Commit Graph

39 Commits (08e1bdb9d810189c006ba21a26454433c63fa012)

Author SHA1 Message Date
neil.ge 859c8ee2a3 perf: enable fused AdamW in full SFT training
Use PyTorch's fused AdamW implementation to reduce optimizer kernel
launch overhead and speed up the training step on CUDA devices.
Guard with device check so the CPU-only fallback path is unaffected.

Co-authored-by: TRAE CLI <traecli@bytedance.com>
2026-09-05 18:37:04 +08:00
jingyaogong 6fc918beb6 [update] remove expert args 2026-09-04 22:16:38 +08:00
Justin Guese fc585a9bf9 [update] 把随机种子暴露到命令行
trainer 里的种子此前硬编码为 42(setup_seed(42 + rank) 与每轮的
setup_seed(42 + epoch)),要跑多种子实验只能改源码。新增 --seed,
默认 42,与改动前完全一致。
2026-09-03 15:36:14 +07:00
Justin Guese c3199c7240 [update] 把 num_experts / num_experts_per_tok 暴露到命令行
此前要改专家数只能去编辑 model_minimind.py。三个入口
(train_pretrain / train_full_sft / eval_llm)的默认值与 MiniMindConfig
一致(4 / 1),默认行为不变;RL 与 agent 相关的 trainer 不动。
2026-09-03 15:13:29 +07:00
jingyaogong 3f1a7cc25b [fix] ddp exit hang 2026-06-01 17:50:39 +08:00
jingyaogong 4497610ec0 [fix] issue#771 2026-05-19 17:40:03 +08:00
jingyaogong 5416a44471 [fix] bugs 2026-04-21 13:03:34 +08:00
jingyaogong b8b3d35257 [update] change default seq_len 2026-03-26 10:09:06 +08:00
jingyaogong 101d7df2da [update] minimind-3 2026-03-25 23:57:45 +08:00
readlnh cf4b49a348 [fix] align log/save last-step check and ETA with 1-indexed step 2026-03-24 02:01:40 +01:00
readlnh d25500d363 [fix] gradient accumulation step alignment 2026-03-24 01:45:04 +01:00
jingyaogong fea69cf338 [fix] data skip 2026-01-18 16:56:29 +08:00
jingyaogong f7ffdf1fdb [update] shuffle data 2026-01-18 16:39:34 +08:00
jingyaogong c090b69c4d [update] align loss 2026-01-15 00:56:32 +08:00
jingyaogong e119db8478 [fix] compile unpack 2026-01-14 20:13:32 +08:00
jingyaogong 81d24a4f16 [feat] add compile 2026-01-14 14:42:30 +08:00
jingyaogong 42a4e8c86a [fix] dist cleanup 2026-01-02 22:25:55 +08:00
jingyaogong 9d898576ac [update] aux loss 2026-01-01 22:41:46 +08:00
jingyaogong c65335b56f [fix] experts unused 2025-12-31 21:47:04 +08:00
jingyaogong bc8fd82166 [fix] layers set 8 2025-12-31 21:06:37 +08:00
jingyaogong 5dd4df7e18 [fix] moe unused 2025-12-31 21:00:06 +08:00
jingyaogong 6242980917 [feat] update lr 2025-12-31 10:27:09 +08:00
jingyaogong 7eae14f3ce [feat] remove empty_cache 2025-12-27 07:14:36 +08:00
jingyaogong fe24501602 [feat] adjust seq length 2025-12-14 20:41:58 +08:00
jingyaogong 5e1447b913 [fix] cuda memory #559 2025-12-01 16:17:43 +08:00
jingyaogong 6b86ea399a [feat] release memory 2025-11-27 19:39:49 +08:00
jingyaogong 509d8dacf1 [feat] clear cache 2025-11-06 13:12:28 +08:00
jingyaogong bf123b585d [feat] add args 2025-10-30 10:05:12 +08:00
jingyaogong 1713c24114 [fix] model device 2025-10-29 10:36:28 +08:00
jingyaogong 8f7e07b8ef [feat] update trainer 2025-10-28 23:30:10 +08:00
jingyaogong e8484874f5 [feat] pause-training 2025-10-26 18:49:52 +08:00
jingyaogong a82526da11 [feat] shuffle data 2025-10-23 20:13:28 +08:00
jingyaogong 805744e60a [fix] loss-issues-430 2025-10-23 19:08:42 +08:00
jingyaogong 463044e92a [fix] sampler-ddp 2025-10-23 15:03:19 +08:00
jingyaogong fa7dff8291 [fix] graph-oom & ddp-pos_cis 2025-10-23 14:22:13 +08:00
jingyaogong 7d877db79b [feat] minimind-2510 2025-10-21 21:19:47 +08:00
jingyaogong caae54a89e fix bugs 2025-04-29 20:45:39 +08:00
jingyaogong 29454c31af fix bugs 2025-04-27 09:56:49 +08:00
jingyaogong a62faf34bd 250426 2025-04-26 10:05:47 +08:00