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This PR involves:

  1. Support Mixture-of-Experts model and Expert Parallel (EP) in ChatLearn;
  2. Implement an example on Mixtral-8x7B SFT, Reward, RLHF, DPO, and other alignment algorithms.

@haolin-nju haolin-nju changed the title [WIP] Feature: Support Mixtral-8x7B SFT, Reward, Alignment [WIP] Feature: Support Mixtral-8x7B SFT, Reward, and Alignment Sep 24, 2024
@haolin-nju haolin-nju changed the title [WIP] Feature: Support Mixtral-8x7B SFT, Reward, and Alignment [WIP][feature] Support Mixtral-8x7B SFT, Reward, and Alignment Oct 22, 2024
NNODES=$WORLD_SIZE


dp=$(($WORLD_SIZE * $GPUS_PER_NODE / $tp / $pp / $ep))

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it's not supposed to divide ep here.

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done

NNODES=$WORLD_SIZE


dp=$(($WORLD_SIZE * $GPUS_PER_NODE / $tp / $pp / $ep))

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neither is here.

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done

cd ${CHATLEARN}/examples/megatron/alignment/reward

torchrun $DISTRIBUTED_ARGS \
finetune_reward.py \

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should be entry/train_reward.py

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done

cd ${CHATLEARN}/examples/megatron/sft

torchrun $DISTRIBUTED_ARGS \
finetune_sft.py \

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shold be entry/train_sft.py

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done

@haolin-nju haolin-nju changed the title [WIP][feature] Support Mixtral-8x7B SFT, Reward, and Alignment [WIP]feature(mixtral): support Mixtral-8x7B SFT, Reward, and Alignment Jan 6, 2025
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3 participants