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726a4b613f
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naive, rudimentary DeepSpeed support (just live with the LoRA weights living with the original weights, they can be split later)
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2024-06-17 13:17:24 -05:00 |
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bd0bc10ec0
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added LoRA policy to decide what layer of the model gets adapted based on simple inclusion/exclusion terms
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2024-06-17 13:05:06 -05:00 |
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be051d9544
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added other LoRA method using parametrization rather than linear injection
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2024-06-17 09:58:34 -05:00 |
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45a39fb79f
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very rudimentary lora support (no deepspeed support, tested training and saving but not loading yet)
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2024-06-17 00:09:16 -05:00 |
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19410a919e
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ugh
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2024-06-15 12:29:03 -05:00 |
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d343bde09b
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residual_in_fp32=False for mamba arch backends because it breaks the classifier (output projection / lm head / what-have-you) under AMP
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2024-06-15 12:08:03 -05:00 |
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ccb14c06ef
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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)
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2024-06-14 19:42:17 -05:00 |
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31f71fa134
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sampler update (some brainworm just never actually had a sampler for sample_type=path)
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2024-06-14 16:55:40 -05:00 |
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b3b67f34ac
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added option to sort paths by durations to better group equally lengthed sequences together (and there was maybe a logic error from creating the samplers and then interleave-reordering paths, desyncing them, maybe)
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2024-06-13 22:37:34 -05:00 |
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83eab4fa59
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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
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2024-06-13 20:08:22 -05:00 |
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26da24fd8d
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mamba updated to fix that pesky NaN error during training
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2024-06-13 12:38:33 -05:00 |
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bcf3910a17
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the NAR only dream is dead (it just won't work)
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2024-06-12 19:49:47 -05:00 |
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a9353cf9fa
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ugh
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2024-06-12 00:14:29 -05:00 |
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cca542a4c0
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ugh
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2024-06-11 23:59:28 -05:00 |
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65a8960305
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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)
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2024-06-11 22:28:59 -05:00 |
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a7a6e0ac76
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validated that inferencing works, changed some defaults (NAR benefits from greedy sampling)
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2024-06-09 17:11:38 -05:00 |
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234f9efc6e
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ugh
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2024-06-09 11:39:43 -05:00 |
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132a02c48b
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sanity cleanup, backup config yaml for each log file
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2024-06-09 11:22:52 -05:00 |
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8d92dac829
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forgot I renamed this
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2024-06-09 11:12:30 -05:00 |
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80f9530840
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ugh
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2024-06-09 01:43:44 -05:00 |
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5c732b72ee
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ugh
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2024-06-08 20:34:00 -05:00 |
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8d068fa3f9
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reticulating splines
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2024-06-08 20:30:15 -05:00 |
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ead3e2f0cb
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ugh
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2024-06-08 16:14:57 -05:00 |
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b072f9b96b
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fixes
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2024-06-08 16:01:34 -05:00 |
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58fb0a84db
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added experimental NAR only model (inferences text length, need more experimenting), AudioEmbedding logic cleanup (I still think it's being done wrong)
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2024-06-08 15:42:02 -05:00 |
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e35a91c67a
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ugh
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2024-06-07 21:56:14 -05:00 |
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7d6fff24f9
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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)
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2024-06-07 20:46:22 -05:00 |
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b0158a61d5
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fixed some logic errors with training (grabbing wrong quant level...)
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2024-06-07 20:34:36 -05:00 |
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eafa622be2
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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)
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2024-06-07 20:29:25 -05:00 |
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da8242d086
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finally got around to removing omegaconf
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2024-06-07 20:23:53 -05:00 |
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4ade2b60ee
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ugh
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2024-06-06 21:57:11 -05:00 |
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f9f309281a
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ugh
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2024-06-06 20:55:27 -05:00 |
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a5c90348d9
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head hurt
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2024-06-06 20:51:31 -05:00 |
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516b0894d7
|
m
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2024-06-06 19:41:26 -05:00 |
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ee25d2e62e
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removed the need to supply targ_list + different AudioEmbedding + other things
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2024-06-06 18:52:41 -05:00 |
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fcac9503e2
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cleanup
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2024-06-06 13:08:02 -05:00 |
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b2194b859a
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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)
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2024-06-06 09:48:43 -05:00 |
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b05a905b95
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ugh
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2024-06-05 21:02:05 -05:00 |
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4073656293
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oops
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2024-06-05 20:53:10 -05:00 |
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ff6fe6f1bc
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cleanup
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2024-06-05 20:30:43 -05:00 |
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880b4ecd1b
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cleanup, putting some thoughts in comments before I forget about them
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2024-06-05 19:50:06 -05:00 |
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3cfc8a96bb
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oops
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2024-06-05 10:30:04 -05:00 |
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48cd1054f9
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madness
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2024-06-04 23:48:51 -05:00 |
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9e3f2e300f
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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)
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2024-06-04 23:23:31 -05:00 |
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e0886c5a78
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re-added mamba as a possible non-experimental arch backend (test trainer will set it as AR only, doing any NAR tasks lobotomizes it)
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2024-06-04 22:41:22 -05:00 |
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687c71e028
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disable accuracy calc because it breaks with actual batched training even though it shouldn't
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2024-06-04 22:13:44 -05:00 |
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d005e24953
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oops
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2024-06-04 22:10:04 -05:00 |
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0f7f3ae754
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added loss calc split and acc for experimental model
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2024-06-04 22:04:40 -05:00 |
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014e565c4b
|
tweaks
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2024-06-04 20:41:13 -05:00 |
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6d5bd0156a
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fixes
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2024-06-04 18:50:48 -05:00 |
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ed3aeaf3a1
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copy pasted from test to actual trainer
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2024-06-04 18:40:30 -05:00 |
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0aa01ba31a
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forgot one crucial detail (you *need* the previous RVQ level to keep coherence between all RVQ levels) (experimental deinterleaved is a bit crusty though)
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2024-06-04 18:30:30 -05:00 |
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2ffad5cb6f
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typo
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2024-06-04 14:20:57 -05:00 |
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406ff7bbe1
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re-implemented config.model.interleave for the HF-compat experimental method
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2024-06-04 14:19:52 -05:00 |
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c93d5863fd
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fixes
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2024-06-04 00:07:00 -05:00 |
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186b93a77e
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oops
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2024-06-03 22:35:55 -05:00 |
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e50edc3b48
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added a flag to convert to a HF compatible model on export by stitching things
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2024-06-03 22:34:47 -05:00 |
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934672252b
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feverish cleanup
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2024-06-03 21:28:49 -05:00 |
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7feeb944a0
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probably insane with even entertaining going this route
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2024-06-03 20:26:27 -05:00 |
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c2a436d368
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somehow between training sessions grad_norm = None even though it worked before
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2024-06-02 08:29:27 -05:00 |
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c1fcd889d5
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reverted automatically disabling split loss calc, since it seems that it's actually cacling loss on prom causes the oddities, maybe
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2024-06-01 12:34:59 -05:00 |
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8cf176ab46
|
ugh
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2024-06-01 10:46:42 -05:00 |
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827cf632e7
|
report current loss scale and adjust grad norm by loss scale (for deepspeed)
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2024-06-01 10:44:32 -05:00 |
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d0ebce6bac
|
ugh
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2024-06-01 10:30:13 -05:00 |
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39bc019142
|
actually save per-rank sampler states
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2024-06-01 09:46:32 -05:00 |
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74df2f5332
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split sampler dict by global_rank, also handle splitting dataset paths by global_rank if sampler_type == path (because I do not trust DistributedSampler) (need to test)
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2024-06-01 09:29:49 -05:00 |
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31785f4eeb
|
actually don't default to compute split losses, test bitnet model doesn't seem to be doing things right (despite debug printouts showing theyre roughly the same logit/loss sequences, could just be bitnet linears being not up to par on actual models)
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2024-06-01 09:12:51 -05:00 |
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e9c87060df
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oops
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2024-05-31 22:22:28 -05:00 |
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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)
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2024-05-31 19:32:37 -05:00 |
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e15c6c74c3
|
correctness
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2024-05-30 20:50:45 -05:00 |
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da473295b7
|
better way to compute per-segment losses
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2024-05-28 19:29:54 -05:00 |
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6c49ad06a3
|
forgot to reinclude mult by loss factors
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2024-05-27 20:40:21 -05:00 |
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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)
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2024-05-27 19:47:58 -05:00 |
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c0ac84c795
|
uh
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2024-05-27 19:05:56 -05:00 |
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197d517181
|
ugh
|
2024-05-27 17:09:35 -05:00 |
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5af6f41c94
|
added loss calcs against prom (requires the right settings for not shit results, disabled by default)
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2024-05-27 08:43:00 -05:00 |
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05cd8b797e
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nevermind it breaks training
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2024-05-25 18:03:43 -05:00 |
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85f9684720
|
some cleanup
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2024-05-25 17:46:52 -05:00 |
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d760924719
|
added kludgy eval only so I don't have to start training, type eval, stop training, then delete the logs for that session
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2024-05-25 17:39:51 -05:00 |
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ddbacde0d1
|
DAC just doesn't work well enough......
|
2024-05-25 11:07:52 -05:00 |
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e3ef89f5aa
|
100x better for subtrain/eval to be by group instead
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2024-05-19 16:40:14 -05:00 |
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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 |
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74e531d391
|
ugh
|
2024-05-18 12:02:56 -05:00 |
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|
4bc7e5a6d1
|
fix loading without needing an hdf5 dataset already prepped (and some other incidental speedups during dataloader prep)
|
2024-05-18 07:14:26 -05:00 |
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|
d88a5ca183
|
ugh
|
2024-05-16 07:25:33 -05:00 |
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d9aabfa3ae
|
final tweaks, hopefully, again
|
2024-05-15 23:04:19 -05:00 |
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8d79f78e0a
|
god I need to replace omegaconf
|
2024-05-12 14:01:52 -05:00 |
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5eb5db7f7f
|
just don't use DAC 24Khz, it's bad
|
2024-05-12 13:41:17 -05:00 |
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230da8b559
|
should be the final things to scramble around for, DAC's 24KHz model is unusable for this, but both encodec's 24KHz and DAC's 44KHz work
|
2024-05-12 13:22:08 -05:00 |
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|
2437a86efa
|
ugh
|
2024-05-12 13:02:15 -05:00 |
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|
4f1593c8db
|
a bunch of shit to salvage my old encodec-quantized audio because dac-encoded audio just does not want to converge
|
2024-05-12 10:17:29 -05:00 |
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917eeb40d2
|
ughhh
|
2024-05-12 08:22:39 -05:00 |
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9910c75d5a
|
checkpointing for bitnet impl
|
2024-05-12 07:52:54 -05:00 |
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14709ac67f
|
ughh
|
2024-05-12 07:30:59 -05:00 |
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|
3774fcbdee
|
ugh
|
2024-05-11 22:58:38 -05:00 |
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|
856545f8bb
|
nan loss detection (should have added it earlier), loss scaling for local backend + fp16
|
2024-05-11 22:23:29 -05:00 |
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a755eb3c62
|
ugh
|
2024-05-11 17:34:45 -05:00 |
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|
88e9b9caff
|
local ddp fix
|
2024-05-11 17:29:01 -05:00 |
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3337c69e5a
|
leverage between xformers and torch.backends.cuda.sdp_kernel for attention
|
2024-05-11 17:14:05 -05:00 |
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d33c7bb7cf
|
ugh
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2024-05-11 16:47:19 -05:00 |
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