昇思25天学习打卡营第四天|初学入门/初学教程/10-使用静态图加速

🕗 发布于 2024-07-16 13:45 学习 python 开发语言

心得

本课程，讲述了使用静态图加速的这种高级技巧的好处及其使用的方法。使用静态图高级编程技巧可以有效地提高编译效率以及执行效率，并可以使程序运行的更加稳定。当然实际是否有很好的效果，还需要将来的课程实践中去体会。目前就是头脑里有一个基本概念。知道有这么一个优化的选项。

打卡截图；

使用静态图加速

背景介绍

AI编译框架分为两种运行模式，分别是动态图模式以及静态图模式。MindSpore默认情况下是以动态图模式运行，但也支持手工切换为静态图模式。两种运行模式的详细介绍如下：

动态图模式

动态图的特点是计算图的构建和计算同时发生（Define by run），其符合Python的解释执行方式，在计算图中定义一个Tensor时，其值就已经被计算且确定，因此在调试模型时较为方便，能够实时得到中间结果的值，但由于所有节点都需要被保存，导致难以对整个计算图进行优化。

在MindSpore中，动态图模式又被称为PyNative模式。由于动态图的解释执行特性，在脚本开发和网络流程调试过程中，推荐使用动态图模式进行调试。如需要手动控制框架采用PyNative模式，可以通过以下代码进行网络构建：

[1]:

%%capture captured_output

# 实验环境已经预装了mindspore==2.2.14，如需更换mindspore版本，可更改下面mindspore的版本号

!pip uninstall mindspore -y

!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14

[2]:

import numpy as np

import mindspore as ms

from mindspore import nn, Tensor

ms.set_context(mode=ms.PYNATIVE_MODE)  # 使用set_context进行动态图模式的配置

class Network(nn.Cell):

    def __init__(self):

        super().__init__()

        self.flatten = nn.Flatten()

        self.dense_relu_sequential = nn.SequentialCell(

            nn.Dense(28*28, 512),

            nn.ReLU(),

            nn.Dense(512, 512),

            nn.ReLU(),

            nn.Dense(512, 10)

    def construct(self, x):

        x = self.flatten(x)

        logits = self.dense_relu_sequential(x)

        return logits

model = Network()

input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))

output = model(input)

print(output)

[[-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]
 [-0.00811011  0.15987587  0.2620123   0.06532933  0.09810102  0.1448662
   0.06367216 -0.1082783  -0.05877057  0.08763404]]

静态图模式

相较于动态图而言，静态图的特点是将计算图的构建和实际计算分开（Define and run）。有关静态图模式的运行原理，可以参考静态图语法支持。

在MindSpore中，静态图模式又被称为Graph模式，在Graph模式下，基于图优化、计算图整图下沉等技术，编译器可以针对图进行全局的优化，获得较好的性能，因此比较适合网络固定且需要高性能的场景。

如需要手动控制框架采用静态图模式，可以通过以下代码进行网络构建：

[3]:

import numpy as np

import mindspore as ms

from mindspore import nn, Tensor

ms.set_context(mode=ms.GRAPH_MODE)  # 使用set_context进行运行静态图模式的配置

class Network(nn.Cell):

    def __init__(self):

        super().__init__()

        self.flatten = nn.Flatten()

        self.dense_relu_sequential = nn.SequentialCell(

            nn.Dense(28*28, 512),

            nn.ReLU(),

            nn.Dense(512, 512),

            nn.ReLU(),

            nn.Dense(512, 10)

    def construct(self, x):

        x = self.flatten(x)

        logits = self.dense_relu_sequential(x)

        return logits

model = Network()

input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))

output = model(input)

print(output)

[ERROR] CORE(2172,ffffa9734930,python):2024-07-14-14:41:02.083.790 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_2172/4016835682.py]

[[ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]
 [ 0.09814478 -0.04893116  0.14850566 -0.12082472  0.01449197  0.01702559
  -0.13880742  0.17012078 -0.03684565 -0.07816261]]

静态图模式的使用场景

MindSpore编译器重点面向Tensor数据的计算以及其微分处理。因此使用MindSpore API以及基于Tensor对象的操作更适合使用静态图编译优化。其他操作虽然可以部分入图编译，但实际优化作用有限。另外，静态图模式先编译后执行的模式导致其存在编译耗时。因此，如果函数无需反复执行，那么使用静态图加速也可能没有价值。

有关使用静态图来进行网络编译的示例，请参考网络构建。

静态图模式开启方式

通常情况下，由于动态图的灵活性，我们会选择使用PyNative模式来进行自由的神经网络构建，以实现模型的创新和优化。但是当需要进行性能加速时，我们需要对神经网络部分或整体进行加速。MindSpore提供了两种切换为图模式的方式，分别是基于装饰器的开启方式以及基于全局context的开启方式。

基于装饰器的开启方式

MindSpore提供了jit装饰器，可以通过修饰Python函数或者Python类的成员函数使其被编译成计算图，通过图优化等技术提高运行速度。此时我们可以简单的对想要进行性能优化的模块进行图编译加速，而模型其他部分，仍旧使用解释执行方式，不丢失动态图的灵活性。无论全局context是设置成静态图模式还是动态图模式，被jit修饰的部分始终会以静态图模式进行运行。

在需要对Tensor的某些运算进行编译加速时，可以在其定义的函数上使用jit修饰器，在调用该函数时，该模块自动被编译为静态图。需要注意的是，jit装饰器只能用来修饰函数，无法对类进行修饰。jit的使用示例如下：

[4]:

import numpy as np

import mindspore as ms

from mindspore import nn, Tensor

class Network(nn.Cell):

    def __init__(self):

        super().__init__()

        self.flatten = nn.Flatten()

        self.dense_relu_sequential = nn.SequentialCell(

            nn.Dense(28*28, 512),

            nn.ReLU(),

            nn.Dense(512, 512),

            nn.ReLU(),

            nn.Dense(512, 10)

    def construct(self, x):

        x = self.flatten(x)

        logits = self.dense_relu_sequential(x)

        return logits

input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))

@ms.jit  # 使用ms.jit装饰器，使被装饰的函数以静态图模式运行

def run(x):

    model = Network()

    return model(x)

output = run(input)

print(output)

[[ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]
 [ 0.08865208  0.00029834  0.10078245  0.08796116 -0.10756826 -0.00097168
  -0.20773146 -0.0500758   0.1373357   0.05541402]]

[ERROR] CORE(2172,ffffa9734930,python):2024-07-14-14:41:23.024.422 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_2172/4227391393.py]

除使用修饰器外，也可使用函数变换方式调用jit方法，示例如下：

[5]:

import numpy as np

import mindspore as ms

from mindspore import nn, Tensor

class Network(nn.Cell):

    def __init__(self):

        super().__init__()

        self.flatten = nn.Flatten()

        self.dense_relu_sequential = nn.SequentialCell(

            nn.Dense(28*28, 512),

            nn.ReLU(),

            nn.Dense(512, 512),

            nn.ReLU(),

            nn.Dense(512, 10)

    def construct(self, x):

        x = self.flatten(x)

        logits = self.dense_relu_sequential(x)

        return logits

input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))

def run(x):

    model = Network()

    return model(x)

run_with_jit = ms.jit(run)  # 通过调用jit将函数转换为以静态图方式执行

output = run(input)

print(output)

[[ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]
 [ 0.05947255 -0.00597872 -0.04577087  0.0868237   0.10475684 -0.08337088
   0.18083248  0.04452055 -0.02432532  0.06350204]]

[ERROR] CORE(2172,ffffa9734930,python):2024-07-14-14:41:23.393.000 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_2172/3365896085.py]

当我们需要对神经网络的某部分进行加速时，可以直接在construct方法上使用jit修饰器，在调用实例化对象时，该模块自动被编译为静态图。示例如下：

[6]:

import numpy as np

import mindspore as ms

from mindspore import nn, Tensor

class Network(nn.Cell):

    def __init__(self):

        super().__init__()

        self.flatten = nn.Flatten()

        self.dense_relu_sequential = nn.SequentialCell(

            nn.Dense(28*28, 512),

            nn.ReLU(),

            nn.Dense(512, 512),

            nn.ReLU(),

            nn.Dense(512, 10)

    @ms.jit  # 使用ms.jit装饰器，使被装饰的函数以静态图模式运行

    def construct(self, x):

        x = self.flatten(x)

        logits = self.dense_relu_sequential(x)

        return logits

input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))

model = Network()

output = model(input)

print(output)

[[-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]
 [-1.30204856e-01  5.91173768e-03  1.04503669e-01 -7.16054142e-02
  -4.59905714e-05  2.10415870e-02 -8.84020403e-02  1.56900808e-01
  -1.00288436e-01 -8.98179412e-02]]

[ERROR] CORE(2172,ffffa9734930,python):2024-07-14-14:41:23.771.260 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_2172/2029473088.py]

基于context的开启方式

context模式是一种全局的设置模式。代码示例如下：

[7]:

import numpy as np

import mindspore as ms

from mindspore import nn, Tensor

ms.set_context(mode=ms.GRAPH_MODE)  # 使用set_context进行运行静态图模式的配置

class Network(nn.Cell):

    def __init__(self):

        super().__init__()

        self.flatten = nn.Flatten()

        self.dense_relu_sequential = nn.SequentialCell(

            nn.Dense(28*28, 512),

            nn.ReLU(),

            nn.Dense(512, 512),

            nn.ReLU(),

            nn.Dense(512, 10)

    def construct(self, x):

        x = self.flatten(x)

        logits = self.dense_relu_sequential(x)

        return logits

model = Network()

input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))

output = model(input)

print(output)

[[-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]
 [-0.13375047 -0.07846902 -0.08079751 -0.07430333  0.08891288 -0.01976799
   0.08481503 -0.01389463 -0.0025824  -0.09483818]]

[ERROR] CORE(2172,ffffa9734930,python):2024-07-14-14:41:24.134.568 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_2172/4016835682.py]

静态图的语法约束

在Graph模式下，Python代码并不是由Python解释器去执行，而是将代码编译成静态计算图，然后执行静态计算图。因此，编译器无法支持全量的Python语法。MindSpore的静态图编译器维护了Python常用语法子集，以支持神经网络的构建及训练。详情可参考静态图语法支持。

JitConfig配置选项

在图模式下，可以通过使用JitConfig配置选项来一定程度的自定义编译流程，目前JitConfig支持的配置参数如下：

jit_level: 用于控制优化等级。
exec_mode: 用于控制模型执行方式。
jit_syntax_level: 设置静态图语法支持级别，详细介绍请见静态图语法支持。

静态图高级编程技巧

使用静态图高级编程技巧可以有效地提高编译效率以及执行效率，并可以使程序运行的更加稳定。详情可参考静态图高级编程技巧。

[8]:

import time

print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),'guojun0718')

2024-07-14 15:03:16 guojun0718

原文地址：https://blog.csdn.net/guojun0718/article/details/140425149

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