## RandomUniform <a name="RandomUniform"></a> {#openvino_docs_ops_generation_RandomUniform_8}

**Versioned name**: *RandomUniform-8*

**Category**: *Generation*

**Short description**: *RandomUniform* operation generates a sequence of random values from a uniform distribution.

**Detailed description**:

*RandomUniform* operation generates random numbers from a uniform distribution in the range `[minval, maxval)`. 
The generation algorithm is based on underlying random integer generator that uses Philox algorithm. Philox algorithm 
is a counter-based pseudo-random generator, which produces uint32 values. Single invocation of Philox algorithm returns 
four result random values, depending on the given *key* and *counter* values. *Key* and *counter* are initialized 
with *global_seed* and *op_seed* attributes respectively.

If both seed values equal to zero, RandomUniform generates non-deterministic sequence.

\f[
key = global_seed\\
counter = op_seed
\f]

Link to the original paper [Parallel Random Numbers: As Easy as 1, 2, 3](https://www.thesalmons.org/john/random123/papers/random123sc11.pdf)

The result of Philox is calculated by applying a fixed number of *key* and *counter* updating so-called "rounds". 
This implementation uses 4x32_10 version of Philox algorithm, where number of rounds = 10.

Suppose we have *n* which determines *n*-th 4 elements of random sequence.
In each round *key*, *counter* and *n* are splitted to pairs of uint32 values:

\f[
R = cast\_to\_uint32(value)\\
L = cast\_to\_uint32(value >> 32),
\f]
where *cast\_to\_uint32* - static cast to uint32, *value* - uint64 input value, *L*, *R* - uint32 
result values, >> - bitwise right shift.

Then *n* and *counter* are updated with the following formula:

\f[
L'= mullo(R, M)\\
R' = mulhi(R, M) {\oplus} k {\oplus} L \\
mulhi(a, b) = floor((a {\times} b) / 2^{32}) \\
mullo(a, b) = (a {\times} b) \mod 2^{32}
\f]
where \f${\oplus}\f$ - bitwise xor, *k* = \f$R_{key}\f$ for updating counter, *k* = \f$L_{key}\f$ for updating *n*, 
*M* = `0xD2511F53` for updating *n*, *M* = `0xCD9E8D57` for updating *counter*.

After each round *key* is raised by summing with another pair of const values:
\f[
L += 0x9E3779B9 \\
R += 0xBB67AE85
\f]
Values \f$L'_{n}, R'_{n}, L'_{counter}, R'_{counter}\f$ are resulting four random numbers.

Float values between [0..1) are obtained from 32-bit integers by the following rules.

Float16 is formatted as follows: *sign*(1 bit) *exponent*(5 bits) *mantissa*(10 bits). The value is interpreted 
using following formula:
\f[
(-1)^{sign} * 1, mantissa * 2 ^{exponent - 15}
\f]

so to obtain float16 values *sign*, *exponent* and *mantissa* are set as follows:
``` 
sign = 0
exponent = 15 - representation of a zero exponent.
mantissa = 10 right bits from generated uint32 random value.
``` 

So the resulting float16 value is:
``` 
x_uint16 = x // Truncate the upper 16 bits.
val = ((exponent << 10) | x_uint16 & 0x3ffu) - 1.0,
```
where x is uint32 generated random value.

Float32 is formatted as follows: *sign*(1 bit) *exponent*(8 bits) *mantissa*(23 bits). The value is interpreted 
using following formula:
\f[
(-1)^{sign} * 1, mantissa * 2 ^{exponent - 127}
\f]

so to obtain float values *sign*, *exponent* and *mantissa* are set as follows:
``` 
sign = 0
exponent = 127 - representation of a zero exponent.
mantissa = 23 right bits from generated uint32 random value.
``` 

So the resulting float value is:
``` 
val = ((exponent << 23) | x & 0x7fffffu) - 1.0,
```
where x is uint32 generated random value.

Double is formatted as follows: *sign*(1 bit) *exponent*(11 bits) *mantissa*(52 bits). The value is interpreted 
using following formula:
\f[
(-1)^{sign} * 1, mantissa * 2 ^{exponent - 1023}
\f]

so to obtain double values *sign*, *exponent* and *mantissa* are set as follows:
``` 
sign = 0
exponent = 1023 - representation of a zero exponent.
mantissa = 52 right bits from two concatinated uint32 values from random integer generator.
``` 

So the resulting double is obtained as follows:
``` 
mantissa_h = x0 & 0xfffffu;  // upper 20 bits of mantissa
mantissa_l = x1;             // lower 32 bits of mantissa
mantissa = (mantissa_h << 32) | mantissa_l;
val = ((exponent << 52) | mantissa) - 1.0,
```
where x0, x1 are uint32 generated random values.

To obtain a value in a specified range each value is processed with the following formulas:

For float values:
\f[
result = x * (maxval - minval) + minval,
\f]
where *x* is random float or double value between [0..1).

For integer values:
\f[
result = x \mod (maxval - minval) + minval,
\f]
where *x* is uint32 random value.


Example 1. *RandomUniform* output with `global_seed` = 150, `op_seed` = 10, `output_type` = f32:

``` 
input_shape    = [ 3, 3 ]
output  = [[0.7011236  0.30539632 0.93931055]
          [0.9456035   0.11694777 0.50770056]
          [0.5197197   0.22727466 0.991374  ]]
```

Example 2. *RandomUniform* output with `global_seed` = 80, `op_seed` = 100, `output_type` = double:

``` 
input_shape    = [ 2, 2 ]

minval = 2

maxval = 10

output  = [[5.65927959 4.23122376]
          [2.67008206 2.36423758]]
```

Example 3. *RandomUniform* output with `global_seed` = 80, `op_seed` = 100, `output_type` = i32:

``` 
input_shape    = [ 2, 3 ]

minval = 50

maxval = 100

output  = [[65 70 56]
          [59 82 92]]
```

**Attributes**:

* *output_type*

    * **Description**: the type of the output. Determines generation algorithm and affects resulting values. 
      Output numbers generated for different values of *output_type* may not be equal.
    * **Range of values**: "i32", "i64", "f16", "bf16", "f32", "f64".
    * **Type**: string
    * **Required**: *Yes*

* *global_seed*

    * **Description**: global seed value.
    * **Range of values**: positive integers
    * **Type**: `int`
    * **Default value**: 0
    * **Required**: *Yes*

* *op_seed*

    * **Description**: operational seed value.
    * **Range of values**: positive integers
    * **Type**: `int`
    * **Default value**: 0
    * **Required**: *Yes*

**Inputs**:

*   **1**: `shape` - 1D tensor of type *T_SHAPE* describing output shape. **Required.**

*   **2**: `minval` - scalar or 1D tensor with 1 element with type specified by the attribute *output_type*, 
    defines the lower bound on the range of random values to generate (inclusive). **Required.**

*   **3**: `maxval` - scalar or 1D tensor with 1 element with type specified by the attribute *output_type*, 
    defines the upper bound on the range of random values to generate (exclusive). **Required.**


**Outputs**:

* **1**: A tensor with type specified by the attribute *output_type* and shape defined by `shape` input tensor.

**Types**

* *T_SHAPE*: `int32` or `int64`.

*Example 1: IR example.*

```xml
<layer ... name="RandomUniform" type="RandomUniform">
    <data output_type="f32" global_seed="234" op_seed="148"/>
    <input>
        <port id="0" precision="I32">  <!-- shape value: [2, 3, 10] -->
            <dim>3</dim>
        </port>
        <port id="1" precision="FP32"/> <!-- min value -->
        <port id="2" precision="FP32"/> <!-- max value -->
    </input>
    <output>
        <port id="3" precision="FP32" names="RandomUniform:0">
            <dim>2</dim>
            <dim>3</dim>
            <dim>10</dim>
        </port>
    </output>
</layer>
```
