Usage Constraints During Operator Parallel

API name	constraints	Config layout constraints
mindspore.ops.Abs	None	Not support config layout
mindspore.ops.ACos	None	Not support config layout
mindspore.ops.Acosh	None	Not support config layout
mindspore.ops.Add	None	Support config layout. Tensor Layout of the inputs should be the same or broadcastable. Output Layout is not open for configuration.
mindspore.ops.AddN	None	Not support config layout
mindspore.ops.ApproximateEqual	None	Not support config layout
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.	Not support config layout
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.	Not support config layout
mindspore.ops.Asin	None	Not support config layout
mindspore.ops.Asinh	None	Not support config layout
mindspore.ops.Assign	None	Not support config layout
mindspore.ops.AssignAdd	None	Not support config layout
mindspore.ops.AssignSub	None	Not support config layout
mindspore.ops.Atan	None	Not support config layout
mindspore.ops.Atan2	None	Not support config layout
mindspore.ops.Atanh	None	Not support config layout
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.	Not support config layout
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.	Not support config layout
mindspore.ops.BatchMatMul	Not support config layout
None	Not support config layout
mindspore.ops.BatchNorm	It does not support GPU.	Not support config layout
mindspore.ops.BesselI0e	None	Not support config layout
mindspore.ops.BesselI1e	None	Not support config layout
mindspore.ops.BiasAdd	None	Support config layout. The second input, bias, should have ths same tensor layout as the last dimension of input_x. Output Layout is not open for configuration.
mindspore.ops.BitwiseAnd	None	Not support config layout
mindspore.ops.BitwiseOr	None	Not support config layout
mindspore.ops.BitwiseXor	None	Not support config layout
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.	Not support config layout
mindspore.ops.BroadcastTo	None	Not support config layout
mindspore.ops.Cast	The shard strategy is ignored in the Auto Parallel and Semi Auto Parallel mode.	Not support config layout
mindspore.ops.Cdist	1. The strategy for ‘B’ dimension must be the same; 2.M dimension can’t be split.	Not support config layout
mindspore.ops.Ceil	None	Not support config layout
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.	Not support config layout
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;	Not support config layout
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.	Not support config layout
mindspore.ops.Cos	None	Not support config layout
mindspore.ops.Cosh	None	Not support config layout
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.	Not support config layout
mindspore.ops.CumProd	The axis dimension for input can’t be split.	Not support config layout
mindspore.ops.CumSum	The same as CumProd.	Not support config layout
mindspore.ops.Div	None	Not support config layout
mindspore.ops.DivNoNan	None	Not support config layout
mindspore.ops.Dropout	None	Not support config layout
mindspore.ops.Elu	None	Not support config layout
mindspore.ops.EmbeddingLookup	The same as Gather.	Not support config layout
mindspore.ops.Equal	None	Not support config layout
mindspore.ops.Erf	None	Not support config layout
mindspore.ops.Erfc	None	Not support config layout
mindspore.ops.Erfinv	None	Not support config layout
mindspore.ops.Exp	None	Not support config layout
mindspore.ops.ExpandDims	None	Not support config layout
mindspore.ops.Expm1	None	Not support config layout
mindspore.ops.Floor	None	Not support config layout
mindspore.ops.FloorDiv	None	Not support config layout
mindspore.ops.FloorMod	None	Not support config layout
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.	Not support config layout
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.	Not support config layout
mindspore.ops.GatherD	The dimension corresponding to dim cannot be segmented; In auto_parallel mode, the dual recursive algorithm is not supported.	Not support config layout
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.	Not support config layout
mindspore.ops.GeLU	None	Support config input layout. Output Layout is not open for configuration.
mindspore.ops.Greater	None	Not support config layout
mindspore.ops.GreaterEqual	None	Not support config layout
mindspore.ops.HShrink	None	Not support config layout
mindspore.ops.HSigmoid	None	Not support config layout
mindspore.ops.InplaceAdd	The first dimension of x and input_v can’t be split.	Not support config layout
mindspore.ops.InplaceSub	The same as InplaceAdd.	Not support config layout
mindspore.ops.InplaceUpdate	The same as InplaceAdd.	Not support config layout
mindspore.ops.Inv	None	Not support config layout
mindspore.ops.IOU	The first dimension of the anchor_boxes and gt_boxes can be spilt.	Not support config layout
mindspore.ops.IsFinite	None	Not support config layout
mindspore.ops.KLDivLoss	None	Not support config layout
mindspore.ops.LayerNorm	The strategy for the second input gamma and the third input beta needs to be equal to the input_x_strategy[begin_params.axis:], input_x_strategy is the strategy for the first input.	Support config layout. The layout configuration for the second input gamma and the third input beta needs to be equal to the input_x_layout_tuple[begin_params.axis:], input_x_layout_tuple is the layout configuration for the first input.
mindspore.ops.L2Loss	None	Not support config layout
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.	Not support config layout
mindspore.ops.Lerp	None	Not support config layout
mindspore.ops.Less	None	Not support config layout
mindspore.ops.LessEqual	None	Not support config layout
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.	Not support config layout
mindspore.ops.LogicalAnd	None	Not support config layout
mindspore.ops.LogicalNot	None	Not support config layout
mindspore.ops.LogicalOr	None	Not support config layout
mindspore.ops.Log	None	Not support config layout
mindspore.ops.Log1p	None	Not support config layout
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.	Not support config layout
mindspore.ops.MaskedFill	None	Not support config layout
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),)	Support config layout. 1. When transpose_b=True is set, the input’s layout configuration must be in the form of (layout(A, B), layout(C, B)), A/B/C is the alias name of device axis or the alias name tuple; 2. When transpose_b=False is set, the input’s layout configuration must be in the form of (layout(A, B), layout(B, C)), A/B/C is the alias name of device axis or the alias name tuple; 3. It is supported to config the output’s layout, the legal output’s layout configuration is (layout(A, C),) or (layout((A, B), C),), A/B/C is the alias name of device axis; When A is tuple of alias name (A1, A2), the legal output’s layout configuration is (layout((A1, A2), C),) or (layout((A1, A2, B), C),)
mindspore.ops.Maximum	None	Not support config layout
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.	Not support config layout
mindspore.ops.MaxPool3D	The same as AvgPool3D.	Not support config layout
mindspore.ops.Minimum	None	Not support config layout
mindspore.ops.Mish	None	Not support config layout
mindspore.ops.Mod	None	Not support config layout
mindspore.ops.Mul	None	Not support config layout
mindspore.ops.MulNoNan	None	Not support config layout
mindspore.ops.Neg	None	Not support config layout
mindspore.ops.NotEqual	None	Not support config layout
mindspore.ops.OneHot	Only support 1-dim indices. Must configure strategy for the output and the first and second inputs.	Not support config layout
mindspore.ops.OnesLike	None	Not support config layout
mindspore.ops.Pow	None	Not support config layout
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.	Not support config layout
mindspore.ops.RandomChoiceWithMask	Only the all-1 strategy is supported.	Not support config layout
mindspore.ops.RealDiv	None	Not support config layout
mindspore.ops.Reciprocal	None	Not support config layout
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.	Not support config layout
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.	Not support config layout
mindspore.ops.ReduceSum	None	Not support config layout
mindspore.ops.ReduceMean	None	Not support config layout
mindspore.ops.ReLU	None	Not support config layout
mindspore.ops.ReLU6	None	Not support config layout
mindspore.ops.Reshape	Configuring sharding 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.	Not support config layout
mindspore.ops.Rint	None	Not support config layout
mindspore.ops.ResizeNearestNeighbor	When align_corners=True is set, only the first dimension and the second dimension can be split.	Not support config layout
mindspore.ops.ROIAlign	Sharding the H/W dimension of the input(features) and the second dimension of input(rois) is not supported.	Not support config layout
mindspore.ops.Round	None	Not support config layout
mindspore.ops.Rsqrt	None	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
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.	Not support config layout
mindspore.ops.Select	In auto_parallel mode, the dual recursive algorithm is not supported.	Not support config layout
mindspore.ops.SeLU	None	Not support config layout
mindspore.ops.Sigmoid	None	Not support config layout
mindspore.ops.SigmoidCrossEntropyWithLogits	None	Not support config layout
mindspore.ops.Sign	None	Not support config layout
mindspore.ops.Sin	None	Not support config layout
mindspore.ops.Sinh	None	Not support config layout
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.	Not support config layout
mindspore.ops.SoftmaxCrossEntropyWithLogits	The last dimension of logits and labels can’t be splited; Only supports using output[0].	Not support config layout
mindspore.ops.Softplus	None	Not support config layout
mindspore.ops.Softsign	None	Not support config layout
mindspore.ops.SoftShrink	None	Not support config layout
mindspore.ops.SparseGatherV2	The same as Gather.	Not support config layout
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.	Not support config layout
mindspore.ops.Sqrt	None	Not support config layout
mindspore.ops.Square	None	Not support config layout
mindspore.ops.SquaredDifference	None	Not support config layout
mindspore.ops.Squeeze	None	Not support config layout
mindspore.ops.Stack	None	Not support config layout
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.	Not support config layout
mindspore.ops.Slice	The dimension needs to be split should be all extracted.	Not support config layout
mindspore.ops.Sub	None	Not support config layout
mindspore.ops.Tan	None	Not support config layout
mindspore.ops.Tanh	None	Not support config layout
mindspore.ops.Tile	Only support configuring shard strategy for multiples.	Not support config layout
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.	Not support config layout
mindspore.ops.Transpose	None	Not support config layout
mindspore.ops.TruncateDiv	None	Not support config layout
mindspore.ops.TruncateMod	None	Not support config layout
mindspore.ops.Unique	Only support the repeat calculate shard strategy (1,).	Not support config layout
mindspore.ops.UnsortedSegmentSum	The shard of input_x and segment_ids must be the same as the dimension of segment_ids.	Not support config layout
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.	Not support config layout
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.	Not support config layout
mindspore.ops.Xdivy	None	Not support config layout
mindspore.ops.Xlogy	None	Not support config layout
mindspore.ops.ZerosLike	None	Not support config layout

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.