| PyTorch的自动微分 | MindSpore的自动微分 |
```python
# torch.autograd:
# backward是累计的，更新完之后需清空optimizer
import torch
from torch.autograd import Variable
x = Variable(torch.ones(2, 2),
requires_grad=True)
x = x * 2
y = x - 1
y.backward(x)
```
|
```python
# ms.grad:
# 使用grad接口，输入正向图，输出反向图
import mindspore as ms
from mindspore import nn
class GradNetWrtX(nn.Cell):
def __init__(self, net):
super(GradNetWrtX, self).__init__()
self.net = net
def construct(self, x, y):
gradient_function = ms.grad(self.net)
return gradient_function(x, y)
```
|
| PyTorch | MindSpore |
```python
# 在调用backward函数之前，x.grad和y.grad函数为空
# backward计算过后，x.grad和y.grad分别代表导数计算后的值
import torch
print("=== tensor.backward ===")
x = torch.tensor(1.0, requires_grad=True)
y = torch.tensor(2.0, requires_grad=True)
z = x**2+y
print("x.grad before backward", x.grad)
print("y.grad before backward", y.grad)
z.backward()
print("z", z)
print("x.grad", x.grad)
print("y.grad", y.grad)
print("=== torch.autograd.backward ===")
x = torch.tensor(1.0, requires_grad=True)
y = torch.tensor(2.0, requires_grad=True)
z = x**2+y
torch.autograd.backward(z)
print("z", z)
print("x.grad", x.grad)
print("y.grad", y.grad)
```
|
```python
import mindspore
print("=== mindspore.grad ===")
x = mindspore.Tensor(1.0)
y = mindspore.Tensor(2.0)
def net(x, y):
return x**2+y
out = mindspore.grad(net, grad_position=0)(x, y)
print("out", out)
out1 = mindspore.grad(net, grad_position=1)(x, y)
print("out1", out1)
```
|
运行结果： ```text === tensor.backward === x.grad before backward None y.grad before backward None z tensor(3., grad_fn= |
运行结果： ```text === mindspore.grad === out 2.0 out1 1.0 ``` |
| PyTorch | MindSpore |
```python
# 不支持多个输出
import torch
print("=== torch.autograd.backward 不支持多个output ===")
x = torch.tensor(1.0, requires_grad=True)
y = torch.tensor(2.0, requires_grad=True)
z = x**2+y
torch.autograd.backward(z)
print("z", z)
print("x.grad", x.grad)
print("y.grad", y.grad)
```
|
```python
# 支持多个输出
import mindspore
print("=== mindspore.grad 多个output ===")
x = mindspore.Tensor(1.0)
y = mindspore.Tensor(2.0)
def net(x, y):
return x**2+y, x
out = mindspore.grad(net, grad_position=0)(x, y)
print("out", out)
out1 = mindspore.grad(net, grad_position=1)(x, y)
print("out1", out)
```
|
运行结果： ```text === torch.autograd.backward 不支持多个output === z tensor(3., grad_fn= |
运行结果： ```text === mindspore.grad 多个output === out 3.0 out1 3.0 ``` |