chatlearn/utils/vllm_utils.py [814:833]:
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    state_dict = load_rank0_state_dict(args)

    megatron_args = state_dict.get("args", None)
    if "checkpoint_version" in state_dict.keys():
        checkpoint_version = state_dict["checkpoint_version"]
    else:
        checkpoint_version = 0.0
    if megatron_args is None:
        raise ValueError(
            "Megatron-LM checkpoint does not contain arguments. This utility only supports Megatron-LM checkpoints"
            " containing all the megatron arguments. This is because it loads all config related to model"
            " architecture, the tensor and pipeline model parallel size from the checkpoint insead of user having to"
            " manually specify all the details. Please save Megatron-LM checkpoint along with all the megatron"
            " arguments to use this utility."
        )

    output_state_dict = {}

    tp_size = megatron_args.tensor_model_parallel_size
    pp_size = megatron_args.pipeline_model_parallel_size
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chatlearn/utils/vllm_utils.py [995:1014]:
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    state_dict = load_rank0_state_dict(args)

    megatron_args = state_dict.get("args", None)
    if "checkpoint_version" in state_dict.keys():
        checkpoint_version = state_dict["checkpoint_version"]
    else:
        checkpoint_version = 0.0
    if megatron_args is None:
        raise ValueError(
            "Megatron-LM checkpoint does not contain arguments. This utility only supports Megatron-LM checkpoints"
            " containing all the megatron arguments. This is because it loads all config related to model"
            " architecture, the tensor and pipeline model parallel size from the checkpoint insead of user having to"
            " manually specify all the details. Please save Megatron-LM checkpoint along with all the megatron"
            " arguments to use this utility."
        )

    output_state_dict = {}

    tp_size = megatron_args.tensor_model_parallel_size
    pp_size = megatron_args.pipeline_model_parallel_size
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