Function auto_configure_device_map

demo/gpus.py:8–42

(num_gpus: int)

def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
    # transformer.word_embeddings 占用1层
    # transformer.final_layernorm 和 lm_head 占用1层
    # transformer.layers 占用 28 层
    # 总共30层分配到num_gpus张卡上
    num_trans_layers = 28
    per_gpu_layers = 30 / num_gpus

    # bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
    # windows下 model.device 会被设置成 transformer.word_embeddings.device
    # linux下 model.device 会被设置成 lm_head.device
    # 在调用chat或者stream_chat时,input_ids会被放到model.device上
    # 如果transformer.word_embeddings.device和model.device不同,则会导致RuntimeError
    # 因此这里将transformer.word_embeddings,transformer.final_layernorm,lm_head都放到第一张卡上
    # 本文件来源于https://github.com/THUDM/ChatGLM-6B/blob/main/utils.py
    # 仅此处做少许修改以支持ChatGLM2,CodeGeeX2
    device_map = {
        'transformer.embedding.word_embeddings': 0,
        'transformer.encoder.final_layernorm': 0,
        'transformer.output_layer': 0,
        'transformer.rotary_pos_emb': 0,
        'lm_head': 0
    }

    used = 2
    gpu_target = 0
    for i in range(num_trans_layers):
        if used >= per_gpu_layers:
            gpu_target += 1
            used = 0
        assert gpu_target < num_gpus
        device_map[f'transformer.encoder.layers.{i}'] = gpu_target
        used += 1

    return device_map


def load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,