Function Differences with torch.autograd.backward and torch.autograd.grad

torch.autograd.backward

torch.autograd.backward(
  tensors,
  grad_tensors=None,
  retain_graph=None,
  create_graph=False,
  grad_variables=None
)

For more information, see torch.autograd.backward.

torch.autograd.grad

torch.autograd.grad(
  outputs,
  inputs,
  grad_outputs=None,
  retain_graph=None,
  create_graph=False,
  only_inputs=True,
  allow_unused=False
)

For more information, see torch.autograd.grad.

mindspore.grad

mindspore.grad(
  fn,
  grad_position=0,
  weights=None,
  has_aux=False
)

For more information, see mindspore.grad.

Differences

PyTorch: Use torch.autograd.backward to compute the sum of gradients of given Tensors with respect to graph leaves. When calculating the gradient of the Tensor with backpropagation, only the gradient of graph leaves with requires_grad=True will be calculated. Use torch.autograd.grad to compute and return the sum of gradients of outputs with respect to the inputs. If only_inputs is True, the function will only return a list of gradients with respect to the specified inputs.

MindSpore: Compute the first derivative. When grad_position is set to int or tuple of int, the corresponding input derivatives are computed. if weights is set, the network parameters derivatives will be computed. If has_aux is True, only the first output of fn participates in the computation, in this case, the fn should has at least two outputs.

Code Example

# In MindSpore：
import numpy as np
import mindspore as ms
import mindspore.nn as nn
from mindspore import ops

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.matmul = ops.MatMul()
        self.z = ms.Parameter(ms.Tensor(np.array([1.0], np.float32)), name='z')
    def construct(self, x, y):
        x = x * self.z
        out = self.matmul(x, y)
        return out

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)

x = ms.Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=ms.float32)
y = ms.Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=ms.float32)
output = GradNetWrtX(Net())(x, y)
print(output)
# Out:
# [[1.4100001 1.5999999 6.6      ]
#  [1.4100001 1.5999999 6.6      ]]

# In torch:
import torch
x = torch.tensor(2., requires_grad=True)
y = torch.tensor(3., requires_grad=True)
z = x * x * y
z.backward()
print(x.grad, y.grad)
# Out:
# tensor(12.) tensor(4.)

x = torch.tensor(2.).requires_grad_()
y = torch.tensor(3.).requires_grad_()
z = x * x * y
grad_x = torch.autograd.grad(outputs=z, inputs=x)
print(grad_x[0])
# Out:
# tensor(12.)