org.apache.sysds.runtime.data

## Class TensorBlock

- java.lang.Object
- - org.apache.sysds.runtime.data.TensorBlock

- All Implemented Interfaces:
    Externalizable, Serializable, org.apache.hadoop.io.Writable, CacheBlock

      public class TensorBlock
      extends Object
      implements CacheBlock, Externalizable

  A `TensorBlock` is the most top level representation of a tensor. There are two types of data representation which can be used: Basic/Homogeneous and Data/Heterogeneous Basic supports only one `ValueType`, while Data supports multiple `ValueType`s along the column axis. The format determines if the `TensorBlock` uses a `BasicTensorBlock` or a `DataTensorBlock` for storing the data.
  See Also:
    Serialized Form

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static int[]` | `DEFAULT_DIMS`
    `static Types.ValueType` | `DEFAULT_VTYPE`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `TensorBlock()` Create a `TensorBlock` with [0,0] dimension and homogeneous representation (aka.
    `TensorBlock(BasicTensorBlock basicTensor)` Wrap the given `BasicTensorBlock` inside a `TensorBlock`.
    `TensorBlock(DataTensorBlock dataTensor)` Wrap the given `DataTensorBlock` inside a `TensorBlock`.
    `TensorBlock(double value)` Create a [1,1] basic FP64 `TensorBlock` containing the given value.
    `TensorBlock(int[] dims, boolean basic)` Create a `TensorBlock` with the given dimensions and the given data representation (basic/data).
    `TensorBlock(TensorBlock that)` Copy constructor
    `TensorBlock(Types.ValueType[] schema, int[] dims)` Create a data `TensorBlock` with the given schema and the given dimensions.
    `TensorBlock(Types.ValueType vt, int[] dims)` Create a basic `TensorBlock` with the given `ValueType` and the given dimensions.

  - ### Method Summary

    All Methods Static Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `TensorBlock` | `allocateBlock()` If data is not yet allocated, allocate.
    `TensorBlock` | `binaryOperations(BinaryOperator op, TensorBlock thatValue, TensorBlock result)`
    `void` | `compactEmptyBlock()` Free unnecessarily allocated empty block.
    `TensorBlock` | `copy(int[] lower, int[] upper, TensorBlock src)` Copy a part of another `TensorBlock`
    `TensorBlock` | `copy(TensorBlock src)`
    `TensorBlock` | `copyExact(int[] lower, int[] upper, TensorBlock src)` Copy a part of another `TensorBlock`.
    `Object` | `get(int[] ix)`
    `double` | `get(int r, int c)`
    `BasicTensorBlock` | `getBasicTensor()`
    `DataTensorBlock` | `getDataTensor()`
    `int` | `getDim(int i)`
    `int[]` | `getDims()`
    `long` | `getExactBlockDataSerializedSize(BasicTensorBlock bt)` Get the exact serialized size of a `BasicTensorBlock` if written by `TensorBlock.writeBlockData(DataOutput,BasicTensorBlock)`.
    `long` | `getExactSerializedSize()` Get the exact serialized size in bytes of the cache block.
    `long` | `getInMemorySize()` Get the in-memory size in bytes of the cache block.
    `long` | `getLength()`
    `long[]` | `getLongDims()`
    `void` | `getNextIndexes(int[] ix)` Calculates the next index array.
    `static void` | `getNextIndexes(int[] dims, int[] ix)` Calculates the next index array.
    `long` | `getNonZeros()`
    `int` | `getNumColumns()`
    `int` | `getNumDims()`
    `int` | `getNumRows()`
    `Types.ValueType[]` | `getSchema()` Get the schema if this `TensorBlock` is heterogeneous.
    `Types.ValueType` | `getValueType()` Get the `ValueType` if this `TensorBlock` is homogeneous.
    `boolean` | `isAllocated()`
    `boolean` | `isBasic()`
    `boolean` | `isEmpty()`
    `boolean` | `isEmpty(boolean safe)`
    `boolean` | `isMatrix()`
    `boolean` | `isShallowSerialize()` Indicates if the cache block is subject to shallow serialized, which is generally true if in-memory size and serialized size are almost identical allowing to avoid unnecessary deep serialize.
    `boolean` | `isShallowSerialize(boolean inclConvert)` Indicates if the cache block is subject to shallow serialized, which is generally true if in-memory size and serialized size are almost identical allowing to avoid unnecessary deep serialize.
    `boolean` | `isVector()`
    `void` | `merge(CacheBlock that, boolean appendOnly)` Merge the given block into the current block.
    `void` | `readExternal(ObjectInput in)`
    `void` | `readFields(DataInput in)`
    `void` | `reset()` Reset all cells to 0.
    `void` | `reset(int[] dims)` Reset data with new dimensions.
    `static Types.ValueType` | `resultValueType(Types.ValueType in1, Types.ValueType in2)`
    `void` | `set(int[] ix, Object v)` Set a cell to the value given as an `Object`.
    `void` | `set(int r, int c, double v)` Set a cell in a 2-dimensional tensor.
    `void` | `set(MatrixBlock other)`
    `void` | `set(Object v)`
    `TensorBlock` | `slice(int[] offsets, TensorBlock outBlock)` Slice the current block and write into the outBlock.
    `CacheBlock` | `slice(int rl, int ru, int cl, int cu, CacheBlock block)` Slice a sub block out of the current block and write into the given output block.
    `void` | `toShallowSerializeBlock()` Converts a cache block that is not shallow serializable into a form that is shallow serializable.
    `void` | `write(DataOutput out)`
    `void` | `writeBlockData(DataOutput out, BasicTensorBlock bt)` Write a `BasicTensorBlock`.
    `void` | `writeExternal(ObjectOutput out)`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- - ### Field Detail

    - #### DEFAULT_DIMS

          public static final int[] DEFAULT_DIMS

    - #### DEFAULT_VTYPE

          public static final Types.ValueType DEFAULT_VTYPE

  - ### Constructor Detail

    - #### TensorBlock

          public TensorBlock()

      Create a `TensorBlock` with [0,0] dimension and homogeneous representation (aka. basic).

    - #### TensorBlock

          public TensorBlock(int[] dims,
                             boolean basic)

      Create a `TensorBlock` with the given dimensions and the given data representation (basic/data).
      Parameters:
        `dims` - dimensions
        `basic` - if true then basic `TensorBlock` else a data type of `TensorBlock`.

    - #### TensorBlock

          public TensorBlock(Types.ValueType vt,
                             int[] dims)

      Create a basic `TensorBlock` with the given `ValueType` and the given dimensions.
      Parameters:
        `vt` - value type
        `dims` - dimensions

    - #### TensorBlock

          public TensorBlock(Types.ValueType[] schema,
                             int[] dims)

      Create a data `TensorBlock` with the given schema and the given dimensions.
      Parameters:
        `schema` - schema of the columns
        `dims` - dimensions

    - #### TensorBlock

          public TensorBlock(double value)

      Create a [1,1] basic FP64 `TensorBlock` containing the given value.
      Parameters:
        `value` - value to put inside

    - #### TensorBlock

          public TensorBlock(BasicTensorBlock basicTensor)

      Wrap the given `BasicTensorBlock` inside a `TensorBlock`.
      Parameters:
        `basicTensor` - basic tensor block

    - #### TensorBlock

          public TensorBlock(DataTensorBlock dataTensor)

      Wrap the given `DataTensorBlock` inside a `TensorBlock`.
      Parameters:
        `dataTensor` - basic tensor block

    - #### TensorBlock

          public TensorBlock(TensorBlock that)

      Copy constructor
      Parameters:
        `that` - `TensorBlock` to copy

  - ### Method Detail

    - #### reset

          public void reset()

      Reset all cells to 0.

    - #### reset

          public void reset(int[] dims)

      Reset data with new dimensions.
      Parameters:
        `dims` - new dimensions

    - #### isBasic

          public boolean isBasic()

    - #### isAllocated

          public boolean isAllocated()

    - #### allocateBlock

          public TensorBlock allocateBlock()

      If data is not yet allocated, allocate.
      Returns:
        this `TensorBlock`

    - #### getBasicTensor

          public BasicTensorBlock getBasicTensor()

    - #### getDataTensor

          public DataTensorBlock getDataTensor()

    - #### getValueType

          public Types.ValueType getValueType()

      Get the `ValueType` if this `TensorBlock` is homogeneous.
      Returns:
        `ValueType` if homogeneous, null otherwise

    - #### getSchema

          public Types.ValueType[] getSchema()

      Get the schema if this `TensorBlock` is heterogeneous.
      Returns:
        value type if heterogeneous, null otherwise

    - #### getNumDims

          public int getNumDims()

    - #### getNumRows

          public int getNumRows()

      Specified by:
        `getNumRows` in interface `CacheBlock`

    - #### getNumColumns

          public int getNumColumns()

      Specified by:
        `getNumColumns` in interface `CacheBlock`

    - #### getInMemorySize

          public long getInMemorySize()

      Description copied from interface: `CacheBlock`
      Get the in-memory size in bytes of the cache block.
      Specified by:
        `getInMemorySize` in interface `CacheBlock`
      Returns:
        in-memory size in bytes of cache block

    - #### isShallowSerialize

          public boolean isShallowSerialize()

      Description copied from interface: `CacheBlock`
      Indicates if the cache block is subject to shallow serialized, which is generally true if in-memory size and serialized size are almost identical allowing to avoid unnecessary deep serialize.
      Specified by:
        `isShallowSerialize` in interface `CacheBlock`
      Returns:
        true if shallow serialized

    - #### isShallowSerialize

          public boolean isShallowSerialize(boolean inclConvert)

      Description copied from interface: `CacheBlock`
      Indicates if the cache block is subject to shallow serialized, which is generally true if in-memory size and serialized size are almost identical allowing to avoid unnecessary deep serialize.
      Specified by:
        `isShallowSerialize` in interface `CacheBlock`
      Parameters:
        `inclConvert` - if true report blocks as shallow serialize that are currently not amenable but can be brought into an amenable form via `toShallowSerializeBlock`.
      Returns:
        true if shallow serialized

    - #### toShallowSerializeBlock

          public void toShallowSerializeBlock()

      Description copied from interface: `CacheBlock`
      Converts a cache block that is not shallow serializable into a form that is shallow serializable. This methods has no affect if the given cache block is not amenable.
      Specified by:
        `toShallowSerializeBlock` in interface `CacheBlock`

    - #### compactEmptyBlock

          public void compactEmptyBlock()

      Description copied from interface: `CacheBlock`
      Free unnecessarily allocated empty block.
      Specified by:
        `compactEmptyBlock` in interface `CacheBlock`

    - #### slice

          public CacheBlock slice(int rl,
                                  int ru,
                                  int cl,
                                  int cu,
                                  CacheBlock block)

      Description copied from interface: `CacheBlock`
      Slice a sub block out of the current block and write into the given output block. This method returns the passed instance if not null.
      Specified by:
        `slice` in interface `CacheBlock`
      Parameters:
        `rl` - row lower
        `ru` - row upper
        `cl` - column lower
        `cu` - column upper
        `block` - cache block
      Returns:
        sub-block of cache block

    - #### merge

          public void merge(CacheBlock that,
                            boolean appendOnly)

      Description copied from interface: `CacheBlock`
      Merge the given block into the current block. Both blocks needs to be of equal dimensions and contain disjoint non-zero cells.
      Specified by:
        `merge` in interface `CacheBlock`
      Parameters:
        `that` - cache block
        `appendOnly` - ?

    - #### getDim

          public int getDim(int i)

    - #### getDims

          public int[] getDims()

    - #### getLongDims

          public long[] getLongDims()

    - #### getNextIndexes

          public static void getNextIndexes(int[] dims,
                                            int[] ix)

      Calculates the next index array. Note that if the given index array was the last element, the next index will be the first one.
      Parameters:
        `dims` - the dims array for which we have to decide the next index
        `ix` - the index array which will be incremented to the next index array

    - #### getNextIndexes

          public void getNextIndexes(int[] ix)

      Calculates the next index array. Note that if the given index array was the last element, the next index will be the first one.
      Parameters:
        `ix` - the index array which will be incremented to the next index array

    - #### isVector

          public boolean isVector()

    - #### isMatrix

          public boolean isMatrix()

    - #### getLength

          public long getLength()

    - #### isEmpty

          public boolean isEmpty()

    - #### isEmpty

          public boolean isEmpty(boolean safe)

    - #### getNonZeros

          public long getNonZeros()

    - #### get

          public Object get(int[] ix)

    - #### get

          public double get(int r,
                            int c)

    - #### set

          public void set(Object v)

    - #### set

          public void set(MatrixBlock other)

    - #### set

          public void set(int[] ix,
                          Object v)

      Set a cell to the value given as an `Object`.
      Parameters:
        `ix` - indexes in each dimension, starting with 0
        `v` - value to set

    - #### set

          public void set(int r,
                          int c,
                          double v)

      Set a cell in a 2-dimensional tensor.
      Parameters:
        `r` - row of the cell
        `c` - column of the cell
        `v` - value to set

    - #### slice

          public TensorBlock slice(int[] offsets,
                                   TensorBlock outBlock)

      Slice the current block and write into the outBlock. The offsets determines where the slice starts, the length of the blocks is given by the outBlock dimensions.
      Parameters:
        `offsets` - offsets where the slice starts
        `outBlock` - sliced result block
      Returns:
        the sliced result block

    - #### copy

          public TensorBlock copy(TensorBlock src)

    - #### copy

          public TensorBlock copy(int[] lower,
                                  int[] upper,
                                  TensorBlock src)

      Copy a part of another `TensorBlock`
      Parameters:
        `lower` - lower index of elements to copy (inclusive)
        `upper` - upper index of elements to copy (exclusive)
        `src` - source `TensorBlock`
      Returns:
        the shallow copy of the src `TensorBlock`

    - #### copyExact

          public TensorBlock copyExact(int[] lower,
                                       int[] upper,
                                       TensorBlock src)

      Copy a part of another `TensorBlock`. The difference to `copy()` is that this allows for exact sub-blocks instead of taking all consecutive data elements from lower to upper.
      Parameters:
        `lower` - lower index of elements to copy (inclusive)
        `upper` - upper index of elements to copy (exclusive)
        `src` - source `TensorBlock`
      Returns:
        the deep copy of the src `TensorBlock`

    - #### getExactSerializedSize

          public long getExactSerializedSize()

      Description copied from interface: `CacheBlock`
      Get the exact serialized size in bytes of the cache block.
      Specified by:
        `getExactSerializedSize` in interface `CacheBlock`
      Returns:
        exact serialized size in bytes of cache block

    - #### getExactBlockDataSerializedSize

          public long getExactBlockDataSerializedSize(BasicTensorBlock bt)

      Get the exact serialized size of a `BasicTensorBlock` if written by `TensorBlock.writeBlockData(DataOutput,BasicTensorBlock)`.
      Parameters:
        `bt` - `BasicTensorBlock`
      Returns:
        the size of the block data in serialized form

    - #### write

          public void write(DataOutput out)
                     throws IOException

      Specified by:
        `write` in interface `org.apache.hadoop.io.Writable`
      Throws:
        `IOException`

    - #### writeBlockData

          public void writeBlockData(DataOutput out,
                                     BasicTensorBlock bt)
                              throws IOException

      Write a `BasicTensorBlock`.
      Parameters:
        `out` - output stream
        `bt` - source `BasicTensorBlock`
      Throws:
        `IOException` - if writing with the output stream fails

    - #### readFields

          public void readFields(DataInput in)
                          throws IOException

      Specified by:
        `readFields` in interface `org.apache.hadoop.io.Writable`
      Throws:
        `IOException`

    - #### writeExternal

          public void writeExternal(ObjectOutput out)
                             throws IOException

      Specified by:
        `writeExternal` in interface `Externalizable`
      Throws:
        `IOException`

    - #### readExternal

          public void readExternal(ObjectInput in)
                            throws IOException

      Specified by:
        `readExternal` in interface `Externalizable`
      Throws:
        `IOException`

    - #### binaryOperations

          public TensorBlock binaryOperations(BinaryOperator op,
                                              TensorBlock thatValue,
                                              TensorBlock result)

    - #### resultValueType

          public static Types.ValueType resultValueType(Types.ValueType in1,
                                                        Types.ValueType in2)

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