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ca31da0a95
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sageattn (forgot to bother with testing this the other day, seems ifne)
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2024-12-03 15:14:57 -06:00 |
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84a05acb6d
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touch ups in docs
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2024-12-02 19:10:42 -06:00 |
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dcaf38b359
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fixed training tqdm being stubborn
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2024-11-23 09:45:23 -06:00 |
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41d7c30ea5
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added much cleaner non-causal mask generation
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2024-11-22 19:43:32 -06:00 |
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c99a74e834
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actually generate a causal mask because it seems sometimes it does not actually generate one because it makes assumptions
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2024-11-22 18:30:24 -06:00 |
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ccee5fc11c
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that was actually all pointless since sdpa always had an attention mask fed to it and does not need is_causal to implicitly generate one
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2024-11-22 16:51:50 -06:00 |
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4aa685e749
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what has science done
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2024-11-22 16:45:40 -06:00 |
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147219a5e0
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huge oversight in the attention masking......... (i realized I have not been providing a non-causal mask to non-causal tasks)
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2024-11-22 13:44:43 -06:00 |
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24d888c47c
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temporarily dropping support for xformers because it's breaking when using an attention mask (which i dont remember commenting it out when being passed), default to not use wandb because it's being a pain when doing tests and not actual sessionsS)
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2024-11-22 11:29:12 -06:00 |
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2cef97e43f
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cleanup
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2024-11-21 23:08:43 -06:00 |
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c6a38693a2
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This better work
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2024-11-09 18:04:59 -06:00 |
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c83670c38c
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Windows specific fixes (to-do: find libespeak-ng.dll automatically because it cannot be trusted to do it by default)
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2024-11-03 19:19:15 -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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fb8faa295b
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actually float16(+AMP) and layerskip is bad and will kill the model......
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2024-11-01 18:36:44 -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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76ebef45dc
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off-by-one...
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2024-10-31 13:24:48 -05:00 |
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b63293cbbe
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ugh
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2024-10-30 22:49:11 -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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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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84005c5b00
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entropix apparently processes the entire sequence of logits but it falls apart when doing that
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2024-10-13 12:01:12 -05:00 |
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c800d28bb8
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respect attention defined in the yaml for web UI (which might explain why theres been a discrepancy in outputs for me)
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2024-10-13 11:02:24 -05:00 |
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ed6b7a690f
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ugh.........
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2024-10-13 00:26:46 -05:00 |
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d405f243d4
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at wits end in trying to output the right attention scores
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2024-10-12 23:53:13 -05:00 |
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70cf694cfd
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output attention scores for SDPA/flash, since naive attention seems broken
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2024-10-12 12:09:17 -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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3d6ef9666b
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overridden naive llama attention to get the right score values that entropix needs
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2024-10-12 10:05:47 -05:00 |
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168e203942
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ugh
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2024-08-30 14:39:07 -05:00 |
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685f4faec0
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ugh
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2024-08-30 10:46:26 -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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d423bc03c2
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fixed attentions for MoE
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2024-08-27 17:02:42 -05:00 |
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0d706ec6a1
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added fused_attn (triton-based fused attention) and simply just query for flash_attn under rocm
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2024-08-26 19:13:34 -05:00 |
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6b0891448c
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pain (some shit to try and get some flash attention for ROCm (gfx1100) through triton fused attention but no good)
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2024-08-25 20:07:27 -05:00 |
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40e1799adc
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fixed xformers and flash_attn to actually work now
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2024-08-19 01:03:35 -05:00 |
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29c35528e5
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the sooner I accept there's no FA for V100s the sooner I'll go to bed
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2024-08-18 23:54:33 -05:00 |
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d636edd3a2
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added flash_attn LlamaAttention (including flash_attn==1.0.9)
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2024-08-18 20:51:14 -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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d04f6911b4
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oops
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2024-08-08 19:38:55 -05:00 |
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949339a3fa
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do not include SDPA attention if there's no available SDPA backends
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2024-08-06 20:42:39 -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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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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9564ecda43
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wrapper attention class for other sdpa backends + xformers seems to have broke...
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2024-08-03 15:12:11 -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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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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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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ff6fe6f1bc
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cleanup
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2024-06-05 20:30:43 -05:00 |
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