mrq 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
..
emb	added prom-less training / inferencing, some other things	2024-07-22 19:36:07 -05:00
engines	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
ext	fixes	2024-06-04 00:07:00 -05:00
models	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
utils	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
__init__.py	Rewrite init	2023-08-02 21:53:35 +00:00
__main__.py	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
config.py	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
data.py	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
demo.py	actually pass language into dataset process script, fix coercing japanese into hiragana because espeak does not like kanji	2024-07-21 23:21:37 -05:00
export.py	fix weird regression in handling checkpoints when backend is local, but deepspeed checkpoints are in (it was handled with LoRA loading but not real loading...)	2024-07-30 22:15:56 -05:00
inference.py	fix weird regression in handling checkpoints when backend is local, but deepspeed checkpoints are in (it was handled with LoRA loading but not real loading...)	2024-07-30 22:15:56 -05:00
plot.py	ugh	2024-06-09 11:39:43 -05:00
samplers.py	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
train.py	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
webui.py	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