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591d3ac848
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have eval dataloader use eval batch size for batchedordersampler
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2024-06-28 22:44:00 -05:00 |
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83075c1505
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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
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2024-06-28 22:28:54 -05:00 |
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8a986eb480
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load exported LoRA weights if exists (to-do: make a better LoRA loading mechanism)
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2024-06-18 21:45:46 -05:00 |
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2bfe786ebd
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ban stop token for NAR levels (because sometimes it gets sampled and causes problems)
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2024-06-17 22:14:43 -05:00 |
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7cfb78fa64
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enable LoRA for targetted RVQ levels (to experiment with, seems to help)
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2024-06-17 21:45:03 -05:00 |
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7047fcc6e2
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actually make deepspeed work with LoRAs
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2024-06-17 13:55:37 -05:00 |
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1d159b1476
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updated export routine to split LoRA weights from the state dict (should work with deepspeed)
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2024-06-17 13:28:18 -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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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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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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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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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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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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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
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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
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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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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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b482ca19ff
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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
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better way to compute per-segment losses
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2024-05-28 19:29:54 -05:00 |
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6c49ad06a3
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forgot to reinclude mult by loss factors
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2024-05-27 20:40:21 -05:00 |
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b82f0d5c0c
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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
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2024-05-27 17:09:35 -05:00 |
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5af6f41c94
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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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ddbacde0d1
|
DAC just doesn't work well enough......
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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
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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
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2024-05-19 11:23:56 -05:00 |
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917eeb40d2
|
ughhh
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2024-05-12 08:22:39 -05:00 |
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9910c75d5a
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checkpointing for bitnet impl
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2024-05-12 07:52:54 -05:00 |
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14709ac67f
|
ughh
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2024-05-12 07:30:59 -05:00 |
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a755eb3c62
|
ugh
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2024-05-11 17:34:45 -05:00 |
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88e9b9caff
|
local ddp fix
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2024-05-11 17:29:01 -05:00 |
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3337c69e5a
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leverage between xformers and torch.backends.cuda.sdp_kernel for attention
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2024-05-11 17:14:05 -05:00 |
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d33c7bb7cf
|
ugh
|
2024-05-11 16:47:19 -05:00 |
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0b6499601b
|
sanitizing
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2024-05-11 16:31:05 -05:00 |
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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 |
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1547de5020
|
haha...
|
2024-05-09 23:15:52 -05:00 |
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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 |
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33b7f81b94
|
small cleanups
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2024-05-04 22:37:22 -05:00 |
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253441b750
|
forgot to disable verbose flag
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2024-05-04 13:13:52 -05:00 |
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3dca1125f5
|
implemented xformers in HF's Llama (because theres no flash attention for Volta cards)
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2024-05-04 13:07:45 -05:00 |
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ffa200eec7
|
added option to specify frames per second for the given audio representation (Encodec is 75Hz, DAC is 41Hz (at 24K sources))
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2024-05-04 12:05:41 -05:00 |
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c494894261
|
simple DDP wrapper (for my NVlink test)
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2024-05-04 11:48:26 -05:00 |
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a7b43b98b5
|
renamed cfg.bitsandbytes to cfg.optimizations (and having it serve as cfg.optimizations.bitsandbytes)
|
2024-05-02 20:08:59 -05:00 |
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b5d1456a09
|
backwards compat for my shitty old weights (was testing if disabling AudioEmbedding summing magically made things better (it did not))
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2024-04-29 22:14:01 -05:00 |
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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 |
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caad7ee3c9
|
final tweaks, hopefully
|
2024-04-28 22:28:29 -05:00 |
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b251669536
|
forgot to fix up the test trainer
|
2024-04-21 14:58:04 -05:00 |
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4f5c9e518a
|
actually use the passed-through sample rate from encode for DAC because it does its own resampling I guess
|
2024-04-18 13:32:41 -05:00 |
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5ff2b4aab5
|
finally swallowing the Descript-Audio-Codec pill (I guess I'm going to have to regenerate my entire dataset)
|
2024-04-17 20:39:35 -05:00 |
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b0bd88833c
|
refractor cleanup, had a revelation on how I can handle a batch of varying tasks
|
2024-04-16 21:04:48 -05:00 |
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467fa1c5ee
|
wrapper fixes
|
2024-04-16 10:19:02 -05:00 |
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aa1e25fbf5
|
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.
|
2024-04-16 10:02:31 -05:00 |
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545162195b
|
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
|
2024-04-15 19:54:32 -05:00 |
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d69a00e389
|
Properly pass retention_mask for retnet-HF, attempt to fix recurrent forward for retnet (doesn't work still)
|
2024-04-14 13:12:50 -05:00 |
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f0c4baeb25
|
added Adagrad (experimenting with it), added 'extended' model size (16 layers instead of 12, experimenting with it)
|
2024-04-09 22:04:01 -05:00 |
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4d75ee066c
|
actually do the Linear replacement with TE's Linear
|
2024-04-09 14:41:13 -05:00 |
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9d97eb5104
|
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
|
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)
|
2024-04-04 19:11:49 -05:00 |
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91062361af
|
tweaks
|
2024-03-01 20:38:06 -06:00 |
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f3c59c3e7e
|
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)
|
2024-03-01 20:18:43 -06:00 |
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