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.bernoulli	Sample from the Bernoulli distribution and randomly set the i^{th} element of the output to (0 or 1) according to the i^{th} probability value given in the input.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.bincount	Count the occurrences of each value in the input.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.clone	Returns a copy of the input tensor.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.eye	Returns a tensor with ones on the diagonal and zeros in the rest.	Ascend	None
mindspore.mint.einsum	According to the Einstein summation Convention (Einsum), the product of the input tensor elements is summed along the specified dimension.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.empty	Creates a tensor with uninitialized data, whose shape, dtype and device are described by the argument size, dtype and device respectively.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.empty_like	Returns an uninitialized Tensor with the same shape as the input.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.full	Create a Tensor of the specified shape and fill it with the specified value.	Ascend	None
mindspore.mint.full_like	Return a Tensor of the same shape as input and filled with fill_value.	Ascend	This is an experimental API that is subject to change or deletion.
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	Atlas training series does not support int16 dtype currently.
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.randint	Returns a new tensor filled with integer numbers from the uniform distribution over an interval \([low, high)\) based on the given shape and dtype.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.randint_like	Returns a new tensor filled with integer numbers from the uniform distribution over an interval \([low, high)\) based on the given dtype and shape of the input tensor.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.randn	Returns a new tensor filled with numbers from the normal distribution over an interval \([0, 1)\) based on the given shape and dtype.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.randn_like	Returns a new tensor filled with numbers from the normal distribution over an interval \([0, 1)\) based on the given dtype and shape of the input tensor.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.randperm	Generates random permutation of integers from 0 to n-1.	Ascend	This is an experimental API that is subject to change or deletion.
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.chunk	Cut the input Tensor into chunks sub-tensors along the specified axis.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.concat	Alias for mindspore.mint.cat().	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.count_nonzero	Counts the number of non-zero values in the tensor input along the given dim.	Ascend	This is an experimental API that is subject to change or deletion.
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.reshape	Rearranges the input Tensor based on the given shape.	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	Obtains a tensor of a specified length at a specified start position along a specified axis.	Ascend	None
mindspore.mint.nonzero	Return the positions of all non-zero values.	Ascend	None
mindspore.mint.tile	Creates a new tensor by repeating 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.select	Slices the input tensor along the selected dimension at the given index.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.squeeze	Return the Tensor after deleting the dimension of size 1 in the specified dim.	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.swapaxes	Alias for mindspore.mint.transpose() .	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.transpose	Interchange two axes of a tensor.	Ascend	This is an experimental API that is subject to change or deletion.
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.unbind	Unbind a tensor dimension in specified axis.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.unique_consecutive	Returns the elements that are unique in each consecutive group of equivalent elements in the input tensor.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.unsqueeze	Adds an additional dimension to input at the given 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.multinomial	Returns a tensor sampled from the multinomial probability distribution located in the corresponding row of the input tensor.	Ascend	This is an experimental API that is subject to change or deletion.
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 self.	Ascend	None
mindspore.mint.addmv	Performs a matrix-vector product of mat and vec, and add the input vector input to the final result.	Ascend	This is an experimental API that is subject to change or deletion.
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.diff	Computes the n-th forward difference along the given dimension.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.div	Divides each element of the input by the corresponding element of the other .	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.exp2	Calculates the base-2 exponent of the Tensor input element by element.	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.float_power	Computes input to the power of exponent element-wise in double precision, and always returns a mindspore.float64 tensor.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.floor	Rounds a tensor down to the closest integer element-wise.	Ascend	None
mindspore.mint.fmod	Computes the floating-point remainder of the division operation input/other.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.frac	Calculates the fractional part of each element in the input.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.lerp	Perform a linear interpolation of two tensors input and end based on a float or tensor weight.	Ascend	This is an experimental API that is subject to change or deletion.
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.log2	Returns the logarithm to the base 2 of a tensor element-wise.	Ascend	This is an experimental API that is subject to change or deletion. If the input value of operator Log2 is within the range (0, 0.01] or [0.95, 1.05], the output accuracy may be affacted.
mindspore.mint.log10	Returns the logarithm to the base 10 of a tensor element-wise.	Ascend	This is an experimental API that is subject to change or deletion. If the input value of operator Log10 is within the range (0, 0.01] or [0.95, 1.05], the output accuracy may be affacted.
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.logaddexp2	Logarithm of the sum of exponentiations of the inputs in base of 2.	Ascend	None
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	Multiply other value by input Tensor.	Ascend	None
mindspore.mint.mv	Multiply matrix input and vector vec.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nansum	Computes sum of input over a given dimension, treating NaNs as zero.	Ascend	It is only supported on Atlas A2 Training Series Products. This is an experimental API that is subject to change or deletion.
mindspore.mint.nan_to_num	Replace the NaN, positive infinity and negative infinity values in input with the specified values in nan, posinf and neginf respectively.	Ascend	For Ascend, it is only supported on Atlas A2 Training Series Products. This is an experimental API that is subject to change or deletion.
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	This is an experimental API that is subject to change or deletion.
mindspore.mint.polar	Converts polar coordinates to Cartesian coordinates.	Ascend	None
mindspore.mint.ravel	Expand the multidimensional Tensor into 1D along the 0 axis direction.	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.softmax	Alias for mindspore.mint.nn.functional.softmax().	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 self Tensor.	Ascend	None
mindspore.mint.t	Transpose the input tensor.	Ascend	This is an experimental API that is subject to change or deletion.
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.amax	Computes the maximum value of of all elements along the specified dim dimension of the input, and retains the dimension based on the keepdim parameter.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.amin	Computes the minimum value of of all elements along the specified dim dimension of the input, and retains the dimension based on the keepdim parameter.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.argmax	Return the indices of the maximum values of a tensor.	Ascend	None
mindspore.mint.argmin	Return the indices of the minimum values of a tensor across a dimension.	Ascend	None
mindspore.mint.all	Reduces all elements of input by the "logical AND".	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.cumprod	Computes the cumulative product of the input tensor along dimension dim.	Ascend	None
mindspore.mint.histc	Computes the histogram of a tensor.	Ascend	This is an experimental API that is subject to change or deletion. If input is int64, valid values fit within int32; exceeding this may cause precision errors.
mindspore.mint.logsumexp	Computes the logarithm of the sum of exponentiations of all elements along the specified dim dimension of the input (with numerical stabilization), and retains the dimension based on the keepdim parameter.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.max	Returns the maximum value of the input tensor.	Ascend	None
mindspore.mint.mean	Reduces all dimension of a tensor by averaging all elements.	Ascend	None
mindspore.mint.median	Output the median on the specified dimension dim and its corresponding index.	Ascend	None
mindspore.mint.min	Returns the minimum value of the input tensor.	Ascend	None
mindspore.mint.norm	Returns the matrix norm or vector norm of a given tensor.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.prod	Multiplying all elements of input.	Ascend	None
mindspore.mint.sum	Calculate sum of all elements in Tensor.	Ascend	None
mindspore.mint.std	Calculates the standard deviation over the dimensions specified by dim.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.std_mean	By default, return the standard deviation and mean of each dimension in Tensor.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.unique	Returns the unique elements of input tensor.	Ascend	None
mindspore.mint.var	Calculates the variance over the dimensions specified by dim.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.var_mean	By default, return the variance and mean of each dimension in Tensor.	Ascend	This is an experimental API that is subject to change or deletion.

Comparison Operations

API Name	Description	Supported Platforms	Warning
mindspore.mint.allclose	Returns a new Tensor with boolean elements representing if each element of input is “close” to the corresponding element of other.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.argsort	Sorts the input tensor along the given dimension in specified order and return the sorted indices.	Ascend	This is an experimental optimizer API that is subject to change.
mindspore.mint.eq	Computes the equivalence between two tensors element-wise.	Ascend	None
mindspore.mint.equal	Computes the equivalence between two tensors.	Ascend	This is an experimental API that is subject to change or deletion.
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	Computes the boolean value of \(input >= other\) element-wise.	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.isinf	Determines which elements are inf or -inf for each position.	Ascend	This is an experimental API that is subject to change. For Ascend, it is only supported on platforms above Atlas A2.
mindspore.mint.isneginf	Determines which elements are -inf for each position.	Ascend	This is an experimental API that is subject to change. This API can be used only on the Atlas A2 training series.
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.not_equal	Alias for mindspore.mint.ne() .	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.addbmm	Applies batch matrix multiplication to batch1 and batch2, with a reduced add step and add input to the result.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.addmm	Performs a matrix multiplication of the 2-D matrices mat1 and mat2.	Ascend	This is an experimental API that is subject to change or deletion.
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.dot	Computes the dot product of two 1D tensor.	Ascend	This is an experimental API that is subject to change or deletion.
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.meshgrid	Generates coordinate matrices from given coordinate tensors.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.mm	Returns the matrix product of two arrays.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.outer	Return outer product of input and vec2.	Ascend	This is an experimental API that is subject to change or deletion.
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.cdist	Computes p-norm distance between each pair of row vectors of two input Tensors.	Ascend	This is an experimental optimizer API that is subject to change.
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.diag	If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.	Ascend	This is an experimental API that is subject to change or deletion.
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 mindspore.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.triangular_solve	Solves a system of equations with a square upper or lower triangular invertible matrix A and multiple right-hand sides b.	Ascend	This is an experimental API that is subject to change or deletion.

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.Conv2d	2D convolution layer.	Ascend	None
mindspore.mint.nn.Conv3d	3D convolution layer.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ConvTranspose2d	Applies a 2D transposed convolution operator over an input image composed of several input planes.	Ascend	This is an experimental API that is subject to change or deletion. In the scenario where inputs are non-contiguous, output_padding must be less than stride . For Atlas training products, when the dtype of input is float32, the groups only supports 1.
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.BatchNorm1d	Applies Batch Normalization over a 2D or 3D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .	Ascend	This API does not support Dynamic Rank. This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.BatchNorm2d	Applies Batch Normalization over a 4D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .	Ascend	This API does not support Dynamic Rank. This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.BatchNorm3d	Applies Batch Normalization over a 5D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .	Ascend	This API does not support Dynamic Rank. This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.GroupNorm	Group Normalization over a mini-batch of inputs.	Ascend	None
mindspore.mint.nn.LayerNorm	Applies Layer Normalization over a mini-batch of inputs.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.SyncBatchNorm	Sync Batch Normalization layer over a N-dimension input.	Ascend	This is an experimental API that is subject to change or deletion.

Non-linear Activations (weighted sum, nonlinearity)

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.ELU	Exponential Linear Unit activation function	Ascend	This is an experimental API that is subject to change or deletion.
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.LogSigmoid	Applies logsigmoid activation element-wise.	Ascend	This is an experimental API that is subject to change or deletion.
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.ReLU6	Activation function ReLU6.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.SELU	Activation function SELU (Scaled exponential Linear Unit).	Ascend	None
mindspore.mint.nn.SiLU	Calculates the SiLU activation function element-wise.	Ascend	This is an experimental API that is subject to change or deletion.
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
mindspore.mint.nn.Tanh	Applies the Tanh function element-wise, returns a new tensor with the hyperbolic tangent of the elements of input.	Ascend	This is an experimental API that is subject to change or deletion.

Embedding Layers

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.Embedding	The value in input is used as the index, and the corresponding embedding vector is queried from weight .	Ascend	This is an experimental API that is subject to change or deletion. On Ascend, the behavior is unpredictable when the value of input is invalid.

Linear Layers

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.Linear	The linear connected layer.	Ascend	On the Ascend platform, if bias is False , the x cannot be greater than 6D in PYNATIVE or KBK mode.

Dropout Layers

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.Dropout	Dropout layer for the input.	Ascend	None
mindspore.mint.nn.Dropout2d	During training, randomly zeroes some channels of the input tensor with probability p from a Bernoulli distribution (For a 4-dimensional tensor with a shape of \(NCHW\), the channel feature map refers to a 2-dimensional feature map with the shape of \(HW\)).	Ascend	This is an experimental API that is subject to change or deletion.

Pooling Layers

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.AdaptiveAvgPool1d	Applies a 1D adaptive average pooling over an input signal composed of several input planes.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.AdaptiveAvgPool2d	Applies a 2D adaptive average pooling over an input signal composed of several input planes.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.AdaptiveAvgPool3d	This operator applies a 3D adaptive average pooling to an input signal composed of multiple input planes.	Ascend	For Ascend, it is only supported on Atlas A2 Training Series Products. This is an experimental optimizer API that is subject to change or deletion.
mindspore.mint.nn.AdaptiveMaxPool1d	Applies a 1D adaptive max pooling over an input signal composed of several input planes.	Ascend	This is an experimental API that is subject to change or deletion.
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
mindspore.mint.nn.AvgPool3d	Applies a 3D average pooling over an input Tensor which can be regarded as a composition of 3D input planes.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.MaxUnpool2d	Computes the inverse of Maxpool2d.	Ascend	This is an experimental API that is subject to change or deletion.

Padding Layers

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.ConstantPad1d	Pad the last dimension of input tensor using padding and value.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ConstantPad2d	Pad the last 2 dimensions of input tensor using padding and value.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ConstantPad3d	Pad the last 3 dimension of input tensor using padding and value.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ReflectionPad1d	Pad the last dimension of input tensor using the reflection of the input boundary.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ReflectionPad2d	Pad the last 2 dimension of input tensor using the reflection of the input boundary.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ReflectionPad3d	Pad the last 3 dimension of input tensor using the reflection of the input boundary.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ReplicationPad1d	Pad the last dimension of input tensor using the replication of the input boundary.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ReplicationPad2d	Pad the last 2 dimension of input tensor using the replication of the input boundary.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ReplicationPad3d	Pad the last 3 dimension of input tensor using the replication of the input boundary.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ZeroPad1d	Pad the last dimension of input tensor with 0 using padding.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ZeroPad2d	Pad the last 2 dimension of input tensor with 0 using padding.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.ZeroPad3d	Pad the last 3 dimension of input tensor with 0 using padding.	Ascend	This is an experimental API that is subject to change or deletion.

Loss Functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.BCELoss	Compute the binary cross entropy between the true labels and predicted labels.	Ascend	This is an experimental API that is subject to change or deletion.
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.CrossEntropyLoss	The cross entropy loss between input and target.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.MSELoss	Calculates the mean squared error between the predicted value and the label value.	Ascend	None
mindspore.mint.nn.NLLLoss	Gets the negative log likelihood loss between inputs and target.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.SmoothL1Loss	Computes smooth L1 loss, a robust L1 loss.	Ascend	This is an experimental API that is subject to change or deletion.

Image Processing Layer

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.Upsample	For details, please refer to mindspore.mint.nn.functional.interpolate().	Ascend	This is an experimental API that is subject to change or deletion.

Tools

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.Identity	A placeholder identity operator that returns the same as input.	Ascend	None

mindspore.mint.nn.functional

Convolution functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.conv2d	Applies a 2D convolution over an input tensor.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.functional.conv3d	Applies a 3D convolution over an input tensor.	Ascend	This API does not support Atlas series products. This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.functional.conv_transpose2d	Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called deconvolution (although it is not an actual deconvolution).	Ascend	This is an experimental API that is subject to change or deletion. In the scenario where inputs are non-contiguous, output_padding must be less than stride . For Atlas training products, when the dtype of input is float32, the groups only supports 1.
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.adaptive_avg_pool1d	Performs 1D adaptive average pooling on a multi-plane input signal.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.functional.adaptive_avg_pool2d	Performs 2D adaptive average pooling on a multi-plane input signal.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.functional.adaptive_max_pool1d	Performs 1D adaptive max pooling on a multi-plane input signal.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.functional.avg_pool1d	Applies a 1D average pooling over an input Tensor which can be regarded as a composition of 1D input planes.	Ascend	This is an experimental API that is subject to change or deletion.
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.avg_pool3d	Applies a 3D average pooling over an input Tensor which can be regarded as a composition of 3D input planes.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.functional.max_pool2d	Performs a 2D max pooling on the input Tensor.	Ascend	Only support on Atlas A2 training series.
mindspore.mint.nn.functional.max_unpool2d	Computes the inverse of max_pool2d.	Ascend	This is an experimental API that is subject to change or deletion.

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	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.functional.elu_	Exponential Linear Unit activation function	Ascend	This is an experimental API that is subject to change or deletion.
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.logsigmoid	Applies logsigmoid activation element-wise.	Ascend	This is an experimental API that is subject to change or deletion.
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.relu6	Computes ReLU (Rectified Linear Unit) upper bounded by 6 of input tensors element-wise.	Ascend	This is an experimental optimizer API that is subject to change.
mindspore.mint.nn.functional.relu_	ReLuComputes ReLU (Rectified Linear Unit activation function) inplace of input tensors element-wise.	Ascend	This is an experimental API that is subject to change or deletion.
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

Normalization functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.normalize	Perform normalization of inputs over specified dimension	Ascend	This is an experimental API that is subject to change or deletion.

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. On the Ascend platform, if bias is not 1D, the input cannot be greater than 6D in PYNATIVE or KBK mode.

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
mindspore.mint.nn.functional.dropout2d	During training, randomly zeroes some channels of the input tensor with probability p from a Bernoulli distribution(For a 4-dimensional tensor with a shape of \(NCHW\), the channel feature map refers to a 2-dimensional feature map with the shape of \(HW\)).	Ascend	This is an experimental API that is subject to change or deletion.

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
mindspore.mint.nn.functional.nll_loss	Gets the negative log likelihood loss between input and target.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.nn.functional.smooth_l1_loss	Computes smooth L1 loss, a robust L1 loss.	Ascend	This is an experimental optimizer API that is subject to change.

Vision functions

API Name	Description	Supported Platforms	Warning
mindspore.mint.nn.functional.interpolate	Samples the input Tensor to the given size or scale_factor by using one of the interpolate algorithms.	Ascend	This is an experimental API that is subject to change or deletion.
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.Adam	Implements Adaptive Moment Estimation (Adam) algorithm.	Ascend	This is an experimental API that is subject to change or deletion.
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.linalg.matrix_norm	Returns the matrix norm of a given tensor on the specified dimensions.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.linalg.norm	Returns the matrix norm or vector norm of a given tensor.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.linalg.vector_norm	Returns the vector norm of the given tensor on the specified dimensions.	Ascend	This is an experimental API that is subject to change or deletion.
mindspore.mint.linalg.qr	Orthogonal decomposition of the input \(A = QR\).	Ascend	This is an experimental API that is subject to change or deletion.

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.exp2	Calculates the base-2 exponent of the Tensor input element by element.	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.all_gather	Gathers tensors from the specified communication group and returns the tensor list which is all gathered.	Ascend	None
mindspore.mint.distributed.all_gather_into_tensor	Gathers tensors from the specified communication group and returns the tensor which is all gathered.	Ascend	None
mindspore.mint.distributed.all_gather_object	Aggregates Python objects in a specified communication group.	Ascend	None
mindspore.mint.distributed.all_reduce	Reduce tensors across all devices in such a way that all deviceswill get the same final result, returns the tensor which is all reduced.	Ascend	None
mindspore.mint.distributed.all_to_all	scatter and gather list of tensor to/from all rank according to input/output tensor list.	Ascend	None
mindspore.mint.distributed.all_to_all_single	scatter and gather input with split size to/from all rank, and return result in a single tensor.	Ascend	None
mindspore.mint.distributed.barrier	Synchronizes all processes in the specified group.	Ascend	None
mindspore.mint.distributed.batch_isend_irecv	Batch send and recv tensors asynchronously.	Ascend	None
mindspore.mint.distributed.broadcast	Broadcasts the tensor to the whole group.	Ascend	None
mindspore.mint.distributed.broadcast_object_list	Broadcasts the entire group of input Python objects.	Ascend	None
mindspore.mint.distributed.destroy_process_group	Destroy the user collective communication group.	Ascend	None
mindspore.mint.distributed.gather	Gathers tensors from the specified communication group.	Ascend	None
mindspore.mint.distributed.gather_object	Gathers python objects from the whole group in a single process.	Ascend	None
mindspore.mint.distributed.get_backend	Get the backend of communication process groups.	Ascend	None
mindspore.mint.distributed.get_global_rank	A function that returns the rank id in the world group corresponding to the rank which id is 'group_rank' in the user group.	Ascend	None
mindspore.mint.distributed.get_group_rank	Get the rank ID in the specified user communication group corresponding to the rank ID in the world communication group.	Ascend	None
mindspore.mint.distributed.get_process_group_ranks	Gets the ranks of the specific group and returns the process ranks in the communication group as a list.	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
mindspore.mint.distributed.init_process_group	Init collective communication lib.	Ascend	None
mindspore.mint.distributed.irecv	Receive tensors from src asynchronously.	Ascend	None
mindspore.mint.distributed.isend	Send tensors to the specified dest_rank asynchronously.	Ascend	None
mindspore.mint.distributed.is_available	Checks if distributed module is available.	Ascend	None
mindspore.mint.distributed.is_initialized	Checks if default process group has been initialized.	Ascend	None
mindspore.mint.distributed.new_group	Create a new distributed group.	Ascend	None
mindspore.mint.distributed.P2POp	Object for batch_isend_irecv input, to store information of "isend" and "irecv".	Ascend	None
mindspore.mint.distributed.recv	Receive tensors from src.	Ascend	None
mindspore.mint.distributed.reduce	Reduces tensors across the processes in the specified communication group, sends the result to the target dst(global rank), and returns the tensor which is sent to the target process.	Ascend	None
mindspore.mint.distributed.reduce_scatter	Reduces and scatters tensors from the specified communication group and returns the tensor which is reduced and scattered.	Ascend	None
mindspore.mint.distributed.reduce_scatter_tensor	Reduces and scatters tensors from the specified communication group and returns the tensor which is reduced and scattered.	Ascend	None
mindspore.mint.distributed.scatter	Scatter tensor evently across the processes in the specified communication group.	Ascend	None
mindspore.mint.distributed.scatter_object_list	Scatters picklable objects in scatter_object_input_list to the whole group.	Ascend	None
mindspore.mint.distributed.send	Send tensors to the specified dest_rank.	Ascend	None