Function Differences with torch.range

torch.range

torch.range(start=0,
            end,
            step=1,
            *,
            out=None,
            dtype=None,
            layout=torch.strided,
            device=None,
            requires_grad=False
            )

For more information, see torch.range.

mindspore.ops.range

mindspore.ops.range(start,
                    end,
                    step
                    )

For more information, see mindspore.ops.range.

Differences

MindSpore: The dtype of the output tensor depends on the input tensor.

PyTorch: The dtype of the output tensor depends on the parameter dtype.

Categories	Subcategories	PyTorch	MindSpore	Difference
input	input 1	start	start	MindSpore must be a Tensor, whereas, PyTorch is float
	input 2	end	end	MindSpore must be a Tensor, whereas, PyTorch is float
	input 3	step	step	MindSpore must be a Tensor, whereas, PyTorch is float
	input 4	out	-	Not involved
	input 5	dtype	-	Not involved
	input 6	layout	-	Not involved
	input 7	device	-	Not involved
	input 8	requires_grad	-	Not involved

Code Example

import mindspore as ms
import torch
from mindspore import Tensor, ops

# PyTorch
torch.range(0, 10, 4)
# tensor([0., 4., 8.])

# MindSpore
start = Tensor(0, ms.int32)
limit = Tensor(10, ms.int32)
delta = Tensor(4, ms.int32)
output = ops.range(start, limit, delta)
print(output)
# [0 4 8]