mirror of https://github.com/hpcaitech/ColossalAI
edd75a59ea
* feat: remove on_learn_epoch fn as not used * revert: add _on_learn_epoch fn * to: remove the use of NaiveStrategy * test: remove NaiveStrategy tests * feat: remove NaiveStrategy * style: modify comments and params * feat: split ColossalAIStrategy into LowLevelZeroStrategy and GeminiStrategy * fix: remove naive * fix: align with modified colossal strategy * fix: fix ddp _try_init_dist arg |
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| README.md | ||
| benchmark_opt_lora_dummy.py | ||
README.md
Benchmarks
Benchmark OPT with LoRA on dummy prompt data
We provide various OPT models (string in parentheses is the corresponding model name used in this script):
- OPT-125M (125m)
- OPT-350M (350m)
- OPT-700M (700m)
- OPT-1.3B (1.3b)
- OPT-2.7B (2.7b)
- OPT-3.5B (3.5b)
- OPT-5.5B (5.5b)
- OPT-6.7B (6.7b)
- OPT-10B (10b)
- OPT-13B (13b)
We also provide various training strategies:
- ddp: torch DDP
- colossalai_gemini: ColossalAI GeminiDDP with
placement_policy="cuda", like zero3 - colossalai_gemini_cpu: ColossalAI GeminiDDP with
placement_policy="cpu", like zero3-offload - colossalai_zero2: ColossalAI zero2
- colossalai_zero2_cpu: ColossalAI zero2-offload
- colossalai_zero1: ColossalAI zero1
- colossalai_zero1_cpu: ColossalAI zero1-offload
We only support torchrun to launch now. E.g.
# run OPT-125M with no lora (lora_rank=0) on single-node single-GPU with min batch size
torchrun --standalone --nproc_per_node 1 benchmark_opt_lora_dummy.py --model 125m --critic_model 125m --strategy ddp --experience_batch_size 1 --train_batch_size 1 --lora_rank 0
# run Actor (OPT-1.3B) and Critic (OPT-350M) with lora_rank=4 on single-node 4-GPU
torchrun --standalone --nproc_per_node 4 benchmark_opt_lora_dummy.py --model 1.3b --critic_model 350m --strategy colossalai_zero2 --lora_rank 4