Supported Operators

Ascend GPU CPU Environmental Setup Beginner Intermediate Expert

Q: What is the function of the TransData operator? Can the performance be optimized?

A: The TransData operator is used in the scenario where the data formats (such as NC1HWC0) used by interconnected operators on the network are inconsistent. In this case, the framework automatically inserts the TransData operator to convert the data formats into the same format and then performs computation. You can consider using the amp for mixed-precision training. In this way, some FP32 operations and the invocation of some TransData operators can be reduced.

Q: An error occurs when the Concat operator concatenates tuples containing multiple tensors. An error occurs when the number of tensor list elements entered is greater than or equal to 192. What is a better solution (running in dynamic mode) for Concat to concatenate tuples containing multiple Tensors?

A: The number of tensors to be concatenated at a time cannot exceed 192 according to the bottom-layer specifications of the Ascend operator. You can try to concatenate them twice.

Q: When Conv2D is used to define convolution, the group parameter is used. Is it necessary to ensure that the value of group can be exactly divided by the input and output dimensions? How is the group parameter transferred?

A: The Conv2d operator has the following constraint: When the value of group is greater than 1, the value must be the same as the number of input and output channels. Do not use ops.Conv2D. Currently, this operator does not support a value of group that is greater than 1. Currently, only the nn.Conv2d API of MindSpore supports group convolution. However, the number of groups must be the same as the number of input and output channels. The Conv2D operator function is as follows:

def __init__(self,
                 out_channel,
                 kernel_size,
                 mode=1,
                 pad_mode="valid",
                 pad=0,
                 stride=1,
                 dilation=1,
                 group=1,
                 data_format="NCHW"):

If the function contains a group parameter, the parameter will be transferred to the C++ layer by default.

Q: Does MindSpore provide 3D convolutional layers?

A: 3D convolutional layers on Ascend are coming soon. Go to the Operator List on the official website to view the operators that are supported.

Q: Does MindSpore support matrix transposition?

A: Yes. For details, see mindspore.ops.Transpose.

Q: Can MindSpore calculate the variance of any tensor?

A: Currently, MindSpore does not have APIs or operators similar to variance which can directly calculate the variance of a tensor. However, MindSpore has sufficient small operators to support such operations. For details, see class Moments(Cell).

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, ops.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
import mindspore.ops as ops

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.concat = ops.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.

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 MindSpore Operator List 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.