speedup for samplers that require a list of previous tokens (the DRY sampler made me realize that I should copy the tolist() thing from the rep pen sampler for everything else)
|
2024-07-29 20:23:26 -05:00 |
|
|
|
55b0121b1a
|
trying (and failing) to nail a weird regression in fancier attentions
|
2024-07-29 19:53:37 -05:00 |
|
|
|
c2f5b916fc
|
added what I think is DRY sampling
|
2024-07-29 19:15:07 -05:00 |
|
|
|
ce8bb1e4f7
|
sanity cleanups with weird off-by-one-ness, cleaned up and validated vall_e.models.experimental works again
|
2024-07-27 15:36:05 -05:00 |
|
|
|
06e948aec1
|
suppress warning on exit about distributed not being cleaned up (because I updated my system)
|
2024-07-25 16:50:47 -05:00 |
|
|
|
1acb0e9c84
|
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)
|
2024-07-24 19:35:17 -05:00 |
|
|
|
188d116222
|
some weird fixes for an equally weird regression with LoRA loading
|
2024-07-22 20:47:24 -05:00 |
|
|
|
75b04686f8
|
added prom-less training / inferencing, some other things
|
2024-07-22 19:36:07 -05:00 |
|
|
|
e19aa643a6
|
cleaned up demo page creation, added option to pass in RVQ level sampling distribution for training
|
2024-07-21 19:12:03 -05:00 |
|
|
|
d87b492295
|
added rudimentary demo page creator (currently just embeds base64 wavs into the page, need to test not doing that)
|
2024-07-19 20:49:40 -05:00 |
|
|
|
d53038a9e4
|
actually have split classifiers working
|
2024-07-19 15:33:31 -05:00 |
|
|
|
28a674e0f1
|
fixes...
|
2024-07-18 23:25:32 -05:00 |
|
|
|
39f961abcd
|
test trainer (vall_e.models.ar_nar) tests some SpeechX features
|
2024-07-18 18:46:45 -05:00 |
|
|
|
83a0954f85
|
fixes for re-introducing SpeechX tasks (need to actually validate if these all do the right things)
|
2024-07-18 17:16:32 -05:00 |
|
|
|
97e768601c
|
re-introducing SpeechX tasks (need to validate them all, everything works with base tts anyways)
|
2024-07-18 16:16:14 -05:00 |
|
|
|
c2b8035e74
|
oops, kept forgetting to actually pass in lang/tone tokens (despite not really using these at the moment)
|
2024-07-18 14:18:34 -05:00 |
|
|
|
22fe53508c
|
added experimental disjointed position IDs (because I *think* this might help because technically a sequence is made up of several parts, and the position embeddings shouldn't be unified)
|
2024-07-16 19:52:41 -05:00 |
|
|
|
fe0f235335
|
mechanism to store the model config inside the weights and load them, some other things to allow LoRA training on the RetNet (gradient checkpointing will gripe about inputs not having require_grad and nothing seems to remedy it)
|
2024-07-16 18:23:13 -05:00 |
|
|
|
3acc54df22
|
allow loading a different model within the web ui (apparently I did not have the web UI in the documentation)
|
2024-07-15 19:59:48 -05:00 |
|
|
|
7b210d9738
|
sanity cleanup
|
2024-07-04 15:58:08 -05:00 |
|
|
|
f770467eb3
|
stuff
|
2024-07-01 18:13:29 -05:00 |
|
|
|
dced595391
|
more cleanup
|
2024-06-30 11:00:12 -05:00 |
|
|
|
bc2a6fa756
|
sanity cleanup: moved experimental features under its own thing
|
2024-06-30 10:37:33 -05:00 |
|
|
|
b21f74a5c5
|
added summing of external embeddings (at this point i dont think any amount of cope bandaids will get DAC to train nicely, I think the RVQ levels the NAR tends add too much noise if they're not accurate)
|
2024-06-29 23:42:30 -05:00 |
|
|
|
793ccb16fb
|
ugh
|
2024-06-29 22:14:35 -05:00 |
|
|
|
2808f881c8
|
cleaned up subjugated audio embedding into a flag, flag can also have it include the original, underlying embedding as well (it seems to do better when set to inclusive)
|
2024-06-29 21:46:35 -05:00 |
|
|
|
ec5eaebcbc
|
experimental method of using DACs quantizer ""embeddings"" to see if it helps with model quality
|
2024-06-29 19:46:11 -05:00 |
|
|
|
a8718d35a4
|
nasty bandaid because some of my DAC dataset only has 8 RVQ levels instead of the full 9
|
2024-06-29 10:16:37 -05:00 |
|
|
|
591d3ac848
|
have eval dataloader use eval batch size for batchedordersampler
|
2024-06-28 22:44:00 -05:00 |
|
|
|
83075c1505
|
sort duration buckets to ensure that paths sorted-by-duration are actually sorted by duration (because i didnt know that python dicts can have non-strings as keys), added batching samples based on total duration to ensure best training throughput
|
2024-06-28 22:28:54 -05:00 |
|
|
|
8a986eb480
|
load exported LoRA weights if exists (to-do: make a better LoRA loading mechanism)
|
2024-06-18 21:45:46 -05:00 |
|
|
|
2bfe786ebd
|
ban stop token for NAR levels (because sometimes it gets sampled and causes problems)
|
2024-06-17 22:14:43 -05:00 |
|
|
|
7cfb78fa64
|
enable LoRA for targetted RVQ levels (to experiment with, seems to help)
|
2024-06-17 21:45:03 -05:00 |
|
|
|
7047fcc6e2
|
actually make deepspeed work with LoRAs
|
2024-06-17 13:55:37 -05:00 |
|
|
|
1d159b1476
|
updated export routine to split LoRA weights from the state dict (should work with deepspeed)
|
2024-06-17 13:28:18 -05:00 |
|
|
|
bd0bc10ec0
|
added LoRA policy to decide what layer of the model gets adapted based on simple inclusion/exclusion terms
|
2024-06-17 13:05:06 -05:00 |
|
|
|
be051d9544
|
added other LoRA method using parametrization rather than linear injection
|
2024-06-17 09:58:34 -05:00 |
|
|
|
45a39fb79f
|
very rudimentary lora support (no deepspeed support, tested training and saving but not loading yet)
|
2024-06-17 00:09:16 -05:00 |
|
|
|
19410a919e
|
ugh
|
2024-06-15 12:29:03 -05:00 |
|
|
|
d343bde09b
|
residual_in_fp32=False for mamba arch backends because it breaks the classifier (output projection / lm head / what-have-you) under AMP
|
2024-06-15 12:08:03 -05:00 |
|
|
|
ccb14c06ef
|
mamba2-hf using vasqu/mamba2-torch because it lets me use mamba2 without triton ops (training with my 4xV100s are not happy with mamba2 because of triton)
|
2024-06-14 19:42:17 -05:00 |
|
|
|
83eab4fa59
|
actually going for the suggested "2x layers, no intermediate scaling" is wrong for VALL-E, directly copying the normal transformer structure fixes mamba2 performance in the test trainer
|
2024-06-13 20:08:22 -05:00 |
|
|
|
26da24fd8d
|
mamba updated to fix that pesky NaN error during training
|
2024-06-13 12:38:33 -05:00 |
|
|
|
bcf3910a17
|
the NAR only dream is dead (it just won't work)
|
2024-06-12 19:49:47 -05:00 |
|
|
|
a9353cf9fa
|
ugh
|
2024-06-12 00:14:29 -05:00 |
|
|
|
cca542a4c0
|
ugh
|
2024-06-11 23:59:28 -05:00 |
|
|
|
65a8960305
|
option to split classifier per-level instead of sharing one (at this point I'm just scrambling to try and cope with training a DAC model, the NAR is being a pain)
|
2024-06-11 22:28:59 -05:00 |
|
|
|
a7a6e0ac76
|
validated that inferencing works, changed some defaults (NAR benefits from greedy sampling)
|
2024-06-09 17:11:38 -05:00 |
|
|
|
132a02c48b
|
sanity cleanup, backup config yaml for each log file
|
2024-06-09 11:22:52 -05:00 |
|
|
|
80f9530840
|
ugh
|
2024-06-09 01:43:44 -05:00 |
|
|
|
5c732b72ee
|
ugh
|
2024-06-08 20:34:00 -05:00 |
|
|
|
8d068fa3f9
|
reticulating splines
|
2024-06-08 20:30:15 -05:00 |
|
|
|
b072f9b96b
|
fixes
|
2024-06-08 16:01:34 -05:00 |
|
|
|
58fb0a84db
|
added experimental NAR only model (inferences text length, need more experimenting), AudioEmbedding logic cleanup (I still think it's being done wrong)
|
2024-06-08 15:42:02 -05:00 |
|
|
|
7d6fff24f9
|
un-tensor'd quant_level marker since it doesn't need to be one (I forgot why I had it as one but nothing seems to need it as a tensor that didn't already make it one)
|
2024-06-07 20:46:22 -05:00 |
|
|
|
b0158a61d5
|
fixed some logic errors with training (grabbing wrong quant level...)
|
2024-06-07 20:34:36 -05:00 |
|
|
|
eafa622be2
|
I forgot the actual reason I was cleaning things up was to re-include prom loss calculation (I realized the reason I did this was because of an prom embedding oversight, it seems to work now)
|
2024-06-07 20:29:25 -05:00 |
|
|
|
f9f309281a
|
ugh
|
2024-06-06 20:55:27 -05:00 |
|
|
|
a5c90348d9
|
head hurt
|
2024-06-06 20:51:31 -05:00 |
|
|
|
516b0894d7
|
m
|
2024-06-06 19:41:26 -05:00 |
|
|
|
ee25d2e62e
|
removed the need to supply targ_list + different AudioEmbedding + other things
|
2024-06-06 18:52:41 -05:00 |
|
|
|
fcac9503e2
|
cleanup
|
2024-06-06 13:08:02 -05:00 |
|
|
|
b2194b859a
|
re-added loading multiple models because I'm now entertaining having split AR/NAR models again (and need a way to load both at once)
|
2024-06-06 09:48:43 -05:00 |
|
|
|
b05a905b95
|
ugh
|
2024-06-05 21:02:05 -05:00 |
|
|
|
4073656293
|
oops
|
2024-06-05 20:53:10 -05:00 |
|
|
|
ff6fe6f1bc
|
cleanup
|
2024-06-05 20:30:43 -05:00 |
|
|
|
880b4ecd1b
|
cleanup, putting some thoughts in comments before I forget about them
|
2024-06-05 19:50:06 -05:00 |
|
|
|
3cfc8a96bb
|
oops
|
2024-06-05 10:30:04 -05:00 |
|
|
|
48cd1054f9
|
madness
|
2024-06-04 23:48:51 -05:00 |
|
|
|
9e3f2e300f
|
experimental "just have a token for what rvq level we're on" that seems to help all models (mamba almost works, but it might just have to be relegated as a pure AR model)
|
2024-06-04 23:23:31 -05:00 |
|
|
|
e0886c5a78
|
re-added mamba as a possible non-experimental arch backend (test trainer will set it as AR only, doing any NAR tasks lobotomizes it)
|
2024-06-04 22:41:22 -05:00 |
|
|
|
687c71e028
|
disable accuracy calc because it breaks with actual batched training even though it shouldn't
|
2024-06-04 22:13:44 -05:00 |
|
|
|
d005e24953
|
oops
|
2024-06-04 22:10:04 -05:00 |
|
|
|
0f7f3ae754
|
added loss calc split and acc for experimental model
|
2024-06-04 22:04:40 -05:00 |
|
|
|
014e565c4b
|
tweaks
|
2024-06-04 20:41:13 -05:00 |
|
|
|
6d5bd0156a
|
fixes
|
2024-06-04 18:50:48 -05:00 |
|
|
|
ed3aeaf3a1
|
copy pasted from test to actual trainer
|
2024-06-04 18:40:30 -05:00 |
|
|
|
0aa01ba31a
|
forgot one crucial detail (you *need* the previous RVQ level to keep coherence between all RVQ levels) (experimental deinterleaved is a bit crusty though)
|
2024-06-04 18:30:30 -05:00 |
|
|
|
2ffad5cb6f
|
typo
|
2024-06-04 14:20:57 -05:00 |
|
|
|
406ff7bbe1
|
re-implemented config.model.interleave for the HF-compat experimental method
|
2024-06-04 14:19:52 -05:00 |
|
|
|
c93d5863fd
|
fixes
|
2024-06-04 00:07:00 -05:00 |
|
|
|
934672252b
|
feverish cleanup
|
2024-06-03 21:28:49 -05:00 |
|
|
|
7feeb944a0
|
probably insane with even entertaining going this route
|
2024-06-03 20:26:27 -05:00 |
|
|
|
b482ca19ff
|
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)
|
2024-05-31 19:32:37 -05:00 |
|
|
|
e15c6c74c3
|
correctness
|
2024-05-30 20:50:45 -05:00 |
|
|
|
da473295b7
|
better way to compute per-segment losses
|
2024-05-28 19:29:54 -05:00 |
|
|
|
6c49ad06a3
|
forgot to reinclude mult by loss factors
|
2024-05-27 20:40:21 -05:00 |
|
|
|
b82f0d5c0c
|
finally nailed the issue that caused logging to break on one machine but not another (bitnet includes zetascale which is a parasite that will break logging)
|
2024-05-27 19:47:58 -05:00 |
|
|
|
c0ac84c795
|
uh
|
2024-05-27 19:05:56 -05:00 |
|
|
|
197d517181
|
ugh
|
2024-05-27 17:09:35 -05:00 |
|
|
|
5af6f41c94
|
added loss calcs against prom (requires the right settings for not shit results, disabled by default)
|
2024-05-27 08:43:00 -05:00 |
|
|
|
ddbacde0d1
|
DAC just doesn't work well enough......
|
2024-05-25 11:07:52 -05:00 |
|
|
|
e3ef89f5aa
|
100x better for subtrain/eval to be by group instead
|
2024-05-19 16:40:14 -05:00 |
|
|
|
458b95d196
|
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
|
2024-05-19 11:23:56 -05:00 |
|
|
|
917eeb40d2
|
ughhh
|
2024-05-12 08:22:39 -05:00 |
|
|
|
9910c75d5a
|
checkpointing for bitnet impl
|
2024-05-12 07:52:54 -05:00 |
|
|
|
14709ac67f
|
ughh
|
2024-05-12 07:30:59 -05:00 |
|
|
|
a755eb3c62
|
ugh
|
2024-05-11 17:34:45 -05:00 |
|
|
|
88e9b9caff
|
local ddp fix
|
2024-05-11 17:29:01 -05:00 |
|
|
|
3337c69e5a
|
leverage between xformers and torch.backends.cuda.sdp_kernel for attention
|
2024-05-11 17:14:05 -05:00 |
|
|
|
d33c7bb7cf
|
ugh
|
2024-05-11 16:47:19 -05:00 |
|
|
|
0b6499601b
|
sanitizing
|
2024-05-11 16:31:05 -05:00 |
|
|
|
2109712e5b
|
resolve deprecation warning that doesn't show on my old training rig but does on my new one
|
2024-05-09 23:25:44 -05:00 |
|
|
|
1547de5020
|
haha...
|
2024-05-09 23:15:52 -05:00 |
|
|
|
0d5d545a40
|
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.
|
2024-05-09 20:28:20 -05:00 |
|
|
|
33b7f81b94
|
small cleanups
|
2024-05-04 22:37:22 -05:00 |
|
|
|
253441b750
|
forgot to disable verbose flag
|
2024-05-04 13:13:52 -05:00 |
|
|
|
3dca1125f5
|
implemented xformers in HF's Llama (because theres no flash attention for Volta cards)
|
2024-05-04 13:07:45 -05:00 |
|
|
|
ffa200eec7
|
added option to specify frames per second for the given audio representation (Encodec is 75Hz, DAC is 41Hz (at 24K sources))
|
2024-05-04 12:05:41 -05:00 |
|
|
|
c494894261
|
simple DDP wrapper (for my NVlink test)
|
2024-05-04 11:48:26 -05:00 |
|
|
|
a7b43b98b5
|
renamed cfg.bitsandbytes to cfg.optimizations (and having it serve as cfg.optimizations.bitsandbytes)
|
2024-05-02 20:08:59 -05:00 |
|
|
|
b5d1456a09
|
backwards compat for my shitty old weights (was testing if disabling AudioEmbedding summing magically made things better (it did not))
|
2024-04-29 22:14:01 -05:00 |
|
|
|
5120ffdda7
|
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
|
2024-04-29 18:24:05 -05:00 |
|
|
|
caad7ee3c9
|
final tweaks, hopefully
|
2024-04-28 22:28:29 -05:00 |
|
|
|
b251669536
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forgot to fix up the test trainer
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2024-04-21 14:58:04 -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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467fa1c5ee
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wrapper fixes
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2024-04-16 10:19:02 -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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d69a00e389
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Properly pass retention_mask for retnet-HF, attempt to fix recurrent forward for retnet (doesn't work still)
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2024-04-14 13:12:50 -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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4d75ee066c
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actually do the Linear replacement with TE's Linear
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2024-04-09 14:41:13 -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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91062361af
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tweaks
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2024-03-01 20:38:06 -06:00 |
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f3c59c3e7e
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cleaner replacement code (because I realized BitNet had an implementation for it too), added calculating gradient norm and performing gradient clipping in local trainer (non-deepspeed)
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2024-03-01 20:18:43 -06:00 |
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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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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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3da1518ace
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added Mistral (non-Mixtral) backend, useless optimization when not training, proper adjustment of the LR for Prodigyopt through d_coeff (maybe), recurrent sampling for LLaMA/Mistral/Mixtral backends (again, doesn't actually work)
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2024-01-31 21:48:36 -06:00 |
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cce929e136
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nasty hotfix for transformer's Mixtral throwing an error when batch sizes > 1
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2024-01-26 19:41:12 -06:00 |
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e799665759
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experimental weighting of prom/resp embeds
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2024-01-25 12:18:48 -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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e513d2ef19
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experts weren't forwarded into constructer (wasted a few days of training garbage)
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2023-12-23 16:08:17 -06:00 |
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0db3203b21
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added LLaMA/Mixtral (if experts>1) model arches, utilize XMoE's loss as well, set MoE frequency to 1 to make every layer MoE'd for RetNet, etc. (going to do tests without burning out again to see how things go)
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2023-12-22 19:27:36 -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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ed54f4ebec
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un 'experimental' the better target sequence preparation
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2023-10-22 09:06:59 -05:00 |
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9a6040383e
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make validation samplers ignore sampler type
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2023-10-22 09:01:47 -05:00 |
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09cda7d3f9
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added sampling by speaker group name (might be better to de-emphasize the LibriVox/Audiobooks that are in large numbers, and emphasize the smaller pools), log cleanup
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2023-10-16 19:30:38 -05:00 |
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a539f6889f
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mucked around with the loss calculation, this seems better?
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2023-10-13 18:22:21 -05:00 |
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65f500083d
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tweaks to try and get deepspeed quantized inferencing, validating bitsandbytes and deepspeed quantization, nothing seems to work
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2023-10-12 22:21:43 -05:00 |
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08bae355eb
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actually use langs from the dataloader
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2023-10-11 21:21:50 -05:00 |
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3af19d79fd
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oops
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2023-10-11 20:49:54 -05:00 |
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8740cdefc6
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added initial support for languages (still testing, marked as model version 3), added experimental 'context extend by limiting the resp context' (untested)
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2023-10-11 20:38:40 -05:00 |
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7facacf7c9
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separated samplers into its own file, don't bother copying the logits back to the GPU after sampling, it's not necessary
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2023-10-11 12:25:31 -05:00 |
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47b3077415
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fixed mirostat issue
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2023-10-10 18:09:49 -05:00 |
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99e980d323
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documentation and more better-er attribution
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2023-10-10 17:15:16 -05:00 |
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e727b6e5c1
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changed dynamic temperature trigger to be a min-(n)ar-temp value between [0,(n)ar-temp), flags to set min temp, checkbox in web UI to request it
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2023-10-10 17:02:33 -05:00 |
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ec25f56bd9
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used torch.max fixes things, somehow, for dynamic temp sampling
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2023-10-10 16:42:24 -05:00 |
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87db03dd93
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trim the input prompt to 3 seconds when training NAR tasks (marked as experimental; the paper mentions doing so, but I don't know how much this would harm the retention heads)
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2023-10-09 22:03:58 -05:00 |
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26fbb92ec6
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reduced dynamic temperature threshold to > 1.0, as it seems to not quite be useful for audio LMs, sped up any sampling that touches logits by copying them to CPU first, as accessing tensors on the GPU is slow as balls)
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2023-10-09 14:46:17 -05:00 |
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27483e56f0
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disabled preparing of SpeechX tasks, added dynamic temperature testing (to-do: test it, credited in the function)
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2023-10-09 13:01:40 -05:00 |
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2deb995cc9
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updated setup script
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2023-10-06 20:08:28 -05:00 |
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63cc9cf37a
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added compat flags for torchscale because the maintainer for torchscale broke compat for existing models
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2023-10-05 16:39:46 -05:00 |
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777ba43305
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oops
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2023-10-03 15:01:37 -05:00 |
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d12877ee09
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added option to set probability of selecting the AR during training under a monolithic AR+NAR, added some more to-dos while I have them in mind
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2023-10-02 16:52:42 -05:00 |
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c0b25541e3
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restructured some things with the model to remove dead weights
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2023-09-20 19:10:59 -05:00 |
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a6bfe43590
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added mirostat sampling (given a partially trained model, it got far decent output than I expected, need to test on a better trained model)
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2023-09-18 18:55:41 -05:00 |
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2567e082b5
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UGH
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2023-09-16 00:26:13 -05:00 |
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22ffaf3a33
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have loss for the NAR not-ignore the text prompt, I imagine this should help the NAR and explain why it's always had a bit of an issue with training
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2023-09-15 19:08:44 -05:00 |
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4aef798135
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added picking final candidate based on sum of score instead of first candidate (this changes nothing).
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2023-09-13 13:19:11 -05:00 |
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23a5fdd645
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implemented a naive beam search (I really should be taking a break)
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2023-09-12 21:28:07 -05:00 |
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a6ae344e5b
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some comments
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2023-09-12 16:04:45 -05:00 |
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d07c63b9d8
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unified more things with training the AR+NAR monolothic model
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2023-09-12 15:54:41 -05:00 |
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40ef34e1ca
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this embedding class definitely works, and migrating from the previous embedding weights seems to work.
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2023-09-11 14:13:42 -05:00 |
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a1f250ffac
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set default max_levels for NAR to 0 and implicitly set it to max resps levels because the previous way was implicitly assuming all models were outputting at 1+7 RVQ bins.
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2023-09-10 20:33:33 -05:00 |
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671dca88ee
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throw error when no reference audio is provided in the web UI because someone keeps doing that in the HF space
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2023-09-10 15:50:50 -05:00 |
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ba71020318
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added option to limit (or exceed) inferenced RVQ-bin levels through the NAR
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2023-09-10 13:50:13 -05:00 |
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10c34c5b98
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added a length-based decay factor for repetition penalty
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2023-09-08 21:02:00 -05:00 |
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b922f35b6b
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added documentation on how these new sampling parameters are very iffy and you really need to know what you are doing to use them because this is audio generation and not text generation
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2023-09-08 20:43:36 -05:00 |
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14c78bae39
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added lots of sampling options (top-k/top-p, repetition penalty, length penalty)
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2023-09-08 20:30:54 -05:00 |
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f69aad9c65
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some day I'll get it right
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2023-09-08 15:36:26 -05:00 |
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b2907ae7e0
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seems that my PromEmbedding/RespEmbedding doesn't actually work all that well, naively using dedicated MultiEmbeddings for AR/NAR in the monolithic model is the best way to go
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2023-09-08 01:03:24 -05:00 |
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c47fc3274e
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added backwards compat flag
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2023-09-07 17:12:17 -05:00 |
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ab5134f385
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tweaks and fixes
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2023-09-07 17:08:38 -05:00 |
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b2c2dec291
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added homebrewed per-RVQ-bin embedding solutions
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2023-09-07 16:48:02 -05:00 |
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e7a67410d1
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oops
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2023-09-07 09:14:03 -05:00 |
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712808494f
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added support for optional prodigy optimizer (https://github.com/konstmish/prodigy) although it consumes a lot more VRAM per parameter
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2023-09-06 20:33:16 -05:00 |
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7ce06432fd
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fixed the AR+NAR dual model, the resp_emb has to be split up (classifier might too)
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2023-09-06 19:33:39 -05:00 |
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