Function Differences with torch.dot

torch.dot

torch.dot(
    input,
    tensor
)

For more information, see torch.dot.

mindspore.ops.tensor_dot

mindspore.ops.tensor_dot(
    x1,
    x2,
    axes
)

For more information, see mindspore.ops.tensor_dot.

Differences

PyTorch: Calculates the dot product(inner product) of two tensors of the same shape, only 1D is supported.

MindSpore：Calculates the dot product of two tensors on any axis. Support tensor of any dimension, but the shape corresponding to the specified axis should be equal. The function of the PyTorch is the same when the input is 1D and the axis is set to 0.

Code Example

import mindspore as ms
import mindspore.ops as ops
import torch
import numpy as np

# In MindSpore, tensor of any dimension will be supported.
# And parameters will be set to specify how to compute among dimensions.
input_x1 = ms.Tensor(np.array([2, 3, 4]), ms.float32)
input_x2 = ms.Tensor(np.array([2, 1, 3]), ms.float32)
output = ops.tensor_dot(input_x1, input_x2, 1)
print(output)
# Out：
# 19.0

# In torch, only 1D tensor's computation will be supported.
input_x1 = torch.tensor([2, 3, 4])
input_x2 = torch.tensor([2, 1, 3])
output = torch.dot(input_x1, input_x2)
print(output)
# Out：
# tensor(19)