mirror of https://github.com/THUDM/ChatGLM-6B
243 lines
9.7 KiB
Python
243 lines
9.7 KiB
Python
import os
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from typing import Dict, Tuple, Union, Optional, List
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import torch
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from torch.nn import Module
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from transformers import AutoModel, AutoTokenizer
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from transformers.tokenization_utils import PreTrainedTokenizer
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def calculate_per_gpu_layers(gpu_list: List[int], total_layers: int) -> Dict[int, int]:
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"""
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Calculate the number of layers to be allocated to each GPU based on the memory ratio.
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Args:
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gpu_list (List[int]): A list of GPU indices.
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total_layers (int): The total number of layers in the model.
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Returns:
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Dict[int, int]: A dictionary mapping GPU indices to the number of layers assigned to each GPU.
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>>> from unittest import mock
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>>> import torch
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>>> mock_get_device_properties = mock.Mock()
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>>> fake_device_properties = lambda gpu: type('', (), {'total_memory': (gpu + 1) * 1024})()
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>>> mock_get_device_properties.side_effect = fake_device_properties
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>>> torch.cuda.get_device_properties = mock_get_device_properties
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>>> calculate_per_gpu_layers([0, 1, 2], 30)
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{0: 5, 1: 10, 2: 15}
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"""
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# 根据每个GPU的显存大小，计算每个GPU应分配的层数
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# 获取每个gpu的显存大小
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gpu_memory_map = {
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gpu: torch.cuda.get_device_properties(gpu).total_memory
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for gpu in gpu_list
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}
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# 计算总显存大小
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total_memory = sum(gpu_memory_map.values())
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# 计算每个GPU的显存比例
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gpu_memory_ratios = {
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gpu: memory / total_memory
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for gpu, memory in gpu_memory_map.items()
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}
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# 计算每个 GPU 应分配的层数
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per_gpu_layers = {
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gpu: int(round(total_layers * ratio))
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for gpu, ratio in gpu_memory_ratios.items()
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}
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# 修正分配误差，确保总层数为total_layers
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while True:
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diff = total_layers - sum(per_gpu_layers.values())
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if diff > 0:
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gpu_with_max_memory = max(gpu_memory_ratios, key=gpu_memory_ratios.get)
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per_gpu_layers[gpu_with_max_memory] += diff
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elif diff < 0:
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gpu_with_min_memory = min(gpu_memory_ratios, key=gpu_memory_ratios.get)
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per_gpu_layers[gpu_with_min_memory] -= -diff
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else:
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break
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return per_gpu_layers
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def auto_configure_device_map(num_gpus: int = 2, gpu_list: Optional[List[int]] = None) -> Dict[str, int]:
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"""
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Automatically configure the device map for model parallelism based on the number of GPUs and their memory ratios.
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Args:
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num_gpus (int): The number of GPUs to be used.
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gpu_list (Optional[List[int]]): An optional list of GPU indices. Defaults to None.
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Returns:
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Dict[str, int]: A dictionary representing the device map for model parallelism.
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>>> from unittest import mock
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>>> import torch
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>>> # mock torch.cuda.get_device_properties
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>>> mock_get_device_properties = mock.Mock()
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>>> fake_device_properties = lambda gpu: type('', (), {'total_memory': (gpu + 1) * 1024})()
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>>> mock_get_device_properties.side_effect = fake_device_properties
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>>> torch.cuda.get_device_properties = mock_get_device_properties
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>>> # mock torch.cuda.device_count
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>>> mock_device_count = mock.Mock()
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>>> mock_device_count.return_value = 3
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>>> torch.cuda.device_count = mock_device_count
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>>> for k, v in auto_configure_device_map(3).items():
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... print(f"{k}: {v}")
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transformer.word_embeddings: 0
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transformer.final_layernorm: 0
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lm_head: 0
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transformer.layers.0: 0
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transformer.layers.1: 0
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transformer.layers.2: 0
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transformer.layers.3: 1
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transformer.layers.4: 1
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transformer.layers.5: 1
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transformer.layers.6: 1
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transformer.layers.7: 1
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transformer.layers.8: 1
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transformer.layers.9: 1
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transformer.layers.10: 1
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transformer.layers.11: 1
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transformer.layers.12: 1
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transformer.layers.13: 2
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transformer.layers.14: 2
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transformer.layers.15: 2
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transformer.layers.16: 2
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transformer.layers.17: 2
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transformer.layers.18: 2
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transformer.layers.19: 2
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transformer.layers.20: 2
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transformer.layers.21: 2
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transformer.layers.22: 2
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transformer.layers.23: 2
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transformer.layers.24: 2
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transformer.layers.25: 2
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transformer.layers.26: 2
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transformer.layers.27: 2
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"""
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# transformer.word_embeddings 占用1层
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# transformer.final_layernorm 和 lm_head 占用1层
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# transformer.layers 占用 28 层
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# 总共30层分配到num_gpus张卡上
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num_trans_layers = 28
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if gpu_list is None:
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gpu_list = list(range(num_gpus))
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assert len(gpu_list) <= torch.cuda.device_count(), "分配的GPU数量超过了实际可用的GPU数量"
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# 获取每个gpu的承载的层数
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per_gpu_layer_dict = calculate_per_gpu_layers(gpu_list, total_layers=num_trans_layers + 2)
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# bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
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# windows下 model.device 会被设置成 transformer.word_embeddings.device
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# linux下 model.device 会被设置成 lm_head.device
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# 在调用chat或者stream_chat时,input_ids会被放到model.device上
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# 如果transformer.word_embeddings.device和model.device不同,则会导致RuntimeError
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# 因此这里将transformer.word_embeddings,transformer.final_layernorm,lm_head都放到第一张卡上
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current_gpu_index = 0
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current_gpu = gpu_list[current_gpu_index]
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device_map = {
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'transformer.word_embeddings': current_gpu,
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'transformer.final_layernorm': current_gpu,
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'lm_head': current_gpu
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}
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used = 2
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# 分配剩余的层数
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for i in range(num_trans_layers):
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if used < per_gpu_layer_dict[current_gpu]:
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used += 1
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else:
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# 当前 GPU 的层数已分配完，切换到下一个 GPU
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current_gpu_index += 1
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current_gpu = gpu_list[current_gpu_index]
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used = 1
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device_map[f"transformer.layers.{i}"] = current_gpu
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return device_map
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def load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
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gpu_list: Optional[List[int]] = None,
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multi_gpu_model_cache_dir: Union[str, os.PathLike] = "./temp_model_dir",
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device_map: Optional[Dict[str, int]] = None,
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tokenizer: Optional[PreTrainedTokenizer] = None, **kwargs) -> Module:
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"""
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Load a pretrained model on multiple GPUs.
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Args:
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checkpoint_path (Union[str, os.PathLike]): The path to the checkpoint or model directory.
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num_gpus (int, optional): The number of GPUs to use. Defaults to 2.
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gpu_list (Optional[List[int]], optional): A list of GPU indices. Defaults to None.
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multi_gpu_model_cache_dir (Union[str, os.PathLike], optional): A directory to cache the multi-GPU model.
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device_map (Optional[Dict[str, int]], optional): A dictionary representing the device map for model parallelism.
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tokenizer (Optional[PreTrainedTokenizer], optional): The tokenizer to be used with the model. Defaults to None.
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**kwargs: Additional keyword arguments for loading the model.
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Returns:
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Module: The pretrained model on multiple GPUs.
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"""
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from accelerate import load_checkpoint_and_dispatch
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model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True, **kwargs)
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model = model.eval()
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if device_map is None:
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device_map = auto_configure_device_map(num_gpus, gpu_list)
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try:
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model = load_checkpoint_and_dispatch(
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model, checkpoint_path, device_map=device_map, offload_folder="offload", offload_state_dict=True).half()
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except ValueError:
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# index.json not found
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print(f"index.json not found, auto fixing and saving model to {multi_gpu_model_cache_dir} ...")
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assert multi_gpu_model_cache_dir is not None, "using auto fix, cache_dir must not be None"
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model.save_pretrained(multi_gpu_model_cache_dir, max_shard_size='2GB')
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model = load_checkpoint_and_dispatch(
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model, multi_gpu_model_cache_dir, device_map=device_map,
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offload_folder="offload", offload_state_dict=True).half()
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if tokenizer is not None:
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tokenizer.save_pretrained(multi_gpu_model_cache_dir)
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print(f"loading model successfully, you should use checkpoint_path={multi_gpu_model_cache_dir} next time")
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return model
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def load_model_and_tokenizer(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 1,
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multi_gpu_model_cache_dir: Union[str, os.PathLike] = "./temp_model_dir",
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gpu_list: Optional[List[int]] = None,
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**kwargs) -> Tuple[Module, PreTrainedTokenizer]:
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"""
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Load a pretrained model and its tokenizer.
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Args:
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checkpoint_path (Union[str, os.PathLike]): The path to the checkpoint or model directory.
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num_gpus (int, optional): The number of GPUs to use. Defaults to 1.
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multi_gpu_model_cache_dir (Union[str, os.PathLike], optional): A directory to cache the multi-GPU model.
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gpu_list (Optional[List[int]], optional): A list of GPU indices. Defaults to None.
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**kwargs: Additional keyword arguments for loading the model and tokenizer.
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Returns:
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Tuple[Module, PreTrainedTokenizer]: A tuple containing the loaded model and tokenizer.
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"""
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tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, trust_remote_code=True, **kwargs)
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if num_gpus < 2:
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model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True, **kwargs).half().cuda()
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model = model.eval()
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else:
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model = load_model_on_gpus(checkpoint_path, num_gpus=num_gpus, gpu_list=gpu_list,
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multi_gpu_model_cache_dir=multi_gpu_model_cache_dir,
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tokenizer=tokenizer, **kwargs)
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return model, tokenizer
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