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