mindspore.mint

mindspore.mint provides a large number of functional, nn, optimizer interfaces. The API usages and functions are consistent with the mainstream usage in the industry for easy reference. The mint interface is currently an experimental interface and performs better than ops in graph mode of O0 and PyNative mode. Currently, the graph sinking mode and CPU/GPU backend are not supported, and it will be gradually improved in the future.

The module import method is as follows:

from mindspore import mint

Compared with the previous version, the added, deleted and supported platforms change information of mindspore.mint operators in MindSpore, please refer to the link mindspore.mint API Interface Change .

Tensor

Creation Operations

API Name	Description	Supported Platforms	Warning
mindspore.mint.arange	Creates a sequence of numbers that begins at start and extends by increments of step up to but not including end.	Ascend	None
mindspore.mint.eye	Creates a tensor with ones on the diagonal and zeros in the rest.	Ascend	None
mindspore.mint.full	Create a Tensor of the specified shape and fill it with the specified value.	Ascend	None
mindspore.mint.linspace	Returns a Tensor whose value is steps evenly spaced in the interval start and end (including start and end), and the length of the output Tensor is steps.	Ascend	None
mindspore.mint.ones	Creates a tensor filled with value ones.	Ascend	None
mindspore.mint.ones_like	Creates a tensor filled with 1, with the same shape as input, and its data type is determined by the given dtype.	Ascend	None
mindspore.mint.zeros	Creates a tensor filled with 0 with shape described by size and fills it with value 0 in type of dtype.	Ascend	None
mindspore.mint.zeros_like	Creates a tensor filled with 0, with the same size as input.	Ascend	None

Indexing, Slicing, Joining, Mutating Operations

API Name	Description	Supported Platforms	Warning
mindspore.mint.cat	Connect input tensors along with the given dimension.	Ascend	None
mindspore.mint.gather	Gather data from a tensor by indices.	Ascend	On Ascend, the behavior is unpredictable in the following cases: the value of index is not in the range [-input.shape[dim], input.shape[dim]) in forward; the value of index is not in the range [0, input.shape[dim]) in backward.
mindspore.mint.index_select	Generates a new Tensor that accesses the values of input along the specified dim dimension using the indices specified in index.	Ascend	None
mindspore.mint.masked_select	Returns a new 1-D Tensor which indexes the input tensor according to the boolean mask.	Ascend	None
mindspore.mint.permute	Permutes the dimensions of the input tensor according to input dims .	Ascend	None
mindspore.mint.scatter	Update the value in src to input according to the specified index.	Ascend	None
mindspore.mint.scatter_add	Add all elements in src to the index specified by index to input along dimension specified by dim.	Ascend	None
mindspore.mint.split	Splits the Tensor into chunks along the given dim.	Ascend	None
mindspore.mint.narrow	Returns a narrowed tensor from input tensor, and the dimension axis is input from start to start + length.	Ascend	None
mindspore.mint.nonzero	Return the positions of all non-zero values.	Ascend	None
mindspore.mint.tile	Creates a new tensor by replicating input dims times.	Ascend	None
mindspore.mint.tril	Returns the lower triangle part of input (elements that contain the diagonal and below), and set the other elements to zeros.	Ascend	None
mindspore.mint.triu	Returns the upper triangle part of input (elements that contain the diagonal and below), and set the other elements to zeros.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.stack	Stacks a list of tensors in specified dim.	Ascend	None
mindspore.mint.where	Selects elements from input or other based on condition and returns a tensor.	Ascend	None

Random Sampling

API Name	Description	Supported Platforms	Warning
mindspore.mint.normal	Generates random numbers according to the standard Normal (or Gaussian) random number distribution.	Ascend	None
mindspore.mint.rand_like	Returns a new tensor that fills numbers from the uniform distribution over an interval \([0, 1)\) based on the given dtype and shape of the input tensor.	Ascend	None
mindspore.mint.rand	Returns a new tensor that fills numbers from the uniform distribution over an interval \([0, 1)\) based on the given shape and dtype.	Ascend	None

Math Operations

Pointwise Operations

API Name	Description	Supported Platforms	Warning
mindspore.mint.abs	Returns absolute value of a tensor element-wise.	Ascend	None
mindspore.mint.add	Adds scaled other value to input Tensor.	Ascend	None
mindspore.mint.acos	Computes arccosine of input tensors element-wise.	Ascend	None
mindspore.mint.acosh	Computes inverse hyperbolic cosine of the inputs element-wise.	Ascend	None
mindspore.mint.arccos	Alias for mindspore.mint.acos() .	Ascend	None
mindspore.mint.arccosh	Alias for mindspore.mint.acosh().	Ascend	None
mindspore.mint.arcsin	Alias for mindspore.mint.asin().	Ascend	None
mindspore.mint.arcsinh	Alias for mindspore.mint.asinh().	Ascend	None
mindspore.mint.arctan	Alias for mindspore.mint.atan().	Ascend	None
mindspore.mint.arctan2	Alias for mindspore.mint.atan2().	Ascend	None
mindspore.mint.arctanh	Alias for mindspore.mint.atanh().	Ascend	None
mindspore.mint.asin	Computes arcsine of input tensors element-wise.	Ascend	None
mindspore.mint.asinh	Computes inverse hyperbolic sine of the input element-wise.	Ascend	None
mindspore.mint.atan	Computes the trigonometric inverse tangent of the input element-wise.	Ascend	None
mindspore.mint.atan2	Returns arctangent of input/other element-wise.	Ascend	None
mindspore.mint.atanh	Computes inverse hyperbolic tangent of the input element-wise.	Ascend	None
mindspore.mint.bitwise_and	Returns bitwise and of two tensors element-wise.	Ascend	None
mindspore.mint.bitwise_or	Returns bitwise or of two tensors element-wise.	Ascend	None
mindspore.mint.bitwise_xor	Returns bitwise xor of two tensors element-wise.	Ascend	None
mindspore.mint.ceil	Rounds a tensor up to the closest integer element-wise.	Ascend	None
mindspore.mint.clamp	Clamps tensor values between the specified minimum value and maximum value.	Ascend	None
mindspore.mint.cos	Computes cosine of input element-wise.	Ascend	Using float64 may cause a problem of missing precision.
mindspore.mint.cosh	Computes hyperbolic cosine of input element-wise.	Ascend	None
mindspore.mint.cross	Computes the cross product of input and other in dimension dim.	Ascend	None
mindspore.mint.div	Divides the first input tensor by the second input tensor in floating-point type element-wise.	Ascend	None
mindspore.mint.divide	Alias for mindspore.mint.div() .	Ascend	None
mindspore.mint.erf	Computes the Gauss error function of input element-wise.	Ascend	None
mindspore.mint.erfc	Computes the complementary error function of input element-wise.	Ascend	None
mindspore.mint.erfinv	Returns the result of the inverse error function with input.	Ascend	None
mindspore.mint.exp	Returns exponential of a tensor element-wise.	Ascend	None
mindspore.mint.expm1	Returns exponential then minus 1 of a tensor element-wise.	Ascend	None
mindspore.mint.fix	Alias for mindspore.mint.trunc() .	Ascend	None
mindspore.mint.floor	Rounds a tensor down to the closest integer element-wise.	Ascend	None
mindspore.mint.log	Returns the natural logarithm of a tensor element-wise.	Ascend	If the input value of operator Log is within the range (0, 0.01] or [0.95, 1.05], the output accuracy may be affacted.
mindspore.mint.log1p	Returns the natural logarithm of one plus the input tensor element-wise.	Ascend	None
mindspore.mint.logaddexp	Computes the logarithm of the sum of exponentiations of the inputs.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.logical_and	Computes the "logical AND" of two tensors element-wise.	Ascend	None
mindspore.mint.logical_not	Computes the "logical NOT" of a tensor element-wise.	Ascend	None
mindspore.mint.logical_or	Computes the "logical OR" of two tensors element-wise.	Ascend	None
mindspore.mint.logical_xor	Computes the "logical XOR" of two tensors element-wise.	Ascend	None
mindspore.mint.mul	Multiplies two tensors element-wise.	Ascend	None
mindspore.mint.neg	Returns a tensor with negative values of the input tensor element-wise.	Ascend	None
mindspore.mint.negative	Alias for mindspore.mint.neg() .	Ascend	None
mindspore.mint.pow	Calculates the exponent power of each element in input.	Ascend	None
mindspore.mint.reciprocal	Returns reciprocal of a tensor element-wise.	Ascend	None
mindspore.mint.remainder	Computes the remainder of input divided by other element-wise.	Ascend	None
mindspore.mint.roll	Rolls the elements of a tensor along an axis.	Ascend	None
mindspore.mint.round	Returns half to even of a tensor element-wise.	Ascend	None
mindspore.mint.rsqrt	Computes reciprocal of square root of input tensor element-wise.	Ascend	None
mindspore.mint.sigmoid	Computes Sigmoid of input element-wise.	Ascend	None
mindspore.mint.sign	Returns an element-wise indication of the sign of a number.	Ascend	None
mindspore.mint.sin	Computes sine of the input element-wise.	Ascend	None
mindspore.mint.sinc	Computes the normalized sinc of input.	Ascend	None
mindspore.mint.sinh	Computes hyperbolic sine of the input element-wise.	Ascend	None
mindspore.mint.sqrt	Returns sqrt of a tensor element-wise.	Ascend	None
mindspore.mint.square	Returns square of a tensor element-wise.	Ascend	None
mindspore.mint.sub	Subtracts scaled other value from input Tensor.	Ascend	None
mindspore.mint.tan	Computes tangent of input element-wise.	Ascend	None
mindspore.mint.tanh	Computes hyperbolic tangent of input element-wise.	Ascend	None
mindspore.mint.trunc	Returns a new tensor with the truncated integer values of the elements of the input tensor.	Ascend	None
mindspore.mint.xlogy	Computes the first input multiplied by the logarithm of second input element-wise.	Ascend	None

Reduction Operations

API Name	Description	Supported Platforms	Warning
mindspore.mint.argmax	Return the indices of the maximum values of a tensor across a dimension.	Ascend	None
mindspore.mint.argmin	Return the indices of the minimum values of a tensor across a dimension.	Ascend	None
mindspore.mint.all	Reduces a dimension of input by the "logical AND" of all elements in the dimension, by default.	Ascend	None
mindspore.mint.any	Reduces a dimension of input by the "logical OR" of all elements in the dimension, by default.	Ascend	None
mindspore.mint.max	Calculates the maximum value along with the given dimension for the input tensor.	Ascend	None
mindspore.mint.mean	Reduces all dimension of a tensor by averaging all elements in the dimension, by default.	Ascend	None
mindspore.mint.median	Output the median on the specified dimension dim and its corresponding index.	Ascend	None
mindspore.mint.min	Calculates the minimum value along with the given dimension for the input tensor.	Ascend	None
mindspore.mint.prod	Reduces a dimension of a tensor by multiplying all elements in the dimension, by default.	Ascend	None
mindspore.mint.sum	Calculate sum of Tensor elements over a given dim.	Ascend	None
mindspore.mint.unique	Returns the unique elements of input tensor.	Ascend	None

Comparison Operations

API Name	Description	Supported Platforms	Warning
mindspore.mint.eq	Computes the equivalence between two tensors element-wise.	Ascend	None
mindspore.mint.greater	Compare the value of the input parameters \(input > other\) element-wise, and the output result is a bool value.	Ascend	None
mindspore.mint.greater_equal	Given two Tensors, compares them element-wise to check if each element in the first Tensor is greater than or equal to the corresponding element in the second Tensor.	Ascend	None
mindspore.mint.gt	Compare the value of the input parameters \(input,other\) element-wise, and the output result is a bool value.	Ascend	None
mindspore.mint.isclose	Returns a new Tensor with boolean elements representing if each element of input is “close” to the corresponding element of other.	Ascend	None
mindspore.mint.isfinite	Determine which elements are finite for each position.	Ascend	None
mindspore.mint.le	Computes the boolean value of \(input <= other\) element-wise.	Ascend	None
mindspore.mint.less	Computes the boolean value of \(input < other\) element-wise.	Ascend	None
mindspore.mint.less_equal	Computes the boolean value of \(input <= other\) element-wise.	Ascend	None
mindspore.mint.lt	Alias for mindspore.mint.less() .	Ascend	None
mindspore.mint.maximum	Computes the maximum of input tensors element-wise.	Ascend	If all inputs are scalar of integers. In GRAPH mode, the output will be Tensor of int32, while in PYNATIVE mode, the output will be Tensor of int64.
mindspore.mint.minimum	Computes the minimum of input tensors element-wise.	Ascend	None
mindspore.mint.ne	Computes the non-equivalence of two tensors element-wise.	Ascend	None
mindspore.mint.topk	Finds values and indices of the k largest or smallest entries along a given dimension.	Ascend	If sorted is set to False, due to different memory layout and traversal methods on different platforms, the display order of calculation results may be inconsistent when sorted is False.
mindspore.mint.sort	Sorts the elements of the input tensor along the given dimension in the specified order.	Ascend	Currently, the data types of float16, uint8, int8, int16, int32, int64 are well supported. If use float32, it may cause loss of accuracy.

BLAS and LAPACK Operations

API Name	Description	Supported Platforms	Warning
mindspore.mint.baddbmm	The result is the sum of the input and a batch matrix-matrix product of matrices in batch1 and batch2.	Ascend	None
mindspore.mint.bmm	Performs batch matrix-matrix multiplication of two three-dimensional tensors.	Ascend	None
mindspore.mint.inverse	Compute the inverse of the input matrix.	Ascend	None
mindspore.mint.matmul	Returns the matrix product of two tensors.	Ascend	None
mindspore.mint.trace	Returns a new tensor that is the sum of the input main trace.	Ascend	None

Other Operations

API Name	Description	Supported Platforms	Warning
mindspore.mint.broadcast_to	Broadcasts input tensor to a given shape.	Ascend	None
mindspore.mint.cummax	Returns a tuple (values, indices) where values is the cumulative maximum value of input Tensor input along the dimension dim, and indices is the index location of each maximum value.	Ascend	None
mindspore.mint.cummin	Returns a tuple (values, indices) where values is the cumulative minimum value of input Tensor input along the dimension dim, and indices is the index location of each minimum value.	Ascend	None
mindspore.mint.cumsum	Computes the cumulative sum of input Tensor along dim.	Ascend	None
mindspore.mint.flatten	Flatten a tensor along dimensions from start_dim to end_dim.	Ascend	None
mindspore.mint.flip	Reverses the order of elements in a tensor along the given axis.	Ascend	None
mindspore.mint.repeat_interleave	Repeat elements of a tensor along an axis, like numpy.repeat.	Ascend	Only support on Atlas A2 training series.
mindspore.mint.searchsorted	Return the position indices such that after inserting the values into the sorted_sequence, the order of innermost dimension of the sorted_sequence remains unchanged.	Ascend	None
mindspore.mint.tril	Returns the lower triangle part of input (elements that contain the diagonal and below), and set the other elements to zeros.	Ascend	None

mindspore.mint.nn

Loss Functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.L1Loss	L1Loss is used to calculate the mean absolute error between the predicted value and the target value.	Ascend	None

Convolution Layers

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.Fold	Combines an array of sliding local blocks into a large containing tensor.	Ascend	None
mindspore.mint.nn.Unfold	Extracts sliding local blocks from a batched input tensor.	Ascend	None

Normalization Layers

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.GroupNorm	Group Normalization over a mini-batch of inputs.	Ascend	None

Non-linear Activations (weighted sum, nonlinearity)

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.GELU	Activation function GELU (Gaussian Error Linear Unit).	Ascend	None
mindspore.mint.nn.Hardshrink	Applies Hard Shrink activation function element-wise.	Ascend	None
mindspore.mint.nn.Hardsigmoid	Applies Hard Sigmoid activation function element-wise.	Ascend	None
mindspore.mint.nn.Hardswish	Applies Hard Swish activation function element-wise.	Ascend	None
mindspore.mint.nn.LogSoftmax	Applies the Log Softmax function to the input tensor on the specified axis.	Ascend	None
mindspore.mint.nn.Mish	Computes MISH (A Self Regularized Non-Monotonic Neural Activation Function) of input tensors element-wise.	Ascend	None
mindspore.mint.nn.PReLU	Applies PReLU activation function element-wise.	Ascend	None
mindspore.mint.nn.ReLU	Applies ReLU (Rectified Linear Unit activation function) element-wise.	Ascend	None
mindspore.mint.nn.SELU	Activation function SELU (Scaled exponential Linear Unit).	Ascend	None
mindspore.mint.nn.Softmax	Applies the Softmax function to an n-dimensional input Tensor.	Ascend	None
mindspore.mint.nn.Softshrink	Applies the SoftShrink function element-wise.	Ascend	None

Linear Layers

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.Linear	The linear connected layer.	Ascend	None

Dropout Layers

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.Dropout	Dropout layer for the input.	Ascend	None

Pooling Layers

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.AvgPool2d	Applies a 2D average pooling over an input Tensor which can be regarded as a composition of 2D input planes.	Ascend	None

Loss Functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.BCEWithLogitsLoss	Adds sigmoid activation function to input as logits, and uses this logits to compute binary cross entropy between the logits and the target.	Ascend	None
mindspore.mint.nn.MSELoss	Calculates the mean squared error between the predicted value and the label value.	Ascend	None

mindspore.mint.nn.functional

Convolution functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.fold	Combines an array of sliding local blocks into a large containing tensor.	Ascend	Currently, only unbatched(3D) or batched(4D) image-like output tensors are supported.
mindspore.mint.nn.functional.unfold	Extracts sliding local blocks from a batched input tensor.	Ascend	Currently, batched(4D) image-like tensors are supported. For Ascend, it is only supported on platforms above Atlas A2.

Pooling functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.avg_pool2d	Applies a 2D average pooling over an input Tensor which can be regarded as a composition of 2D input planes.	Ascend	None
mindspore.mint.nn.functional.max_pool2d	Performs a 2D max pooling on the input Tensor.	Ascend	Only support on Atlas A2 training series.

Non-linear activation functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.batch_norm	Batch Normalization for input data and updated parameters.	Ascend	None
mindspore.mint.nn.functional.elu	Exponential Linear Unit activation function.	Ascend	None
mindspore.mint.nn.functional.gelu	Gaussian Error Linear Units activation function.	Ascend	None
mindspore.mint.nn.functional.group_norm	Group Normalization over a mini-batch of inputs.	Ascend	None
mindspore.mint.nn.functional.hardshrink	Hard Shrink activation function.	Ascend	None
mindspore.mint.nn.functional.hardsigmoid	Hard Sigmoid activation function.	Ascend	None
mindspore.mint.nn.functional.hardswish	Hard Swish activation function.	Ascend	None
mindspore.mint.nn.functional.layer_norm	Applies the Layer Normalization on the mini-batch input.	Ascend	None
mindspore.mint.nn.functional.leaky_relu	leaky_relu activation function.	Ascend	None
mindspore.mint.nn.functional.log_softmax	Applies the Log Softmax function to the input tensor on the specified axis.	Ascend	None
mindspore.mint.nn.functional.mish	Computes MISH (A Self Regularized Non-Monotonic Neural Activation Function) of input tensors element-wise.	Ascend	None
mindspore.mint.nn.functional.prelu	Parametric Rectified Linear Unit activation function.	Ascend	None
mindspore.mint.nn.functional.relu	Computes ReLU (Rectified Linear Unit activation function) of input tensors element-wise.	Ascend	None
mindspore.mint.nn.functional.selu	Activation function SELU (Scaled exponential Linear Unit).	Ascend	None
mindspore.mint.nn.functional.sigmoid	Computes Sigmoid of input element-wise.	Ascend	None
mindspore.mint.nn.functional.silu	Computes Sigmoid Linear Unit of input element-wise.	Ascend	None
mindspore.mint.nn.functional.softmax	Applies the Softmax operation to the input tensor on the specified axis.	Ascend	None
mindspore.mint.nn.functional.softplus	Applies softplus function to input element-wise.	Ascend	None
mindspore.mint.nn.functional.softshrink	Soft Shrink activation function.	Ascend	None
mindspore.mint.nn.functional.tanh	Computes hyperbolic tangent of input element-wise.	Ascend	None

Linear functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.linear	Applies the dense connected operation to the input.	Ascend	This is an experimental API that is subject to change or deletion.

Dropout functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.dropout	During training, randomly zeroes some of the elements of the input tensor with probability p from a Bernoulli distribution.	Ascend	None

Sparse functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.embedding	Retrieve the word embeddings in weight using indices specified in input.	Ascend	On Ascend, the behavior is unpredictable when the value of input is invalid.
mindspore.mint.nn.functional.one_hot	Computes a one-hot tensor.	Ascend	None

Loss Functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.binary_cross_entropy	Computes the binary cross entropy(Measure the difference information between two probability distributions) between predictive value input and target value target.	Ascend	The value of input must range from 0 to l.
mindspore.mint.nn.functional.binary_cross_entropy_with_logits	Adds sigmoid activation function to input as logits, and uses this logits to compute binary cross entropy between the logits and the target.	Ascend	None
mindspore.mint.nn.functional.l1_loss	Calculate the mean absolute error between the input value and the target value.	Ascend	None
mindspore.mint.nn.functional.mse_loss	Calculates the mean squared error between the predicted value and the label value.	Ascend	None

Vision functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.grid_sample	Given an input and a flow-field grid, computes the output using input values and pixel locations from grid.	Ascend	None
mindspore.mint.nn.functional.pad	Pads the input tensor according to the pad.	Ascend	circular mode has poor performance and is not recommended.

mindspore.mint.optim

API Name	Description	Supported Platforms	Warning
mindspore.mint.optim.AdamW	Implements Adam Weight Decay algorithm.	Ascend	This is an experimental optimizer API that is subject to change. This module must be used with lr scheduler module in LRScheduler Class . For Ascend, it is only supported on platforms above Atlas A2.

mindspore.mint.linalg

Inverses

API Name	Description	Supported Platforms	Warning
mindspore.mint.linalg.inv	Compute the inverse of the input matrix.	Ascend	None

mindspore.mint.special

Pointwise Operations

API Name	Description	Supported Platforms	Warning
mindspore.mint.special.erfc	Computes the complementary error function of input element-wise.	Ascend	None
mindspore.mint.special.expm1	Returns exponential then minus 1 of a tensor element-wise.	Ascend	None
mindspore.mint.special.log1p	Returns the natural logarithm of one plus the input tensor element-wise.	Ascend	None
mindspore.mint.special.log_softmax	Applies the Log Softmax function to the input tensor on the specified axis.	Ascend	None
mindspore.mint.special.round	Returns half to even of a tensor element-wise.	Ascend	None
mindspore.mint.special.sinc	Computes the normalized sinc of input.	Ascend	None

mindspore.mint.distributed

API Name	Description	Supported Platforms	Warning
mindspore.mint.distributed.init_process_group	Init collective communication lib.	Ascend	None
mindspore.mint.distributed.destroy_process_group	Destroy the user collective communication group.	Ascend	None
mindspore.mint.distributed.get_rank	Get the rank ID for the current device in the specified collective communication group.	Ascend	None
mindspore.mint.distributed.get_world_size	Get the rank size of the specified collective communication group.	Ascend	None