Differences with torch.cat

torch.cat

torch.cat(
    tensors,
    dim=0,
    *,
    out=None
) -> Tensor

For more information, see torch.cat.

mindspore.ops.cat

mindspore.ops.cat(tensors, axis=0) -> Tensor

For more information, see mindspore.ops.cat.

Differences

API function of MindSpore is consistent with that of PyTorch.

PyTorch: Splice the input Tensor on the specified axis. When the data precision of the input Tensors is different, the low precision Tensor will be automatically converted to high precision Tensor.

MindSpore: Currently, the data type and precision of the the input Tensors are required to remain the same. If not, the low-precision Tensor can be converted to a high-precision Tensor through ops.cast and then call the concat operator.

Categories	Subcategories	PyTorch	MindSpore	Differences
Input	Single input	tensors	tensors	The data type and precision of the tensors in MindSpore are required to remain the same, while the precision of the tensors in PyTorch can be different
Parameters	Parameter 1	dim	axis	Different parameter names
	Parameter 2	out	-	For details, see General Difference Parameter Table

Code Example

MindSpore currently requires that the data type and precision of the input Tensors are consistent. If it is inconsistent, the low-precision tensor can be converted to a high-precision type through ops.cast before calling the concat operator.

# PyTorch
import torch

torch_x1 = torch.Tensor([[0, 1], [2, 3]]).type(torch.float32)
torch_x2 = torch.Tensor([[0, 1], [2, 3]]).type(torch.float32)
torch_x3 = torch.Tensor([[0, 1], [2, 3]]).type(torch.float16)

torch_output = torch.cat((torch_x1, torch_x2, torch_x3))
print(torch_output.numpy())
# [[0. 1.]
#  [2. 3.]
#  [0. 1.]
#  [2. 3.]
#  [0. 1.]
#  [2. 3.]]

# MindSpore
import mindspore
import numpy as np
from mindspore import Tensor

# In MindSpore，converting low precision to high precision is needed before cat.
ms_x1 = Tensor(np.array([[0, 1], [2, 3]]).astype(np.float32))
ms_x2 = Tensor(np.array([[0, 1], [2, 3]]).astype(np.float32))
ms_x3 = Tensor(np.array([[0, 1], [2, 3]]).astype(np.float16))

ms_x3 = mindspore.ops.cast(ms_x2, mindspore.float32)
output = mindspore.ops.cat((ms_x1, ms_x2, ms_x3))
print(output)
# [[0. 1.]
#  [2. 3.]
#  [0. 1.]
#  [2. 3.]
#  [0. 1.]
#  [2. 3.]]