|
|
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 |
|
|
|
e35a91c67a
|
ugh
|
2024-06-07 21:56:14 -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 |
|
|
|
da8242d086
|
finally got around to removing omegaconf
|
2024-06-07 20:23:53 -05:00 |
|
|
|
4ade2b60ee
|
ugh
|
2024-06-06 21:57:11 -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 |
|
|
|
186b93a77e
|
oops
|
2024-06-03 22:35:55 -05:00 |
|
|
|
e50edc3b48
|
added a flag to convert to a HF compatible model on export by stitching things
|
2024-06-03 22:34:47 -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 |
|
|
|
c2a436d368
|
somehow between training sessions grad_norm = None even though it worked before
|
2024-06-02 08:29:27 -05:00 |
|
|
|
c1fcd889d5
|
reverted automatically disabling split loss calc, since it seems that it's actually cacling loss on prom causes the oddities, maybe
|
2024-06-01 12:34:59 -05:00 |
|
|
|
8cf176ab46
|
ugh
|
2024-06-01 10:46:42 -05:00 |
|
|
|
827cf632e7
|
report current loss scale and adjust grad norm by loss scale (for deepspeed)
|
2024-06-01 10:44:32 -05:00 |
|
|
|
d0ebce6bac
|
ugh
|
2024-06-01 10:30:13 -05:00 |
|
|
|
39bc019142
|
actually save per-rank sampler states
|
2024-06-01 09:46:32 -05:00 |
|
|
|
74df2f5332
|
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)
|
2024-06-01 09:29:49 -05:00 |
|
|
|
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)
|
2024-06-01 09:12:51 -05:00 |
|
|
|
e9c87060df
|
oops
|
2024-05-31 22:22:28 -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 |
|
