Commit Graph

16 Commits

Author SHA1 Message Date
mrq
3337c69e5a leverage between xformers and torch.backends.cuda.sdp_kernel for attention 2024-05-11 17:14:05 -05:00
mrq
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
mrq
c494894261 simple DDP wrapper (for my NVlink test) 2024-05-04 11:48:26 -05:00
mrq
a7b43b98b5 renamed cfg.bitsandbytes to cfg.optimizations (and having it serve as cfg.optimizations.bitsandbytes) 2024-05-02 20:08:59 -05:00
mrq
467fa1c5ee wrapper fixes 2024-04-16 10:19:02 -05:00
mrq
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
mrq
4d75ee066c actually do the Linear replacement with TE's Linear 2024-04-09 14:41:13 -05:00
mrq
9d97eb5104 added FP8 support through NVIDIA/TransformerEngine, added RetNet_HF through syncdoth/RetNet (as an alternative to branch away from torchscale) 2024-04-08 20:14:51 -05:00
mrq
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
mrq
47435207f7 Added cfg.bitsandbytes.replace as a less intrusive alternative to cfg.bitsandbytes.inject to replace all Linear modules in a model 2024-03-01 19:20:10 -06:00
mrq
35d78a2bb0 Yet Another Underlying Transformer Implementation (BitNet, will give it a few days to see how it fares) 2024-02-29 20:29:17 -06:00
mrq
65f500083d tweaks to try and get deepspeed quantized inferencing, validating bitsandbytes and deepspeed quantization, nothing seems to work 2023-10-12 22:21:43 -05:00
mrq
712808494f added support for optional prodigy optimizer (https://github.com/konstmish/prodigy) although it consumes a lot more VRAM per parameter 2023-09-06 20:33:16 -05:00
mrq
100ca6b7d0 added option to use SGD optimizer through the YAML, added option to pass in additional optimizer parameters through the YAML, added experimental unified AR+NAR model (does not seem fruitful in testing) 2023-09-06 18:58:35 -05:00
mrq
f6597e2dfe adjustments 2023-08-02 18:36:26 -05:00
mrq
bf8cedc9dd Rewrite init 2023-08-02 21:53:35 +00:00