MindSpore Distributed Operator List

Linux Ascend GPU CPU Model Development Beginner Intermediate Expert

Distributed Operator

op name	constraints
mindspore.ops.Abs	None
mindspore.ops.ACos	None
mindspore.ops.Acosh	None
mindspore.ops.Add	None
mindspore.ops.ApproximateEqual	None
mindspore.ops.ArgMaxWithValue	When the input_x is splited on the axis dimension, the distributed result may be inconsistent with that on the single machine.
mindspore.ops.ArgMinWithValue	When the input_x is splited on the axis dimension, the distributed result may be inconsistent with that on the single machine.
mindspore.ops.Asin	None
mindspore.ops.Asinh	None
mindspore.ops.Assign	None
mindspore.ops.AssignAdd	None
mindspore.ops.AssignSub	None
mindspore.ops.Atan	None
mindspore.ops.Atan2	None
mindspore.ops.Atanh	None
mindspore.ops.BatchMatMul	transpore_a=True is not supported.
mindspore.ops.BesselI0e	None
mindspore.ops.BesselI1e	None
mindspore.ops.BiasAdd	None
mindspore.ops.BroadcastTo	None
mindspore.ops.Cast	The shard strategy is ignored in the Auto Parallel and Semi Auto Parallel mode.
mindspore.ops.Ceil	None
mindspore.ops.Concat	The input_x can’t be split into the dimension of axis, otherwise it’s inconsistent with the single machine in the mathematical logic.
mindspore.ops.Cos	None
mindspore.ops.Cosh	None
mindspore.ops.Div	None
mindspore.ops.DivNoNan	None
mindspore.ops.DropoutDoMask	Need to be used in conjunction with DropoutGenMask，configuring shard strategy is not supported.
mindspore.ops.DropoutGenMask	Need to be used in conjunction with DropoutDoMask.
mindspore.ops.Elu	None
mindspore.ops.EmbeddingLookup	The same as GatherV2.
mindspore.ops.Equal	None
mindspore.ops.Erf	None
mindspore.ops.Erfc	None
mindspore.ops.Exp	None
mindspore.ops.ExpandDims	None
mindspore.ops.Expm1	None
mindspore.ops.Floor	None
mindspore.ops.FloorDiv	None
mindspore.ops.FloorMod	None
mindspore.ops.GatherV2	Only support 1-dim and 2-dim parameters and the last dimension of the input_params should be 32-byte aligned; Scalar input_indices is not supported; Repeated calculation is not supported when the parameters are split in the dimension of the axis; Split input_indices and input_params at the same time is not supported.
mindspore.ops.Gather	Only support 1-dim and 2-dim parameters and the last dimension of the input_params should be 32-byte aligned; Scalar input_indices is not supported; Repeated calculation is not supported when the parameters are split in the dimension of the axis; Split input_indices and input_params at the same time is not supported.
mindspore.ops.Gelu	None
mindspore.ops.GeLU	None
mindspore.ops.Greater	None
mindspore.ops.GreaterEqual	None
mindspore.ops.Inv	None
mindspore.ops.L2Normalize	The input_x can’t be split into the dimension of axis, otherwise it’s inconsistent with the single machine in the mathematical logic.
mindspore.ops.Less	None
mindspore.ops.LessEqual	None
mindspore.ops.LogicalAnd	None
mindspore.ops.LogicalNot	None
mindspore.ops.LogicalOr	None
mindspore.ops.Log	None
mindspore.ops.Log1p	None
mindspore.ops.LogSoftmax	The logits can’t be split into the dimension of axis, otherwise it’s inconsistent with the single machine in the mathematical logic.
mindspore.ops.MatMul	transpose_a=True is not supported.
mindspore.ops.Maximum	None
mindspore.ops.Minimum	None
mindspore.ops.Mod	None
mindspore.ops.Mul	None
mindspore.ops.Neg	None
mindspore.ops.NotEqual	None
mindspore.ops.OneHot	Only support 1-dim indices. Must configure strategy for the output and the first and second inputs.
mindspore.ops.OnesLike	None
mindspore.ops.Pack	None
mindspore.ops.Pow	None
mindspore.ops.PReLU	When the shape of weight is not [1], the shard strategy in channel dimension of input_x should be consistent with weight.
mindspore.ops.RealDiv	None
mindspore.ops.Reciprocal	None
mindspore.ops.ReduceMax	When the input_x is splited on the axis dimension, the distributed result may be inconsistent with that on the single machine.
mindspore.ops.ReduceMin	When the input_x is splited on the axis dimension, the distributed result may be inconsistent with that on the single machine.
mindspore.ops.ReduceSum	None
mindspore.ops.ReduceMean	None
mindspore.ops.ReLU	None
mindspore.ops.ReLU6	None
mindspore.ops.ReLUV2	None
mindspore.ops.Reshape	Configuring shard strategy is not supported. In auto parallel mode, if multiple operators are followed by the reshape operator, different shard strategys are not allowed to be configured for these operators.
mindspore.ops.Round	None
mindspore.ops.Rsqrt	None
mindspore.ops.Sigmoid	None
mindspore.ops.SigmoidCrossEntropyWithLogits	None
mindspore.ops.Sign	None
mindspore.ops.Sin	None
mindspore.ops.Sinh	None
mindspore.ops.Softmax	The logits can’t be split into the dimension of axis, otherwise it’s inconsistent with the single machine in the mathematical logic.
mindspore.ops.SoftmaxCrossEntropyWithLogits	The last dimension of logits and labels can’t be splited; Only supports using output[0].
mindspore.ops.Softplus	None
mindspore.ops.Softsign	None
mindspore.ops.SparseGatherV2	The same as GatherV2.
mindspore.ops.Split	The input_x can’t be split into the dimension of axis, otherwise it’s inconsistent with the single machine in the mathematical logic.
mindspore.ops.Sqrt	None
mindspore.ops.Square	None
mindspore.ops.Squeeze	None
mindspore.ops.StridedSlice	Only support mask with all 0 values; The dimension needs to be split should be all extracted; Split is supported when the strides of dimension is 1.
mindspore.ops.Slice	The dimension needs to be split should be all extracted.
mindspore.ops.Sub	None
mindspore.ops.Tan	None
mindspore.ops.Tanh	None
mindspore.ops.TensorAdd	None
mindspore.ops.Tile	Only support configuring shard strategy for multiples.
mindspore.ops.TopK	The input_x can’t be split into the last dimension, otherwise it’s inconsistent with the single machine in the mathematical logic.
mindspore.ops.Transpose	None
mindspore.ops.Unique	Only support the repeat calculate shard strategy (1,).
mindspore.ops.UnsortedSegmentSum	The shard of input_x and segment_ids must be the same as the dimension of segment_ids.
mindspore.ops.UnsortedSegmentMin	The shard of input_x and segment_ids must be the same as the dimension of segment_ids. Note that if the segment id i is missing, then the output[i] will be filled with the maximum of the input type. The user needs to mask the maximum value to avoid value overflow. The communication operation such as AllReudce will raise an Run Task Error due to overflow.
mindspore.ops.UnsortedSegmentMax	The shard of input_x and segment_ids must be the same as the dimension of segment_ids. Note that if the segment id i is missing, then the output[i] will be filled with the minimum of the input type. The user needs to mask the minimum value to avoid value overflow. The communication operation such as AllReudce will raise an Run Task Error due to overflow.
mindspore.ops.ZerosLike	None

Repeated calculation means that the device is not fully used. For example, the cluster has 8 devices to run distributed training, the splitting strategy only cuts the input into 4 copies. In this case, double counting will occur.