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 strategy’s searching algorithm can not use “recursive_programming”. |
mindspore.ops.BatchMatMul |
transpore_a=True is not supported.
|
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) (Total input length including pad - kernel_size) 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.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. Uniform split:
1) Only support 1-dim and 2-dim parameters and the last dimension of the input_params should be 32-byte aligned;
2) Scalar input_indices is not supported;
3) Repeated calculation is not supported when the parameters are split in the dimension of the axis;
4) Splitting input_indices and input_params at the same time is not supported;
5) 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:
1) Only support axis = 0;
2) 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;
3) The number of slices in the 0th dimension of input_params should be equal to that of the last dimension of input_indices;
4) Each dimension of input_params can be split, but input_indices can only split the last dimension, and does not support repeated calculations;
5) 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 strategy’s searching algorithm can not use “recursive_programming”. |
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 strategy’s searching algorithm can not use “recursive_programming”. |
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. transpose_a=True is not supported;
2. When transpose_b=True is set, the input’s split strategy must be in the form of ((A, B), (C, B));
3. When transpose_b=False is set, the input’s split strategy must be in the form of ((A, B), (B, C));
4. 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 strategy’s searching algorithm can not use “recursive_programming”. |
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 are supported to 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.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 strategy’s searching algorithm can not use “recursive_programming”. |
mindspore.ops.Select |
In auto_parallel mode, the strategy’s searching algorithm can not use “recursive_programming”. |
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 |
