mindspore.ops
Operators can be used in the construct function of Cell.
Examples
>>> import mindspore.ops as ops
operations
The Primitive operators in operations need to be instantiated before being used.
Neural Network Operators
API Name |
Description |
Supported Platforms |
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Computes inverse hyperbolic cosine of the input element-wise. |
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Updates gradients by the Adaptive Moment Estimation (Adam) algorithm. |
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Updates gradients by Adaptive Moment Estimation (Adam) algorithm. |
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Updates relevant entries according to the adadelta scheme. |
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Updates relevant entries according to the adagrad scheme. |
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Updates relevant entries according to the adagradv2 scheme. |
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Updates relevant entries according to the adamax scheme. |
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Updates relevant entries according to the AddSign algorithm. |
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Optimizer that implements the centered RMSProp algorithm. |
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Updates relevant entries according to the following. |
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Optimizer that implements the Momentum algorithm. |
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Updates relevant entries according to the AddSign algorithm. |
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Updates relevant entries according to the proximal adagrad algorithm. |
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Updates relevant entries according to the FOBOS(Forward Backward Splitting) algorithm. |
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Optimizer that implements the Root Mean Square prop(RMSProp) algorithm. |
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Average pooling operation. |
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Applies the long short-term memory (LSTM) to the input. |
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Batch Normalization for input data and updated parameters. |
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Returns sum of input and bias tensor. |
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Computes the binary cross entropy between the target and the output. |
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For the BatchNorm operation this operator update the moving averages for training and is used in conjunction with BNTrainingUpdate. |
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For the BatchNorm operation, this operator update the moving averages for training and is used in conjunction with BNTrainingReduce. |
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Compute accidental hits of sampled classes which happen to match target classes. |
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2D convolution layer. |
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Computes the gradients of convolution with respect to the input. |
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Performs greedy decoding on the logits given in inputs. |
To Be Developed |
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Calculates the CTC (Connectionist Temporal Classification) loss and the gradient. |
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Returns the dimension index in the destination data format given in the source data format. |
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Returns the depth-wise convolution value for the input. |
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Applies dropout mask on the input tensor. |
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Generates the mask value for the input shape. |
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Applies a single-layer gated recurrent unit (GRU) to an input sequence. |
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DynamicRNN Operator. |
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Computes exponential linear: |
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Fast Gaussian Error Linear Units activation function. |
To Be Developed |
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Fast Gaussian Error Linear Units activation function. |
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Flattens a tensor without changing its batch size on the 0-th axis. |
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Computes the remainder of division element-wise. |
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FusedBatchNorm is a BatchNorm. |
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FusedBatchNormEx is an extension of FusedBatchNorm, FusedBatchNormEx has one more output(output reserve) than FusedBatchNorm, reserve will be used in backpropagation phase. |
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Merges the duplicate value of the gradient and then updates parameters by the Adaptive Moment Estimation (Adam) algorithm. |
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Merges the duplicate value of the gradient and then updates parameters by the Adaptive Moment Estimation (LazyAdam) algorithm. |
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Merges the duplicate value of the gradient and then updates relevant entries according to the proximal adagrad algorithm. |
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Gaussian Error Linear Units activation function. |
To Be Developed |
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Gaussian Error Linear Units activation function. |
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Returns the next element in the dataset queue. |
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Hard sigmoid activation function. |
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Hard swish activation function. |
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Computes the Kullback-Leibler divergence between the target and the output. |
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Calculates half of the L2 norm of a tensor without using the sqrt. |
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L2 normalization Operator. |
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Conducts LARS (layer-wise adaptive rate scaling) update on the sum of squares of gradient. |
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Applies the Layer Normalization to the input tensor. |
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Log Softmax activation function. |
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Local Response Normalization. |
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Performs the Long Short-Term Memory (LSTM) on the input. |
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Max pooling operation. |
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Performs max pooling on the input Tensor and returns both max values and indices. |
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Pads the input tensor according to the paddings and mode. |
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Computes a one-hot tensor. |
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Packs a list of tensors in specified axis. |
To Be Developed |
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Pads the input tensor according to the paddings. |
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Parametric Rectified Linear Unit activation function. |
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Computes ReLU (Rectified Linear Unit) of input tensors element-wise. |
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Computes ReLU (Rectified Linear Unit) upper bounded by 6 of input tensors element-wise. |
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Computes ReLU (Rectified Linear Unit) of input tensors element-wise. |
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Resizes an image to a certain size using the bilinear interpolation. |
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Computes the RNNTLoss and its gradient with respect to the softmax outputs. |
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Computes the Region of Interest (RoI) Align operator. |
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Computes the stochastic gradient descent. |
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Sigmoid activation function. |
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Uses the given logits to compute sigmoid cross entropy. |
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Computes smooth L1 loss, a robust L1 loss. |
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Softmax operation. |
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Gets the softmax cross-entropy value between logits and labels with one-hot encoding. |
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Softplus activation function. |
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Softsign activation function. |
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Updates relevant entries according to the adagrad scheme. |
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Updates relevant entries according to the adagrad scheme, one more epsilon attribute than SparseApplyAdagrad. |
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Updates relevant entries according to the proximal adagrad algorithm. |
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Computes the softmax cross-entropy value between logits and sparse encoding labels. |
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Stacks a list of tensors in specified axis. |
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Tanh activation function. |
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Finds values and indices of the k largest entries along the last dimension. |
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Unpacks tensor in specified axis. |
To Be Developed |
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Unstacks tensor in specified axis. |
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Math Operators
API Name |
Description |
Supported Platforms |
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Returns absolute value of a tensor element-wise. |
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Computes accumulation of all input tensors element-wise. |
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Computes arccosine of input tensors element-wise. |
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Adds two input tensors element-wise. |
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Computes addition of all input tensors element-wise. |
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Returns True if abs(x1-x2) is smaller than tolerance element-wise, otherwise False. |
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Computes arcsine of input tensors element-wise. |
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Computes inverse hyperbolic sine of the input element-wise. |
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Updates a Parameter by adding a value to it. |
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Updates a Parameter by subtracting a value from it. |
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Computes the trigonometric inverse tangent of the input element-wise. |
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Returns arctangent of input_x/input_y element-wise. |
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Computes inverse hyperbolic tangent of the input element-wise. |
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Computes matrix multiplication between two tensors by batch |
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Computes BesselI0e of input element-wise. |
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Computes BesselI1e of input element-wise. |
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Returns bitwise and of two tensors element-wise. |
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Returns bitwise or of two tensors element-wise. |
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Returns bitwise xor of two tensors element-wise. |
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Rounds a tensor up to the closest integer element-wise. |
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Computes cosine of input element-wise. |
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Computes hyperbolic cosine of input element-wise. |
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Computes the cumulative product of the tensor x along axis. |
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Computes the cumulative sum of input tensor along axis. |
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Computes the quotient of dividing the first input tensor by the second input tensor element-wise. |
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Computes a safe divide and returns 0 if the y is zero. |
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Creates a tensor filled with input_x dtype minimum value. |
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Computes the equivalence between two tensors element-wise. |
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Computes the number of the same elements of two tensors. |
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Computes the Gauss error function of input_x element-wise. |
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Computes the complementary error function of input_x element-wise. |
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Returns exponential of a tensor element-wise. |
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Returns exponential then minus 1 of a tensor element-wise. |
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Determines if the elements contain Not a Number(NaN), infinite or negative infinite. |
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Rounds a tensor down to the closest integer element-wise. |
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Divides the first input tensor by the second input tensor element-wise and round down to the closest integer. |
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Computes the boolean value of \(x > y\) element-wise. |
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Computes the boolean value of \(x >= y\) element-wise. |
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Returns a rank 1 histogram counting the number of entries in values that fall into every bin. |
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Adds v into specified rows of x. |
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Subtracts v into specified rows of x. |
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Computes Inv(Reciprocal) of input tensor element-wise. |
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Flips all bits of input tensor element-wise. |
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Determines which elements are inf or -inf for each position |
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Determines which elements are NaN for each position. |
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Computes the boolean value of \(x < y\) element-wise. |
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Computes the boolean value of \(x <= y\) element-wise. |
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Generates values in an interval (inclusive of start and stop) and returns the corresponding interpolated array with num number of ticks. |
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Returns the natural logarithm of a tensor element-wise. |
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Returns the natural logarithm of one plus the input tensor element-wise. |
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Computes the "logical AND" of two tensors element-wise. |
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Computes the "logical NOT" of a tensor element-wise. |
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Computes the "logical OR" of two tensors element-wise. |
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Multiplies matrix a and matrix b. |
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Computes the maximum of input tensors element-wise. |
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Computes the minimum of input tensors element-wise. |
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Computes the remainder of dividing the first input tensor by the second input tensor element-wise. |
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Multiplies two tensors element-wise. |
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Returns a tensor with negative values of the input tensor element-wise. |
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Selects some bounding boxes in descending order of score. |
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Computes the non-equivalence of two tensors element-wise. |
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Allocates a flag to store the overflow status. |
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Clears the flag which stores the overflow status. |
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Updates the flag which is the output tensor of NPUAllocFloatStatus with the latest overflow status. |
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Computes a tensor to the power of the second input. |
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Divides the first input tensor by the second input tensor in floating-point type element-wise. |
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Returns reciprocal of a tensor element-wise. |
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Reduces a dimension of a tensor by the "logicalAND" of all elements in the dimension. |
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Reduces a dimension of a tensor by the "logical OR" of all elements in the dimension. |
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Reduces a dimension of a tensor by the maximum value in this dimension. |
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Reduces a dimension of a tensor by averaging all elements in the dimension. |
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Reduces a dimension of a tensor by the minimum value in the dimension. |
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Reduces a dimension of a tensor by multiplying all elements in the dimension. |
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Reduces a dimension of a tensor by summing all elements in the dimension. |
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Returns half to even of a tensor element-wise. |
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Computes reciprocal of square root of input tensor element-wise. |
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Performs sign on the tensor element-wise. |
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Computes sine of the input element-wise. |
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Computes hyperbolic sine of the input element-wise. |
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Returns square root of a tensor element-wise. |
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Returns square of a tensor element-wise. |
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Subtracts the second input tensor from the first input tensor element-wise and returns square of it. |
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Returns the square sum of a tensor element-wise |
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Subtracts the second input tensor from the first input tensor element-wise. |
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Computes tangent of input_x element-wise. |
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Adds two input tensors element-wise. |
To Be Developed |
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Divides the first input tensor by the second input tensor element-wise for integer types, negative numbers will round fractional quantities towards zero. |
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Returns the remainder of division element-wise. |
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Divides the first input tensor by the second input tensor element-wise. |
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Computes the first input tensor multiplied by the logarithm of second input tensor element-wise. |
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Array Operators
API Name |
Description |
Supported Platforms |
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Updates relevant entries according to the FTRL scheme. |
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Returns the indices of the maximum value of a tensor across the axis. |
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Calculates the maximum value with the corresponding index. |
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Returns the indices of the minimum value of a tensor across the axis. |
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Calculates the minimum value with corresponding index, and returns indices and values. |
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Divides batch dimension with blocks and interleaves these blocks back into spatial dimensions. |
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Divides batch dimension with blocks and interleaves these blocks back into spatial dimensions. |
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Broadcasts input tensor to a given shape. |
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Returns a tensor with the new specified data type. |
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Connect tensor in the specified axis. |
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Rearranges blocks of depth data into spatial dimensions. |
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Returns the data type of the input tensor as mindspore.dtype. |
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Returns the shape of the input tensor. |
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Computes the Levenshtein Edit Distance. |
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Returns a slice of input tensor based on the specified indices. |
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Adds an additional dimension at the given axis. |
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Creates a tensor with ones on the diagonal and zeros the rest. |
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Creates a tensor filled with a scalar value. |
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Merges the duplicate value of the gradient and then updates relevant entries according to the FTRL-proximal scheme. |
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Gathers values along an axis specified by dim. |
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Gathers slices from a tensor by indices. |
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Returns a slice of the input tensor based on the specified indices and axis. |
To Be Developed |
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Returns a slice of the input tensor based on the specified indices and axis. |
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Returns a Tensor with the same shape and contents as input. |
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Updates specified rows with values in v. |
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Computes the inverse of an index permutation. |
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Determines which elements are finite for each position. |
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Checks whether an object is an instance of a target type. |
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Checks whether this type is a sub-class of another type. |
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Generates coordinate matrices from given coordinate tensors. |
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Creates a tensor filled with value ones. |
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Creates a new tensor. |
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Extends the last dimension of the input tensor from 1 to pad_dim_size, by filling with 0. |
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Concats tensor in the first dimension. |
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Returns the rank of a tensor. |
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Reshapes the input tensor with the same values based on a given shape tuple. |
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Resizes the input tensor by using the nearest neighbor algorithm. |
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Reverses variable length slices. |
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Reverses specific dimensions of a tensor. |
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Returns an integer that is closest to x element-wise. |
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Checks whether the data type and shape of two tensors are the same. |
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Converts a scalar to a Tensor. |
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Converts a scalar to a Tensor, and converts the data type to the specified type. |
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Updates the value of the input tensor through the addition operation. |
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Updates the value of the input tensor through the divide operation. |
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Updates the value of the input tensor through the maximum operation. |
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Updates the value of the input tensor through the minimum operation. |
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Updates the value of the input tensor through the multiply operation. |
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Scatters a tensor into a new tensor depending on the specified indices. |
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Applies sparse addition to individual values or slices in a tensor. |
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Applies sparse subtraction to individual values or slices in a tensor. |
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Updates tensor values by using input indices and value. |
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Applies sparse addition to the input using individual values or slices. |
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Updates the value of the input tensor through the subtraction operation. |
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Updates tensor values by using input indices and value. |
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Returns the selected elements, either from input \(x\) or input \(y\), depending on the condition. |
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Returns the shape of the input tensor. |
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Returns the size of a tensor. |
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Slices a tensor in the specified shape. |
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Sorts the elements of the input tensor along a given dimension in ascending order by value. |
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Divides spatial dimensions into blocks and combines the block size with the original batch. |
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Divides spatial dimensions into blocks and combines the block size with the original batch. |
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Rearranges blocks of spatial data into depth. |
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Updates relevant entries according to the FTRL-proximal scheme. |
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Updates relevant entries according to the FTRL-proximal scheme. |
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Returns a slice of input tensor based on the specified indices and axis. |
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Splits the input tensor into output_num of tensors along the given axis and output numbers. |
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Returns a tensor with the same type but dimensions of 1 are removed based on axis. |
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Extracts a strided slice of a tensor. |
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Updates tensor values using given values, along with the input indices. |
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Replicates a tensor with given multiples times. |
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Permutes the dimensions of the input tensor according to input permutation. |
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Converts a tuple to a tensor. |
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Returns the unique elements of input tensor and also return a tensor containing the index of each value of input tensor corresponding to the output unique tensor. |
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Returns unique elements and relative indexes in 1-D tensor, filled with padding num. |
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Computes the maximum along segments of a tensor. |
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Computes the minimum of a tensor along segments. |
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Computes the product of a tensor along segments. |
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Computes the sum of a tensor along segments. |
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Creates a tensor filled with value zeros. |
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Creates a new tensor. |
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Common Operators
API Name |
Description |
Supported Platforms |
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Gathers tensors from the specified communication group. |
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Reduces the tensor data across all devices in such a way that all devices will get the same final result. |
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Broadcasts the tensor to the whole group. |
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Operation options for reducing tensors. |
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Reduces and scatters tensors from the specified communication group. |
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Control Flows
API Name |
Description |
Supported Platforms |
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Adds control dependency relation between source and destination operations. |
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Debug Operators
API Name |
Description |
Supported Platforms |
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Outputs the tensor to protocol buffer through histogram summary operator. |
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Outputs the image tensor to protocol buffer through image summary operator. |
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Attaches callback to the graph node that will be invoked on the node's gradient. |
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Outputs the tensor or string to stdout. |
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Outputs a scalar to a protocol buffer through a scalar summary operator. |
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Outputs a tensor to a protocol buffer through a tensor summary operator. |
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Random Operators
API Name |
Description |
Supported Platforms |
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Produces random positive floating-point values x, distributed according to probability density function: |
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Generates random labels with a log-uniform distribution for sampled_candidates. |
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Returns a tensor sampled from the multinomial probability distribution located in the corresponding row of tensor input. |
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Produces random non-negative integer values i, distributed according to discrete probability function: |
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Generates random samples from a given categorical distribution tensor. |
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Generates a random sample as index tensor with a mask tensor from a given tensor. |
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Generates random numbers according to the Laplace random number distribution (mean=0, lambda=1). |
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Generates random numbers according to the standard Normal (or Gaussian) random number distribution. |
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Uniform candidate sampler. |
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Produces random integer values i, uniformly distributed on the closed interval [minval, maxval), that is, distributed according to the discrete probability function: |
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Produces random floating-point values i, uniformly distributed to the interval [0, 1). |
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Inner Operators
API Name |
Description |
Supported Platforms |
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Casts the input scalar to another type. |
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Image Operators
API Name |
Description |
Supported Platforms |
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Extracts crops from the input image tensor and resizes them. |
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Other Operators
API Name |
Description |
Supported Platforms |
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Assigns Parameter with a value. |
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Decodes bounding boxes locations. |
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Encodes bounding boxes locations. |
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Checks bounding box. |
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Depend is used for processing dependency operations. |
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Determines whether the targets are in the top k predictions. |
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Calculates intersection over union for boxes. |
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Makes a RefKey instance by string. |
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Calculates population count. |
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composite
The composite operators are the pre-defined combination of operators.
API Name |
Description |
Supported Platforms |
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A decorator that adds a flag to the function. |
To Be Developed |
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Clips tensor values by the ratio of the sum of their norms. |
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Clips tensor values to a specified min and max. |
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A decorator that adds a flag to the function. |
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Count number of nonzero elements across axis of input tensor |
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Generates random numbers according to the Gamma random number distribution. |
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A higher-order function which is used to generate the gradient function for the input function. |
To Be Developed |
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Hypermap will apply the set operation to input sequences. |
To Be Developed |
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Generates random numbers according to the Laplace random number distribution. |
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Returns a tensor sampled from the multinomial probability distribution located in the corresponding row of the input tensor. |
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Generates overloaded functions. |
To Be Developed |
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Generates random numbers according to the Normal (or Gaussian) random number distribution. |
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Generates random numbers according to the Poisson random number distribution. |
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Repeat elements of a tensor along an axis, like np.repeat. |
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Returns a mask tensor representing the first N positions of each cell. |
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Computation of Tensor contraction on arbitrary axes between tensors a and b. |
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Generates random numbers according to the Uniform random number distribution. |
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functional
The functional operators are the pre-instantiated Primitive operators, which can be used directly as a function.
functional |
operations |
|---|---|
mindspore.ops.addn |
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mindspore.ops.array_reduce |
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mindspore.ops.array_to_scalar |
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mindspore.ops.assign |
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mindspore.ops.assign_add |
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mindspore.ops.assign_sub |
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mindspore.ops.bool_and |
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mindspore.ops.bool_eq |
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mindspore.ops.bool_not |
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mindspore.ops.bool_or |
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mindspore.ops.cast |
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mindspore.ops.control_depend |
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mindspore.ops.distribute |
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mindspore.ops.dtype |
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mindspore.ops.equal |
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mindspore.ops.expand_dims |
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mindspore.ops.fill |
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mindspore.ops.gather |
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mindspore.ops.gather_nd |
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mindspore.ops.hastype |
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mindspore.ops.in_dict |
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mindspore.ops.is_not |
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mindspore.ops.is_ |
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mindspore.ops.isconstant |
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mindspore.ops.isinstance_ |
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mindspore.ops.issubclass_ |
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mindspore.ops.logical_and |
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mindspore.ops.logical_not |
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mindspore.ops.logical_or |
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mindspore.ops.make_row_tensor |
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mindspore.ops.make_sparse_tensor |
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mindspore.ops.mixed_precision_cast |
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mindspore.ops.neg_tensor |
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mindspore.ops.not_equal |
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mindspore.ops.not_in_dict |
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mindspore.ops.ones_like |
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mindspore.ops.pack |
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mindspore.ops.print_ |
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mindspore.ops.rank |
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mindspore.ops.reduced_shape |
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mindspore.ops.reshape |
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mindspore.ops.row_tensor_get_dense_shape |
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mindspore.ops.row_tensor_get_indices |
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mindspore.ops.row_tensor_get_values |
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mindspore.ops.same_type_shape |
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mindspore.ops.scalar_add |
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mindspore.ops.scalar_cast |
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mindspore.ops.scalar_div |
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mindspore.ops.scalar_eq |
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mindspore.ops.scalar_floordiv |
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mindspore.ops.scalar_ge |
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mindspore.ops.scalar_gt |
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mindspore.ops.scalar_le |
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mindspore.ops.scalar_log |
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mindspore.ops.scalar_lt |
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mindspore.ops.scalar_mod |
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mindspore.ops.scalar_mul |
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mindspore.ops.scalar_ne |
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mindspore.ops.scalar_pow |
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mindspore.ops.scalar_sub |
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mindspore.ops.scalar_to_array |
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mindspore.ops.scalar_to_tensor |
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mindspore.ops.scalar_uadd |
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mindspore.ops.scalar_usub |
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mindspore.ops.scatter_nd |
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mindspore.ops.scatter_nd_update |
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mindspore.ops.scatter_update |
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mindspore.ops.select |
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mindspore.ops.shape |
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mindspore.ops.shape_mul |
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mindspore.ops.size |
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mindspore.ops.sparse_tensor_get_dense_shape |
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mindspore.ops.sparse_tensor_get_indices |
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mindspore.ops.sparse_tensor_get_values |
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mindspore.ops.sqrt |
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mindspore.ops.square |
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mindspore.ops.stop_gradient |
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mindspore.ops.strided_slice |
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mindspore.ops.string_concat |
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mindspore.ops.string_eq |
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mindspore.ops.tensor_add |
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mindspore.ops.tensor_div |
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mindspore.ops.tensor_floordiv |
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mindspore.ops.tensor_ge |
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mindspore.ops.tensor_gt |
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mindspore.ops.tensor_le |
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mindspore.ops.tensor_lt |
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mindspore.ops.tensor_mod |
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mindspore.ops.tensor_mul |
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mindspore.ops.tensor_pow |
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mindspore.ops.tensor_sub |
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mindspore.ops.tile |
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mindspore.ops.tuple_to_array |
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mindspore.ops.typeof |
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mindspore.ops.zeros_like |
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primitive
Creates a PrimitiveWithInfer operator that can infer the value at compile time. |
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Primitive attributes register. |
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Primitive is the base class of primitives in python. |
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PrimitiveWithCheck is the base class of primitives in python defines functions for checking operator input arguments but used the infer method registered in c++ source codes. |
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PrimitiveWithInfer is the base class of primitives in python and defines functions for tracking inference in python. |
vm_impl_registry
Gets the virtual implementation function by a primitive object or primitive name. |
op_info_register
Class for AiCPU op info register |
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Various combinations of dtype and format. |
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A decorator which is used to register an operator. |
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Class for TBE operator information register. |