ColossalAI/applications/Chat/benchmarks/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