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@ -8,7 +8,7 @@ from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic
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from coati.models.gpt import GPTRM, GPTActor, GPTCritic |
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from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM |
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from coati.models.opt import OPTRM, OPTActor, OPTCritic |
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from coati.models.roberta import RoBERTaRM, RoBERTaActor, RoBERTaCritic |
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from coati.models.roberta import RoBERTaActor, RoBERTaCritic, RoBERTaRM |
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from coati.trainer import PPOTrainer |
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from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy |
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from coati.utils import prepare_llama_tokenizer_and_embedding |
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@ -143,6 +143,8 @@ def main(args):
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prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_path, max_datasets_size=16384) |
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if dist.is_initialized() and dist.get_world_size() > 1: |
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prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True) |
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else: |
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prompt_sampler = None |
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prompt_dataloader = DataLoader(prompt_dataset, |
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shuffle=(prompt_sampler is None), |
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sampler=prompt_sampler, |
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@ -151,6 +153,8 @@ def main(args):
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pretrain_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.pretrain_dataset, max_datasets_size=16384) |
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if dist.is_initialized() and dist.get_world_size() > 1: |
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pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True) |
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else: |
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pretrain_sampler = None |
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pretrain_dataloader = DataLoader(pretrain_dataset, |
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shuffle=(pretrain_sampler is None), |
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sampler=pretrain_sampler, |
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