Supported Operators

Ascend GPU CPU Environmental Setup Beginner Intermediate Expert

Q: Why is data loading abnormal when MindSpore1.0.1 is used in graph data offload mode?

A: An operator with the axis attribute, for example, P.Concat(axis=1)((x1, x2)), is directly used in construct. You are advised to initialize the operator in __init__ as follows:

from mindspore import nn
from mindspore.ops import operations as P

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.concat = P.Concat(axis=1)
    def construct(self, x, y):
        out = self.concat((x, y))
        return out

Q: When the Tile module in operations executes __infer__, the value is None. Why is the value lost?

A: The multiples input of the Tile operator must be a constant. (The value cannot directly or indirectly come from the input of the graph.) Otherwise, the None data will be obtained during graph composition because the graph input is transferred only during graph execution and the input data cannot be obtained during graph composition. For details, see “Other Constraints” in the Constraints on Network Construction.

Q: Compared with PyTorch, the nn.Embedding layer lacks the padding operation. Can other operators implement this operation?

A: In PyTorch, padding_idx is used to set the word vector in the padding_idx position in the embedding matrix to 0, and the word vector in the padding_idx position is not updated during backward propagation. In MindSpore, you can manually initialize the weight corresponding to the padding_idx position of embedding to 0. In addition, the loss corresponding to padding_idx is filtered out through the mask operation during training.

Q: What can I do if the LSTM example on the official website cannot run on Ascend?

A: Currently, the LSTM runs only on a GPU or CPU and does not support the hardware environment. You can click here to view the supported operators.

Q: When conv2d is set to (3,10), Tensor[2,2,10,10] and it runs on Ascend on ModelArts, the error message FM_W+pad_left+pad_right-KW>=strideW is displayed. However, no error message is displayed when it runs on a CPU. What should I do?

A: This is a TBE operator restriction that the width of x must be greater than that of the kernel. The CPU does not have this operator restriction. Therefore, no error is reported.