Release Notes

MindSpore Lite 2.7.1 Release Notes

主要特性及增强

  • MindSpore Lite与MindSpore解耦,CPU算子库等相关动态库独立于MindSpore进行演进。

  • 支持图片生成等AIGC模型中的Cache算法基于图模式实现。

API 变更

  • 新增MultiModelRunner、ModelExecutor接口,支持Cache算法的图模式实现。

    import mindspore_lite as mslite
    import numpy as np
    dtype_map = {
        mslite.DataType.FLOAT32: np.float32,
        mslite.DataType.INT32: np.int32,
        mslite.DataType.FLOAT16: np.float16,
        mslite.DataType.INT8: np.int8
    }
    context = mslite.Context()
    context.target = ["ascend"]
    context.ascend.devcie_id = 0
    runner = mslite.MultiModelRunner()
    model_path = "path_to_model"
    runner.build_from_file(model_path, mslite.ModelType.MINDIR, context)
    execs = runner.get_model_executor()
    for exec_ in execs:
        exec_inputs = exec_.get_inputs()
        for input_ in exec_inputs:
            data = np.random.randn(*input_.shape).astype(dtype_map[input_.dtype])
            input_.set_data_from_numpy(data)
        exec_.predict(exec_inputs)
    
  • 离线转换工具conver_lite通过配置SplitGraph参数以及split_node_name参数实现子图切分。

    [SplitGraph]
    split_node_name=[[node_name_1],[node_name_2]]
    

贡献者

YeFeng_24,xiong-pan,jjfeing,liuf9,zhangzhugucheng,xu_anyue,yiguangzheng,zxx_xxz,jianghui58,hbhu_bin,chenyihang5,qll1998,yangyingchun1999,liuchengji3,cheng-chao23,gemini524,yangly,yanghui00