摘要：
昇思MindSpore AI框架支持动态图、静态图两种模式。默认动态图模式便于调试，静态图模式用于加速改善性能场景。开启静态图有jit装饰器和set_context方法2种开启方式。
一、概念
AI编译框架两种运行模式
动态图模式(PyNative模式，默认选项)
计算图构建和计算同时发生（Define by run）
计算图在定义Tensor时，其值已经确定
便于Python调试模型，可实时得到中间结果。
由于要保存所有节点，优化困难。
静态图模式(Graph模式)
计算图的构建和实际计算分开（Define and run）
基于图优化、计算图整图下沉等技术，对图进行全局优化，性能较好
适合神经网络固定、高性能场景。
张量Tensor数据的计算以及其微分处理
反复执行
性能加速
部分Python语法不支持。参考静态图语法支持
二、环境准备
安装minspore模块
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.3.0rc1导入numpy、minspore、nn、Tensor等相关模块
import numpy as np
import mindspore as ms
from mindspore import nn, Tensor三、动态图模式(PyNative模式)
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)输出：
[[-0.00134926 -0.13563682 -0.02863023 -0.05452826 0.03290743 -0.12423715
-0.0582641 -0.10854103 -0.08558805 0.06099342]
[-0.00134926 -0.13563682 -0.02863023 -0.05452826 0.03290743 -0.12423715
-0.0582641 -0.10854103 -0.08558805 0.06099342]
[-0.00134926 -0.13563682 -0.02863023 -0.05452826 0.03290743 -0.12423715
-0.0582641 -0.10854103 -0.08558805 0.06099342]
[-0.00134926 -0.13563682 -0.02863023 -0.05452826 0.03290743 -0.12423715
-0.0582641 -0.10854103 -0.08558805 0.06099342]
[-0.00134926 -0.13563682 -0.02863023 -0.05452826 0.03290743 -0.12423715
-0.0582641 -0.10854103 -0.08558805 0.06099342]
...
[-0.00134926 -0.13563682 -0.02863023 -0.05452826 0.03290743 -0.12423715
-0.0582641 -0.10854103 -0.08558805 0.06099342]
[-0.00134926 -0.13563682 -0.02863023 -0.05452826 0.03290743 -0.12423715
-0.0582641 -0.10854103 -0.08558805 0.06099342]
[-0.00134926 -0.13563682 -0.02863023 -0.05452826 0.03290743 -0.12423715
-0.0582641 -0.10854103 -0.08558805 0.06099342]
[-0.00134926 -0.13563682 -0.02863023 -0.05452826 0.03290743 -0.12423715
-0.0582641 -0.10854103 -0.08558805 0.06099342]]四、静态图模式
下例手动控制框架采用静态图模式。
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)输出：
[[ 0.05363735 0.05117104 -0.03343301 0.06347139 0.07546629 0.03263091
0.02790363 0.06269836 0.01838502 0.04387159]
[ 0.05363735 0.05117104 -0.03343301 0.06347139 0.07546629 0.03263091
0.02790363 0.06269836 0.01838502 0.04387159]
[ 0.05363735 0.05117104 -0.03343301 0.06347139 0.07546629 0.03263091
0.02790363 0.06269836 0.01838502 0.04387159]
[ 0.05363735 0.05117104 -0.03343301 0.06347139 0.07546629 0.03263091
0.02790363 0.06269836 0.01838502 0.04387159]
...
[ 0.05363735 0.05117104 -0.03343301 0.06347139 0.07546629 0.03263091
0.02790363 0.06269836 0.01838502 0.04387159]
[ 0.05363735 0.05117104 -0.03343301 0.06347139 0.07546629 0.03263091
0.02790363 0.06269836 0.01838502 0.04387159]
[ 0.05363735 0.05117104 -0.03343301 0.06347139 0.07546629 0.03263091
0.02790363 0.06269836 0.01838502 0.04387159]
[ 0.05363735 0.05117104 -0.03343301 0.06347139 0.07546629 0.03263091
0.02790363 0.06269836 0.01838502 0.04387159]]五、静态图模式开启方式
1.基于jit装饰器的开启方式
示例：
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)输出：
[[-0.12126954 0.06986676 -0.2230821 -0.07087803 -0.01003947 0.01063392
0.10143848 -0.0200909 -0.09724037 0.0114444 ]
[-0.12126954 0.06986676 -0.2230821 -0.07087803 -0.01003947 0.01063392
0.10143848 -0.0200909 -0.09724037 0.0114444 ]
[-0.12126954 0.06986676 -0.2230821 -0.07087803 -0.01003947 0.01063392
0.10143848 -0.0200909 -0.09724037 0.0114444 ]
[-0.12126954 0.06986676 -0.2230821 -0.07087803 -0.01003947 0.01063392
0.10143848 -0.0200909 -0.09724037 0.0114444 ]
...
[-0.12126954 0.06986676 -0.2230821 -0.07087803 -0.01003947 0.01063392
0.10143848 -0.0200909 -0.09724037 0.0114444 ]
[-0.12126954 0.06986676 -0.2230821 -0.07087803 -0.01003947 0.01063392
0.10143848 -0.0200909 -0.09724037 0.0114444 ]
[-0.12126954 0.06986676 -0.2230821 -0.07087803 -0.01003947 0.01063392
0.10143848 -0.0200909 -0.09724037 0.0114444 ]
[-0.12126954 0.06986676 -0.2230821 -0.07087803 -0.01003947 0.01063392
0.10143848 -0.0200909 -0.09724037 0.0114444 ]]使用修饰器函数变换方式调用jit方法，示例如下：
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)输出：
[[ 0.11027216 -0.09628229 0.0457969 0.05396656 -0.06958974 0.0428197
-0.1572069 -0.14151613 -0.04531277 0.07521383]
[ 0.11027216 -0.09628229 0.0457969 0.05396656 -0.06958974 0.0428197
-0.1572069 -0.14151613 -0.04531277 0.07521383]
[ 0.11027216 -0.09628229 0.0457969 0.05396656 -0.06958974 0.0428197
-0.1572069 -0.14151613 -0.04531277 0.07521383]
[ 0.11027216 -0.09628229 0.0457969 0.05396656 -0.06958974 0.0428197
-0.1572069 -0.14151613 -0.04531277 0.07521383]
...
[ 0.11027216 -0.09628229 0.0457969 0.05396656 -0.06958974 0.0428197
-0.1572069 -0.14151613 -0.04531277 0.07521383]
[ 0.11027216 -0.09628229 0.0457969 0.05396656 -0.06958974 0.0428197
-0.1572069 -0.14151613 -0.04531277 0.07521383]
[ 0.11027216 -0.09628229 0.0457969 0.05396656 -0.06958974 0.0428197
-0.1572069 -0.14151613 -0.04531277 0.07521383]
[ 0.11027216 -0.09628229 0.0457969 0.05396656 -0.06958974 0.0428197
-0.1572069 -0.14151613 -0.04531277 0.07521383]]对神经网络的某部分进行加速时，直接在construct方法上使用jit修饰器，实例化对象时该模块自动被编译为静态图。示例如下：
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)输出：
[[ 0.10522258 0.06597593 -0.09440921 -0.04883489 0.07194916 0.1343117
-0.06813788 0.01986085 0.0216996 -0.05345828]
[ 0.10522258 0.06597593 -0.09440921 -0.04883489 0.07194916 0.1343117
-0.06813788 0.01986085 0.0216996 -0.05345828]
[ 0.10522258 0.06597593 -0.09440921 -0.04883489 0.07194916 0.1343117
-0.06813788 0.01986085 0.0216996 -0.05345828]
[ 0.10522258 0.06597593 -0.09440921 -0.04883489 0.07194916 0.1343117
-0.06813788 0.01986085 0.0216996 -0.05345828]
...
[ 0.10522258 0.06597593 -0.09440921 -0.04883489 0.07194916 0.1343117
-0.06813788 0.01986085 0.0216996 -0.05345828]
[ 0.10522258 0.06597593 -0.09440921 -0.04883489 0.07194916 0.1343117
-0.06813788 0.01986085 0.0216996 -0.05345828]
[ 0.10522258 0.06597593 -0.09440921 -0.04883489 0.07194916 0.1343117
-0.06813788 0.01986085 0.0216996 -0.05345828]
[ 0.10522258 0.06597593 -0.09440921 -0.04883489 0.07194916 0.1343117
-0.06813788 0.01986085 0.0216996 -0.05345828]]2.基于全局context的开启方式
示例：
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)输出：
[[ 0.08501796 -0.04404321 -0.05165704 0.00357929 0.00051521 0.00946456
0.02748473 -0.19415936 -0.00278988 0.04024826]
[ 0.08501796 -0.04404321 -0.05165704 0.00357929 0.00051521 0.00946456
0.02748473 -0.19415936 -0.00278988 0.04024826]
[ 0.08501796 -0.04404321 -0.05165704 0.00357929 0.00051521 0.00946456
0.02748473 -0.19415936 -0.00278988 0.04024826]
[ 0.08501796 -0.04404321 -0.05165704 0.00357929 0.00051521 0.00946456
0.02748473 -0.19415936 -0.00278988 0.04024826]
...
[ 0.08501796 -0.04404321 -0.05165704 0.00357929 0.00051521 0.00946456
0.02748473 -0.19415936 -0.00278988 0.04024826]
[ 0.08501796 -0.04404321 -0.05165704 0.00357929 0.00051521 0.00946456
0.02748473 -0.19415936 -0.00278988 0.04024826]
[ 0.08501796 -0.04404321 -0.05165704 0.00357929 0.00051521 0.00946456
0.02748473 -0.19415936 -0.00278988 0.04024826]
[ 0.08501796 -0.04404321 -0.05165704 0.00357929 0.00051521 0.00946456
0.02748473 -0.19415936 -0.00278988 0.04024826]]六、静态图JitConfig配置选项
自定义编译流程，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。
参考静态图高级编程技巧。