Usage Constraints During Operator Parallel

API name	constraints
mindspore.ops.Abs	None
mindspore.ops.ACos	None
mindspore.ops.Acosh	None
mindspore.ops.Add	None
mindspore.ops.AddN	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.AvgPool	1. The data format only supports ‘NCHW’; 2. The shapes of output H/W dimension must be divisible by the split strategies of input H/W dimension; 3. If H/W is split: 1) If the kernel_size <= stride, the input slice size must be divisible by stride; 2) It does not support kernel_size > stride; 4. In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.AvgPool3D	1. The data format only supports ‘NCDHW’; 2. If data exchange between adjacent nodes is involved, only Ascend is supported; 3. The W dimensions can not be split; 4. The output shape of D/H dimension must be divisible by the strategy of input D/H dimensions; 5. In valid mode: If D/H dimension is split: 1) When the kernel_size <= stride, the input‘s slice shape of D/H dimension must be divisible by stride; 2) It does not support that kernel_size > stride; 6. In the same/pad mode: If D/H dimension is split: 1) If kernel_size >= stride, (Total input length including pad - kernel_size) must be divisible by stride. Otherwise, the pad must be 0 and the slice shape of D/H dimension must be divisible by stride; 2) (Output length* stride - input length) must be divisible by strategy: 3) The length of data sent and received between adjacent cards must be greater than or equal to 0 and less than or equal to the slice size; 7. In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.BatchMatMul
None
mindspore.ops.BatchNorm	It does not support GPU.
mindspore.ops.BesselI0e	None
mindspore.ops.BesselI1e	None
mindspore.ops.BiasAdd	None
mindspore.ops.BitwiseAnd	None
mindspore.ops.BitwiseOr	None
mindspore.ops.BitwiseXor	None
mindspore.ops.BoundingBoxEncode	1. The first dimension of input (anchor_box) and input (groundtruth_box) can be split; 2. The sharding strategies of input (anchor_box) and input (groundtruth_box) must be the same.
mindspore.ops.BroadcastTo	None
mindspore.ops.Cast	The shard strategy is ignored in the Auto Parallel and Semi Auto Parallel mode.
mindspore.ops.Cdist	1. The strategy for ‘B’ dimension must be the same; 2.M dimension can’t be split.
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.Conv2D	1. The data format only supports ‘NCHW’; 2. If data exchange between adjacent nodes is involved, only Ascend is supported; 3. When the value of group is not 1, can not split C-in or C-out; 4. The last two dimensions of weight can not be split; 5. The output shape of H/W dimension must be divisible by the strategy of input H/W dimensions; 6. In valid mode: If H/W dimension is split: 1) When the kernel_size <= stride (kernel_size is dilation (kernel_size - 1) + 1, the same below), the input‘s slice shape of H/W dimension must be divisible by stride; 2) It does not support that kernel_size > stride; 7. In the same/pad mode: If H/W dimension is split: 1) If kernel_size >= stride, (Total input length including pad - kernel_size) must be divisible by stride. Otherwise, the pad must be 0 and the slice shape of H/W dimension must be divisible by stride; 2) (Output length stride - input length) must be divisible by strategy: 3) The length of data sent and received between adjacent cards must be greater than or equal to 0 and less than or equal to the slice size;
mindspore.ops.Conv3D	1. The data format only supports ‘NCDHW’; 2. If data exchange between adjacent nodes is involved, only Ascend is supported; 3. When the value of group is not 1, can not split C-in or C-out; 4. The W dimension and the last three dimensions of weight can not be split; 5. The output shape of D/H dimension must be divisible by the strategy of input D/H dimensions; 6. In valid mode: If D/H dimension is split: 1) When the kernel_size <= stride (kernel_size is dilation (kernel_size - 1) + 1, the same below), the input‘s slice shape of D/H dimension must be divisible by stride; 2) It does not support that kernel_size > stride; 7. In the same/pad mode: If D/H dimension is split: 1) If kernel_size >= stride, (Total input length including pad - kernel_size) must be divisible by stride. Otherwise, the pad must be 0 and the slice shape of D/H dimension must be divisible by stride; 2) (Output length stride - input length) must be divisible by strategy: 3) The length of data sent and received between adjacent cards must be greater than or equal to 0 and less than or equal to the slice size; 8. In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.Cos	None
mindspore.ops.Cosh	None
mindspore.ops.CropAndResize	1. Sharding of the H/W dimension of input (x) and the second dimension of input (boxes) is not supported. 2. The shard strategy for the first dimension of inputs (boxes) and (box_index) must be the same.
mindspore.ops.CumProd	The axis dimension for input can’t be split.
mindspore.ops.CumSum	The same as CumProd.
mindspore.ops.Div	None
mindspore.ops.DivNoNan	None
mindspore.ops.Dropout	None
mindspore.ops.Elu	None
mindspore.ops.EmbeddingLookup	The same as Gather.
mindspore.ops.Equal	None
mindspore.ops.Erf	None
mindspore.ops.Erfc	None
mindspore.ops.Erfinv	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.Gamma	1. Set the strategy for shape. e.g shape=(8, 16), the corresponding policy can be (2, 4); 2. The strategy for alpha and beta must be all-1; 3. When the setting for shard is not all-1 strategy, the result is inconsistent with standalone.
mindspore.ops.Gather	1. When batch_dims > 0: 1) The axis dimension of input_params can not be split; 2) Non-uniform split is not supported; 2. When batch_dims = 0: 1) If uniform split: a) and if the axis dimensions of input_params is not split, other dimensions can be split; b) If the axis dimensions of input_params is split: The input_params only supports 1 and 2 dimensions; The input_indices can not be scalar tensor; Does not support to split input_params and input_indices at the same time; When axis = 0 and the parameter is split in the dimension of axis, the output strategy can be configured. The legal output shard strategy is (indices_strategy, param_strategy[1:]) or ((indices_strategy[0]*param_strategy[0], indices_strategy[1:]), param_strategy[1:]) 2) Non-uniform split: a) Only support axis = 0; b) The non-uniform split only represents the non-uniformity of the 0th dimension of input_params, and the last dimension of the params slice should be aligned by 32 bytes; c) The number of slices in the 0th dimension of input_params should be equal to that of the last dimension of input_indices; d) Each dimension of input_params can be split, but input_indices can only split the last dimension, and does not support repeated calculations; e) Input_indices shall meet the following requirements: the Tensor value of the next slice shall be greater than that of the previous slice.
mindspore.ops.GatherD	The dimension corresponding to dim cannot be segmented; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.GatherNd	The first input can’t be split, and the last dimension of the second input can’t be split; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.GeLU	None
mindspore.ops.Greater	None
mindspore.ops.GreaterEqual	None
mindspore.ops.HShrink	None
mindspore.ops.HSigmoid	None
mindspore.ops.InplaceAdd	The first dimension of x and input_v can’t be split.
mindspore.ops.InplaceSub	The same as InplaceAdd.
mindspore.ops.InplaceUpdate	The same as InplaceAdd.
mindspore.ops.Inv	None
mindspore.ops.IOU	The first dimension of the anchor_boxes and gt_boxes can be spilt.
mindspore.ops.IsFinite	None
mindspore.ops.KLDivLoss	None
mindspore.ops.L2Loss	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.Lerp	None
mindspore.ops.Less	None
mindspore.ops.LessEqual	None
mindspore.ops.LinSpace	You don’t need to configure strategy for start and end. You just need to pass in a strategy of length 1 whose value divisible into num.
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.MaskedFill	None
mindspore.ops.MatMul	1. When transpose_b=True is set, the input’s split strategy must be in the form of ((A, B), (C, B)); 2. When transpose_b=False is set, the input’s split strategy must be in the form of ((A, B), (B, C)); 3. It is supported to set the output’s split strategy, the legal output’s split strategy is ((A, C),) or ((A * B, C),)
mindspore.ops.Maximum	None
mindspore.ops.MaxPool	1. The data format only supports ‘NCHW’; 2. The shapes of output H/W dimension must be divisible by the split strategies of input H/W dimension; 3. If H/W is split: 1) If the kernel_size <= stride, the input slice size must be divisible by stride; 2) It does not support kernel_size > stride; 4. In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.MaxPool3D	The same as AvgPool3D.
mindspore.ops.Minimum	None
mindspore.ops.Mish	None
mindspore.ops.Mod	None
mindspore.ops.Mul	None
mindspore.ops.MulNoNan	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.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.RandomChoiceWithMask	Only the all-1 strategy is supported.
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.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.ResizeBilinear	Under GPU platform, can not split H or W dimension; Under Ascend platform, can not split H dimension, and the output shape of W dimension can be divided by the strategy.
mindspore.ops.Rint	None
mindspore.ops.ResizeNearestNeighbor	When align_corners=True is set, only the first dimension and the second dimension can be split.
mindspore.ops.ROIAlign	Sharding the H/W dimension of the input(features) and the second dimension of input(rois) is not supported.
mindspore.ops.Round	None
mindspore.ops.Rsqrt	None
mindspore.ops.ScatterAdd	The second input cannot be split, and the top n dimensions of the third input (n is the dimension of the second input) cannot be split; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.ScatterDiv	The second input cannot be split, and the top n dimensions of the third input (n is the dimension of the second input) cannot be split; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.ScatterMax	The first dimension of the first input cannot be split, the second input cannot be split, and the top n dimensions of the third input (n is the dimension of the second input) cannot be split; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.ScatterMin	The first dimension of the first input cannot be split, the second input cannot be split, and the top n dimensions of the third input (n is the dimension of the second input) cannot be split; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.ScatterMul	The second input cannot be split, and the top n dimensions of the third input (n is the dimension of the second input) cannot be split; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.ScatterNdAdd	The second input cannot be split, the top n-1 dimension of the third input (n is the dimension of the second input) cannot be split, and the remaining k dimensions (excluding the top n-1 dimension) of the third input are consistent with the last k partitions of the first input; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.ScatterNdSub	The second input cannot be split, the top n-1 dimension of the third input (n is the dimension of the second input) cannot be split, and the remaining k dimensions (excluding the top n-1 dimension) of the third input are consistent with the last k partitions of the first input; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.ScatterNdUpdate	The top m dimension of the first input cannot be cut (m is the value of the last dimension of the second input indexes [- 1]). The second input cannot be split. The top n-1 dimension of the third input (n is the dimension of the second input) cannot be split. The partitions of the remaining k dimensions (excluding the top n-1 dimension) of the third input is consistent with the last k partitions of the first input; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.ScatterSub	The second input cannot be split, and the top n dimensions of the third input (n is the dimension of the second input) cannot be split; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.ScatterUpdate	The first dimension of first input can not be split, the second input can not be split, and the first n dimensions (n is the dimension size of the second input) of the third input can not be split; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.TensorScatterAdd	The second input cannot be split, the top n-1 dimension of the third input (n is the dimension of the second input) cannot be split, and the remaining k dimensions (excluding the top n-1 dimension) of the third input are consistent with the last k partitions of the first input; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.TensorScatterDiv	The second input cannot be split, the top n-1 dimension of the third input (n is the dimension of the second input) cannot be split, and the remaining k dimensions (excluding the top n-1 dimension) of the third input are consistent with the last k partitions of the first input; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.TensorScatterMax	The top m dimension of the first input cannot be cut (m is the value of the last dimension of the second input indexes [- 1]). The second input cannot be split. The top n-1 dimension of the third input (n is the dimension of the second input) cannot be split. The partitions of the remaining k dimensions (excluding the top n-1 dimension) of the third input is consistent with the last k partitions of the first input; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.TensorScatterMax	The top m dimension of the first input cannot be cut (m is the value of the last dimension of the second input indexes [- 1]). The second input cannot be split. The top n-1 dimension of the third input (n is the dimension of the second input) cannot be split. The partitions of the remaining k dimensions (excluding the top n-1 dimension) of the third input is consistent with the last k partitions of the first input; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.TensorScatterMul	The second input cannot be split, the top n-1 dimension of the third input (n is the dimension of the second input) cannot be split, and the remaining k dimensions (excluding the top n-1 dimension) of the third input are consistent with the last k partitions of the first input; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.TensorScatterAdd	The second input cannot be split, the top n-1 dimension of the third input (n is the dimension of the second input) cannot be split, and the remaining k dimensions (excluding the top n-1 dimension) of the third input are consistent with the last k partitions of the first input; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.TensorScatterUpdate	The top m dimension of the first input cannot be cut (m is the value of the last dimension of the second input indexes [- 1]). The second input cannot be split. The top n-1 dimension of the third input (n is the dimension of the second input) cannot be split. The partitions of the remaining k dimensions (excluding the top n-1 dimension) of the third input is consistent with the last k partitions of the first input; In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.Select	In auto_parallel mode, the dual recursive algorithm is not supported.
mindspore.ops.SeLU	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.SoftShrink	None
mindspore.ops.SparseGatherV2	The same as Gather.
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.SquaredDifference	None
mindspore.ops.Squeeze	None
mindspore.ops.Stack	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.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.TruncateDiv	None
mindspore.ops.TruncateMod	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.Xdivy	None
mindspore.ops.Xlogy	None
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.