Differences with torch.dot

torch.dot

torch.dot(input, other, *, out=None)

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

API function of MindSpore is not consistent with that of PyTorch.

PyTorch: Calculates the dot product (inner product) of two tensors of the same shape, only 1D is supported. The supported input data types include uint8, int8/16/32/64, float32/64.

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. The supported input data types are float16 or float32.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	input	x1	Different parameter names
	Parameter 2	other	x2	Different parameter names
	Parameter 3	out	-	For details, see General Difference Parameter Table
	Parameter 4	-	axes	The function of the PyTorch is the same when the input is 1D and the axis is set to 0.

Code Example 1

The data type of the input is int, and the data type of the output is also int.

import torch
input_x1 = torch.tensor([2, 3, 4], dtype=torch.int32)
input_x2 = torch.tensor([2, 1, 3], dtype=torch.int32)
output = torch.dot(input_x1, input_x2)
print(output)
# tensor(19, dtype=torch.int32)
print(output.dtype)
# torch.int32

# MindSpore doesn't support this feature currently.

Code Example 2

The data type of the input is float, and the data type of the output is also float.

import torch
input_x1 = torch.tensor([2, 3, 4], dtype=torch.float32)
input_x2 = torch.tensor([2, 1, 3], dtype=torch.float32)
output = torch.dot(input_x1, input_x2)
print(output)
# tensor(19.)
print(output.dtype)
# torch.float32

import mindspore as ms
import mindspore.ops as ops
import numpy as np
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)
# 19.0
print(output.dtype)
# Float32