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Split specification refactoring (#5150)

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* Review spec of Split operation

* Address review comments

   * Changed detailed description
   * Added more description for attribute
   * Changed T1 and T2 for T and T_AXIS
   * Changed range of values in axis input description
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Gabriele Galiero Casay 2021-04-13 14:43:48 +02:00 committed by GitHub
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docs/ops/movement/Split_1.md
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**Short description**: *Split* operation splits an input tensor into pieces of the same length along some axis.
**Detailed Description**
*Split* operation splits a given input tensor `data` into chunks of the same length along a scalar `axis`. It produces multiple output tensors based on *num_splits* attribute.
The i-th output tensor shape is equal to the input tensor `data` shape, except for dimension along `axis` which is `data.shape[axis]/num_splits`.
\f[
shape\_output\_tensor = [data.shape[0], data.shape[1], \dotsc , data.shape[axis]/num\_splits, \dotsc data.shape[D-1]]
\f]
Where D is the rank of input tensor `data`. The axis being split must be evenly divided by *num_splits* attribute.
**Attributes**
* *num_splits*
* **Description**: it specifies the number of outputs into which the initial "*data*" tensor will be split along *"axis"*
* **Range of values**: a positive integer less than or equal to the size of the dimension being split over
* **Type**: any integer type
* **Default value**: None
* **Required**: *Yes*
* **Description**: number of outputs into which the input tensor `data` will be split along `axis` dimension. The dimension of `data` shape along `axis` must be evenly divisible by *num_splits*
* **Range of values**: a positive integer less than or equal to the value of `axis` dimension being split over
* **Type**: `int`
* **Default value**: none
* **Required**: *yes*
**Inputs**
* **1**: "data" - A tensor of type T1. **Required.**
* **2**: "axis" - axis along *"data"* to split. A scalar of type T2 with value from range `-rank(data) .. rank(data)-1`. Negative values address dimensions from the end. **Required.**
* **1**: `data`. A tensor of type `T` and arbitrary shape. **Required.**
* **2**: `axis`. Axis along `data` to split. A scalar of type `T_AXIS` within the range `[-rank(data), rank(data) - 1]`. Negative values address dimensions from the end. **Required.**
* **Note**: The dimension of input tensor `data` shape along `axis` must be evenly divisible by *num_splits* attribute.
**Outputs**
* **Multiple outputs**: Tensors of the same type as the 1st input tensor. The shape of the i-th output has the same shape as the *"data"* except along dimension *"axis"* where the size is `data.shape[i]/num_splits`.
**Detailed Description**
*Split* operation splits the *"data"* input tensor into pieces of the same length along *"axis"*. The i-th shape of output tensor will be equal to the *"data"* shape except along dimension *"axis"* where the shape will be `data.shape[i]/num_splits`. The sum of elements of split_lengths must match `data.shape[axis]`.
Shape of output tensor will be:
\f[
shape_output_tensor = shape_input_tensor[shape_input_tensor[0], shape_input_tensor[1], ... ,split_lengths[axis], ... shape_input_tensor[D-1]], where D rank of input tensor.
\f]
* **Multiple outputs**: Tensors of type `T`. The i-th output has the same shape as `data` input tensor except for dimension along `axis` which is `data.shape[axis]/num_splits`.
**Types**
* *T1*: arbitrary supported type.
* *T2*: any integer type.
* *T*: any arbitrary supported type.
* *T_AXIS*: any integer type.
**Example**