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ColossalAI/applications/ColossalChat/benchmarks
Hongxin Liu 7f8b16635b
[misc] refactor launch API and tensor constructor (#5666)
7 months ago
..
ray [ColossalChat] Update RLHF V2 (#5286) 8 months ago
Opt.json [ColossalChat] Update RLHF V2 (#5286) 8 months ago
README.md [ColossalChat] Update RLHF V2 (#5286) 8 months ago
benchmark_memory_consumption.txt [ColossalChat] Update RLHF V2 (#5286) 8 months ago
benchmark_performance_summarization.txt [ColossalChat] Update RLHF V2 (#5286) 8 months ago
benchmark_ppo.py [misc] refactor launch API and tensor constructor (#5666) 7 months ago
benchmark_ppo.sh [ColossalChat] Update RLHF V2 (#5286) 8 months ago
data_preparation.sh [ColossalChat] Update RLHF V2 (#5286) 8 months ago

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:

  • gemini: ColossalAI GeminiPlugin with placement_policy="cuda", like zero3
  • gemini_auto: ColossalAI GeminiPlugin with placement_policy="cpu", like zero3-offload
  • zero2: ColossalAI zero2
  • zero2_cpu: ColossalAI zero2-offload
  • 3d: ColossalAI HybridParallelPlugin with TP, DP support

How to Run

cd ../tests
# Prepare data for benchmark
SFT_DATASET=/path/to/sft/data/ \
PROMPT_DATASET=/path/to/prompt/data/ \
PRETRAIN_DATASET=/path/to/ptx/data/ \
PREFERENCE_DATASET=/path/to/preference/data \
./test_data_preparation.sh
# Start benchmark
./benchmark_ppo.sh